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- Get it together
- Nat Med 17(9):1021 (2011)
Nature Medicine | Editorial Get it together Journal name:Nature MedicineVolume: 17,Page:1021Year published:(2011)DOI:doi:10.1038/nm0911-1021Published online07 September 2011 Global health programs have made great strides in the last ten years, mobilizing billions of dollars to provide life-saving drugs and immunizations to people in resource-poor settings. But these myriad initiatives need to get in step to improve integration of healthcare delivery. View full text Additional data - Cheap drugs pulled despite wealth gap in middle-income countries
- Nat Med 17(9):1023 (2011)
Article preview View full access options Nature Medicine | News Cheap drugs pulled despite wealth gap in middle-income countries * Hannah WatersJournal name:Nature MedicineVolume: 17,Page:1023Year published:(2011)DOI:doi:10.1038/nm0911-1023Published online07 September 2011 Most pharmaceutical and aid groups base their tiered pricing schemes for drug discounts on average per-capita income levels by country. But the growing wealth of many developing countries is adding a wrinkle to the calculus of which nations get cheap access to essential medicines. Some experts assert that basing prices on average income doesn't work for the growing pool of middle-income countries, where many people are left below the poverty line and are therefore unable to afford medicine despite booms in local industries. "The poor people in the countries are stuck in the middle," says Prashant Yadav, the director of healthcare delivery research at the University of Michigan's William Davidson Institute in Ann Arbor. "That's where the big problem lies." According to data from the World Bank, around 70% of the world's poor now live in middle-income countries—defined as those with a gross national income of between $1,006 and $12,275 per capita—up from less than 10% two decades ago. But supporters of cheap drug access are retreating from making their medicines available at subsidized rates in these countries, opting instead to negotiate prices on a country-by-country basis. An 18 July report from Geneva's Médecins Sans Frontières (MSF) notes that in the past year Merck scrapped their discount pricing tier for middle-income economies for HIV drugs, joining Johnson & Johnson and ViiV Healthcare (a partnership between Pfizer and GlaxoSmithKline) in negotiating prices on a per-country basis. bp1/ZUMA Press/Newscom Countries such as India have updated patent laws to deal with rising drug prices. Even well-meaning measures to expand access can miss the mark. The nonprofit GAVI Alliance adjusted its criteria this year for discounted vaccine eligibility, taking countries' economic growth into account. Countries with gross national incomes under $1,500 per capita now qualify for discounted vaccines, up from under $1,000 last year. Despite the effort to be inclusive, however, only the 56 poorest countries in the world were eligible to apply to GAVI for cheap meds this year, compared to 72 in 2004. And, whereas the California drugmaker Gilead Sciences has garnered praise for licensing patents for HIV medications to UNITAID's Medicines Patent Pool for generic distribution on 12 July, the drugmaker's deal excludes a number of middle-income countries from its discounted intellectual property. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Hannah Waters Search for this author in: * NPG journals * PubMed * Google Scholar - Combination products neglected by FDA device evaluation
- Nat Med 17(9):1024 (2011)
Article preview View full access options Nature Medicine | News Combination products neglected by FDA device evaluation * Hannah WatersJournal name:Nature MedicineVolume: 17,Page:1024Year published:(2011)DOI:doi:10.1038/nm0911-1024aPublished online07 September 2011 In a damning evaluation of the US Food and Drug Administration's 510(k) program, which is used to clear medical devices similar to those approved previously, the US Institute of Medicine (IOM) issued a report on 29 July calling for a complete overhaul of the 35-year-old device regulation system. The IOM's conclusions harshly criticized the 510(k) process for clearing new devices on the basis of their similarity to existing products without evaluating safety and effectiveness data. But the 245-page report committed a mere two pages to what some consider an equally insidious problem affecting the approval of some kinds of medical devices: the review of combination products that straddle the divide between a device and a small-molecule– or biologic–based drug. "We'd like to see the FDA be proactive in however it approaches combination and companion products," says IOM panel member Steven Gutman, a pathologist who directed the FDA's Office of In Vitro Diagnostic Device Evaluation and Safety for six years and now serves as associate director of the BlueCross and BlueShield Association's Technology Evaluation Center in Alexandria, Virginia. Although the report focused on pure devices, he concedes that "we don't think it would be inappropriate if the FDA stepped back and looked at the whole panoply of products it regulates." Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Hannah Waters Search for this author in: * NPG journals * PubMed * Google Scholar - FDA reorganization inspires hope for better coordination
- Nat Med 17(9):1024 (2011)
Article preview View full access options Nature Medicine | News FDA reorganization inspires hope for better coordination * Mike MayJournal name:Nature MedicineVolume: 17,Page:1024Year published:(2011)DOI:doi:10.1038/nm0911-1024bPublished online07 September 2011 In an agency-wide e-mail message on 13 July, US Food and Drug Administration Commissioner Margaret Hamburg unveiled a massive reorganization of the regulatory watchdog's 41-year-old management structure. "The most obvious change you will see," she wrote, "is that the Agency's programs, in terms of a reporting chain to me, will be divided into 'directorates' that reflect the core functions and responsibilities of the Agency." In addition to the existing offices, she said the FDA would also create a new Office of Operations as well as a deputy commissioner for Global Regulatory Operations and Policy "focused on grappling with the truly global nature of today's world." For the pharmaceutical industry, the most important change could be the addition of the Office of Medical Products and Tobacco, which will be directed by Stephen Spielberg, who will assume the new post by the end of September. Speilberg is tasked with helping to coordinate work across the FDA's centers for drug, biologics, medical devices and tobacco products. In the past, each of these centers reported directly to the commissioner. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Mike May Search for this author in: * NPG journals * PubMed * Google Scholar - Proposed centralization of trial oversight stirs mixed reaction
- Nat Med 17(9):1025 (2011)
Article preview View full access options Nature Medicine | News Proposed centralization of trial oversight stirs mixed reaction * Meredith WadmanJournal name:Nature MedicineVolume: 17,Page:1025Year published:(2011)DOI:doi:10.1038/nm0911-1025Published online07 September 2011 Over the last two decades, scientists have increasingly followed the mantra that "bigger is better" when planning drug trials. Large, multisite trials have become staples of clinical investigation, enabling wider enrollment and more statistically meaningful research results. But, as the number of participating sites per study has grown, so has the administrative red tape. And, nowadays, dozens of local ethics committees—known as institutional review boards (IRBs)—are commonly involved in approving multisite studies, routinely suggesting changes to protocols and consent forms that then need to be reapproved by all the other parties involved. As a result, trials can take months to launch, delaying progress, and meaning that study participants don't benefit from the oversight of one central committee with ultimate responsibility for the research. The current system "is time consuming and slows research," says Kathy Hudson, deputy director for science, outreach and policy at the US National Institutes of Health (NIH) in Bethesda, Maryland. "It also may introduce vulnerability for subjects, because if there are many, many IRBs involved, does any single IRB feel like they have the real responsibility to examine the risks and benefits to research participants in exquisite detail?" To remedy the situation, on 22 July the NIH's parent agency, the Department of Health and Human Services (HHS), proposed that multisite studies conducted in the US should each be overseen by a single IRB for that study. Under the proposal—made as part of a sweeping overhaul to the Common Rule, the 1991 regulation that governs human research funded by 17 federal agencies, including HHS—this centralized IRB would approve protocols on behalf of all institutions involved and oversee midcourse corrections in the study in response to any unexpected adverse events. The proposed change, which is currently open for public comment, "would make a lot less work for all the individual IRBs and allow them to focus on the studies they should be focusing on: the homegrown studies that haven't been reviewed by anybody else," says Richard Galbraith, director of the University of Vermont Center for Clinical and Translational Science in Burlington, who has studied the issue for the Federation of American Societies for Experimental Biology (FASEB). Moving to a single IRB for multisite trials "is basically a very good idea," says Robert Levine, who teaches at the Yale University School of Medicine in New Haven, Connecticut and chaired the Yale–New Haven Hospital's IRB for 31 years. But he's concerned that the proposed rule does not specify the location or quality of a central IRB. "I wish they could strengthen the requirement to say that they are talking about a highly qualified, prestigious collection of people," he says. Without such clear guidelines, the HHS proposal could conceivably lead trial investigators to outsource their study oversight to freestanding, commercial IRBs—although Levine is not convinced this scenario will be borne out. "I don't think it would increase the business of the for-profit IRB appreciably," he says. Instead, Levine expects IRBs similar to the ones set up over the last decade at two federal agencies to have a greater role. One, at the US Department of Veterans Affairs (VA), has been in place since 2008 and is now mandated for all the multisite studies funded by the agency. Similarly, the US National Cancer Institute (NCI) created its own central IRB in 2001 and a corresponding pediatric IRB in 2004—both of which are available on a voluntary basis to organizers of NCI-funded oncology trials. Faster, cheaper—better? Both boards "are working very well," says Jacquelyn Goldberg, who heads the NCI's central IRB initiative. According to a study published last year by Goldberg and her colleagues, sites affiliated with the NCI's central IRB reviewed trial protocols on average 34 calendar days faster than unaffiliated sites that used their local IRB, at a savings of around $700 in staff wages for each initial review (J. Clin. Oncol., 662–666, 2010). "Review by the NCI's IRB was also associated with faster and less variable review times," Goldberg notes. Other studies have also found that local IRBs often make consent forms longer and more complicated, sometimes even introducing errors in the description of the study and its attendant risks (Clin. Infect. Dis., 328–335, 2009). istockphoto Centralized oversight proposed. Other NIH institutes are taking notice of the NCI's and VA's initiatives. The agency's National Heart, Lung and Blood Institute, for example, held a workshop in June examining the use of central IRBs, with an eye to endorsing their use for its grantees. "We have many studies with a large number of sites and no evidence that requiring 70 to 140 separate IRB reviews, annual reviews and reporting improves the protection of the participants or contributes to the quality of the science," says Susan Shurin, the institute's acting director. She notes that most EU countries now have single national IRBs for multicenter trials, too. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Meredith Wadman Search for this author in: * NPG journals * PubMed * Google Scholar - French law to make conflict of interest disclosure mandatory
- Nat Med 17(9):1026 (2011)
Article preview View full access options Nature Medicine | News French law to make conflict of interest disclosure mandatory * Sabine LouëtJournal name:Nature MedicineVolume: 17,Page:1026Year published:(2011)DOI:doi:10.1038/nm0911-1026aPublished online07 September 2011 France, still reeling from the Mediator scandal in which the diabetes drug (also known by its generic name, benfluorex) remained on the market until November 2009 despite earlier indications that it carried a risk of fatal heart valve trouble, is contemplating a revamp of its drug approval system. Lawmakers are due to discuss updates to the rules governing disclosures of conflict of interests by experts involved in the country's drug approval process when the French National Assembly reconvenes at the end of September. As part of a draft bill reforming the drugs approval and safety system, France may soon require that conflicts are publicly disclosed by directors and experts at regulatory agencies and made available publicly. Failure to do so would now incur sanctions including fines of up to €30,000 ($43,000). Etienne Caniard, president of Mutualité Française, a federation of most of France's nonprofit private health insurers, contends that the new rules will have a positive effect. "This proposal will help uncover the sectors where the state has given free rein to the pharmaceutical industry and where it should take its responsibility and regain control, such as continuous medical education," he says. istockphoto Conflicts to be publicly disclosed. To gain a clean start, the new bill also suggests renaming the country's drug regulatory authority, from the French Health Products Safety Agency (AFSSAPS) to the National Agency for Medicine Safety (ANSM). The legislation would also make the renamed agency's drug approval committees smaller than before to ensure that only experts with a track record in relevant therapeutic areas are involved. These experts would not be allowed to sit on drug approval committees longer than four or five years, and the decisions made by the committees would be more transparent. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Sabine Louët Search for this author in: * NPG journals * PubMed * Google Scholar - Patent protection brings hope to insurers
- Nat Med 17(9):1026 (2011)
Article preview View full access options Nature Medicine | News Patent protection brings hope to insurers * Georgina KenyonJournal name:Nature MedicineVolume: 17,Page:1026Year published:(2011)DOI:doi:10.1038/nm0911-1026bPublished online07 September 2011 Insurance companies are stepping up their marketing of damage-protection products to pharmaceutical and life science companies, some say in response to a June report by the US Food and Drug Administration laying out a collaborative strategy to more closely track the quality of goods globally. In response to increasing regulatory activity, UK-based JLT Specialty, part of the Jardine Lloyd Thompson Group, has begun marketing such 'nondamage' products more aggressively this past summer—and they're dropping their prices. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Georgina Kenyon Search for this author in: * NPG journals * PubMed * Google Scholar - Nonprofit disease groups earmark grants for drug repositioning
- Nat Med 17(9):1027 (2011)
Article preview View full access options Nature Medicine | News Nonprofit disease groups earmark grants for drug repositioning * Elie DolginJournal name:Nature MedicineVolume: 17,Page:1027Year published:(2011)DOI:doi:10.1038/nm0911-1027Published online07 September 2011 Prompted by the low success rates and high costs of drug development, pharmaceutical companies have increasingly turned to drug repositioning, also known as repurposing, to refurbish dwindling product pipelines—but the trend has spilled beyond industry. With an increasing academic focus on translational medicine, nonprofit research organizations are also looking to encourage new uses for old drugs, and some are earmarking money specifically for the effort. "Certainly, this is an area that seems ripe for some further investigation," Francis Collins, director of the US National Institutes of Health (NIH) in Bethesda, Maryland, told Nature Medicine. Although the NIH has yet to formally launch any drug repositioning–specific grant schemes, some nonprofit organizations and academic institutions already have. In late July, for example, the Michael J. Fox Foundation for Parkinson's Research (MJFF) announced the recipients of its first awards designated specifically for repositioning studies. Five of the six projects, funded for a total of $2.4 million, will take drugs originally developed for a range of ailments, including tuberculosis, depression and diabetes, and test them in animal and cellular models of Parkinson's disease. The sixth grant will fund a human trial of a pupil-dilating eye drug called tropicamide to treat uncontrolled drooling in people with Parkinson's. "That concept of repurposing a drug is a really powerful one," says Brian Fiske, director of research programs at the New York–based MJFF. "It really just speaks to our broader mission, which is to push treatments to the clinic and to accelerate the whole process." Fiske says the inspiration for the recent request for applications (RFA) was an earlier project, funded through the MJFF's general grant scheme, showing that the antidiabetic drug Actos (pioglitazone), although under public scrutiny for its possible link to bladder cancer, can be neuroprotective in a primate model of Parkinson's disease. The foundation, together with the US National Institute of Neurological Disorders and Stroke, is now supporting a phase 2 human clinical trial. "Stories and examples like this really make the case for a repositioning program," Fiske says. Creative Commons Funders set money aside for drug repositioning. Also in July, the University of New Mexico (UNM) Health Sciences Center's Clinical and Translational Science Center in Albuquerque—one of 60 members of the NIH's Clinical and Translational Science Awards (CTSA) consortium—issued its own call for repurposing grant applications. Although these awards are small, ranging from $5,000 to $50,000, they are intended to help investigators secure much larger NIH grants afterward. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Elie Dolgin Search for this author in: * NPG journals * PubMed * Google Scholar - US budget quagmire leaves global health funding in the lurch
- Nat Med 17(9):1028 (2011)
Article preview View full access options Nature Medicine | News US budget quagmire leaves global health funding in the lurch * Trevor StokesJournal name:Nature MedicineVolume: 17,Page:1028Year published:(2011)DOI:doi:10.1038/nm0911-1028aPublished online07 September 2011 In August, after a tense run-up to the default deadline, US lawmakers passed the Budget Control Act. The legislation that increased the debt ceiling contains $917 billion in cuts through 2021, which will probably affect core research agencies such as the National Institutes of Health, the National Science Foundation and the science office of the Department of Defense. But individuals involved in global health programs are also bracing for a hit come September, when Congress scrutinizes how to appropriate next year's federal budget, given the nation's tightened purse strings. Global health leaders say they expect cuts across the board for the next fiscal year in programs that tackle HIV transmission, tropical disease reduction and infant mortality. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Trevor Stokes Search for this author in: * NPG journals * PubMed * Google Scholar - PrEP trial successes prompt cost-effectiveness questions
- Nat Med 17(9):1028 (2011)
Article preview View full access options Nature Medicine | News PrEP trial successes prompt cost-effectiveness questions * Roxanne KhamsiJournal name:Nature MedicineVolume: 17,Page:1028Year published:(2011)DOI:doi:10.1038/nm0911-1028bPublished online07 September 2011 Clinical trial data are starting to pour in demonstrating that the HIV prevention strategy known as 'pre-exposure prophylaxis' is an effective way of keeping people at high risk of infection disease free. In July, researchers reported at the International AIDS Society Conference in Rome that taking an antiretroviral drug called Truvada offered a 73% protection rate for heterosexual couples in East Africa in which only one person had HIV. At the same meeting, the US Centers for Disease Control and Prevention also announced trial results demonstrating a 63% reduction in transmission among young adults in Botswana taking the pill. Buoyed by these and similar findings reported last year among men who have sex with men, health policy experts and economists are now debating how best to roll out the strategy to those who might benefit most. Preliminary analyses, experts say, indicate that PrEP should be a cost-effective tool to address the HIV epidemic until more testing and treatment for the disease becomes available. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Roxanne Khamsi Search for this author in: * NPG journals * PubMed * Google Scholar - Straight talk with...Alexander von Gabain
- Nat Med 17(9):1029 (2011)
Nature Medicine | News Straight talk with...Alexander von Gabain * Hannah WatersJournal name:Nature MedicineVolume: 17,Page:1029Year published:(2011)DOI:doi:10.1038/nm0911-1029Published online07 September 2011 Abstract Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although many of the world's best known drugmakers hail from Europe, historically the continent's academic institutions haven't been as adept as their US counterparts at spinning off companies. So, in 2008, the European Commission founded the European Institute of Innovation and Technology (EIT) to bring technology and ideas developed at universities to market. The EIT was modeled after the Massachusetts Institute of Technology in Cambridge—but it doesn't bring its students and researchers to a common location. Instead, EIT-funded projects are based within virtual Knowledge and Innovation Communities (KICs) spread across the continent. So far, the institute has established three subject-specific KICs focused on climate change, sustainable energy and information technology, with a total of 75 collaborating universities, companies and other investors. On 15 September, microbiologist Alexander von Gabain will take over from physicist and founding chairman Martin Schuurmans as head of the EIT. A professor at the Max Perutz Laboratories in Vienna and a cofounder of the Austrian biotech Intercell, von Gabain brings with him a new focus on advancing biomedical innovations at the institute. spoke with him about how he plans to move the EIT into the biomedical arena. View full text Additional data Author Details * Hannah Waters Search for this author in: * NPG journals * PubMed * Google Scholar - News in brief: Biomedical briefing
- Nat Med 17(9):1030-1031 (2011)
Article preview View full access options Nature Medicine | News News in brief: Biomedical briefing Journal name:Nature MedicineVolume: 17,Pages:1030–1031Year published:(2011)DOI:doi:10.1038/nm0911-1030Published online07 September 2011 On 20 July, the World Health Organization (WHO) issued an urgent warning against the use of blood tests to detect active tuberculosis. In its first-ever explicit 'negative' policy recommendation, the Geneva-based health body emphasized that sputum smears and lab cultures remain the gold standard for active disease and that most serological assays sold by private clinics throughout the developing world suffer from both low sensitivity and low specificity, leading to high rates of false diagnoses. "It's a case of inappropriate marketing," says Mario Raviglione, director of the WHO's Stop TB department. Three weeks later, a WHO-commissioned literature review that analyzed close to 100 studies involving the commercial blood tests reported extremely wide variability in results (PLoS Med., e1001062, 2011). The US lawsuit that has cast a shadow of uncertainty over human embryonic stem cell research for the past year was dismissed on 27 July. In a 38-page opinion, Judge Royce Lamberth, who had previously issued a temporary injunction that halted federally funded embryonic stem cell studies for 17 days last summer, acknowledged that an April 2011 appeals court decision that overturned his injunction obliged him to side with the US National Institutes of Health (NIH). The plaintiffs, however, could still appeal, possibly all the way up to the Supreme Court. "We intend to review all of our options for appeal of this decision," Steven Aden, one of the plaintiffs' attorneys, said in a statement. New regulations are needed to oversee how animal-human hybrids are used in research, according to the UK Academy of Medical Sciences. In a report released on 21 July, a working group led by geneticist Martin Bobrow of the University of Cambridge proposed that some experiments involving animals containing human material should undergo extra scrutiny, whereas others should be banned altogether. Specifically, the panel said that breeding animals with human germ cells, implanting monkeys with enough human-derived neurons to impart human-like behavior, and embryos containing a mix of human and nonhuman primate cells that develop beyond 14 days should be off limits. Bobrow notes, however, that these definitions will probably shift as the science progresses. "It is not our stance to draw a line in the sand for all time," he says. "We suggest these categories as a starting point, but we emphasize that they are undoubtedly bound to change." J. Zirato, University of Arizona BioCommunications An antivenom that neutralizes the fatal nerve-poisoning effects of scorpion stings won US regulatory approval last month on the back of a placebo-controlled trial of just 15 subjects. Leslie Boyer, from the University of Arizona in Tucson, recalls physicians involved with the study telling her: "If you unblind this data and it's as unequivocal as we think it is, we'll never do [the trial] again." Indeed, the results were so overwhelming that further placebo-controlled trials became unfeasible—and unethical. So, Boyer and her colleagues moved to an open-label study of more than 1,500 people to prove the safety and utility of the antivenom, called Anascorp. As Boyer notes, the drug's clinical development "was unusual and not without some debate along the way." See go.nature.com/RfT1Qu for more. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Taking tissue engineering to heart
- Nat Med 17(9):1032-1035 (2011)
Nature Medicine | News | News Feature Taking tissue engineering to heart * Elie Dolgin1Journal name:Nature MedicineVolume: 17,Pages:1032–1035Year published:(2011)DOI:doi:10.1038/nm0911-1032Published online07 September 2011 More than a decade after Japanese scientists implanted the first bioengineered blood vessel into a child with a congenital heart defect, the experimental treatment has finally made its way into clinical testing in the US. asks what took so long and what lessons have been learned along the way. View full text Additional data Affiliations * Elie Dolgin is a news editor with Nature Medicine in New York. Author Details * Elie Dolgin Search for this author in: * NPG journals * PubMed * Google Scholar - Consistent clinical research standards benefit patients around the world
- Nat Med 17(9):1036 (2011)
Nature Medicine | News | Opinion Consistent clinical research standards benefit patients around the world * Joe Herring1Journal name:Nature MedicineVolume: 17,Page:1036Year published:(2011)DOI:doi:10.1038/nm0911-1036Published online07 September 2011 Although the globalization of clinical trials has provided benefits to host countries, critics have focused on the rare but egregious examples of unethical practices. But large, coordinated trials by the contract research industry can encourage best practice, particularly if local countries adopt more consistent standards and oversight. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Joe Herring is chairman and chief executive officer of Covance, headquartered in Princeton, New Jersey, USA. Currently, he also serves as chairman of the board of the Washington, DC–based Association of Clinical Research Organizations. Author Details * Joe Herring Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Too much treatment?
- Nat Med 17(9):1037 (2011)
Article preview View full access options Nature Medicine | Book Review Too much treatment? * Robert Aronowitz1Journal name:Nature MedicineVolume: 17,Page:1037Year published:(2011)DOI:doi:10.1038/nm0911-1037Published online07 September 2011 Overdiagnosed: Making People Sick in the Pursuit of Health H. Gilbert Welch, Lisa Schwartz and Steve Woloshin Beacon Press, 2011 248 pp., hardcover, $24.95 ISBN: 0807022004 Buy this book: USUKJapan Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A friend recently developed a stress fracture that might have been caused by a medicine she was taking to prevent osteoporosis. Shortly afterward, her husband's blood pressure was found to be slightly elevated during a routine checkup. I would have thought that he would hesitate to treat asymptomatic risk factors after his wife's experience, but instead he demanded that his wavering physician immediately prescribe an antihypertensive medication. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Robert Aronowitz is in the History and Sociology of Science Department at the University of Pennsylvania, Philadelphia, Pennsylvania, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Robert Aronowitz Author Details * Robert Aronowitz Contact Robert Aronowitz Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Tissue-specific adult stem cells in the human lung
- Nat Med 17(9):1038-1039 (2011)
Article preview View full access options Nature Medicine | Correspondence Tissue-specific adult stem cells in the human lung * Piero Anversa1, 2 * Jan Kajstura1, 2 * Annarosa Leri1, 2 * Joseph Loscalzo2 * AffiliationsJournal name:Nature MedicineVolume: 17,Pages:1038–1039Year published:(2011)DOI:doi:10.1038/nm.2463Published online07 September 2011 To the Editor: We read with interest the Community Corner that appeared in Nature Medicine1 on the topic of our recent work on human lung stem cells (hLSCs)2. In their comments, Brigid Hogan and Barry Stripp have made a number of rather strong remarks concerning the actual validity of our results, some of which clearly lack acceptable scientific objectivity and border on personal attacks. Putting aside the healthy skepticism that confronts new findings, we are at a loss to explain the negative tone with which these comments were written. There is an important philosophical principle in medicine that has apparently been overlooked in these comments. Human studies constitute the foundation for animal work, not the reverse; this is also the policy of the US National Institutes of Health. The fact that new information does not coincide with established views should create excitement rather than unqualified criticisms. Contrary to the opinions expressed in the Community Corner, we think that the impossibility of performing lineage-tracing studies in humans does not diminish in any way the validity of our data. Fate-mapping strategies using fluorescent reporter genes are commonly used to track the origin of cells and their destiny in animals in which genetic manipulations are easily introduced. This approach would represent the ideal retrospective assay for the detection of lung-cell formation, as the expression of the fluorescent label can be placed under the control of promoters of genes coding for epithelial and vascular proteins. However, with lineage-tracing studies, it is impossible to determine whether stem cells divide asymmetrically (that is, they self-renew), or whether the cell types of the tagged progeny derive from activation of an individual or several resident stem cells (that is, they are unipotent or multipotent). To obtain indisputable evidence in favor of the ability of hLSCs to self-renew and create lung parenchyma in vivo, we injected single-cell–derived clonal hLSCs (Fig. 1a) into damaged lungs of immunosuppressed mice. Clonal hLSCs divided asymmetrically2 a! nd generated bronchioles, alveoli (Fig. 1b) and pulmonary vessels (Fig. 1c). Thus, hLSCs have the ability in vivo to form new stem cells and cells destined to acquire specialized function—these are the fundamental characteristics of tissue-specific adult stem cells3, 4. Figure 1: Clonal hLSCs and differentiated progeny in vivo. () Clone derived from deposition of one c-kit–positive hLSC (c-kit, green; nuclei, red). (,) Clonal EGFP-labeled hLSCs injected into a damaged mouse lung form human alveoli (: left, EGFP, green; middle, pan-cytokeratin, red; right, merge) and pulmonary vessels (: left, EGFP, green; middle: a-smooth muscle actin, red, and von Willebrand factor, bright blue; right, merge). Nuclei were stained by DAPI (blue). * Full size image (110 KB) In contrast to these observations, Hogan's view that basal cells are adult airway stem cells is incorrect5, 6, 7. These cells express the transcription factor p63 and the epithelial cell cytoplasmic proteins cytokeratin-5, cytokeratin-8, cytokeratin-14 and cytokeratin-18 (ref. 6). The lack of stem cell antigens and the presence of specific nuclear and cytoplasmic markers make this cell, at most, an amplifying cell; it is simply not a progenitor or a precursor cell8. By definition, stem cells are lineage-negative cells9, 10. The lineage-tracing studies performed using a Clara cell promoter5 further disproves the purported stemness of basal cells. The identification and characterization of tissue-specific adult stem cells is a complex, demanding process. After nearly 50 years since the discovery of hematopoietic stem cells, the search for the long-term multilineage repopulating cells continues and has not been resolved yet10. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Anesthesia, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Piero Anversa, * Jan Kajstura & * Annarosa Leri * Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Piero Anversa, * Jan Kajstura, * Annarosa Leri & * Joseph Loscalzo Competing financial interests P.A. is a member of Autologous LLC, and a patent application has been filed by Partners HealthCare for this class of human lung stem cells. Author Details * Piero Anversa Search for this author in: * NPG journals * PubMed * Google Scholar * Jan Kajstura Search for this author in: * NPG journals * PubMed * Google Scholar * Annarosa Leri Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Loscalzo Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Paraoxonase-1 and clopidogrel efficacy
- Nat Med 17(9):1039 (2011)
Nature Medicine | Correspondence Paraoxonase-1 and clopidogrel efficacy * Thomas Cuisset1, 2 * Pierre-Emmanuel Morange2 * Jacques Quilici1 * Jean Louis Bonnet1 * Christian Gachet3 * Marie-Christine Alessi2 * AffiliationsJournal name:Nature MedicineVolume: 17,Page:1039Year published:(2011)DOI:doi:10.1038/nm.2367Published online07 September 2011 To the Editor: We read with great interest the paper published in Nature Medicine by Bouman et al.1 showing that a common paraoxonase-1 (PON1) polymorphism (rs662, also known as Q192R) is a major determinant of clopidogrel biological efficacy and stent thrombosis, whereas the common CYP2C19*2 allele (rs4244285-A allele) has no effect in this population. This suggests that previous data about the effect of the rs4244285-A allele on clopidogrel efficacy2, 3 were mainly related to linkage disequilibrium with PON1 polymorphisms. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Département de Cardiologie, University Hospital La Timone, Marseille, France. * Thomas Cuisset, * Jacques Quilici & * Jean Louis Bonnet * Institut National de la Santé et de la Recherche Médicale, U626, Faculté de Médecine, Marseille, France. * Thomas Cuisset, * Pierre-Emmanuel Morange & * Marie-Christine Alessi * UMR-S949 Institut National de la Santé et de la Recherche Médicale-Université de Strasbourg, Etablissement Français du Sang-Alsace, Strasbourg, France. * Christian Gachet Competing financial interests The authors declare no competing financial interests. Author Details * Thomas Cuisset Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre-Emmanuel Morange Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Quilici Search for this author in: * NPG journals * PubMed * Google Scholar * Jean Louis Bonnet Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Gachet Search for this author in: * NPG journals * PubMed * Google Scholar * Marie-Christine Alessi Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (106K) Supplementary Tables 1–3 and Supplementary Methods Additional data - Paraoxonase-1 and clopidogrel efficacy
- Nat Med 17(9):1040-1041 (2011)
Nature Medicine | Correspondence Paraoxonase-1 and clopidogrel efficacy * Patrick M Dansette1 * Julien Rosi1 * Gildas Bertho1 * Daniel Mansuy1 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1040–1041Year published:(2011)DOI:doi:10.1038/nm.2436Published online07 September 2011 To the Editor: The mechanism that has generally been accepted for the bioactivation of clopidogrel, a tetrahydrothienopyridine antithrombotic prodrug, is a two-step enzymatic conversion into a biologically active thiol metabolite. The first step is a cytochrome P450 (CYP)-dependent oxidation leading to a 2-oxo-clopidogrel thiolactone metabolite (Fig. 1), and the second step is a CYP-dependent oxidative opening of the 2-oxo-clopidogrel thiolactone ring, with the eventual formation of an active thiol metabolite1, 2, 3. CYP2C19 has been reported to have a key role in clopidogrel bioactivation by contributing to both steps of this bioactivation3, and a common genetic variant within the CYP2C19 gene, the CYP2C19*2 loss-of-function polymorphism, was found to be associated with an attenuated response to clopidogrel and a worse clinical outcome in patients undergoing coronary stenting4. A recent paper published in Nature Medicine5 reported that this second step is not catalyzed by CYP enzymes, but! rather depends on paraoxonase-1 (PON1), and that PON1 is a major determinant of clopidogrel efficacy. Recently, we designed experiments to understand the origin of these contradictory conclusions about the nature of the second step of clopidogrel bioactivation. As described below, we found that although both CYP enzymes and PON1 can catalyze the opening of 2-oxo-clopidogrel thiolactone, they lead to the formation of different isomers of the thiol metabolite. Moreover, the major active thiol isomer that has recently been reported to be present in the plasma of clopidogrel-treated subjects6 is the one that our results indicate is formed via a CYP-dependent pathway. View full text Figures at a glance * Figure 1: Bioactivation of clopidogrel (CLO) and formation of different thiol isomers by esterase- and CYP-catalyzed metabolism of 2-oxo-clopidogrel. The endo-thiol should exist in equilibrium with its thioketone tautomer. * Figure 2: HPLC profiles of incubations of 2-oxo-clopidogrel with or without NADPH. (–) Profiles depicting the results with NADPH (,) and without NADPH (,) using MS detection of the thiols themselves (peaks at m/z = 356) (,) or of their derivatized products after treatment with 3′-methoxyphenacyl bromide (peaks at m/z = 504) (,). Incubations of [aS] 2-oxo-clopidogrel (100 mM) were performed with 1 mg protein per ml of pooled human liver microsomes (BD-Gentest) in 100 mM phosphate buffer pH 7.4 containing 2 mM CaCl2, 100 mM KF and a reducing agent (either ascorbic acid (20 mM) or glutathione (5 mM)). The incubations were performed at 37 °C for 30 min in the absence or presence of an NADPH generating system. RT, retention time. For detailed methodology, see Supplementary Methods. Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 8601, Université Paris Descartes, Paris, France. * Patrick M Dansette, * Julien Rosi, * Gildas Bertho & * Daniel Mansuy Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Patrick M Dansette Author Details * Patrick M Dansette Contact Patrick M Dansette Search for this author in: * NPG journals * PubMed * Google Scholar * Julien Rosi Search for this author in: * NPG journals * PubMed * Google Scholar * Gildas Bertho Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Mansuy Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (147K) Supplementary Methods and Supplementary Figure 1 Additional data - Paraoxonase-1 and clopidogrel efficacy
- Nat Med 17(9):1041-1042 (2011)
Article preview View full access options Nature Medicine | Correspondence Paraoxonase-1 and clopidogrel efficacy * Jordi Camps1 * Jorge Joven1 * Bharti Mackness1 * Michael Mackness1 * Dan Tawfik2 * Dragomir Draganov3 * Lucio G Costa4 * György Paragh5 * Ildikó Seres5 * Sven Horke6 * Richard James7 * Antonio Hernández8 * Srinivasa Reddy9 * Diana Shih9 * Mohamed Navab9 * Daniel Rochu10 * Michael Aviram11 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1041–1042Year published:(2011)DOI:doi:10.1038/nm.2386Published online07 September 2011 To the Editor: The incidence of stent thrombosis is decreasing steadily, in part as a result of the combined treatment of patients with clopidogrel and aspirin. In a recent publication in Nature Medicine, Bouman et al.1 demonstrated in vitro that oxidized clopidogrel is converted to the active thiol metabolite in a reaction catalyzed by PON1 (aryldialkylphosphatase, EC 3.1.8.1). Their results are plausible, as PON1 is known to catalyze the hydrolysis of thiolactones (as is oxo-clopidogrel), albeit at low rates2. They concluded that PON1 is a major determinant of clopidogrel efficacy. This is an important point, because it may imply that clopidogrel effectiveness depends on PON1 status. However, several aspects of the experimental procedures and data concern us and, in our opinion, merit further investigation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Centre de Recerca Biomèdica, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan, Reus, Spain. * Jordi Camps, * Jorge Joven, * Bharti Mackness & * Michael Mackness * Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel. * Dan Tawfik * WIL Research Laboratories, Ashland, Ohio, USA. * Dragomir Draganov * Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA. * Lucio G Costa * Department of Internal Medicine, University of Debrecen, Hungary. * György Paragh & * Ildikó Seres * Institute of Pharmacology, University Medical Center, Meinz, Obere Zahlbacher, Meinz, Germany. * Sven Horke * Department of Internal Medicine, University of Geneva, Switzerland. * Richard James * Department of Toxicology, University of Granada, Granada, Spain. * Antonio Hernández * Division of Cardiology, University of California–Los Angeles, Los Angeles, California, USA. * Srinivasa Reddy, * Diana Shih & * Mohamed Navab * Département de Toxicologie, Centre de Recherches du Service de Santé des Armées, La Tronche Cedex, France. * Daniel Rochu * The Lipid Research Laboratory, Technion Faculty of Medicine, Rambam Medical Center, Haifa, Israel. * Michael Aviram Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jordi Camps Author Details * Jordi Camps Contact Jordi Camps Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge Joven Search for this author in: * NPG journals * PubMed * Google Scholar * Bharti Mackness Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Mackness Search for this author in: * NPG journals * PubMed * Google Scholar * Dan Tawfik Search for this author in: * NPG journals * PubMed * Google Scholar * Dragomir Draganov Search for this author in: * NPG journals * PubMed * Google Scholar * Lucio G Costa Search for this author in: * NPG journals * PubMed * Google Scholar * György Paragh Search for this author in: * NPG journals * PubMed * Google Scholar * Ildikó Seres Search for this author in: * NPG journals * PubMed * Google Scholar * Sven Horke Search for this author in: * NPG journals * PubMed * Google Scholar * Richard James Search for this author in: * NPG journals * PubMed * Google Scholar * Antonio Hernández Search for this author in: * NPG journals * PubMed * Google Scholar * Srinivasa Reddy Search for this author in: * NPG journals * PubMed * Google Scholar * Diana Shih Search for this author in: * NPG journals * PubMed * Google Scholar * Mohamed Navab Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Rochu Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Aviram Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Reply to: "Paraoxonase-1 and clopidogrel efficacy"
- Nat Med 17(9):1042-1044 (2011)
Article preview View full access options Nature Medicine | Correspondence Reply to: "Paraoxonase-1 and clopidogrel efficacy" * Heleen J Bouman1, 2 * Edgar Schömig3 * Jochem W van Werkum1, 2 * Janna Velder4 * Christian M Hackeng2, 5 * Christoph Hirschhäuser4 * Christopher Waldmann6 * Hans-Günther Schmalz4 * Jurriën M ten Berg2 * Dirk Taubert3 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1042–1044Year published:(2011)DOI:doi:10.1038/nm.2469Published online07 September 2011 Bouman et al. reply: We very much appreciate the study of Cuisset et al.1 that complements our investigation2 and gives us the opportunity to focus on two issues: the relevance of differences in study design and the applied methodology for the observed outcomes; and the potential misinterpretation of our results as a clinical recommendation to tailor clopidogrel treatment on the basis of the PON1 genotype. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Biochemistry, Cardiovascular Research Institute Maastricht, University Maastricht, Maastricht, The Netherlands. * Heleen J Bouman & * Jochem W van Werkum * Department of Cardiology, St. Antonius Hospital Nieuwegein, Nieuwegein, The Netherlands. * Heleen J Bouman, * Jochem W van Werkum, * Christian M Hackeng & * Jurriën M ten Berg * Department of Pharmacology, University Hospital of Cologne, Cologne, Germany. * Edgar Schömig & * Dirk Taubert * Department für Chemie, Universität zu Köln, Cologne, Germany. * Janna Velder, * Christoph Hirschhäuser & * Hans-Günther Schmalz * Department of Clinical Chemistry, St. Antonius Hospital Nieuwegein, Nieuwegein, The Netherlands. * Christian M Hackeng * Klinik und Poliklinik für Nuklearmedizin, Universität Münster, Münster, Germany. * Christopher Waldmann Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dirk Taubert Author Details * Heleen J Bouman Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar Schömig Search for this author in: * NPG journals * PubMed * Google Scholar * Jochem W van Werkum Search for this author in: * NPG journals * PubMed * Google Scholar * Janna Velder Search for this author in: * NPG journals * PubMed * Google Scholar * Christian M Hackeng Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph Hirschhäuser Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Waldmann Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Günther Schmalz Search for this author in: * NPG journals * PubMed * Google Scholar * Jurriën M ten Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Taubert Contact Dirk Taubert Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - A new twist in the function of the cardiac lipid droplet
- Nat Med 17(9):1045-1046 (2011)
Article preview View full access options Nature Medicine | Article ATGL-mediated fat catabolism regulates cardiac mitochondrial function via PPAR-α and PGC-1 * Guenter Haemmerle1 * Tarek Moustafa2 * Gerald Woelkart3 * Sabrina Büttner1 * Albrecht Schmidt4 * Tineke van de Weijer5 * Matthijs Hesselink6 * Doris Jaeger1 * Petra C Kienesberger1 * Kathrin Zierler1 * Renate Schreiber1 * Thomas Eichmann1 * Dagmar Kolb1 * Petra Kotzbeck1 * Martina Schweiger1 * Manju Kumari1 * Sandra Eder1 * Gabriele Schoiswohl1 * Nuttaporn Wongsiriroj1 * Nina M Pollak1 * Franz P W Radner1 * Karina Preiss-Landl1 * Thomas Kolbe6 * Thomas Rülicke7 * Burkert Pieske4 * Michael Trauner2 * Achim Lass1 * Robert Zimmermann1 * Gerald Hoefler8 * Saverio Cinti9 * Erin E Kershaw10 * Patrick Schrauwen5 * Frank Madeo1 * Bernd Mayer3 * Rudolf Zechner1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1076–1085Year published:(2011)DOI:doi:10.1038/nm.2439Received20 April 2011Accepted08 July 2011Published online21 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that regulate genes involved in energy metabolism and inflammation. For biological activity, PPARs require cognate lipid ligands, heterodimerization with retinoic X receptors, and coactivation by PPAR-γ coactivator-1α or PPAR-γ coactivator-1β (PGC-1α or PGC-1β, encoded by Ppargc1a and Ppargc1b, respectively). Here we show that lipolysis of cellular triglycerides by adipose triglyceride lipase (patatin-like phospholipase domain containing protein 2, encoded by Pnpla2; hereafter referred to as Atgl) generates essential mediator(s) involved in the generation of lipid ligands for PPAR activation. Atgl deficiency in mice decreases mRNA levels of PPAR-α and PPAR-δ target genes. In the heart, this leads to decreased PGC-1α and PGC-1β expression and severely disrupted mitochondrial substrate oxidation and respiration; this is followed by excessive lipid accumulation, cardiac insufficiency and! lethal cardiomyopathy. Reconstituting normal PPAR target gene expression by pharmacological treatment of Atgl-deficient mice with PPAR-α agonists completely reverses the mitochondrial defects, restores normal heart function and prevents premature death. These findings reveal a potential treatment for the excessive cardiac lipid accumulation and often-lethal cardiomyopathy in people with neutral lipid storage disease, a disease marked by reduced or absent ATGL activity. Figures at a glance * Figure 1: Expression of PPAR-α and PPAR-δ target genes and PGC-1α and PGC-1β in AtglKO, HslKO, and wild-type tissues. mRNA expression levels for selected PPAR-α and PPAR-δ target genes and PGC-1α and PGC-1β were determined by RT-qPCR analysis. (,) Cardiac () and hepatic () mRNA expression of PPAR-α and PPAR-δ target genes were markedly decreased in fasted 8- to 10-week-old female AtglKO mice compared to age-matched HslKO and wild-type mice. (,) mRNA levels of genes encoding PGC-1α and PGC-1β mRNA were also reduced in cardiac muscle () but increased in the liver () of fasted AtglKO mice compared to wild-type mice. n = 4. Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 2: Morphology, glycogen content, mitochondria size and mitochondrial DNA content in cardiac muscle of wild-type and AtglKO mice. () Cardiac muscle glycogen content (measured as glucose after hydrolysis) of 10-week-old female wild-type and AtglKO mice (n = 9). () Transmission electron microscopy of cardiac muscle sections from 10-weeks old female mice. Top images, wild-type cardiac muscle sections show a typical intermyofibrillar network containing mitochondria (M), glycogen (*Gly) and lipid droplets (LD). In AtglKO cardiac muscle (lower panels) lipid droplet size and the number of glycogen granules embedded within the intermyofibrillar network are increased. VE, vessel. Scale bars, 1 μm for upper and lower left images; 0.5 μm for upper and lower right images. (,) Morphometric () and cytofluorimetric () analyses of mitochondria from cardiac muscle of wild-type and AtglKO mice. Size was either determined from sections of 100 randomly selected mitochondria per genotype or from isolated mitochondria (fluorescence-activated cell sorting (FACS) analysis, n = 4). AU, arbitrary units. () Relative mitochondr! ial DNA (mtDNA) content (normalized to the single-copy nuclear gene Ndufv1) in cardiac muscle of 10-week-old female wild-type and AtglKO mice (n = 5). Error bars are means ± s.d. **P < 0.01. * Figure 3: Mitochondrial OXPHOS function and oxidative stress in cardiac muscle of wild-type and AtglKO mice. (,) Oxygen consumption, an indicator for mitochondrial respiration, in AtglKO cardiac homogenates of 4-week-old () and 8-week-old () male mice in the presence of glucose (n = 6). () Triglyceride (TG) content in cardiac muscle of wild-type and AtglKO mice. (,) Oxygen flux of mitochondria isolated from cardiac tissue of 8- to 9-week-old male wild-type and AtglKO mice. ADP-driven (state 3) and uncoupled (state U) oxygen flow was measured in the presence of pyruvate () and palmitoyl-CoA () in subsarcolemmal (SS) and in intramyofibrillar (IMF) mitochondria (n = 6). () Western blotting analysis of mitochondrial respiratory chain proteins NDUFA9 of complex I and SDHA of complex II in mitochondrial preparations of AtglKO mice and wild-type mice. MTCO1, a marker of complex IV, served as loading control. () Mitochondrial membrane potential (tetramethyl-rhodaminemethylester perchlorate (TMRM) staining) in isolated cardiac mitochondria of 8- to 9-week-old female AtglKO compared to wild-! type mice (n = 4). () Relative concentrations of non-oxidized (free) thiol groups in isolated mitochondria of 8- to 9-week-old female AtglKO mice compared to wild-type mice (n = 4). Error bars are means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 4: Changes in PPAR-α and PPAR-δ activated gene expression and OXPHOS in mice lacking or overexpressing Atgl in cardiac muscle. () Cardiac triglyceride content in wild-type and conditional knockout mice lacking Atgl in cardiac and skeletal muscle (muscleAtglKO mice) demonstrating a drastic cardiac steatosis in muscleAtglKO mice (n = 5). Scale bars, 5 mm. () mRNA expression levels of PPAR-α and PPAR-δ target genes and of the gene encoding PGC-1α in cardiac muscle of muscleAtglKO mice compared to wild-type mice (n = 5). (–) Heart weight (), cardiac muscle triglyceride (TG) content (), and white and brown adipose tissue (WAT and BAT) weight () of wild-type, AtglKO and AtglKO-cmAtglTG mice expressing an Atgl transgene on an AtglKO background (n = 6). () mRNA expression levels of PPAR-α and PPAR-δ target genes and genes encoding PGC-1α and PGC-1β in cardiac muscle of wild-type, AtglKO and AtglKO-cmAtglTG mice (n = 4). () Oxygen consumption in cardiac homogenates prepared from 8- to 9-week-old female wild-type and AtglKO-cmAtglTG mice (n = 6). () Relative luciferase activities in lysates of HepG2 ! cells transfected with a PPRE-luciferase reporter plasmid and a PPAR-α expression vector. The additional expression of Atgl increases luciferase activity in the absence or presence of exogenously added linoleic acid (LA). Transfection of the bacterial β-galactosidase gene (lacZ)-containing plasmid and colorimetric determination of β-galactosidase (β-gal) enzyme activity was used for normalization of transfection efficiency. Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 5: Triglyceride content, oxygen consumption and cardiac function in AtglKO mice treated with PPAR-α agonists. () Cardiac and hepatic triglyceride content in 6-week-old female AtglKO mice on chow diet with or without 0.1% Wy14643 for 3 weeks (n = 5). () Cardiac and hepatic triglyceride content in 6-week-old female AtglKO mice on chow diet with or without 0.2% fenofibrate for 10 weeks (n = 4–5). () mRNA expression levels of PPAR-α and PPAR-δ target genes and genes encoding PGC-1α and PGC-1β in cardiac muscle of female wild-type and AtglKO mice fed a chow diet with or without 0.1% Wy14643 for 3 weeks (n = 5). () Oxygen consumption in cardiac muscle preparations under both basal conditions and succinate-stimulated conditions of 9-week-old male wild-type, AtglKO mice and AtglKO mice fed a chow diet with 0.1% (wt/wt) Wy14643 for 3 weeks (n = 5). () Representative echocardiographic images (M- and B-Mode) of a 9-week-old female wild-type and AtglKO mouse on chow diet and a 9-week-old female AtglKO mouse fed a chow diet containing 0.1% (wt/wt) Wy14643 for 3 weeks. We measured intervent! ricular septum (IVS) and posterior wall (PW) thickness from original tracings. We measured left ventricular end-systolic dimensions (ESD) and left ventricular end-diastolic dimensions (EDD) from original tracings according to the leading edge convention of the American Society of Echocardiography. (,) Left ventricular fractional shortening (LVFS) () and left ventricular (LV) mass (), calculated from the echocardiographic tracings as previously described54 (n = 5). Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 6: Life span, tissue triglyceride content and energy substrate utilization in wild-type and AtglKO mice treated with the PPAR-α agonist Wy14643. (,) Treatment of 8-week-old AtglKO mice on chow diet containing 0.1% WY14643 for 12 weeks prevented cardiac death () and lowered tissue triglyceride (TG) content (), including in cardiac muscle and liver, compared to that observed in wild-type animals (n = 4). () Relative whole-body oxygen consumption of 8- to 9-week-old female wild-type and AtglKO mice housed in metabolic cages (n = 5). () Respiratory quotients (calculated from the ratio of carbon dioxide elimination versus oxygen consumption) in AtglKO mice compared to wild-type during the light period and in the fasted state indicating preferential glucose utilization as oxidative fuel (n = 5). Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. () Scheme of the integration of Atgl-mediated lipolysis in PPAR signaling. Fatty acids from exogenous or endogenous sources are not available as ligands for nuclear receptor signaling but instead are activated to acyl-CoAs and subsequently oxidized or esterified ! to triglycerides. Atgl-mediated lipolysis of triglyceride stores preferentially generates ligands or precursors of ligands for nuclear receptors controlling mitochondrial function and OXPHOS. CD36, cluster of differentiation 36; Fatp, fatty acid transport protein; FFA, free fatty acid; TGRLP, triglyceride-rich lipoproteins. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information * Abstract * Author information * Supplementary information Affiliations * Institute of Molecular Biosciences, University of Graz, Graz, Austria. * Guenter Haemmerle, * Sabrina Büttner, * Doris Jaeger, * Petra C Kienesberger, * Kathrin Zierler, * Renate Schreiber, * Thomas Eichmann, * Dagmar Kolb, * Petra Kotzbeck, * Martina Schweiger, * Manju Kumari, * Sandra Eder, * Gabriele Schoiswohl, * Nuttaporn Wongsiriroj, * Nina M Pollak, * Franz P W Radner, * Karina Preiss-Landl, * Achim Lass, * Robert Zimmermann, * Frank Madeo & * Rudolf Zechner * Laboratory of Experimental and Molecular Hepatology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. * Tarek Moustafa & * Michael Trauner * Department of Pharmacology and Toxicology, University of Graz, Graz, Austria. * Gerald Woelkart & * Bernd Mayer * Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria. * Albrecht Schmidt & * Burkert Pieske * Department of Human Biology, School for Nutrition, Toxicology and Metabolism (NUTRIM), Maastricht University Medical Centre, Maastricht, The Netherlands. * Tineke van de Weijer & * Patrick Schrauwen * Department of Human Movement Sciences, NUTRIM, Maastricht University Medical Centre, Maastricht, The Netherlands. * Matthijs Hesselink & * Thomas Kolbe * Biomodels Austria, Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Vienna, Austria. * Thomas Rülicke * Institute of Pathology, Medical University of Graz, Graz, Austria. * Gerald Hoefler * Department of Molecular Pathology and Innovative Therapies, Faculty of Medicine, University of Ancona (Politecnica delle Marche), Ancona, Italy. * Saverio Cinti * Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Erin E Kershaw Contributions G.H. and R.Z. designed the study, were involved in all aspects of the experiments and wrote the manuscript. T.M. and D.J. were responsible for quantitative RT-qPCR–based gene expression analyses and luciferase assays. G.W. and B.M. were responsible for the measurements of tissue oxygen consumption. P. K., D.K. and S.C. were responsible for electron microscopy. S.B., F.M., N.W., T.v.d.W., M.H. and P.S. were responsible for mitochondrial analyses. P.C.K., T.K. and T.R. generated the transgenic mouse strains. K.Z., F.P.W.R., R.S., T.E., M.S., M.K., S.E., G.S. and N.M.P. were responsible for agonist application, dietary studies, plasma and tissue parameter analyses and enzymatic assays. A.S. and B.P. were responsible for echocardiography. E.E.K. generated Atgl-floxed mice. K.P.-L., M.T., A.L., R.Z. and G.H. discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rudolf Zechner Author Details * Guenter Haemmerle Search for this author in: * NPG journals * PubMed * Google Scholar * Tarek Moustafa Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald Woelkart Search for this author in: * NPG journals * PubMed * Google Scholar * Sabrina Büttner Search for this author in: * NPG journals * PubMed * Google Scholar * Albrecht Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Tineke van de Weijer Search for this author in: * NPG journals * PubMed * Google Scholar * Matthijs Hesselink Search for this author in: * NPG journals * PubMed * Google Scholar * Doris Jaeger Search for this author in: * NPG journals * PubMed * Google Scholar * Petra C Kienesberger Search for this author in: * NPG journals * PubMed * Google Scholar * Kathrin Zierler Search for this author in: * NPG journals * PubMed * Google Scholar * Renate Schreiber Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Eichmann Search for this author in: * NPG journals * PubMed * Google Scholar * Dagmar Kolb Search for this author in: * NPG journals * PubMed * Google Scholar * Petra Kotzbeck Search for this author in: * NPG journals * PubMed * Google Scholar * Martina Schweiger Search for this author in: * NPG journals * PubMed * Google Scholar * Manju Kumari Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra Eder Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriele Schoiswohl Search for this author in: * NPG journals * PubMed * Google Scholar * Nuttaporn Wongsiriroj Search for this author in: * NPG journals * PubMed * Google Scholar * Nina M Pollak Search for this author in: * NPG journals * PubMed * Google Scholar * Franz P W Radner Search for this author in: * NPG journals * PubMed * Google Scholar * Karina Preiss-Landl Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Kolbe Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Rülicke Search for this author in: * NPG journals * PubMed * Google Scholar * Burkert Pieske Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Trauner Search for this author in: * NPG journals * PubMed * Google Scholar * Achim Lass Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Zimmermann Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald Hoefler Search for this author in: * NPG journals * PubMed * Google Scholar * Saverio Cinti Search for this author in: * NPG journals * PubMed * Google Scholar * Erin E Kershaw Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Schrauwen Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Madeo Search for this author in: * NPG journals * PubMed * Google Scholar * Bernd Mayer Search for this author in: * NPG journals * PubMed * Google Scholar * Rudolf Zechner Contact Rudolf Zechner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7 and Supplementary Tables 1–4 Additional data - Cancer stem cells renew their impact
- Nat Med 17(9):1046-1048 (2011)
Article preview View full access options Nature Medicine | Article Stem cell gene expression programs influence clinical outcome in human leukemia * Kolja Eppert1 * Katsuto Takenaka2, 12 * Eric R Lechman1, 12 * Levi Waldron3, 12 * Björn Nilsson4, 12 * Peter van Galen1 * Klaus H Metzeler5 * Armando Poeppl1 * Vicki Ling6 * Joseph Beyene6 * Angelo J Canty7 * Jayne S Danska8 * Stefan K Bohlander5 * Christian Buske9 * Mark D Minden10 * Todd R Golub11 * Igor Jurisica3 * Benjamin L Ebert4 * John E Dick1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1086–1093Year published:(2011)DOI:doi:10.1038/nm.2415Received29 September 2010Accepted09 June 2011Published online28 August 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Xenograft studies indicate that some solid tumors and leukemias are organized as cellular hierarchies sustained by cancer stem cells (CSCs). Despite the promise of the CSC model, its relevance in humans remains uncertain. Here we show that acute myeloid leukemia (AML) follows a CSC model on the basis of sorting multiple populations from each of 16 primary human AML samples and identifying which contain leukemia stem cells (LSCs) using a sensitive xenograft assay. Analysis of gene expression from all functionally validated populations yielded an LSC-specific signature. Similarly, a hematopoietic stem cell (HSC) gene signature was established. Bioinformatic analysis identified a core transcriptional program shared by LSCs and HSCs, revealing the molecular machinery underlying 'stemness' properties. Both stem cell programs were highly significant independent predictors of patient survival and were found in existing prognostic signatures. Thus, determinants of stemness influence! the clinical outcome of AML, establishing that LSCs are clinically relevant and not artifacts of xenotransplantation. Figures at a glance * Figure 1: Strategy of transcriptional profiling of stem cell fractions identified by function. () Overview of experimental design. Cells were sorted on CD34 and CD38, with sort gates for AML and cord blood as well as FACS analysis of the resulting sorted fractions. Functional validation of sorted fractions was done in vivo and combined with gene expression profiling to generate stem cell–related gene expression profiles. () Surface marker profiles of AML are variable with respect to coexpression of CD34 and CD38. CD34 and CD38 marker profiles for 16 AML samples were sorted into four populations and assayed for LSCs. * Figure 2: Correlation between LSC-R and HSC-R. () Heat map of genes more highly expressed in LSC than in non-LSC populations (LSC-R gene signature). LSC and non-LSC represent sorted AML fractions with LSCs, as determined by an in vivo reconstitution assay, and no detected LSCs, respectively. () Heat map of genes more highly expressed in HSC populations than in those with no detectable HSCs (HSC-R gene signature) in four different sorted cord blood populations. Sorted fractions include two HSC fractions (HSC1, Lin−CD34+CD38−; HSC2, Lin−CD34+CD38loCD36−), a progenitor-enriched fraction (Prog, Lin− CD34+CD38+) and unsorted cord blood cells (Lin+). () GSEA plot of enrichment of HSC-R gene signature (top) and common lineage–committed progenitor gene signature (bottom) in LSC versus non-LSC gene expression profile. NES denotes normalized enrichment score. () Heat map of HSC-R GSEA plot from (top) showing core enriched HSC-R genes in LSC expression profile (CE-HSC-LSC). Genes separated by slashes are detected by the! same probe set. () Representative protein-protein interaction network of core enriched genes (CE-HSC-LSC) from , generated from known and interologous interactions from I2D. Large circles, proteins from core enriched gene list (CE-HSC-LSC); small squares, proteins that link proteins in core enriched list. Node color corresponds to GO protein function. Visualization was done using NAViGaTOR (Supplementary Data). * Figure 3: LSC-R and HSC-R gene signatures are correlated with disease outcome. Unsorted cytogenetically normal AML samples (160) were divided into two populations of 80 AML samples by expression of stem cell gene signatures. () Correlation of LSC-R and HSC-R signatures and overall survival. Red line, subjects whose AML cells expressed LSC-R (left) or HSC-R (right) signatures greater than the median; black line, those whose AML cells expressed respective stem cell signature less than the median. () Event-free survival of subjects stratified by expression of the LSC-R and HSC-R, as in . () Additive correlation analysis of the LSC-R signature and overall survival. y axis, log-rank P value of each combination of probes. x axis, number of probes included in analysis, starting with top-ranked probe positively correlated with LSCs followed by the addition of each next ranked probe in the LSC-R gene profile (as determined by z-score in the LSC versus non-LSC t-test). () Correlation of an AML signature based on phenotypic markers (CD34+CD38−, stem cell, versu! s CD34+CD38+, progenitor; 23 AML samples) and overall survival. Red line, subjects whose AML expressed the CD34+CD38− gene list greater than the median; black line, those who expressed the CD34+CD38− gene list less than the median. * Figure 4: Correlation of LSC and HSC gene expression signatures and molecular risk status with overall survival in a cohort of cytogenetically normal AML samples. Overall survival curves of 159 cytogenetically normal AML samples divided by expression of the LSC-R (left) or HSC-R (right) signatures and molecular risk. LMR group, NPM1mut/FLT3wt cytogenetically normal AML; HMR group, NPM1wt or FLT3ITD cytogenetically normal AML. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30377 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Katsuto Takenaka, * Eric R Lechman, * Levi Waldron & * Björn Nilsson Affiliations * Division of Stem Cell and Developmental Biology, Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Kolja Eppert, * Eric R Lechman, * Peter van Galen, * Armando Poeppl & * John E Dick * Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan. * Katsuto Takenaka * Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. * Levi Waldron & * Igor Jurisica * Brigham and Women's Hospital, Boston, Massachusetts, USA. * Björn Nilsson & * Benjamin L Ebert * Department of Internal Medicine III, Ludwig-Maximilians-Universität, Munich, Germany. * Klaus H Metzeler & * Stefan K Bohlander * Population Health Sciences, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada. * Vicki Ling & * Joseph Beyene * Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada. * Angelo J Canty * Program in Genetics and Genome Biology, Hospital for Sick Children and Department of Immunology and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. * Jayne S Danska * Institute of Experimental Cancer Research, Comprehensive Cancer Center, University Hospital of Ulm, Ulm, Germany. * Christian Buske * Department of Medicine, University Health Network, Toronto, Ontario, Canada. * Mark D Minden * Broad Institute, Cambridge, Massachusetts, USA. * Todd R Golub Contributions K.E., E.R.L., K.T., B.L.E. and J.E.D. designed the study. K.E., E.R.L., P.v.G., K.T. and A.P. carried out experiments. K.E., K.T., L.W., B.N., E.R.L., P.v.G., V.L. and I.J. analyzed and interpreted data. K.E., J.B., A.J.C., J.S.D., S.K.B., K.H.M., C.B., M.D.M., T.R.G., I.J., B.L.E. and J.E.D. provided research support and conceptual advice. M.D.M. provided samples. K.E. and J.E.D. wrote the paper. E.R.L., K.T., K.H.M., J.S.D., S.K.B., C.B., M.D.M., I.J. and B.L.E. revised the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John E Dick Author Details * Kolja Eppert Search for this author in: * NPG journals * PubMed * Google Scholar * Katsuto Takenaka Search for this author in: * NPG journals * PubMed * Google Scholar * Eric R Lechman Search for this author in: * NPG journals * PubMed * Google Scholar * Levi Waldron Search for this author in: * NPG journals * PubMed * Google Scholar * Björn Nilsson Search for this author in: * NPG journals * PubMed * Google Scholar * Peter van Galen Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus H Metzeler Search for this author in: * NPG journals * PubMed * Google Scholar * Armando Poeppl Search for this author in: * NPG journals * PubMed * Google Scholar * Vicki Ling Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Beyene Search for this author in: * NPG journals * PubMed * Google Scholar * Angelo J Canty Search for this author in: * NPG journals * PubMed * Google Scholar * Jayne S Danska Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan K Bohlander Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Buske Search for this author in: * NPG journals * PubMed * Google Scholar * Mark D Minden Search for this author in: * NPG journals * PubMed * Google Scholar * Todd R Golub Search for this author in: * NPG journals * PubMed * Google Scholar * Igor Jurisica Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin L Ebert Search for this author in: * NPG journals * PubMed * Google Scholar * John E Dick Contact John E Dick Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Tables (3M) Supplementary Tables 1–25 XML files * Supplementary Data (6M) NAViGaTOR file PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–14, Supplementary Results and Supplementary Methods Additional data - The social aspects of EMT-MET plasticity
- Nat Med 17(9):1048-1049 (2011)
Article preview View full access options Nature Medicine | Article Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization * Manav Korpal1 * Brian J Ell1 * Francesca M Buffa2 * Toni Ibrahim3 * Mario A Blanco1 * Toni Celià-Terrassa1, 4 * Laura Mercatali3 * Zia Khan5, 6 * Hani Goodarzi1, 6 * Yuling Hua1 * Yong Wei1 * Guohong Hu1 * Benjamin A Garcia1 * Jiannis Ragoussis7 * Dino Amadori3 * Adrian L Harris2 * Yibin Kang1, 8 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1101–1108Year published:(2011)DOI:doi:10.1038/nm.2401Received08 October 2010Accepted18 May 2011Published online07 August 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although the role of miR-200s in regulating E-cadherin expression and epithelial-to-mesenchymal transition is well established, their influence on metastatic colonization remains controversial. Here we have used clinical and experimental models of breast cancer metastasis to discover a pro-metastatic role of miR-200s that goes beyond their regulation of E-cadherin and epithelial phenotype. Overexpression of miR-200s is associated with increased risk of metastasis in breast cancer and promotes metastatic colonization in mouse models, phenotypes that cannot be recapitulated by E-cadherin expression alone. Genomic and proteomic analyses revealed global shifts in gene expression upon miR-200 overexpression toward that of highly metastatic cells. miR-200s promote metastatic colonization partly through direct targeting of Sec23a, which mediates secretion of metastasis-suppressive proteins, including Igfbp4 and Tinagl1, as validated by functional and clinical correlation studies. O! verall, these findings suggest a pleiotropic role of miR-200s in promoting metastatic colonization by influencing E-cadherin–dependent epithelial traits and Sec23a-mediated tumor cell secretome. Figures at a glance * Figure 1: miR-200s are associated with poor prognosis in breast cancer. () Kaplan-Meier curves showing the DRFS of 210 subjects with high or low expression of the entire miR-200 family (top), miR-429 (middle) and miR-200a (bottom) in breast tumors. P values were computed by a likelihood-ratio test. () Box plots showing miR-200 expression levels in ten human primary and metastasis (met) samples as assessed by qRT-PCR analysis. Data (mean ± s.e.m.) are normalized to U6, and P values were computed by Student's t test. () Heat map showing miRNA expression levels in 4T1 series. 168, 168FARN. () Phase-contrast images (left) and immunofluorescence images of 4TO7 and 4T1 cells stained for E-cadherin (right). () Imaging as in of MCFCA1h and MCFCA1a cells. Insets highlight the membrane localization of E-cadherin. () Kaplan-Meier curves showing the DRFS of 210 subjects with high or low CDH1 expression. P values were computed by a likelihood-ratio test. * Figure 2: Ectopic miR-200 expression enhances spontaneous metastasis and colonization of distant organs. () Western blot showing expression of indicated proteins in various genetically modified 4TO7 cell lines. () Phase-contrast and immunofluorescence images of cell lines stained for E-cadherin (E-cad) and N-cadherin (N-cad). Yellow outline emphasizes cell morphology. () Plated colonies showing lung colonization by various cell lines used to generate orthotopic mammary gland tumors. Average numbers of colonies are listed below representative plate images. Data represent mean ± s.e.m. from a single representative experiment of three independent experiments. (n = 9 or 10). () Relative expression of puromycin-resistance gene, an indicator of circulating tumor cells, by qRT-PCR analysis of genomic DNA from whole-blood samples. Red dotted lines represent median values. P = 0.02 (Student's t test). () Representative gross lung and H&E-stained lung sections from mice intravenously injected with various 4TO7 cell lines. Red arrowheads and dashed lines mark metastatic nodules. Scale ba! r, 4 mm. () Immunohistochemical (IHC) staining for E-cadherin of lung nodules established from indicated cells. () Fold increase in number of pulmonary metastasis nodules for each group. Data represent mean fold increase ± s.e.m. from a single representative experiment of three independent experiments. (n = 9 or 10). () Left, RT-PCR showing expression of Cdh1 in C1+C2 cells with or without stable Cdh1 knockdown. Right, fold change in number of pulmonary lesions after intravenous inoculation of tumor cells. *P < 0.05, **P < 0.01 (Student's t test). * Figure 3: Ectopic miR-200 expression promotes global changes in gene expression. () Unsupervised clustering highlighting genome-wide changes in gene expression upon miR-200 expression in 4TO7 cells. Experiment was performed twice in duplicates. () Gene-set enrichment analysis showing influence of miR-200 overexpression on the overall gene expression profile of 4TO7 cells. Gene sets used are the top 100 (left) and bottom 100 (right) differentially expressed genes in the test lines (C1, C2, C1+C2 and CDH1) compared with control lines. The gene list used included all mouse genes, ranked by their differential expression between 4T1 and 4TO7 variants. Enrichment of top and bottom 100 genes from 4T1 compared with 4TO7 (ranked list) is shown as an example of maximum possible enrichment. NES, normalized enrichment score. Red and blue double-sided arrows denote the relative number of core genes for each analysis. () Venn diagram showing substantial overlap of core genes from top 100 gene sets for C1 (red circle), C2 (blue circle) and C1+C2 (green circle) lines fr! om . Core genes shared among all three lines are listed. Cdh1 is listed in red to emphasize the positive influence of miR-200s on E-cadherin expression. * Figure 4: Identification of putative miR-200 targets using MS. () Protein abundance compared with mRNA abundance, relative to control cells, for 1,562 genes in C1+C2 cells. Red dots represent genes with miR-200 target sites. () Protein and mRNA abundances as in , for only genes containing miR-200 target sites (n = 130). Red dots denote significantly reduced expression at both mRNA and protein levels (n = 9). Orange dotted circle represents little or no difference from gene expression in control cells. () qRT-PCR validation of reduced expression in C1+C2 cells, relative to control cells, for the nine candidate genes highlighted as red dots in . Data represent mean ± s.e.m. *P < 0.05 (Student's t test). () Heat map showing expression (expressed as fold difference) of the nine candidate genes in MDA-MB-231 (left) and TSU-PR1 (right) cells upon transient transfection of miR-200s (200) relative to pre-miR controls. Pre, control pre-miRNA. ZEB1 and ZEB2 were included as positive controls. () Heat map showing average expression (fold differen! ce) of the nine–candidate gene signature (9-gene) and miR-200b and miR-200c in NCI-60 panel of cell lines. () Luciferase assays in HeLa cells testing direct targeting of eight of nine candidate genes by miR-200s. Data represent percentage difference in normalized luciferase activity upon cotransfection of miR-200s, relative to transfection with the negative control pre-miRNA (mean ± s.e.m.). *P < 0.05, **P < 0.01 (Student's t test). * Figure 5: Sec23a knockdown phenocopies miR-200s in inhibiting migration and promoting metastatic colonization. () Transwell migration assays. Shown are ratios of migration of knockdown (KD) lines over migration of parental 4TO7 cells (mean ± s.e.m. from triplicate experiments. () Fold change in number of pulmonary nodules relative to 4TO7 parental line (mean ± s.e.m.). 'Triple KD' denotes knockdown of all three genes. () Representative gross lung images and H&E-stained lung sections (bottom) from mice intravenously injected with indicated cell lines (KD1 and KD2 denote two different knockdown lines for each gene). Red arrows mark metastatic nodules, except in Sec23a knockdown samples, which contained large numbers of nodules that were outlined with red dashed lines. (,) Relative SEC23A expression in ten human primary tumors compared with expression in ten lung metastases (box plots show 25th, 50th and 75th percentiles (horizontal bars) and 1.5 interquartile ranges (error bars)) (), and in matched primary and lung metastasis samples collected from six individuals (). GAPDH was used ! to normalize expression. Error bars show s.e.m. *P < 0.05 (Student's t test). * Figure 6: Sec23a knockdown disrupts secretion of proteins that are correlated with suppression of clinical metastasis. () Correlation of secretome profiles between two different Sec23a knockdown lines (Sec23a-KD2 and Sec23a-KD3) and between Sec23a-KD2 and C1+C2 lines. Proteins in common between different lines were used to generate the plots. Orange, proteins less abundant in both lines; green, more abundant in both lines; gray, discordant expression patterns. () Kaplan-Meier curves showing RFS of subjects with high or low median expression of 35 genes whose secreted products were reduced in Sec23a-knockdown lines. () Fold increase in number of pulmonary metastases in 4TO7-derived lines with stable knockdown of Axl, Tinagl1 or Igfbp4, relative to vector control (KD1 and KD2 signify different knockdown lines). () Representative gross lung images from animals injected via lateral tail vein with knockdown lines from , along with vector control. **P < 0.01 (Student's t test). () Kaplan-Meier plots of distant metastasis-free survival of patients in the EMC286 data set stratified by expression of ! TINAGL1 (top) or IGFBP4 (bottom). P values were computed by log-rank test. () Schematic model of miR-200 function during metastasis. miR-200s simultaneously target several genes including Zeb1 and Zeb2 (Zeb1/2) and Sec23a to inhibit local invasion but promote metastatic colonization. Targeting of Zeb1/2 influences cell-intrinsic epithelial traits, whereas targeting of Sec23a modulates tumor-derived secretion of factors such as Igfbp4 and Tinagl1, which influence metastatic colonization by altering tumor-stromal interactions. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19631 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA. * Manav Korpal, * Brian J Ell, * Mario A Blanco, * Toni Celià-Terrassa, * Hani Goodarzi, * Yuling Hua, * Yong Wei, * Guohong Hu, * Benjamin A Garcia & * Yibin Kang * Cancer Research UK, Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK. * Francesca M Buffa & * Adrian L Harris * Osteoncology Center, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori, Meldola, Italy. * Toni Ibrahim, * Laura Mercatali & * Dino Amadori * Department of Cell Biology, Institut de Biologia Molecular de Barcelona, Consejo Superior de Investegaciones Cientificas, Barcelona, Spain. * Toni Celià-Terrassa * Department of Computer Science, Princeton University, Princeton, New Jersey, USA. * Zia Khan * The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. * Zia Khan & * Hani Goodarzi * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Jiannis Ragoussis * Genomic Instability and Tumor Progression Program, Cancer Institute of New Jersey, New Brunswick, New Jersey, USA. * Yibin Kang Contributions M.K. and Y.K. designed experiments. M.K., B.J.E., T.C.-T. and Y.H. performed the experiments. F.M.B., T.I., L.M., J.R., D.A. and A.L.H. provided clinical samples and associated analyses. M.A.B., Z.K., H.G., Y.W., G.H. and B.A.G. contributed genomic and proteomic analyses. M.K. and Y.K. wrote the manuscript. All authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yibin Kang Author Details * Manav Korpal Search for this author in: * NPG journals * PubMed * Google Scholar * Brian J Ell Search for this author in: * NPG journals * PubMed * Google Scholar * Francesca M Buffa Search for this author in: * NPG journals * PubMed * Google Scholar * Toni Ibrahim Search for this author in: * NPG journals * PubMed * Google Scholar * Mario A Blanco Search for this author in: * NPG journals * PubMed * Google Scholar * Toni Celià-Terrassa Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Mercatali Search for this author in: * NPG journals * PubMed * Google Scholar * Zia Khan Search for this author in: * NPG journals * PubMed * Google Scholar * Hani Goodarzi Search for this author in: * NPG journals * PubMed * Google Scholar * Yuling Hua Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Guohong Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin A Garcia Search for this author in: * NPG journals * PubMed * Google Scholar * Jiannis Ragoussis Search for this author in: * NPG journals * PubMed * Google Scholar * Dino Amadori Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian L Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Yibin Kang Contact Yibin Kang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Results, Supplementary Discussion, Supplementary Figures 1–9, Supplementary Tables 1–5 and Supplementary Methods Additional data - Anticancer effects of imatinib via immunostimulation
- Nat Med 17(9):1050-1051 (2011)
Article preview View full access options Nature Medicine | Article Imatinib potentiates antitumor T cell responses in gastrointestinal stromal tumor through the inhibition of Ido * Vinod P Balachandran1 * Michael J Cavnar1 * Shan Zeng1 * Zubin M Bamboat1 * Lee M Ocuin1 * Hebroon Obaid1 * Eric C Sorenson1 * Rachel Popow1 * Charlotte Ariyan1 * Ferdinand Rossi2 * Peter Besmer2 * Tianhua Guo3 * Cristina R Antonescu3 * Takahiro Taguchi4 * Jianda Yuan5 * Jedd D Wolchok5, 6 * James P Allison5, 7 * Ronald P DeMatteo1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1094–1100Year published:(2011)DOI:doi:10.1038/nm.2438Received28 December 2010Accepted07 July 2011Published online28 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Imatinib mesylate targets mutated KIT oncoproteins in gastrointestinal stromal tumor (GIST) and produces a clinical response in 80% of patients. The mechanism is believed to depend predominantly on the inhibition of KIT-driven signals for tumor-cell survival and proliferation. Using a mouse model of spontaneous GIST, we found that the immune system contributes substantially to the antitumor effects of imatinib. Imatinib therapy activated CD8+ T cells and induced regulatory T cell (Treg cell) apoptosis within the tumor by reducing tumor-cell expression of the immunosuppressive enzyme indoleamine 2,3-dioxygenase (Ido). Concurrent immunotherapy augmented the efficacy of imatinib in mouse GIST. In freshly obtained human GIST specimens, the T cell profile correlated with imatinib sensitivity and IDO expression. Thus, T cells are crucial to the antitumor effects of imatinib in GIST, and concomitant immunotherapy may further improve outcomes in human cancers treated with targeted a! gents. Figures at a glance * Figure 1: CD8+ T cells contribute to antitumor effects of imatinib. GIST and wild-type mice were treated with vehicle or imatinib and analyzed on day 4 () or 8 (–) using flow cytometry, PET and immunohistochemistry. () Tumor weight. () Tumor uptake of 18FDG, shown by PET. H, heart; T, tumor; B, bladder. A decreasing intensity scale is indicated, yellow (maximum intensity) = 25%. () Left, number of CD8+ T cells in the DLN and inguinal node (IN) of GIST mice and mesenteric node of wild-type (WT) mice. Middle and right, expression (MFI, mean fluorescence intensity, arbitrary units) and frequency of CD8+CD69+ T cells in the DLN of GIST mice, as determined by flow cytometry. () ELISPOT assay for IFN-γ secretion, using purified DLN CD8+ T cells from treated GIST mice cultured with T cell–depleted splenocytes (APC) and tumor cells (Tu). Average spots per well ± s.e.m. (n = 3 wells) are shown and represent two independent experiments. () Left, gating of CD8+ T cells (marked with red boxes) as a percentage of CD45+ lymphocytes. Right, absolute ! number of intratumoral CD8+ T cells. () Gating and frequency of intratumoral CD8+Ki67+ T cells (red boxes). () Histograms, MFI and frequency of intratumoral CD8+CD69+ and CD8+ granzyme B+ T cells. () Tumors stained for CD8. Arrows indicate cells staining for CD8 (dark brown). Hematoxylin (blue) was used as a counterstain. () Tumor weight and volume (measured with magnetic resonance imaging, bottom) in GIST mice depleted of CD8+ or CD4+ T cells or NK cells during 1 (top) or 2 weeks (bottom) of imatinib treatment. Data in –, represent means ± s.e.m. with n ≥ 6 per group. *P < 0.05. * Figure 2: Imatinib induces Treg apoptosis selectively within the tumor. GIST mice were treated with vehicle or imatinib and analyzed by flow cytometry on day 4 (D4) or day 8 (D8). () Representative gating, frequency and absolute numbers of DLN and intratumoral Treg cells on day 8. () Contour plots showing representative gating of annexin V expression on intratumoral Treg cells (CD4+FoxP3+). Bar graphs represent frequency of annexin V+ Treg cells in the DLN and tumor. Loss of Treg cell viability was confirmed using propidium iodide staining. () Ratio of CD8+ T cells to Treg cells in the DLN and tumor on day 8. Data represent means ± s.e.m. with n = 6–11 per group. *P < 0.05. * Figure 3: Imatinib alters intratumoral T cells through inhibition of Ido. () Left, Ido1 mRNA in the DLN and tumor, as determined by microarray analysis of vehicle-treated GIST mice and the tumor from imatinib-treated GIST mice after 7 d. Data represent means ± s.e.m. and are shown relative to internal controls (housekeeping gene); n = 3 per group. Right top, western blot staining for Ido in the DLN, spleen and tumor of vehicle-treated GIST mice and the tumor of imatinib-treated GIST mice; MW, molecular weight. Right bottom, intracellular Ido expression in CD45+ intratumoral immune cells and CD45−Kit+ tumor cells as determined by flow cytometry. () Tumor weight of GIST mice treated with 1-MT for 7 d with or without CD8+ T cell depletion. In –, GIST mice were treated for 7 d with combinations of 1-MT or control (Ctrl), imatinib (I) or vehicle (V) and tryptophan metabolites (metabs). Tumors and DLNs were analyzed using flow cytometry. () Frequency of intratumoral CD8+Ki67+ and CD8+CD69+ T cells. () Frequency of intratumoral annexin V+ Treg cells! . () Ratio of intratumoral CD8+ effector T cells to Treg cells. () Tumor weight. () CD69 and granzyme B expression (MFI, arbitrary units) and frequency of CD8+CD69+ and CD8+ granzyme B+ T cells in the tumor of GIST mice. () Frequency of intratumoral Annexin V+ Treg cells. () Ratio of intratumoral CD8+ T cells to Treg cells. Data in – represent means ± s.e.m. with n = 6–12 per group. *P < 0.05. * Figure 4: Imatinib reduces IDO expression through inhibition of oncogenic KIT signaling. () IDO expression (histograms (left) and MFI (right), arbitrary units) in GIST-T1 and GIST-T1R cells, as determined by flow cytometry. () Ido1 mRNA levels in mouse GIST tumors (left; n = 8 per group) and sorted Kit+ tumor cells (right; n = 3 per group) after treatment with vehicle or imatinib. () Intracellular IDO expression in GIST-T1 cells after culture in rapamycin, as in . () Etv4 mRNA levels in the DLN and tumor, determined by microarray analysis from GIST mice after vehicle or imatinib treatment for 7 d. n = 3 per group. () Etv4 mRNA levels in mouse GIST tumors (left; n = 8 per group) and sorted Kit+ tumor cells (right; n = 3 per group) after treatment with vehicle or imatinib. () Western blot staining for KIT, ETV4 and IDO in GIST mice (left) and GIST-T1 cells (right) after treatment with vehicle or imatinib. Both phosphorylated and nonphosphorylated KIT, STAT3 and S6 are shown as components of oncogenic KIT signaling. IDO in GIST-T1 cells was detected via immunopreci! pitation. MW, molecular weight. () ChIP from GIST mice treated with vehicle or imatinib in vivo (n = 3 per group). In vitro culture experiments were performed with 1 μM imatinib. Data in , and were normalized to internal controls and , are shown relative to vehicle treatment. Data represent either means ± s.e.m. with n as indicated above, or representative plots from triplicate wells analyzed individually. *P < 0.05. * Figure 5: Ratio of intratumoral CD8+ T cells to Treg cells correlates with imatinib sensitivity in human GIST. () Frequency of CD3+ and CD8+ T cells and Treg cells (determined by flow cytometry) in peripheral blood and tumor of untreated (U; n = 15), sensitive (S; n = 17) and resistant (R; n = 13) GIST specimens, and representative gating for Treg cells. The red boxes represent Treg cells as a frequency of CD4+ T cells. () CD69 and CD25 expression (MFI, arbitrary units) on CD8+ T cells from matched peripheral blood and tumor samples. () Ratio of CD8+ T cells to Treg cells in blood and tumor. () Ratio of CD8+ T cells to Treg cells in three patients (Pt.) who underwent synchronous resection of a sensitive and a resistant tumor. () Ratio of CD8+ T cell to Treg cells in tumors expressing low (<4,000 MFI; n = 6) or high (≥4,000 MFI; n = 7) levels of IDO protein as determined by flow cytometry. Data in , represent means ± s.e.m. *P < 0.05. * Figure 6: CTLA-4 blockade is synergistic with imatinib. GIST mice were treated with chronic CTLA-4 blockade and either imatinib or vehicle for 7 d. A third group was treated with imatinib for 7 d and chronic isotype control antibody. () Tumor volume, monitored using serial magnetic resonance imaging. (–) Frequency and absolute number of CD4+ and CD8+ T cells in DLN (), frequency of intratumoral CD4+ and CD8+ T cells (), and ratio of intratumoral CD8+ T cells to Treg cells () of GIST mice analyzed on days 16–18. () IFN-γ production in intratumoral CD8+ T cells stimulated for 4 h with phorbol 12-myristate 13-acetate and ionomycin. Contour plots show representative gating of IFN-γ expression on intratumoral CD8+ T cells. Bar graphs represent the intratumoral frequency of CD8+ IFN-γ+ cells; P = 0.09, two-tailed Student's t test. Data in represent means ± s.e.m. of a composite of two independent experiments, each with 3–5 mice per group. Data in – represent means ± s.e.m. with n = 6–8 per group. *P < 0.05. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Surgery, Memorial Hospital, New York, New York, USA. * Vinod P Balachandran, * Michael J Cavnar, * Shan Zeng, * Zubin M Bamboat, * Lee M Ocuin, * Hebroon Obaid, * Eric C Sorenson, * Rachel Popow, * Charlotte Ariyan & * Ronald P DeMatteo * Developmental Biology Program, Sloan-Kettering Institute, New York, New York, USA. * Ferdinand Rossi & * Peter Besmer * Department of Pathology, Memorial Hospital, New York, New York, USA. * Tianhua Guo & * Cristina R Antonescu * Division of Human Health and Medical Science, Graduate School of Kuroshio Science, Kochi University, Kochi, Japan. * Takahiro Taguchi * The Ludwig Center for Cancer Immunotherapy, New York, New York, USA. * Jianda Yuan, * Jedd D Wolchok & * James P Allison * Department of Medicine, Memorial Hospital, New York, New York, USA. * Jedd D Wolchok * Immunology Program, Sloan-Kettering Institute, New York, New York, USA. * James P Allison Contributions All authors contributed to experimental design. V.P.B., M.J.C., S.Z., Z.M.B., H.O., R.P., C.A., T.G., C.R.A. and J.Y. performed the experiments. All authors assisted in data analysis. V.P.B. and R.P.D. wrote and prepared the manuscript with critical comments from all authors. Competing financial interests R.P.D. serves as a consultant for Novartis and has received honoraria. P.B. has received a commercial research grant from Novartis. J.D.W. serves as a consultant to Novartis and Bristol-Meyers Squibb. CTLA-4 blocking antibody is currently in clinical development by Medarex and Bristol-Meyers Squibb. J.P.A. is a consultant for Medarex and Bristol-Meyers Squibb and is an inventor of intellectual property that has been licensed to Medarex and Bristol-Meyers Squibb by the University of California–Berkeley. Corresponding author Correspondence to: * Ronald P DeMatteo Author Details * Vinod P Balachandran Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Cavnar Search for this author in: * NPG journals * PubMed * Google Scholar * Shan Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Zubin M Bamboat Search for this author in: * NPG journals * PubMed * Google Scholar * Lee M Ocuin Search for this author in: * NPG journals * PubMed * Google Scholar * Hebroon Obaid Search for this author in: * NPG journals * PubMed * Google Scholar * Eric C Sorenson Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel Popow Search for this author in: * NPG journals * PubMed * Google Scholar * Charlotte Ariyan Search for this author in: * NPG journals * PubMed * Google Scholar * Ferdinand Rossi Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Besmer Search for this author in: * NPG journals * PubMed * Google Scholar * Tianhua Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina R Antonescu Search for this author in: * NPG journals * PubMed * Google Scholar * Takahiro Taguchi Search for this author in: * NPG journals * PubMed * Google Scholar * Jianda Yuan Search for this author in: * NPG journals * PubMed * Google Scholar * Jedd D Wolchok Search for this author in: * NPG journals * PubMed * Google Scholar * James P Allison Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald P DeMatteo Contact Ronald P DeMatteo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (918K) Supplementary Figures 1–8, Supplementary Tables 1 and 2 and Supplementary Methods Additional data - Starting the scar: a primary role for pericytes?
- Nat Med 17(9):1052-1053 (2011)
Article preview View full access options Nature Medicine | Community Corner Starting the scar: a primary role for pericytes? Journal name:Nature MedicineVolume: 17,Pages:1052–1053Year published:(2011)DOI:doi:10.1038/nm0911-1052Published online07 September 2011 James W Fawcett Most mammalian tissues produce a scar after injury to prevent infection and damage and to regain control of homeostasis. The CNS is no exception, and the mammalian glial scar is a complex structure with several functions. Scarring has adverse consequences such as blocking axon regeneration, but preventing it delays sealing of the blood-brain barrier and permits extensive inflammatory cell invasion2, 3. A particular feature of the glial scar is the core region, formed of fibroblast-like cells with collagen and new vessels, which is surrounded by reactive astrocytes and other glial cells. This core is particularly prominent after spinal cord injury, expresses inhibitory semaphorin-3A and completely blocks axon regeneration. As modulating core formation might be useful in areas such as spinal repair and stroke treatment, the origin of the core cells has been of interest. One theory has been that many of the core cells invade from the fibroblast-like cells of the meninges, but even in nonpenetrating injuries there is usually a fibrotic core, suggesting an alternative origin. Now, through genetic labeling, Göritz et al.1 show that perivascular pericytes can divide, migrate and provide a major contribution to the core. The glutamate aspartate transporter (Glast) label they used also appears to identify cells in the meninges, so some of these cells are also probably present there. The authors also asked whether suppressing formation of the lesion core by preventing division of pericytes would alter scar formation. However, mice whose pericytes could not divide had lesions that failed to close and seal, which may leave the CNS vulnerable to further damage. Pericytes are important for blood-brain barrier formation and for vascular control, so simply removing them from CNS injuries will probably increase brain damage4. However, knowing the origin of the lesion core should make it possible to devise more subtle methods of modulating the glial scar to allow protection and repair. Raghu Kalluri For decades, α-smooth muscle actin (SMA)-positive myofibroblasts have been considered the producers of type I collagen and mediators of tissue fibrosis, but the supporting evidence is descriptive, not functional5. An open question is whether these cells are the sole mediators of fibrosis or whether other cells also contribute. Although myofibroblasts have been a major focus of fibrosis research, it is now becoming clear that fibrosis-generating cells in a given tissue are probably heterogeneous, thus appreciating this possibility is necessary for the open-minded investigation of fibrosis. CNS injury–induced fibrosis is associated with an increase in astrocyte number, and Göritz et al.1 now suggest that pericytes, not astrocytes, are the source of fibrotic cells after spinal cord injury. Using Glast-cre ER transgenic mice as a tool for cellular fate mapping, the authors identify a subset of cells that they term type A pericytes. Their assertion is driven by pseudocoloring of electron microscopy pictures based on immunofluorescence images, but, in these images, the astrocytes show more basement membrane association compared to the 'type A pericytes'. C.J. Guerin, PhD, MRC Toxicology Unit/Photo Researchers, Inc. "Glast is expressed in several CNS types: astrocytes, Bergmann glia cells, Muller cells and neural stem cells." Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Competing financial interests J.W.F. is a paid consultant for Acorda Therapeutics and Novartis. J.S.D. is on the scientific advisory board of Regulus Therapeutics and Promedior. He has stock options in Promedior. Additional data * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * Open Faculty Positions * The University of Alabama * Program Chair * Cold Spring Harbor Laboratory * Senior Research Scientist (Ref: LASRS1) * ReResearch * Post a free job * More science jobs Open innovation challenges * A GRI (Glucose Responsive Insulin) for Better Treatment of Type 1 Diabetes Deadline:Nov 09 2011Reward:$100,000 USD This Challenge, sponsored by the Juvenile Diabetes Research Foundation International (JDRF) (the Se… * Algorithm to Identify Underlying Geometric Features in Noisy 2D Data Deadline:Sep 19 2011Reward:$30,000 USD The Seeker desires an algorithm that can identify the size, shape, and depth of underground feature… * Powered by: * More challenges Top content Emailed * E3 ubiquitin ligase Cblb regulates the acute inflammatory response underlying lung injury Nature Medicine 08 Jul 2007 * In vivo detection of Staphylococcus aureus endocarditis by targeting pathogen-specific prothrombin activation Nature Medicine 21 Aug 2011 * DNA released from dying host cells mediates aluminum adjuvant activity Nature Medicine 17 Jul 2011 * In vivo detection of Staphylococcus aureus endocarditis by targeting pathogen-specific prothrombin activation Nature Medicine 21 Aug 2011 * Killing the messenger to maintain control of HIV Nature Medicine 04 Aug 2011 View all Downloaded * Artificially engineered magnetic nanoparticles for ultra-sensitive molecular imaging Nature Medicine 24 Dec 2006 * Stem cell gene expression programs influence clinical outcome in human leukemia Nature Medicine 28 Aug 2011 * Pathway to diabetes through attenuation of pancreatic beta cell glycosylation and glucose transport Nature Medicine 14 Aug 2011 * Host S-nitrosylation inhibits clostridial small molecule–activated glucosylating toxins Nature Medicine 21 Aug 2011 * Vascular anastomosis using controlled phase transitions in poloxamer gels Nature Medicine 28 Aug 2011 View all Blogged * A recombinant Mycobacterium smegmatis induces potent bactericidal immunity against Mycobacterium tuberculosis Nature Medicine 04 Sep 2011 * OpenFreezer: a reagent information management software system Nature Medicine 28 Jul 2011 * Vascular anastomosis using controlled phase transitions in poloxamer gels Nature Medicine 28 Aug 2011 * Building a better mouse test Nature Medicine 30 Aug 2011 * A viral strategy to ambush tumors Nature Medicine 07 Jul 2011 View all - Autoimmunity's collateral damage: Immunodeficiency hints at autoreactivity to cytokines
- Nat Med 17(9):1054-1055 (2011)
Nature Medicine | Between Bedside and Bench Autoimmunity's collateral damage: Immunodeficiency hints at autoreactivity to cytokines * Michael Waterfield1 * Mark S Anderson1 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1054–1055Year published:(2011)DOI:doi:10.1038/nm0911-1054Published online07 September 2011 Autoimmunity develops when one's own immune cells and pathogenic antibodies react against the body, causing inflammation, degeneration, tissue destruction and even organ failure. But autoimmunity mediators can also evoke other pathological side effects, and individual factors can worsen the morbidity of the people suffering from autoimmune disorders, adding another level of complexity to these diseases. In 'Bedside to Bench', Mark Anderson and Michael Waterfield peruse a potential link between immunodeficiency and autoimmunity. Autoantibodies against cytokines involved in tackling Candida albicans infection may underlie the trait of increased susceptibility to yeast observed in people with such autoantibodies. In 'Bench to Bedside', Daniel Cua and Jonathan Sherlock discuss how the immune response induced by gut microbiota may be responsible for autoimmune attacks at distant sites, such as the joints. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Michael Waterfield is in the Department of Pediatrics and Mark S. Anderson is at the Diabetes Center, University of California–San Francisco, San Francisco, California. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mark S Anderson Author Details * Michael Waterfield Search for this author in: * NPG journals * PubMed * Google Scholar * Mark S Anderson Contact Mark S Anderson Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Autoimmunity's collateral damage: Gut microbiota strikes 'back'
- Nat Med 17(9):1055-1056 (2011)
Article preview View full access options Nature Medicine | Between Bedside and Bench Autoimmunity's collateral damage: Gut microbiota strikes 'back' * Daniel J Cua1 * Jonathan P Sherlock1 * Affiliations * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:1055–1056Year published:(2011)DOI:doi:10.1038/nm0911-1055Published online07 September 2011 The bacteria resident in the intestine of mammals have coevolved with their hosts and represent an important and highly selected metagenome1 that contributes in numerous ways to the hosts' health2. These commensal bacteria provide defense against pathogenic bacteria not simply by competing for nutrients and physical niches but by inducing specific immune responses. Indeed, the development of the mucosal immune system is dependent on bacteria. Gut microbiota influence the balance between T helper type 1 (TH1) and TH2 lymphocytes3 important in host defense, as well as the development of TH17 cells, which have recently been described to be important in antifungal responses and autoimmune pathology. TH17 cells are absent in the lamina propria of germ-free mice, and colonization of these mice with one bacterial strain, segmented filamentous bacteria (SFB), is sufficient for their reconstitution4. The consequences of this dependency of TH17 cells on intestinal bacterial flora have recently been highlighted by Wu et al.5, who show that commensal microbes can have profound effects on clinical disease. SFB promote the development of arthritis in the K/BxN mouse model, with disease being abrogated under germ-free conditions and restored after colonization with these bacteria. These mice, which express a transgenic T cell receptor recognizing peptides derived from glucose phosphate isomerase (GPI), develop an arthritis that depends on formation of antibodies to GPI. In germ-free mice, levels of these antibodies are reduced and arthritis is attenuated; however, transfer of the mice into a conventional animal facility restores antibody formation. IL-17 is crucial in this process through its ability to enhance germinal center formation by acting on B lymphocytes expressing IL-17 receptor. Notably, TH17 development can be abrogated when mice are treated with antibiotics target! ing Gram-positive bacteria, such as SFB, but not Gram-negative organisms, and this also inhibits arthritis. Manipulation of a single commensal bacterial species can therefore result in potent alterations in systemic rheumatic pathology. These principles are of great interest in ankylosing spondylitis and other spondyloarthropathies—rheumatic diseases characterized by inflammation at entheses, which are attachment sites of tendons and ligaments onto bone. Clinical observations demonstrate the importance of bowel involvement in these conditions, as one major subtype of spondyloarthropathy is associated with overt inflammatory bowel disease (IBD), and spondyloarthropathy without gastrointestinal symptoms shows subclinical inflammation of the terminal ileum in up to 68% of affected individuals6. Alterations in host-microbe interactions in the gut may therefore contribute to the pathogenesis of inflammatory disease in distant joint tissues (Fig. 1). Figure 1: Commensal bacteria may have a role in producing inflammatory cytokines that can worsen autoimmune disease in the joints. Gut Gram-positive bacteria, such as SFB, induce IL-1, IL-6 and IL-23 in the mucosa and also a TH17 response, increasing IL-17 and IL-22. The release of these cytokines may initiate IBD, but, when overproduced, they may spill into the systemic circulation. This may promote inflammatory diseases in distal sites, such as the joints, perhaps through action upon joint-resident lymphoid cell populations. Altered sensitivity to IL-23 may predispose people to develop rheumatic diseases, such as ankylosing spondylitis. Katie Vicari * Full size image (101 KB) The terminal ileum is known to constitutively produce inflammatory cytokines, including IL-1 and IL-23, in the presence of commensal microbes7, and their local excessive production is associated with immune pathologies leading to inflammatory bowel disease (IBD). We suggest that the 'spilling over' of these cytokines into the systemic circulation can promote rheumatic disease at distal sites. Indeed, human ankylosing spondylitis is accompanied by IL-23 overproduction in the terminal ileum8. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Medicine for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Daniel J. Cua and Jonathan P. Sherlock are at Merck Research Laboratories, Palo Alto, California. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Daniel J Cua or * Jonathan P Sherlock Author Details * Daniel J Cua Contact Daniel J Cua Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan P Sherlock Contact Jonathan P Sherlock Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Resolving controversies on the path to Alzheimer's therapeutics
- Nat Med 17(9):1060-1065 (2011)
Nature Medicine | Perspective Resolving controversies on the path to Alzheimer's therapeutics * Dennis J Selkoe1Journal name:Nature MedicineVolume: 17,Pages:1060–1065Year published:(2011)DOI:doi:10.1038/nm.2460Published online07 September 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Alzheimer's disease constitutes a personal and societal tragedy of immense proportions. Since 1960, research in laboratories and clinics worldwide has elucidated many features of this insidious and ultimately fatal syndrome, and this progress has led to initial human trials of potentially disease-modifying agents. However, some of these agents have already failed. Gnawing controversies and important gaps in our knowledge seem to cast additional doubt on the ability of the field to move forward effectively. Here I discuss some of these looming concerns and offer possible explanations for the major trial failures that suggest they are not predictive of the future. Rigorous preclinical validation of mechanism-based therapeutic agents followed by meticulously designed trials that focus on the cardinal cognitive symptoms and their associated biomarkers in the mild or presymptomatic phases of Alzheimer's disease are likely to lead to success, perhaps in the not-too-distant future. View full text Figures at a glance * Figure 1: Approximate timeline of some principal discoveries in Alzheimer's disease research since 1960. The list is by no means exhaustive and focuses on findings deemed important for the current stage of general understanding of Alzheimer's disease (AD) pathogenesis and for the development of potentially disease-modifying agents. EM, electron microscopy; PHF, paired helical filaments; HCHWA-D, hereditary cerebral hemorrhage with amyloidosis-Dutch type; CAA, Congophilic amyloid angiopathy; PS, presenilin; tg, transgenic; BACE1, β-secretase 1; FTD, frontotemporal dementia. Red, genetic discoveries; blue, discoveries about molecular pathogenesis in cells and animals; green, clinical trials. * Figure 2: Intersecting disease-modifying agents for Alzheimer's disease with the course of the disease. Blue boxes, sequence of steps in the discovery of compounds or biologics appropriate for investigational new drugs (INDs) in Alzheimer's disease. Red boxes, speculative stages in the long presymptomatic and symptomatic phases of Alzheimer's disease in a hypothetical individual who undergoes Aβ buildup for one of several possible reasons (for example, presenilin or APP mutation; ApoE4 inheritance; increased BACE activity, and so on) and develops MCI by around age 70. Green boxes, clinical trial categories dependent on the stage of Alzheimer's disease. Red X, trials in moderate Alzheimer's disease not recommended. Author information * Abstract * Author information Affiliations * Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Dennis J Selkoe Competing financial interests D.J.S. is a founding scientist of Athena Neurosciences and a consultant to Elan plc. He is also a consultant to Johnson & Johnson in neuroscience. Author Details * Dennis J Selkoe Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Pathway to diabetes through attenuation of pancreatic beta cell glycosylation and glucose transport
- Nat Med 17(9):1067-1075 (2011)
Nature Medicine | Article Pathway to diabetes through attenuation of pancreatic beta cell glycosylation and glucose transport * Kazuaki Ohtsubo1, 2, 3 * Mark Z Chen4 * Jerrold M Olefsky4 * Jamey D Marth1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1067–1075Year published:(2011)DOI:doi:10.1038/nm.2414Received07 April 2011Accepted07 June 2011Published online14 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A connection between diet, obesity and diabetes exists in multiple species and is the basis of an escalating human health problem. The factors responsible provoke both insulin resistance and pancreatic beta cell dysfunction but remain to be fully identified. We report a combination of molecular events in human and mouse pancreatic beta cells, induced by elevated levels of free fatty acids or by administration of a high-fat diet with associated obesity, that comprise a pathogenic pathway to diabetes. Elevated concentrations of free fatty acids caused nuclear exclusion and reduced expression of the transcription factors FOXA2 and HNF1A in beta cells. This resulted in a deficit of GnT-4a glycosyltransferase expression in beta cells that produced signs of metabolic disease, including hyperglycemia, impaired glucose tolerance, hyperinsulinemia, hepatic steatosis and diminished insulin action in muscle and adipose tissues. Protection from disease was conferred by enforced beta cel! l–specific GnT-4a protein glycosylation and involved the maintenance of glucose transporter expression and the preservation of glucose transport. We observed that this pathogenic process was active in human islet cells obtained from donors with type 2 diabetes; thus, illuminating a pathway to disease implicated in the diet- and obesity-associated component of type 2 diabetes mellitus. View full text Figures at a glance * Figure 1: Dietary regulation of Mgat4a and Slc2a2 gene expression by Foxa2 and Hnf1a in mouse pancreatic islet cells. () Abundance of mRNA produced from Mgat4a, Slc2a2 and Ins2 genes in mouse pancreatic islet cells (>90% beta cells) isolated from 18- to 22-week-old WT mice on standard (SD) and high-fat diet (HFD) dietary regimens. (–) ChIP and real-time PCR (rtPCR) analysis of acetylated histone H4 (AcH4) bound to promoter regions of the Mgat4a, Slc2a2 and Ins2 genes () and binding to these regions by Foxa2 () and Hnf1a (). ND, not detected. () In situ localization of Foxa2 and Hnf1a proteins in beta cells from mouse islet tissue in response to diet. Results represent analyses of >24 fields of view consisting of >100 beta cells. Two different antibodies that detect Foxa2 (07-633 and M-20) were used. Image analyses quantified percentage of cellular protein localized to nuclear region. () Total islet cell abundance of Foxa2, Pdx1, Hnf4a, Arnt and Hnf1a proteins determined using antibodies specific for each factor. () Pancreatic beta cells from mice receiving SD transfected with siRNAs to kn! ock down Foxa2 and Hnf1a, or with a siRNA control, were cultured for 72 h followed by mRNA abundance measurements using rtPCR. Mice for study were normal C57BL/6J mice 18–22 weeks old before initial experimentation. Data in – are means ± s.e.m. of triplicate experiments per mouse from ≥12 mice consisting of ≥6 separate littermate pairs. &P = 0.0007; $P = 0.0049; *P = 0.0421; **P = 0.0418; ***P < 0.0001 (Student's t test, –; Bonferroni test after analysis of variance (ANOVA), ). * Figure 2: Effect of palmitic acid on normal mouse and human islet cells. () Subcellular localization of Foxa2 and Hnf1a proteins within beta cells from islet tissue measured in response to palmitic acid (PA) and N-acetylcysteine (NAC). Propidium iodide (PI) staining of the nucleus (red) is also included where indicated. () mRNA expression of Mgat4a, Slc2a2, Foxa2 and Hnf1a measured by rtPCR from WT islet cells isolated and cultured in medium containing 5 mM glucose for 48 h with or without PA and NAC. Islets in and were isolated from normal C57BL/6J mice maintained on standard diet. () In situ localization of FOXA2 and HNF1A proteins in beta cells from normal human islet tissue measured with or without PA. (,) ChIP and rtPCR measurement of FOXA2 () and HNF1A () proteins bound to promoter regions of human MGAT4A, SLC2A1, SLC2A2 and INS genes. ND, not detected; NA, no addition. () Abundance of mRNA from MGAT4A, SLC2A1, SLC2A2 and genes in human islet cells. () Glucose-stimulated insulin secretion assayed in islet cell cultures containing medium bea! ring indicated concentrations of glucose. Results in and represent analyses of >24 fields of view consisting of >100 beta cells. Data are means ± s.e.m. of triplicate experiments per mouse from six mice fed standard diet and four normal human islet donors. *P = 0.01–0.049; **P = 0.005–0.009; ***P = 0.0004; ****P < 0.0001 (Student's t test). * Figure 3: Analyses of human islets from normal donors and donors with type 2 diabetes. () Subcellular localization of FOXA2 and HNF1A proteins in human beta cells from normal donors and donors with type 2 diabetes (T2D). Results represent analyses of six normal islet samples and two T2D islet samples. Propidium iodide (PI) staining of the nucleus (red) is included where indicated. () Abundance of mRNA produced from MGAT4A, SLC2A1, SLC2A2 and INS genes in the designated human islet cells. () Islet cell surface abundance of the DSA lectin-binding glycan produced by the GnT-4a glycosyltransferase. () Islet cell surface expression of human GLUT-1 and GLUT-2 glucose transporters. () Glucose transport activity of the indicated human islets measured using the fluorescent glucose analog 2-NBDG. () Glucose-stimulated insulin secretion assayed in islet cell cultures containing medium bearing the indicated concentrations of glucose. () Left, GLUT-1 and GLUT-2 glycoproteins were immunoprecipitated (IP) from normal human islet cell extracts followed by electrophoresis and ! analyses with the indicated antibodies and lectins. Right, deduced tetra-antennary N-glycan structure residing on both human islet cell GLUT-1 and GLUT-2 bearing undersialylated glycan branch termini (+/−) . Gray circle, core β1-4GlcNAc linkage produced by GnT-4a. () Normal human islet cells were cultured with or without the indicated glycans (10 mM) for 2 h before analyses of cell surface GLUT-1 and GLUT-2 expression by flow cytometry. () Fluorescent glucose analog (2-NBDG) transport (left) and GSIS activity (right) measured among islet cells treated in . The results in and represent analyses of three islet cell samples from normal human donors. () siRNA knockdown of GLUT1 and GLUT2 mRNA in normal human islet cell cultures measured at 72 h (left four graphs). Glucose analog 2-NBDG transport and GSIS activity were also measured at 72 h. Data are expressed as means ± s.e.m. from six normal human islet donors and two human donors with T2D, unless otherwise stated. *P = 0.! 01–0.04; **P = 0.002–0.005; ***P = 0.0002–0.0005; ****P ! < 0.0001 (Student's t test). * Figure 4: Enforced beta cell–specific GnT-4a glycosylation prevents loss of Glut-2 expression and inhibits onset of disease signs including hyperglycemia and failure of GSIS. () Human MGAT4A cDNA was incorporated into a transgene vector that conferred beta cell–specific expression in multiple tissues of two separate founder lines, 978 and 980. Transgene expression was detected using vector-specific primers. () Pancreatic beta cell histology from WT and MGAT4A transgenic littermates that received either standard diet (SD) or high-fat diet (HFD) for the preceding 10 weeks. Antibody binding and visualization of GLUT-2 (green), insulin (red) and DNA (DAPI-blue). () GLUT-2 protein abundance and glycosylation in beta cells from WT or MGAT4A (Tg) littermates as in were analyzed by blotting GLUT-2 immunoprecipitates with antibody to GLUT-2 or DSA lectin. Single analysis shown represents three independent experiments with different littermates. () Blood glucose (left), blood insulin (center) and body weight (right) were measured (unfasted) every 2 weeks for up to 16 weeks of HFD administration (,). In fasted mice receiving SD or the HFD for 10 weeks, gl! ucose was injected into the intraperitoneal space before analysis of glucose tolerance () and insulin release (). () GSIS activity was analyzed ex vivo by perifusion in islet cells isolated from mice that received either SD or HFD for the preceding 4 weeks. Glucose concentration was increased from 2.8 mM to 16.8 mM at the time indicated. Data are mean ± s.e.m. of three independent studies of beta cells from distinct littermates. Data from WT beta cells, red line. () Insulin secretion response to L-arginine injection measured in fasting WT and MGAT4A Tg mice receiving SD. Data are means ± s.e.m. in triplicate experiments per mouse, from six or seven mice of each genotype unless otherwise stated. * Figure 5: Beta cell–specific GnT-4a protein glycosylation promotes systemic insulin sensitivity and inhibits development of hepatic steatosis. () Insulin challenge response measured in 16–18 week old littermates of indicated genotypes after 10 weeks of standard diet (SD) or high-fat diet (HFD) administration. () Phosphorylation of Akt-1 at Thr308 and IRS-1 at Ser307 in equivalent cellular protein preparations from adipose and muscle tissue of mice as in after 2 min of insulin perfusion through inferior vena cava. () Euglycemic and hyperinsulinemic clamp studies after 10 weeks on HFD compared measurements of glucose infusion rate, glucose disposal rate, insulin-stimulated glucose disposal rate, and suppression of hepatic glucose production in response to insulin. The analyses included nine WT and seven Tg mice. (,) Liver tissue observed macroscopically () and by histological analysis stained with H&E (). *P = 0.0017; **P = 0.0235; ***P = 0.0401 (single-tail t test). Data are means ± s.e.m. from three or more littermates unless otherwise indicated. * Figure 6: Enforced beta cell–specific expression of GnT-4a substrate GLUT-2 mitigates diet- and obesity-induced diabetes. () Human SLC2A2 cDNA was incorporated into a transgene vector that conferred beta cell–specific expression in multiple tissues of two separate founder lines, 926 and 930. () Beta cell surface expression of GLUT-2 analyzed by flow cytometry from WT, MGAT4A Tg (978), and SLC2A2 Tg (926) mice receiving standard diet (SD). () Glucose analog 2-NBDG transport into cultured islet cells isolated from WT mice and Tg littermates fed SD. () Pancreatic beta cell histology of WT and MGAT4A Tg littermates that received either SD or high-fat diet (HFD) for the preceding 10 weeks. Antibody binding and visualization of Glut-2 (green), insulin (red) and DNA (blue). () Islet cell Glut-2 and GLUT-2 immunoprecipitates were analyzed for Glut-2 abundance and DSA lectin binding from WT or SLC2A2 Tg littermates administered indicated dietary stimuli. Single analysis shown represents three independent experiments with different littermates. () Histogram of GLUT-2 protein abundance (left) and GLUT-2! glycosylation (right) was calculated from results in . () Blood glucose (left), blood insulin (right) and body weight (bottom) were measured in unfasted mice every 2 weeks for up to 16 weeks of indicated diet administration. () In fasted mice that had received SD or HFD for 10 weeks, glucose tolerance tests measured blood glucose (left) and insulin release (middle). GSIS activity was analyzed ex vivo by perifusion (bottom) in islet cells isolated from mice that received either SD or HFD for preceding 4 weeks. Glucose concentration was increased from 2.8 mM to 16.8 mM at time indicated. Data from WT beta cells, red line. () Insulin secretion response to L-arginine injection in fasting WT and SLC2A2 Tg mice receiving SD. () Euglycemic and hyperinsulinemic clamp studies after 10 weeks on HFD compared measurements of glucose infusion rate, glucose disposal rate, insulin-stimulated glucose disposal rate and hepatic suppression of gluconeogenesis. The clamp studies included eigh! t WT and eight SLC2A2 Tg littermates. Data plotted throughout ! are means ± s.e.m. At least six or seven mice including littermates were studied in each experiment unless otherwise stated. *P = 0.01–0.04; **P = 0.001–0.09; ***P = 0.0001–0.0009; ****P < 0.0001 (Student's t test, , and ; single-tail t test, ). Author information * Abstract * Author information * Supplementary information Affiliations * Center for Nanomedicine, Sanford-Burnham Medical Research Institute, University of California–Santa Barbara, Santa Barbara, California, USA. * Kazuaki Ohtsubo & * Jamey D Marth * Department of Molecular, Cellular and Developmental Biology, University of California–Santa Barbara, Santa Barbara, California, USA. * Kazuaki Ohtsubo & * Jamey D Marth * Disease Glycomics Team, RIKEN Advanced Science Institute, Saitama, Japan. * Kazuaki Ohtsubo * Department of Medicine, University of California–San Diego, La Jolla, California, USA. * Mark Z Chen & * Jerrold M Olefsky Contributions K.O. conducted the majority of experiments and helped write the manuscript. M.Z.C. and J.M.O. carried out the hyperinsulinemic-euglycemic clamp studies and helped write the manuscript. J.D.M. conceived of and supervised the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jamey D Marth Author Details * Kazuaki Ohtsubo Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Z Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jerrold M Olefsky Search for this author in: * NPG journals * PubMed * Google Scholar * Jamey D Marth Contact Jamey D Marth Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (578K) Supplementary Figures 1–4, Supplementary Table 1 and Supplementary Methods Additional data - ATGL-mediated fat catabolism regulates cardiac mitochondrial function via PPAR-α and PGC-1
- Nat Med 17(9):1076-1085 (2011)
Nature Medicine | Article ATGL-mediated fat catabolism regulates cardiac mitochondrial function via PPAR-α and PGC-1 * Guenter Haemmerle1 * Tarek Moustafa2 * Gerald Woelkart3 * Sabrina Büttner1 * Albrecht Schmidt4 * Tineke van de Weijer5 * Matthijs Hesselink6 * Doris Jaeger1 * Petra C Kienesberger1 * Kathrin Zierler1 * Renate Schreiber1 * Thomas Eichmann1 * Dagmar Kolb1 * Petra Kotzbeck1 * Martina Schweiger1 * Manju Kumari1 * Sandra Eder1 * Gabriele Schoiswohl1 * Nuttaporn Wongsiriroj1 * Nina M Pollak1 * Franz P W Radner1 * Karina Preiss-Landl1 * Thomas Kolbe6 * Thomas Rülicke7 * Burkert Pieske4 * Michael Trauner2 * Achim Lass1 * Robert Zimmermann1 * Gerald Hoefler8 * Saverio Cinti9 * Erin E Kershaw10 * Patrick Schrauwen5 * Frank Madeo1 * Bernd Mayer3 * Rudolf Zechner1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1076–1085Year published:(2011)DOI:doi:10.1038/nm.2439Received20 April 2011Accepted08 July 2011Published online21 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that regulate genes involved in energy metabolism and inflammation. For biological activity, PPARs require cognate lipid ligands, heterodimerization with retinoic X receptors, and coactivation by PPAR-γ coactivator-1α or PPAR-γ coactivator-1β (PGC-1α or PGC-1β, encoded by Ppargc1a and Ppargc1b, respectively). Here we show that lipolysis of cellular triglycerides by adipose triglyceride lipase (patatin-like phospholipase domain containing protein 2, encoded by Pnpla2; hereafter referred to as Atgl) generates essential mediator(s) involved in the generation of lipid ligands for PPAR activation. Atgl deficiency in mice decreases mRNA levels of PPAR-α and PPAR-δ target genes. In the heart, this leads to decreased PGC-1α and PGC-1β expression and severely disrupted mitochondrial substrate oxidation and respiration; this is followed by excessive lipid accumulation, cardiac insufficiency and! lethal cardiomyopathy. Reconstituting normal PPAR target gene expression by pharmacological treatment of Atgl-deficient mice with PPAR-α agonists completely reverses the mitochondrial defects, restores normal heart function and prevents premature death. These findings reveal a potential treatment for the excessive cardiac lipid accumulation and often-lethal cardiomyopathy in people with neutral lipid storage disease, a disease marked by reduced or absent ATGL activity. View full text Figures at a glance * Figure 1: Expression of PPAR-α and PPAR-δ target genes and PGC-1α and PGC-1β in AtglKO, HslKO, and wild-type tissues. mRNA expression levels for selected PPAR-α and PPAR-δ target genes and PGC-1α and PGC-1β were determined by RT-qPCR analysis. (,) Cardiac () and hepatic () mRNA expression of PPAR-α and PPAR-δ target genes were markedly decreased in fasted 8- to 10-week-old female AtglKO mice compared to age-matched HslKO and wild-type mice. (,) mRNA levels of genes encoding PGC-1α and PGC-1β mRNA were also reduced in cardiac muscle () but increased in the liver () of fasted AtglKO mice compared to wild-type mice. n = 4. Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 2: Morphology, glycogen content, mitochondria size and mitochondrial DNA content in cardiac muscle of wild-type and AtglKO mice. () Cardiac muscle glycogen content (measured as glucose after hydrolysis) of 10-week-old female wild-type and AtglKO mice (n = 9). () Transmission electron microscopy of cardiac muscle sections from 10-weeks old female mice. Top images, wild-type cardiac muscle sections show a typical intermyofibrillar network containing mitochondria (M), glycogen (*Gly) and lipid droplets (LD). In AtglKO cardiac muscle (lower panels) lipid droplet size and the number of glycogen granules embedded within the intermyofibrillar network are increased. VE, vessel. Scale bars, 1 μm for upper and lower left images; 0.5 μm for upper and lower right images. (,) Morphometric () and cytofluorimetric () analyses of mitochondria from cardiac muscle of wild-type and AtglKO mice. Size was either determined from sections of 100 randomly selected mitochondria per genotype or from isolated mitochondria (fluorescence-activated cell sorting (FACS) analysis, n = 4). AU, arbitrary units. () Relative mitochondr! ial DNA (mtDNA) content (normalized to the single-copy nuclear gene Ndufv1) in cardiac muscle of 10-week-old female wild-type and AtglKO mice (n = 5). Error bars are means ± s.d. **P < 0.01. * Figure 3: Mitochondrial OXPHOS function and oxidative stress in cardiac muscle of wild-type and AtglKO mice. (,) Oxygen consumption, an indicator for mitochondrial respiration, in AtglKO cardiac homogenates of 4-week-old () and 8-week-old () male mice in the presence of glucose (n = 6). () Triglyceride (TG) content in cardiac muscle of wild-type and AtglKO mice. (,) Oxygen flux of mitochondria isolated from cardiac tissue of 8- to 9-week-old male wild-type and AtglKO mice. ADP-driven (state 3) and uncoupled (state U) oxygen flow was measured in the presence of pyruvate () and palmitoyl-CoA () in subsarcolemmal (SS) and in intramyofibrillar (IMF) mitochondria (n = 6). () Western blotting analysis of mitochondrial respiratory chain proteins NDUFA9 of complex I and SDHA of complex II in mitochondrial preparations of AtglKO mice and wild-type mice. MTCO1, a marker of complex IV, served as loading control. () Mitochondrial membrane potential (tetramethyl-rhodaminemethylester perchlorate (TMRM) staining) in isolated cardiac mitochondria of 8- to 9-week-old female AtglKO compared to wild-! type mice (n = 4). () Relative concentrations of non-oxidized (free) thiol groups in isolated mitochondria of 8- to 9-week-old female AtglKO mice compared to wild-type mice (n = 4). Error bars are means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 4: Changes in PPAR-α and PPAR-δ activated gene expression and OXPHOS in mice lacking or overexpressing Atgl in cardiac muscle. () Cardiac triglyceride content in wild-type and conditional knockout mice lacking Atgl in cardiac and skeletal muscle (muscleAtglKO mice) demonstrating a drastic cardiac steatosis in muscleAtglKO mice (n = 5). Scale bars, 5 mm. () mRNA expression levels of PPAR-α and PPAR-δ target genes and of the gene encoding PGC-1α in cardiac muscle of muscleAtglKO mice compared to wild-type mice (n = 5). (–) Heart weight (), cardiac muscle triglyceride (TG) content (), and white and brown adipose tissue (WAT and BAT) weight () of wild-type, AtglKO and AtglKO-cmAtglTG mice expressing an Atgl transgene on an AtglKO background (n = 6). () mRNA expression levels of PPAR-α and PPAR-δ target genes and genes encoding PGC-1α and PGC-1β in cardiac muscle of wild-type, AtglKO and AtglKO-cmAtglTG mice (n = 4). () Oxygen consumption in cardiac homogenates prepared from 8- to 9-week-old female wild-type and AtglKO-cmAtglTG mice (n = 6). () Relative luciferase activities in lysates of HepG2 ! cells transfected with a PPRE-luciferase reporter plasmid and a PPAR-α expression vector. The additional expression of Atgl increases luciferase activity in the absence or presence of exogenously added linoleic acid (LA). Transfection of the bacterial β-galactosidase gene (lacZ)-containing plasmid and colorimetric determination of β-galactosidase (β-gal) enzyme activity was used for normalization of transfection efficiency. Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 5: Triglyceride content, oxygen consumption and cardiac function in AtglKO mice treated with PPAR-α agonists. () Cardiac and hepatic triglyceride content in 6-week-old female AtglKO mice on chow diet with or without 0.1% Wy14643 for 3 weeks (n = 5). () Cardiac and hepatic triglyceride content in 6-week-old female AtglKO mice on chow diet with or without 0.2% fenofibrate for 10 weeks (n = 4–5). () mRNA expression levels of PPAR-α and PPAR-δ target genes and genes encoding PGC-1α and PGC-1β in cardiac muscle of female wild-type and AtglKO mice fed a chow diet with or without 0.1% Wy14643 for 3 weeks (n = 5). () Oxygen consumption in cardiac muscle preparations under both basal conditions and succinate-stimulated conditions of 9-week-old male wild-type, AtglKO mice and AtglKO mice fed a chow diet with 0.1% (wt/wt) Wy14643 for 3 weeks (n = 5). () Representative echocardiographic images (M- and B-Mode) of a 9-week-old female wild-type and AtglKO mouse on chow diet and a 9-week-old female AtglKO mouse fed a chow diet containing 0.1% (wt/wt) Wy14643 for 3 weeks. We measured intervent! ricular septum (IVS) and posterior wall (PW) thickness from original tracings. We measured left ventricular end-systolic dimensions (ESD) and left ventricular end-diastolic dimensions (EDD) from original tracings according to the leading edge convention of the American Society of Echocardiography. (,) Left ventricular fractional shortening (LVFS) () and left ventricular (LV) mass (), calculated from the echocardiographic tracings as previously described54 (n = 5). Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. * Figure 6: Life span, tissue triglyceride content and energy substrate utilization in wild-type and AtglKO mice treated with the PPAR-α agonist Wy14643. (,) Treatment of 8-week-old AtglKO mice on chow diet containing 0.1% WY14643 for 12 weeks prevented cardiac death () and lowered tissue triglyceride (TG) content (), including in cardiac muscle and liver, compared to that observed in wild-type animals (n = 4). () Relative whole-body oxygen consumption of 8- to 9-week-old female wild-type and AtglKO mice housed in metabolic cages (n = 5). () Respiratory quotients (calculated from the ratio of carbon dioxide elimination versus oxygen consumption) in AtglKO mice compared to wild-type during the light period and in the fasted state indicating preferential glucose utilization as oxidative fuel (n = 5). Error bars show means ± s.d. *P < 0.05, **P < 0.01 and ***P < 0.001. () Scheme of the integration of Atgl-mediated lipolysis in PPAR signaling. Fatty acids from exogenous or endogenous sources are not available as ligands for nuclear receptor signaling but instead are activated to acyl-CoAs and subsequently oxidized or esterified ! to triglycerides. Atgl-mediated lipolysis of triglyceride stores preferentially generates ligands or precursors of ligands for nuclear receptors controlling mitochondrial function and OXPHOS. CD36, cluster of differentiation 36; Fatp, fatty acid transport protein; FFA, free fatty acid; TGRLP, triglyceride-rich lipoproteins. Author information * Abstract * Author information * Supplementary information Affiliations * Institute of Molecular Biosciences, University of Graz, Graz, Austria. * Guenter Haemmerle, * Sabrina Büttner, * Doris Jaeger, * Petra C Kienesberger, * Kathrin Zierler, * Renate Schreiber, * Thomas Eichmann, * Dagmar Kolb, * Petra Kotzbeck, * Martina Schweiger, * Manju Kumari, * Sandra Eder, * Gabriele Schoiswohl, * Nuttaporn Wongsiriroj, * Nina M Pollak, * Franz P W Radner, * Karina Preiss-Landl, * Achim Lass, * Robert Zimmermann, * Frank Madeo & * Rudolf Zechner * Laboratory of Experimental and Molecular Hepatology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. * Tarek Moustafa & * Michael Trauner * Department of Pharmacology and Toxicology, University of Graz, Graz, Austria. * Gerald Woelkart & * Bernd Mayer * Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria. * Albrecht Schmidt & * Burkert Pieske * Department of Human Biology, School for Nutrition, Toxicology and Metabolism (NUTRIM), Maastricht University Medical Centre, Maastricht, The Netherlands. * Tineke van de Weijer & * Patrick Schrauwen * Department of Human Movement Sciences, NUTRIM, Maastricht University Medical Centre, Maastricht, The Netherlands. * Matthijs Hesselink & * Thomas Kolbe * Biomodels Austria, Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Vienna, Austria. * Thomas Rülicke * Institute of Pathology, Medical University of Graz, Graz, Austria. * Gerald Hoefler * Department of Molecular Pathology and Innovative Therapies, Faculty of Medicine, University of Ancona (Politecnica delle Marche), Ancona, Italy. * Saverio Cinti * Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Erin E Kershaw Contributions G.H. and R.Z. designed the study, were involved in all aspects of the experiments and wrote the manuscript. T.M. and D.J. were responsible for quantitative RT-qPCR–based gene expression analyses and luciferase assays. G.W. and B.M. were responsible for the measurements of tissue oxygen consumption. P. K., D.K. and S.C. were responsible for electron microscopy. S.B., F.M., N.W., T.v.d.W., M.H. and P.S. were responsible for mitochondrial analyses. P.C.K., T.K. and T.R. generated the transgenic mouse strains. K.Z., F.P.W.R., R.S., T.E., M.S., M.K., S.E., G.S. and N.M.P. were responsible for agonist application, dietary studies, plasma and tissue parameter analyses and enzymatic assays. A.S. and B.P. were responsible for echocardiography. E.E.K. generated Atgl-floxed mice. K.P.-L., M.T., A.L., R.Z. and G.H. discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rudolf Zechner Author Details * Guenter Haemmerle Search for this author in: * NPG journals * PubMed * Google Scholar * Tarek Moustafa Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald Woelkart Search for this author in: * NPG journals * PubMed * Google Scholar * Sabrina Büttner Search for this author in: * NPG journals * PubMed * Google Scholar * Albrecht Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Tineke van de Weijer Search for this author in: * NPG journals * PubMed * Google Scholar * Matthijs Hesselink Search for this author in: * NPG journals * PubMed * Google Scholar * Doris Jaeger Search for this author in: * NPG journals * PubMed * Google Scholar * Petra C Kienesberger Search for this author in: * NPG journals * PubMed * Google Scholar * Kathrin Zierler Search for this author in: * NPG journals * PubMed * Google Scholar * Renate Schreiber Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Eichmann Search for this author in: * NPG journals * PubMed * Google Scholar * Dagmar Kolb Search for this author in: * NPG journals * PubMed * Google Scholar * Petra Kotzbeck Search for this author in: * NPG journals * PubMed * Google Scholar * Martina Schweiger Search for this author in: * NPG journals * PubMed * Google Scholar * Manju Kumari Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra Eder Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriele Schoiswohl Search for this author in: * NPG journals * PubMed * Google Scholar * Nuttaporn Wongsiriroj Search for this author in: * NPG journals * PubMed * Google Scholar * Nina M Pollak Search for this author in: * NPG journals * PubMed * Google Scholar * Franz P W Radner Search for this author in: * NPG journals * PubMed * Google Scholar * Karina Preiss-Landl Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Kolbe Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Rülicke Search for this author in: * NPG journals * PubMed * Google Scholar * Burkert Pieske Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Trauner Search for this author in: * NPG journals * PubMed * Google Scholar * Achim Lass Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Zimmermann Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald Hoefler Search for this author in: * NPG journals * PubMed * Google Scholar * Saverio Cinti Search for this author in: * NPG journals * PubMed * Google Scholar * Erin E Kershaw Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Schrauwen Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Madeo Search for this author in: * NPG journals * PubMed * Google Scholar * Bernd Mayer Search for this author in: * NPG journals * PubMed * Google Scholar * Rudolf Zechner Contact Rudolf Zechner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7 and Supplementary Tables 1–4 Additional data - Stem cell gene expression programs influence clinical outcome in human leukemia
- Nat Med 17(9):1086-1093 (2011)
Nature Medicine | Article Stem cell gene expression programs influence clinical outcome in human leukemia * Kolja Eppert1 * Katsuto Takenaka2, 12 * Eric R Lechman1, 12 * Levi Waldron3, 12 * Björn Nilsson4, 12 * Peter van Galen1 * Klaus H Metzeler5 * Armando Poeppl1 * Vicki Ling6 * Joseph Beyene6 * Angelo J Canty7 * Jayne S Danska8 * Stefan K Bohlander5 * Christian Buske9 * Mark D Minden10 * Todd R Golub11 * Igor Jurisica3 * Benjamin L Ebert4 * John E Dick1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1086–1093Year published:(2011)DOI:doi:10.1038/nm.2415Received29 September 2010Accepted09 June 2011Published online28 August 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Xenograft studies indicate that some solid tumors and leukemias are organized as cellular hierarchies sustained by cancer stem cells (CSCs). Despite the promise of the CSC model, its relevance in humans remains uncertain. Here we show that acute myeloid leukemia (AML) follows a CSC model on the basis of sorting multiple populations from each of 16 primary human AML samples and identifying which contain leukemia stem cells (LSCs) using a sensitive xenograft assay. Analysis of gene expression from all functionally validated populations yielded an LSC-specific signature. Similarly, a hematopoietic stem cell (HSC) gene signature was established. Bioinformatic analysis identified a core transcriptional program shared by LSCs and HSCs, revealing the molecular machinery underlying 'stemness' properties. Both stem cell programs were highly significant independent predictors of patient survival and were found in existing prognostic signatures. Thus, determinants of stemness influence! the clinical outcome of AML, establishing that LSCs are clinically relevant and not artifacts of xenotransplantation. View full text Figures at a glance * Figure 1: Strategy of transcriptional profiling of stem cell fractions identified by function. () Overview of experimental design. Cells were sorted on CD34 and CD38, with sort gates for AML and cord blood as well as FACS analysis of the resulting sorted fractions. Functional validation of sorted fractions was done in vivo and combined with gene expression profiling to generate stem cell–related gene expression profiles. () Surface marker profiles of AML are variable with respect to coexpression of CD34 and CD38. CD34 and CD38 marker profiles for 16 AML samples were sorted into four populations and assayed for LSCs. * Figure 2: Correlation between LSC-R and HSC-R. () Heat map of genes more highly expressed in LSC than in non-LSC populations (LSC-R gene signature). LSC and non-LSC represent sorted AML fractions with LSCs, as determined by an in vivo reconstitution assay, and no detected LSCs, respectively. () Heat map of genes more highly expressed in HSC populations than in those with no detectable HSCs (HSC-R gene signature) in four different sorted cord blood populations. Sorted fractions include two HSC fractions (HSC1, Lin−CD34+CD38−; HSC2, Lin−CD34+CD38loCD36−), a progenitor-enriched fraction (Prog, Lin− CD34+CD38+) and unsorted cord blood cells (Lin+). () GSEA plot of enrichment of HSC-R gene signature (top) and common lineage–committed progenitor gene signature (bottom) in LSC versus non-LSC gene expression profile. NES denotes normalized enrichment score. () Heat map of HSC-R GSEA plot from (top) showing core enriched HSC-R genes in LSC expression profile (CE-HSC-LSC). Genes separated by slashes are detected by the! same probe set. () Representative protein-protein interaction network of core enriched genes (CE-HSC-LSC) from , generated from known and interologous interactions from I2D. Large circles, proteins from core enriched gene list (CE-HSC-LSC); small squares, proteins that link proteins in core enriched list. Node color corresponds to GO protein function. Visualization was done using NAViGaTOR (Supplementary Data). * Figure 3: LSC-R and HSC-R gene signatures are correlated with disease outcome. Unsorted cytogenetically normal AML samples (160) were divided into two populations of 80 AML samples by expression of stem cell gene signatures. () Correlation of LSC-R and HSC-R signatures and overall survival. Red line, subjects whose AML cells expressed LSC-R (left) or HSC-R (right) signatures greater than the median; black line, those whose AML cells expressed respective stem cell signature less than the median. () Event-free survival of subjects stratified by expression of the LSC-R and HSC-R, as in . () Additive correlation analysis of the LSC-R signature and overall survival. y axis, log-rank P value of each combination of probes. x axis, number of probes included in analysis, starting with top-ranked probe positively correlated with LSCs followed by the addition of each next ranked probe in the LSC-R gene profile (as determined by z-score in the LSC versus non-LSC t-test). () Correlation of an AML signature based on phenotypic markers (CD34+CD38−, stem cell, versu! s CD34+CD38+, progenitor; 23 AML samples) and overall survival. Red line, subjects whose AML expressed the CD34+CD38− gene list greater than the median; black line, those who expressed the CD34+CD38− gene list less than the median. * Figure 4: Correlation of LSC and HSC gene expression signatures and molecular risk status with overall survival in a cohort of cytogenetically normal AML samples. Overall survival curves of 159 cytogenetically normal AML samples divided by expression of the LSC-R (left) or HSC-R (right) signatures and molecular risk. LMR group, NPM1mut/FLT3wt cytogenetically normal AML; HMR group, NPM1wt or FLT3ITD cytogenetically normal AML. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30377 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Katsuto Takenaka, * Eric R Lechman, * Levi Waldron & * Björn Nilsson Affiliations * Division of Stem Cell and Developmental Biology, Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Kolja Eppert, * Eric R Lechman, * Peter van Galen, * Armando Poeppl & * John E Dick * Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan. * Katsuto Takenaka * Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. * Levi Waldron & * Igor Jurisica * Brigham and Women's Hospital, Boston, Massachusetts, USA. * Björn Nilsson & * Benjamin L Ebert * Department of Internal Medicine III, Ludwig-Maximilians-Universität, Munich, Germany. * Klaus H Metzeler & * Stefan K Bohlander * Population Health Sciences, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada. * Vicki Ling & * Joseph Beyene * Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada. * Angelo J Canty * Program in Genetics and Genome Biology, Hospital for Sick Children and Department of Immunology and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. * Jayne S Danska * Institute of Experimental Cancer Research, Comprehensive Cancer Center, University Hospital of Ulm, Ulm, Germany. * Christian Buske * Department of Medicine, University Health Network, Toronto, Ontario, Canada. * Mark D Minden * Broad Institute, Cambridge, Massachusetts, USA. * Todd R Golub Contributions K.E., E.R.L., K.T., B.L.E. and J.E.D. designed the study. K.E., E.R.L., P.v.G., K.T. and A.P. carried out experiments. K.E., K.T., L.W., B.N., E.R.L., P.v.G., V.L. and I.J. analyzed and interpreted data. K.E., J.B., A.J.C., J.S.D., S.K.B., K.H.M., C.B., M.D.M., T.R.G., I.J., B.L.E. and J.E.D. provided research support and conceptual advice. M.D.M. provided samples. K.E. and J.E.D. wrote the paper. E.R.L., K.T., K.H.M., J.S.D., S.K.B., C.B., M.D.M., I.J. and B.L.E. revised the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John E Dick Author Details * Kolja Eppert Search for this author in: * NPG journals * PubMed * Google Scholar * Katsuto Takenaka Search for this author in: * NPG journals * PubMed * Google Scholar * Eric R Lechman Search for this author in: * NPG journals * PubMed * Google Scholar * Levi Waldron Search for this author in: * NPG journals * PubMed * Google Scholar * Björn Nilsson Search for this author in: * NPG journals * PubMed * Google Scholar * Peter van Galen Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus H Metzeler Search for this author in: * NPG journals * PubMed * Google Scholar * Armando Poeppl Search for this author in: * NPG journals * PubMed * Google Scholar * Vicki Ling Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Beyene Search for this author in: * NPG journals * PubMed * Google Scholar * Angelo J Canty Search for this author in: * NPG journals * PubMed * Google Scholar * Jayne S Danska Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan K Bohlander Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Buske Search for this author in: * NPG journals * PubMed * Google Scholar * Mark D Minden Search for this author in: * NPG journals * PubMed * Google Scholar * Todd R Golub Search for this author in: * NPG journals * PubMed * Google Scholar * Igor Jurisica Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin L Ebert Search for this author in: * NPG journals * PubMed * Google Scholar * John E Dick Contact John E Dick Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Tables (3M) Supplementary Tables 1–25 XML files * Supplementary Data (6M) NAViGaTOR file PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–14, Supplementary Results and Supplementary Methods Additional data - Imatinib potentiates antitumor T cell responses in gastrointestinal stromal tumor through the inhibition of Ido
- Nat Med 17(9):1094-1100 (2011)
Nature Medicine | Article Imatinib potentiates antitumor T cell responses in gastrointestinal stromal tumor through the inhibition of Ido * Vinod P Balachandran1 * Michael J Cavnar1 * Shan Zeng1 * Zubin M Bamboat1 * Lee M Ocuin1 * Hebroon Obaid1 * Eric C Sorenson1 * Rachel Popow1 * Charlotte Ariyan1 * Ferdinand Rossi2 * Peter Besmer2 * Tianhua Guo3 * Cristina R Antonescu3 * Takahiro Taguchi4 * Jianda Yuan5 * Jedd D Wolchok5, 6 * James P Allison5, 7 * Ronald P DeMatteo1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1094–1100Year published:(2011)DOI:doi:10.1038/nm.2438Received28 December 2010Accepted07 July 2011Published online28 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Imatinib mesylate targets mutated KIT oncoproteins in gastrointestinal stromal tumor (GIST) and produces a clinical response in 80% of patients. The mechanism is believed to depend predominantly on the inhibition of KIT-driven signals for tumor-cell survival and proliferation. Using a mouse model of spontaneous GIST, we found that the immune system contributes substantially to the antitumor effects of imatinib. Imatinib therapy activated CD8+ T cells and induced regulatory T cell (Treg cell) apoptosis within the tumor by reducing tumor-cell expression of the immunosuppressive enzyme indoleamine 2,3-dioxygenase (Ido). Concurrent immunotherapy augmented the efficacy of imatinib in mouse GIST. In freshly obtained human GIST specimens, the T cell profile correlated with imatinib sensitivity and IDO expression. Thus, T cells are crucial to the antitumor effects of imatinib in GIST, and concomitant immunotherapy may further improve outcomes in human cancers treated with targeted a! gents. View full text Figures at a glance * Figure 1: CD8+ T cells contribute to antitumor effects of imatinib. GIST and wild-type mice were treated with vehicle or imatinib and analyzed on day 4 () or 8 (–) using flow cytometry, PET and immunohistochemistry. () Tumor weight. () Tumor uptake of 18FDG, shown by PET. H, heart; T, tumor; B, bladder. A decreasing intensity scale is indicated, yellow (maximum intensity) = 25%. () Left, number of CD8+ T cells in the DLN and inguinal node (IN) of GIST mice and mesenteric node of wild-type (WT) mice. Middle and right, expression (MFI, mean fluorescence intensity, arbitrary units) and frequency of CD8+CD69+ T cells in the DLN of GIST mice, as determined by flow cytometry. () ELISPOT assay for IFN-γ secretion, using purified DLN CD8+ T cells from treated GIST mice cultured with T cell–depleted splenocytes (APC) and tumor cells (Tu). Average spots per well ± s.e.m. (n = 3 wells) are shown and represent two independent experiments. () Left, gating of CD8+ T cells (marked with red boxes) as a percentage of CD45+ lymphocytes. Right, absolute ! number of intratumoral CD8+ T cells. () Gating and frequency of intratumoral CD8+Ki67+ T cells (red boxes). () Histograms, MFI and frequency of intratumoral CD8+CD69+ and CD8+ granzyme B+ T cells. () Tumors stained for CD8. Arrows indicate cells staining for CD8 (dark brown). Hematoxylin (blue) was used as a counterstain. () Tumor weight and volume (measured with magnetic resonance imaging, bottom) in GIST mice depleted of CD8+ or CD4+ T cells or NK cells during 1 (top) or 2 weeks (bottom) of imatinib treatment. Data in –, represent means ± s.e.m. with n ≥ 6 per group. *P < 0.05. * Figure 2: Imatinib induces Treg apoptosis selectively within the tumor. GIST mice were treated with vehicle or imatinib and analyzed by flow cytometry on day 4 (D4) or day 8 (D8). () Representative gating, frequency and absolute numbers of DLN and intratumoral Treg cells on day 8. () Contour plots showing representative gating of annexin V expression on intratumoral Treg cells (CD4+FoxP3+). Bar graphs represent frequency of annexin V+ Treg cells in the DLN and tumor. Loss of Treg cell viability was confirmed using propidium iodide staining. () Ratio of CD8+ T cells to Treg cells in the DLN and tumor on day 8. Data represent means ± s.e.m. with n = 6–11 per group. *P < 0.05. * Figure 3: Imatinib alters intratumoral T cells through inhibition of Ido. () Left, Ido1 mRNA in the DLN and tumor, as determined by microarray analysis of vehicle-treated GIST mice and the tumor from imatinib-treated GIST mice after 7 d. Data represent means ± s.e.m. and are shown relative to internal controls (housekeeping gene); n = 3 per group. Right top, western blot staining for Ido in the DLN, spleen and tumor of vehicle-treated GIST mice and the tumor of imatinib-treated GIST mice; MW, molecular weight. Right bottom, intracellular Ido expression in CD45+ intratumoral immune cells and CD45−Kit+ tumor cells as determined by flow cytometry. () Tumor weight of GIST mice treated with 1-MT for 7 d with or without CD8+ T cell depletion. In –, GIST mice were treated for 7 d with combinations of 1-MT or control (Ctrl), imatinib (I) or vehicle (V) and tryptophan metabolites (metabs). Tumors and DLNs were analyzed using flow cytometry. () Frequency of intratumoral CD8+Ki67+ and CD8+CD69+ T cells. () Frequency of intratumoral annexin V+ Treg cells! . () Ratio of intratumoral CD8+ effector T cells to Treg cells. () Tumor weight. () CD69 and granzyme B expression (MFI, arbitrary units) and frequency of CD8+CD69+ and CD8+ granzyme B+ T cells in the tumor of GIST mice. () Frequency of intratumoral Annexin V+ Treg cells. () Ratio of intratumoral CD8+ T cells to Treg cells. Data in – represent means ± s.e.m. with n = 6–12 per group. *P < 0.05. * Figure 4: Imatinib reduces IDO expression through inhibition of oncogenic KIT signaling. () IDO expression (histograms (left) and MFI (right), arbitrary units) in GIST-T1 and GIST-T1R cells, as determined by flow cytometry. () Ido1 mRNA levels in mouse GIST tumors (left; n = 8 per group) and sorted Kit+ tumor cells (right; n = 3 per group) after treatment with vehicle or imatinib. () Intracellular IDO expression in GIST-T1 cells after culture in rapamycin, as in . () Etv4 mRNA levels in the DLN and tumor, determined by microarray analysis from GIST mice after vehicle or imatinib treatment for 7 d. n = 3 per group. () Etv4 mRNA levels in mouse GIST tumors (left; n = 8 per group) and sorted Kit+ tumor cells (right; n = 3 per group) after treatment with vehicle or imatinib. () Western blot staining for KIT, ETV4 and IDO in GIST mice (left) and GIST-T1 cells (right) after treatment with vehicle or imatinib. Both phosphorylated and nonphosphorylated KIT, STAT3 and S6 are shown as components of oncogenic KIT signaling. IDO in GIST-T1 cells was detected via immunopreci! pitation. MW, molecular weight. () ChIP from GIST mice treated with vehicle or imatinib in vivo (n = 3 per group). In vitro culture experiments were performed with 1 μM imatinib. Data in , and were normalized to internal controls and , are shown relative to vehicle treatment. Data represent either means ± s.e.m. with n as indicated above, or representative plots from triplicate wells analyzed individually. *P < 0.05. * Figure 5: Ratio of intratumoral CD8+ T cells to Treg cells correlates with imatinib sensitivity in human GIST. () Frequency of CD3+ and CD8+ T cells and Treg cells (determined by flow cytometry) in peripheral blood and tumor of untreated (U; n = 15), sensitive (S; n = 17) and resistant (R; n = 13) GIST specimens, and representative gating for Treg cells. The red boxes represent Treg cells as a frequency of CD4+ T cells. () CD69 and CD25 expression (MFI, arbitrary units) on CD8+ T cells from matched peripheral blood and tumor samples. () Ratio of CD8+ T cells to Treg cells in blood and tumor. () Ratio of CD8+ T cells to Treg cells in three patients (Pt.) who underwent synchronous resection of a sensitive and a resistant tumor. () Ratio of CD8+ T cell to Treg cells in tumors expressing low (<4,000 MFI; n = 6) or high (≥4,000 MFI; n = 7) levels of IDO protein as determined by flow cytometry. Data in , represent means ± s.e.m. *P < 0.05. * Figure 6: CTLA-4 blockade is synergistic with imatinib. GIST mice were treated with chronic CTLA-4 blockade and either imatinib or vehicle for 7 d. A third group was treated with imatinib for 7 d and chronic isotype control antibody. () Tumor volume, monitored using serial magnetic resonance imaging. (–) Frequency and absolute number of CD4+ and CD8+ T cells in DLN (), frequency of intratumoral CD4+ and CD8+ T cells (), and ratio of intratumoral CD8+ T cells to Treg cells () of GIST mice analyzed on days 16–18. () IFN-γ production in intratumoral CD8+ T cells stimulated for 4 h with phorbol 12-myristate 13-acetate and ionomycin. Contour plots show representative gating of IFN-γ expression on intratumoral CD8+ T cells. Bar graphs represent the intratumoral frequency of CD8+ IFN-γ+ cells; P = 0.09, two-tailed Student's t test. Data in represent means ± s.e.m. of a composite of two independent experiments, each with 3–5 mice per group. Data in – represent means ± s.e.m. with n = 6–8 per group. *P < 0.05. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Surgery, Memorial Hospital, New York, New York, USA. * Vinod P Balachandran, * Michael J Cavnar, * Shan Zeng, * Zubin M Bamboat, * Lee M Ocuin, * Hebroon Obaid, * Eric C Sorenson, * Rachel Popow, * Charlotte Ariyan & * Ronald P DeMatteo * Developmental Biology Program, Sloan-Kettering Institute, New York, New York, USA. * Ferdinand Rossi & * Peter Besmer * Department of Pathology, Memorial Hospital, New York, New York, USA. * Tianhua Guo & * Cristina R Antonescu * Division of Human Health and Medical Science, Graduate School of Kuroshio Science, Kochi University, Kochi, Japan. * Takahiro Taguchi * The Ludwig Center for Cancer Immunotherapy, New York, New York, USA. * Jianda Yuan, * Jedd D Wolchok & * James P Allison * Department of Medicine, Memorial Hospital, New York, New York, USA. * Jedd D Wolchok * Immunology Program, Sloan-Kettering Institute, New York, New York, USA. * James P Allison Contributions All authors contributed to experimental design. V.P.B., M.J.C., S.Z., Z.M.B., H.O., R.P., C.A., T.G., C.R.A. and J.Y. performed the experiments. All authors assisted in data analysis. V.P.B. and R.P.D. wrote and prepared the manuscript with critical comments from all authors. Competing financial interests R.P.D. serves as a consultant for Novartis and has received honoraria. P.B. has received a commercial research grant from Novartis. J.D.W. serves as a consultant to Novartis and Bristol-Meyers Squibb. CTLA-4 blocking antibody is currently in clinical development by Medarex and Bristol-Meyers Squibb. J.P.A. is a consultant for Medarex and Bristol-Meyers Squibb and is an inventor of intellectual property that has been licensed to Medarex and Bristol-Meyers Squibb by the University of California–Berkeley. Corresponding author Correspondence to: * Ronald P DeMatteo Author Details * Vinod P Balachandran Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Cavnar Search for this author in: * NPG journals * PubMed * Google Scholar * Shan Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Zubin M Bamboat Search for this author in: * NPG journals * PubMed * Google Scholar * Lee M Ocuin Search for this author in: * NPG journals * PubMed * Google Scholar * Hebroon Obaid Search for this author in: * NPG journals * PubMed * Google Scholar * Eric C Sorenson Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel Popow Search for this author in: * NPG journals * PubMed * Google Scholar * Charlotte Ariyan Search for this author in: * NPG journals * PubMed * Google Scholar * Ferdinand Rossi Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Besmer Search for this author in: * NPG journals * PubMed * Google Scholar * Tianhua Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina R Antonescu Search for this author in: * NPG journals * PubMed * Google Scholar * Takahiro Taguchi Search for this author in: * NPG journals * PubMed * Google Scholar * Jianda Yuan Search for this author in: * NPG journals * PubMed * Google Scholar * Jedd D Wolchok Search for this author in: * NPG journals * PubMed * Google Scholar * James P Allison Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald P DeMatteo Contact Ronald P DeMatteo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (918K) Supplementary Figures 1–8, Supplementary Tables 1 and 2 and Supplementary Methods Additional data - Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization
- Nat Med 17(9):1101-1108 (2011)
Nature Medicine | Article Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization * Manav Korpal1 * Brian J Ell1 * Francesca M Buffa2 * Toni Ibrahim3 * Mario A Blanco1 * Toni Celià-Terrassa1, 4 * Laura Mercatali3 * Zia Khan5, 6 * Hani Goodarzi1, 6 * Yuling Hua1 * Yong Wei1 * Guohong Hu1 * Benjamin A Garcia1 * Jiannis Ragoussis7 * Dino Amadori3 * Adrian L Harris2 * Yibin Kang1, 8 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1101–1108Year published:(2011)DOI:doi:10.1038/nm.2401Received08 October 2010Accepted18 May 2011Published online07 August 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although the role of miR-200s in regulating E-cadherin expression and epithelial-to-mesenchymal transition is well established, their influence on metastatic colonization remains controversial. Here we have used clinical and experimental models of breast cancer metastasis to discover a pro-metastatic role of miR-200s that goes beyond their regulation of E-cadherin and epithelial phenotype. Overexpression of miR-200s is associated with increased risk of metastasis in breast cancer and promotes metastatic colonization in mouse models, phenotypes that cannot be recapitulated by E-cadherin expression alone. Genomic and proteomic analyses revealed global shifts in gene expression upon miR-200 overexpression toward that of highly metastatic cells. miR-200s promote metastatic colonization partly through direct targeting of Sec23a, which mediates secretion of metastasis-suppressive proteins, including Igfbp4 and Tinagl1, as validated by functional and clinical correlation studies. O! verall, these findings suggest a pleiotropic role of miR-200s in promoting metastatic colonization by influencing E-cadherin–dependent epithelial traits and Sec23a-mediated tumor cell secretome. View full text Figures at a glance * Figure 1: miR-200s are associated with poor prognosis in breast cancer. () Kaplan-Meier curves showing the DRFS of 210 subjects with high or low expression of the entire miR-200 family (top), miR-429 (middle) and miR-200a (bottom) in breast tumors. P values were computed by a likelihood-ratio test. () Box plots showing miR-200 expression levels in ten human primary and metastasis (met) samples as assessed by qRT-PCR analysis. Data (mean ± s.e.m.) are normalized to U6, and P values were computed by Student's t test. () Heat map showing miRNA expression levels in 4T1 series. 168, 168FARN. () Phase-contrast images (left) and immunofluorescence images of 4TO7 and 4T1 cells stained for E-cadherin (right). () Imaging as in of MCFCA1h and MCFCA1a cells. Insets highlight the membrane localization of E-cadherin. () Kaplan-Meier curves showing the DRFS of 210 subjects with high or low CDH1 expression. P values were computed by a likelihood-ratio test. * Figure 2: Ectopic miR-200 expression enhances spontaneous metastasis and colonization of distant organs. () Western blot showing expression of indicated proteins in various genetically modified 4TO7 cell lines. () Phase-contrast and immunofluorescence images of cell lines stained for E-cadherin (E-cad) and N-cadherin (N-cad). Yellow outline emphasizes cell morphology. () Plated colonies showing lung colonization by various cell lines used to generate orthotopic mammary gland tumors. Average numbers of colonies are listed below representative plate images. Data represent mean ± s.e.m. from a single representative experiment of three independent experiments. (n = 9 or 10). () Relative expression of puromycin-resistance gene, an indicator of circulating tumor cells, by qRT-PCR analysis of genomic DNA from whole-blood samples. Red dotted lines represent median values. P = 0.02 (Student's t test). () Representative gross lung and H&E-stained lung sections from mice intravenously injected with various 4TO7 cell lines. Red arrowheads and dashed lines mark metastatic nodules. Scale ba! r, 4 mm. () Immunohistochemical (IHC) staining for E-cadherin of lung nodules established from indicated cells. () Fold increase in number of pulmonary metastasis nodules for each group. Data represent mean fold increase ± s.e.m. from a single representative experiment of three independent experiments. (n = 9 or 10). () Left, RT-PCR showing expression of Cdh1 in C1+C2 cells with or without stable Cdh1 knockdown. Right, fold change in number of pulmonary lesions after intravenous inoculation of tumor cells. *P < 0.05, **P < 0.01 (Student's t test). * Figure 3: Ectopic miR-200 expression promotes global changes in gene expression. () Unsupervised clustering highlighting genome-wide changes in gene expression upon miR-200 expression in 4TO7 cells. Experiment was performed twice in duplicates. () Gene-set enrichment analysis showing influence of miR-200 overexpression on the overall gene expression profile of 4TO7 cells. Gene sets used are the top 100 (left) and bottom 100 (right) differentially expressed genes in the test lines (C1, C2, C1+C2 and CDH1) compared with control lines. The gene list used included all mouse genes, ranked by their differential expression between 4T1 and 4TO7 variants. Enrichment of top and bottom 100 genes from 4T1 compared with 4TO7 (ranked list) is shown as an example of maximum possible enrichment. NES, normalized enrichment score. Red and blue double-sided arrows denote the relative number of core genes for each analysis. () Venn diagram showing substantial overlap of core genes from top 100 gene sets for C1 (red circle), C2 (blue circle) and C1+C2 (green circle) lines fr! om . Core genes shared among all three lines are listed. Cdh1 is listed in red to emphasize the positive influence of miR-200s on E-cadherin expression. * Figure 4: Identification of putative miR-200 targets using MS. () Protein abundance compared with mRNA abundance, relative to control cells, for 1,562 genes in C1+C2 cells. Red dots represent genes with miR-200 target sites. () Protein and mRNA abundances as in , for only genes containing miR-200 target sites (n = 130). Red dots denote significantly reduced expression at both mRNA and protein levels (n = 9). Orange dotted circle represents little or no difference from gene expression in control cells. () qRT-PCR validation of reduced expression in C1+C2 cells, relative to control cells, for the nine candidate genes highlighted as red dots in . Data represent mean ± s.e.m. *P < 0.05 (Student's t test). () Heat map showing expression (expressed as fold difference) of the nine candidate genes in MDA-MB-231 (left) and TSU-PR1 (right) cells upon transient transfection of miR-200s (200) relative to pre-miR controls. Pre, control pre-miRNA. ZEB1 and ZEB2 were included as positive controls. () Heat map showing average expression (fold differen! ce) of the nine–candidate gene signature (9-gene) and miR-200b and miR-200c in NCI-60 panel of cell lines. () Luciferase assays in HeLa cells testing direct targeting of eight of nine candidate genes by miR-200s. Data represent percentage difference in normalized luciferase activity upon cotransfection of miR-200s, relative to transfection with the negative control pre-miRNA (mean ± s.e.m.). *P < 0.05, **P < 0.01 (Student's t test). * Figure 5: Sec23a knockdown phenocopies miR-200s in inhibiting migration and promoting metastatic colonization. () Transwell migration assays. Shown are ratios of migration of knockdown (KD) lines over migration of parental 4TO7 cells (mean ± s.e.m. from triplicate experiments. () Fold change in number of pulmonary nodules relative to 4TO7 parental line (mean ± s.e.m.). 'Triple KD' denotes knockdown of all three genes. () Representative gross lung images and H&E-stained lung sections (bottom) from mice intravenously injected with indicated cell lines (KD1 and KD2 denote two different knockdown lines for each gene). Red arrows mark metastatic nodules, except in Sec23a knockdown samples, which contained large numbers of nodules that were outlined with red dashed lines. (,) Relative SEC23A expression in ten human primary tumors compared with expression in ten lung metastases (box plots show 25th, 50th and 75th percentiles (horizontal bars) and 1.5 interquartile ranges (error bars)) (), and in matched primary and lung metastasis samples collected from six individuals (). GAPDH was used ! to normalize expression. Error bars show s.e.m. *P < 0.05 (Student's t test). * Figure 6: Sec23a knockdown disrupts secretion of proteins that are correlated with suppression of clinical metastasis. () Correlation of secretome profiles between two different Sec23a knockdown lines (Sec23a-KD2 and Sec23a-KD3) and between Sec23a-KD2 and C1+C2 lines. Proteins in common between different lines were used to generate the plots. Orange, proteins less abundant in both lines; green, more abundant in both lines; gray, discordant expression patterns. () Kaplan-Meier curves showing RFS of subjects with high or low median expression of 35 genes whose secreted products were reduced in Sec23a-knockdown lines. () Fold increase in number of pulmonary metastases in 4TO7-derived lines with stable knockdown of Axl, Tinagl1 or Igfbp4, relative to vector control (KD1 and KD2 signify different knockdown lines). () Representative gross lung images from animals injected via lateral tail vein with knockdown lines from , along with vector control. **P < 0.01 (Student's t test). () Kaplan-Meier plots of distant metastasis-free survival of patients in the EMC286 data set stratified by expression of ! TINAGL1 (top) or IGFBP4 (bottom). P values were computed by log-rank test. () Schematic model of miR-200 function during metastasis. miR-200s simultaneously target several genes including Zeb1 and Zeb2 (Zeb1/2) and Sec23a to inhibit local invasion but promote metastatic colonization. Targeting of Zeb1/2 influences cell-intrinsic epithelial traits, whereas targeting of Sec23a modulates tumor-derived secretion of factors such as Igfbp4 and Tinagl1, which influence metastatic colonization by altering tumor-stromal interactions. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19631 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA. * Manav Korpal, * Brian J Ell, * Mario A Blanco, * Toni Celià-Terrassa, * Hani Goodarzi, * Yuling Hua, * Yong Wei, * Guohong Hu, * Benjamin A Garcia & * Yibin Kang * Cancer Research UK, Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK. * Francesca M Buffa & * Adrian L Harris * Osteoncology Center, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori, Meldola, Italy. * Toni Ibrahim, * Laura Mercatali & * Dino Amadori * Department of Cell Biology, Institut de Biologia Molecular de Barcelona, Consejo Superior de Investegaciones Cientificas, Barcelona, Spain. * Toni Celià-Terrassa * Department of Computer Science, Princeton University, Princeton, New Jersey, USA. * Zia Khan * The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. * Zia Khan & * Hani Goodarzi * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Jiannis Ragoussis * Genomic Instability and Tumor Progression Program, Cancer Institute of New Jersey, New Brunswick, New Jersey, USA. * Yibin Kang Contributions M.K. and Y.K. designed experiments. M.K., B.J.E., T.C.-T. and Y.H. performed the experiments. F.M.B., T.I., L.M., J.R., D.A. and A.L.H. provided clinical samples and associated analyses. M.A.B., Z.K., H.G., Y.W., G.H. and B.A.G. contributed genomic and proteomic analyses. M.K. and Y.K. wrote the manuscript. All authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yibin Kang Author Details * Manav Korpal Search for this author in: * NPG journals * PubMed * Google Scholar * Brian J Ell Search for this author in: * NPG journals * PubMed * Google Scholar * Francesca M Buffa Search for this author in: * NPG journals * PubMed * Google Scholar * Toni Ibrahim Search for this author in: * NPG journals * PubMed * Google Scholar * Mario A Blanco Search for this author in: * NPG journals * PubMed * Google Scholar * Toni Celià-Terrassa Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Mercatali Search for this author in: * NPG journals * PubMed * Google Scholar * Zia Khan Search for this author in: * NPG journals * PubMed * Google Scholar * Hani Goodarzi Search for this author in: * NPG journals * PubMed * Google Scholar * Yuling Hua Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Guohong Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin A Garcia Search for this author in: * NPG journals * PubMed * Google Scholar * Jiannis Ragoussis Search for this author in: * NPG journals * PubMed * Google Scholar * Dino Amadori Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian L Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Yibin Kang Contact Yibin Kang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Results, Supplementary Discussion, Supplementary Figures 1–9, Supplementary Tables 1–5 and Supplementary Methods Additional data - Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2
- Nat Med 17(9):1109-1115 (2011)
Nature Medicine | Article Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2 * Traci R Lyons1, 8 * Jenean O'Brien1, 2, 8 * Virginia F Borges1, 3 * Matthew W Conklin4, 5 * Patricia J Keely4, 5 * Kevin W Eliceiri5 * Andriy Marusyk6 * Aik-Choon Tan1, 3 * Pepper Schedin1, 2, 3, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1109–1115Year published:(2011)DOI:doi:10.1038/nm.2416Received09 July 2010Accepted10 June 2011Published online07 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The prognosis of breast cancer in young women is influenced by reproductive history. Women diagnosed within 5 years postpartum have worse prognosis than nulliparous women or women diagnosed during pregnancy. Here we describe a mouse model of postpartum breast cancer that identifies mammary gland involution as a driving force of tumor progression. In this model, human breast cancer cells exposed to the involuting mammary microenvironment form large tumors that are characterized by abundant fibrillar collagen, high cyclooxygenase-2 (COX-2) expression and an invasive phenotype. In culture, tumor cells are invasive in a fibrillar collagen and COX-2–dependent manner. In the involuting mammary gland, inhibition of COX-2 reduces the collagen fibrillogenesis associated with involution, as well as tumor growth and tumor cell infiltration to the lung. These data support further research to determine whether women at high risk for postpartum breast cancer would benefit from treatment! with nonsteroidal anti-inflammatory drugs (NSAIDs) during postpartum involution. View full text Figures at a glance * Figure 1: The postpartum mammary microenvironment promotes tumor growth in a mammary fat pad xenograft model. () Average primary tumor volume. *P = 0.001, **P = 0.0174, n = 7 (nulliparous (N)), n = 6 (involution (I)), unpaired t test. () Total tumor number per group at 4 weeks after injection. n = 9 (nulliparous) and n = 8 (involution). () Average tumor burden (total tumor volume per injected gland) at 4 weeks after injection. *P = 0.0011, n = 15 (nulliparous), n = 13 (involution), unpaired t test. () Average (black bars) percentage of tumor area positive for Ki67. *P = 0.0009, n = 5 (nulliparous), n = 8 (involution), unpaired t test. () Collagen intensity measured by second harmonic generation (SHG) imaging versus distance from involuting mouse mammary ducts (red, n = 17 ducts, three mice) compared to nulliparous (black, n = 12 ducts, three mice). P < 0.00001, Student's t test. AU, arbitrary units. () Western blot for collagen I (Col I) in mouse mammary tissue lysates. *P = 0.042, n = 3 per group, unpaired t test. () Average (black bars) percentage of tumor area positive for collag! en by trichrome stain. *P = 0.0002, n = 6 (nulliparous), n = 11 (involution), unpaired t test. () Top, trichrome-stained tumor images. Collagen is shown in blue. Bottom, the blue signal converted to black. Scale bars, 100 μm. () Percentage of BrdU-positive cells in three-dimensional culture on Matrigel (Mtgl) or 10% collagen I (Col I). *P < 0.0001, unpaired t test, n = 30 100× fields per group. () BrdU immunohistochemistry images of three-dimensional organoids. Scale bars, 50 μm. Data are shown as mean ± s.e.m. * Figure 2: Postpartum involution drives tumor cell invasion. () GFP-positive tumor cells 3 weeks after injection. Scale bars, 100 μm. () Right, immunohistochemistry image of GFP-positive cells (arrows) in mammary tissue 3 d after injection. Scale bar, 50 μm. Inset, GFP-positive tumor cells. Scale bar, 10 μm. Left, quantification of dispersed GFP-positive cell clusters by group. *P = 0.0129, n = 3 (nulliparous (N) shown in white), n = 5 (involution (I) shown in gray), unpaired t test. () Left, immunohistochemistry image of tumor cells (arrows) in mammary blood vessel 3 d after injection. Scale bars, 10 μm. Right, quantification of tumor cells in peripheral blood 1 d and 3 d after injection. *P = 0.04, n = 3 per group, unpaired t test. () FISH for COT-1 DNA (red, human; green, mouse) and DAPI-stained nuclei (blue). Scale bars, 50 μm. () Left axis, individual mammary tumor volumes (black bars). Right axis, qRT-PCR analysis of lung tissue for human β2M transcripts in arbitrary units (AU) after normalizing to actin. Inset, average β! 2M expression. *P = 0.0046, n = 9 (nulliparous 1–9), n = 8 (involution 10–17), unpaired t test. () Right, average in vitro wound closure of involution and nulliparous group tumor cell populations on collagen 2 h, 4 h and 6 h after scrape. *P = 0.034, **P = 0.045, ***P = 0.040, n = 7 (nulliparous), n = 8 (involution). Left, scrape images of involution group tumor cell populations at 0 h and 6 h. Scale bars, 50 μm. () Left, average number of invasive tumor cells in trans-well assay. n = 5 (nulliparous), n = 6 (involution), *P = 0.0491, unpaired t test. Right, filter images. Scale bars, 50 μm. () Left axis, volumes of tumors used for tumor cell isolation (black bars). Right axis, wound closure of individual tumor cell populations 6 h after scrape. Data are shown as mean ± s.e.m. * Figure 3: Fibrillar collagen and COX-2 mediate tumor cell invasiveness. () Trichrome (top) and COX-2 (bottom) immunohistochemistry-stained tumor. Arrows show dense collagen and arrowheads show sparse collagen. Scale bars, 50 μm. () Left, average (black bars) percentage of tumor cells shown by immunohistochemistry to be COX-2 positive. *P = 0.0275, n = 9 (nulliparous), n = 5 (involution), unpaired t test. Right, COX-2 immunohistochemistry tumor images. Scale bars, 100 μm. () Western blot showing expression of COX-2 in tumor cell populations. *P = 0.0274, n = 3 per group, unpaired t test. AU, arbitrary units. () In vitro wound closure by tumor cells on collagen ± 20 μM COX-2 inhibitor celecoxib (CXB, black) at 6 h after scrape. *P = 0.019, n = 3 cell populations per group, unpaired t test. Inset, individual tumor cell population data. () Left, percentage of organoids identified as COX-2 positive by immunohistochemistry in Matrigel or Matrigel + 10% (10C), 20% (20C) or 40% (40C) collagen. *P < 0.0001, one-way analysis of variance (ANOVA). Right! , three-dimensional organoid COX-2 immunohistochemistry images. Scale bars, 50 μm. (–) Percentage of organoids observed to be rounded (1), dysmorphic (2) or invasive (3) on 0% (0C) and 40% collagen (). *P < 0.0001, n = 3 wells per condition, one-way ANOVA; or on 40% collagen + DMSO solvent, 40% collagen + 2.5 μm CXB, 40% collagen + 5 μm CXB (). *P < 0.0001, n = 3 wells per condition, one-way ANOVA; or on 40% collagen + shGFP and 40% collagen + shCOX-2 (). *P < 0.0001, n = 3 wells per condition, one-way ANOVA. () Left, SHG images of collagen surrounding nulliparous and involuting ducts, higher magnifications (yellow box) below. Scale bars, 10 μm (left). Arrow shows radially aligned collagen. Scale bars, 50 μm (right). () Organoid type on 0% collagen, 20% collagen + 20% gelatin (20/20), 40% collagen and 40% gelatin (40G). *P < 0.0001, n = 3 wells per condition, one-way ANOVA. Data are shown as mean ± s.e.m. and are representative of triplicate studies. * Figure 4: COX-2 inhibition mitigates the tumor-promotional effects of involution. () Average tumor volume for involution group (I) and involution + CXB group (I + CXB) mice at 3 weeks after injection. *P = 0.0205, n = 12, unpaired t test. () Percentage of glands with dispersed GFP-positive tumor cells in mammary glands at 3 d after injection. *P = 0.018, n = 12, unpaired t test. () Percentage of glands with dispersed GFP-positive tumor cells in mammary glands at 3 weeks after injection. *P = 0.0033, n = 12, unpaired t test. Data are shown as mean ± s.e.m. () SHG imaging of collagen surrounding control involuting (I) and CXB-treated involuting ducts (I + CXB). Scale bars, 50 μm. () Collagen intensity by SHG versus distance from involuting mouse mammary ducts (red, n = 21 ducts, three mice) compared to involuting + CXB (blue, n = 22 ducts, three mice) and to nulliparous (black, n = 12 ducts, three mice). *P < 0.001, involution versus involution + CXB between 5–10 μm, Student's t test. AU, arbitrary units. () A model depicting upregulation of collagen f! ibrillogenesis mediated by COX-2 derived from the involuting mammary gland, and subsequent upregulation of COX-2 and invasion (brown cells) in tumor cells exposed to involution collagen. * Figure 5: Evidence that collagen and COX-2 contribute to postpartum breast cancer. () Quantification of collagen intensity by SHG versus the distance from human breast ducts. Black, nulliparous (N); red, involution (I) (13 or 14 ducts per case, three cases per group). P < 0.00001, Student's t test. Data are shown as mean ± s.e.m. AU, arbitrary units. () SHG imaging of collagen in breast tissue from involuting and nulliparous women. Scale bars, 50 μm. () Multivariate Cox analysis of relapse-free survival in 345 breast tumors diagnosed in women ≤45 years of age who relapsed within 5 years of diagnosis for the effect of high COL1A1 and COX-2 (PTGS2) expression (23%) (red), all other combinations of COL1A1 and COX-2 expression (77%) (blue), and estrogen receptor (ER) status. *P = 0.018. () Univariate analysis of high COX-2 (top) and high COL1A1 (bottom) expression and relapse-free survival in 345 breast tumors diagnosed in women ≤45 years of age who relapsed within 5 years of diagnosis. () Left, images of human DCIS lesions stained for COX-2 by immunohis! tochemistry. Scale bars, 50 μm. *Normal adjacent tissue. () Average (black bars) of tumor area positive for COX-2 signal quantified by quantitative immunohistochemistry methods14. *P = 0.0266, n = 11 nulliparous cases and 22 cases diagnosed ≤10 years postpartum (2–13 DCIS lesions examined per case), unpaired t test. See Supplementary Figure 5f for data set characteristics. PP, postpartum; black horizontal bars indicate group average. * Figure 6: Postpartum ibuprofen treatment reduces tumor volume, burden, COX-2 expression and lung infiltration. () In vitro wound closure untreated (U), vehicle treated (V) and ibuprofen treated (200 μg ml−1) (Ibu). *P = 0.0001, n = 4 wells per condition, unpaired t test. () Collagen quantification by SHG as intensity versus distance from rat mammary ducts. Black, nulliparous; red, involution; blue, involution + ibuprofen (3 rats per group). *P < 0.001, involution versus involution + ibuprofen between 5–10 μm from duct, Student's t test. AU, arbitrary units. () H&E-stained images of involution and involution + ibuprofen group mouse mammary tissues 2 weeks after treatment. Scale bars (top), 150 μm; scale bars (bottom), 40 μm. () Average tumor volume in nulliparous (N), involution (I), ± ibuprofen (Ibu) group mice. *P = 0.00373, one-way ANOVA, ‡P ≤ 0.035, type IIIF test for group effect in additive model, n = 6 (nulliparous), n = 8 (involution), n = 4 (nulliparous + ibuprofen), n = 5 (involution + ibuprofen). () Tumor burden, 6-week time point. *P = 0.0255, n = 8 (involutio! n), n = 9 (involution + ibuprofen), unpaired t test. () Quantitation of tumor COX-2 expression at 6-week time point. *P ≤ 0.036, unpaired t test, n = 9 (nulliparous), n = 9 (involution), n = 6 (nulliparous + ibuprofen), n = 9 (involution + ibuprofen). () Statistical modeling of mouse lung signal for human-specific β2M by qRT-PCR across time. ‡P = 0.027, t test of group effect. () Left axis, average mammary tumor volume per group demonstrating size match between groups (light gray). Right axis, mouse lung signal for human-specific β2M by qRT-PCR analysis. *P = 0.011, n = 5, Wilcoxon test. Dark gray, involution; black, involution + ibuprofen. Data shown are mean ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Traci R Lyons & * Jenean O'Brien Affiliations * Department of Medicine, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. * Traci R Lyons, * Jenean O'Brien, * Virginia F Borges, * Aik-Choon Tan & * Pepper Schedin * Program in Cancer Biology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. * Jenean O'Brien & * Pepper Schedin * University of Colorado Cancer Center, Aurora, Colorado, USA. * Virginia F Borges, * Aik-Choon Tan & * Pepper Schedin * Department of Cell and Regenerative Biology and UW Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin, USA. * Matthew W Conklin & * Patricia J Keely * Laboratory of Cell and Molecular Biology, Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, Wisconsin, USA. * Matthew W Conklin, * Patricia J Keely & * Kevin W Eliceiri * Department of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. * Andriy Marusyk * AMC Cancer Research Center, Denver, Colorado, USA. * Pepper Schedin Contributions T.R.L. developed the postpartum mouse model, and designed and performed the in vivo celecoxib, two-dimensional cell culture, protein expression, three-dimensional collagen, celecoxib, COX-2 knockdown and human DCIS studies, and data analyses. J.O. designed and performed the in vivo ibuprofen experiments, the collagen western blot quantification, the three-dimensional gelatin assay and data analyses. P.J.K. and M.W.C. performed quantitative SHG collagen imaging and collagen fiber orientation. K.W.E. provided critical guidance for the SHG imaging. A.M. generated GFP-expressing MCF10DCIS cells and provided MCF10DCIS cells with stable knockdown of COX-2. A.-C.T. performed the human outcome analyses. V.B. and T.R.L. were responsible for regulatory oversight of human tissue acquisition and V.B. and P.S. for human tissue acquisition. P.S. and V.B. were responsible for hypothesis development, conceptual design and all data analysis and interpretation. T.R.L., J.O. and P.S. wrote the! manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Pepper Schedin Author Details * Traci R Lyons Search for this author in: * NPG journals * PubMed * Google Scholar * Jenean O'Brien Search for this author in: * NPG journals * PubMed * Google Scholar * Virginia F Borges Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew W Conklin Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia J Keely Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin W Eliceiri Search for this author in: * NPG journals * PubMed * Google Scholar * Andriy Marusyk Search for this author in: * NPG journals * PubMed * Google Scholar * Aik-Choon Tan Search for this author in: * NPG journals * PubMed * Google Scholar * Pepper Schedin Contact Pepper Schedin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (811K) Supplementary Figures 1–6, Supplementary Tables 1 and 2 and Supplementary Methods Additional data - Oncogenic PIK3CA-driven mammary tumors frequently recur via PI3K pathway–dependent and PI3K pathway–independent mechanisms
- Nat Med 17(9):1116-1120 (2011)
Nature Medicine | Letter Oncogenic PIK3CA-driven mammary tumors frequently recur via PI3K pathway–dependent and PI3K pathway–independent mechanisms * Pixu Liu1, 2, 11 * Hailing Cheng1, 2, 3, 11 * Stephanie Santiago1, 2 * Maria Raeder1, 4 * Fan Zhang5 * Adam Isabella1 * Janet Yang1 * Derek J Semaan1 * Changzhong Chen6 * Edward A Fox6, 7, 8 * Nathanael S Gray1, 2 * John Monahan9 * Robert Schlegel9 * Rameen Beroukhim1, 7, 8, 10 * Gordon B Mills5 * Jean J Zhao1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1116–1120Year published:(2011)DOI:doi:10.1038/nm.2402Received10 December 2010Accepted18 May 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg PIK3CA gain-of-function mutations are a common oncogenic event in human malignancy1, 2, 3, 4, making phosphatidylinositol 3-kinase (PI3K) a target for cancer therapy. Despite the promise of targeted therapy, resistance often develops, leading to treatment failure. To elucidate mechanisms of resistance to PI3K-targeted therapy, we constructed a mouse model of breast cancer conditionally expressing human PIK3CAH1047R. Notably, most PIK3CAH1047R-driven mammary tumors recurred after PIK3CAH1047R inactivation. Genomic analyses of recurrent tumors revealed multiple lesions, including focal amplification of Met or Myc (also known as c-Met and c-Myc, respectively). Whereas Met amplification led to tumor survival dependent on activation of endogenous PI3K, tumors with Myc amplification became independent of the PI3K pathway. Functional analyses showed that Myc contributed to oncogene independence and resistance to PI3K inhibition. Notably, PIK3CA mutations and c-MYC elevation co-occu! r in a substantial fraction of human breast tumors. Together, these data suggest that c-MYC elevation represents a potential mechanism by which tumors develop resistance to current PI3K-targeted therapies. View full text Figures at a glance * Figure 1: Mammary gland–specific expression of PIK3CAH1047R induces mammary tumors. () Generation of a transgenic mouse model expressing hemagglutinin (HA)-tagged human PIK3CAH1047R under the control of TetO. The expression of PIK3CAH1047R is coupled through an internal ribosomal entry site (IRES) with downstream expression of luciferase. These mice were crossed with MMTV-rtTA (MTB) mice to generate bitransgenic iPIK3CAH1047R animals to drive the expression of HA-PIK3CAH1047R in mammary glands. Bottom, bioluminescence imaging of iPIK3CAH1047R mice maintained with or without doxycycline (Dox). () Tumor-free survival curve for iPIK3CAH1047R mice maintained on doxycycline (n = 81, median tumor-free survival 208 d), and three groups of control mice (blue line): MTB (n = 12) and TetO-PIK3CAH1047R (n = 10) mice maintained with doxycycline, and iPIK3CAH1047R (n = 14) mice maintained without doxycycline. () Representative H&E-stained sections of primary mammary tumors from iPIK3CAH1047R mice subjected to chronic doxycycline treatment. Scale bars, 25 μm. () Represe! ntative images of immunohistochemistry for p-AKT (Ser473) and p-S6RP (Ser235 and Ser236) carried out on tumors isolated from iPIK3CAH1047R mice maintained on doxycycline (Dox on) or 6 d after doxycycline withdrawal (Dox off). Scale bars, 50 μm. () Representative images of immunohistochemistry for Ki67 or TUNEL carried out on tumors isolated from iPIK3CAH1047R mice maintained on doxycycline (Dox on) or 3 d after doxycycline withdrawal (Dox off). Scale bars, 50 μm. Data are means ± s.e.m. (n = 6). *P < 0.005 (Student's t test). * Figure 2: Tumor responses to doxycycline withdrawal. () Response of primary tumors (135 primary tumors derived from 107 tumor-bearing bitransgenic mice; 81 mice carried one tumor, 21 mice bore two tumors and 4 mice had three tumors) to doxycycline withdrawal. () Western blot analyses of HA-p110αH1047R, p-Akt and p-S6RP in six recurrent tumors (RCT) without doxycycline and their matched primary tumors (PMT) maintained on doxycycline. Mammary gland tissues from uninduced iPIK3CAH1047R mice were controls. () Responses of recurrent tumor transplants to GDC-0941 or vehicle treatment. Data are means ± s.e.m. (n = 6). *P < 0.001 (Student's t test). * Figure 3: Genetic alterations associated with PIK3CAH1047R-independent tumor recurrence. (,) Mouse SNP6.0 array analyses of six recurrent tumors identified an amplification region encompassing Met in RCT-E565 () and a common focal amplification at the Myc locus in RCT-D419 and RCT-C658 tumors (). () Western blot analyses of p-Akt (Ser473) and p-S6RP (Ser235 and Ser236) in two RCT-E565 transplanted tumors treated with vehicle or PF02341066. Samples were isolated 4 h after the last dose from mice treated with PF02341066 for 3 d. () Responses of RCT-E565 transplanted tumors in athymic mice to PF02341066 or vehicle. Data are means ± s.e.m. (each group, n = 6). *P < 0.005, **P < 0.001 (Student's t test). * Figure 4: Elevation of c-Myc drives mammary tumors to become independent of PIK3CAH1047R and resistant to PI3K inhibition. () shRNA knockdown of c-Myc in primary tumor cells isolated from RCT-D419. Western blot analysis of c-Myc in RCT-D419 parental cells or cells infected with indicated lentiviral shRNAs. Vinculin, loading control. () RCT-D419 cells expressing sh-Luc, sh-Myc1 or sh-Myc2 were transplanted into NOD-SCID mice and tumor formation was monitored. P < 0.001 (log-rank test). () Western blot analysis of ectopically expressed c-Myc or c-MycT58A in D777 tumor cells isolated from a PIK3CAH1047R-dependent primary tumor maintained on doxycycline. () Bioluminescence imaging of tumor establishment in NOD-SCID mice transplanted with D777 cells expressing vector, c-MycT58A or c-Myc. Mice were maintained on doxycycline to sustain PIK3CAH1047R expression. () Responses of tumors established by D777 cells expressing control vector, c-Myc or c-MycT58A to doxycycline withdrawal. Data are means ± s.e.m. (n = 6). () Mice bearing D777-MycT58A tumors were treated with either GDC-0941 or vehicle and tumor! growth was monitored. Data are means ± s.e.m. (n = 6). () Schematic summarizing three outcomes of PIK3CAH1047R-initiated tumors after inactivation of PIK3CAH1047R expression. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE27691 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Pixu Liu & * Hailing Cheng Affiliations * Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Pixu Liu, * Hailing Cheng, * Stephanie Santiago, * Maria Raeder, * Adam Isabella, * Janet Yang, * Derek J Semaan, * Nathanael S Gray, * Rameen Beroukhim & * Jean J Zhao * Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Pixu Liu, * Hailing Cheng, * Stephanie Santiago, * Nathanael S Gray & * Jean J Zhao * Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Hailing Cheng & * Jean J Zhao * Department of Obstetrics and Gynecology, Haukeland University Hospital and Department of Clinical Medicine, University of Bergen, Bergen, Norway. * Maria Raeder * Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA. * Fan Zhang & * Gordon B Mills * Microarray Core, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Changzhong Chen & * Edward A Fox * Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Edward A Fox & * Rameen Beroukhim * Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. * Edward A Fox & * Rameen Beroukhim * Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA. * John Monahan & * Robert Schlegel * Broad Institute, Cambridge, Massachusetts, USA. * Rameen Beroukhim Contributions P.L., H.C. and J.J.Z. designed the experiments, interpreted the data and wrote the paper. P.L. and H.C. carried out most of the experiments. S.S., A.I. and D.J.S. assisted with biochemical analyses and mouse work. J.Y., C.C., E.A.F., J.M. and R.S. carried out genome-wide DNA copy number profiling. N.S.G. provided GDC-0941 inhibitor. M.R. and R.B. analyzed co-occurrence of PIK3CA mutation with c-MYC amplification and overexpression in human breast tumors. F.Z. and G.B.M. provided the reverse-phase protein array data on the co-occurrence of PIK3CA mutation with increased c-MYC expression in human breast tumors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jean J Zhao Author Details * Pixu Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Hailing Cheng Search for this author in: * NPG journals * PubMed * Google Scholar * Stephanie Santiago Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Raeder Search for this author in: * NPG journals * PubMed * Google Scholar * Fan Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Isabella Search for this author in: * NPG journals * PubMed * Google Scholar * Janet Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Derek J Semaan Search for this author in: * NPG journals * PubMed * Google Scholar * Changzhong Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Edward A Fox Search for this author in: * NPG journals * PubMed * Google Scholar * Nathanael S Gray Search for this author in: * NPG journals * PubMed * Google Scholar * John Monahan Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Schlegel Search for this author in: * NPG journals * PubMed * Google Scholar * Rameen Beroukhim Search for this author in: * NPG journals * PubMed * Google Scholar * Gordon B Mills Search for this author in: * NPG journals * PubMed * Google Scholar * Jean J Zhao Contact Jean J Zhao Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–16, Supplementary Tables 1–3 and Supplementary Methods Additional data - Peroxisome proliferation–associated control of reactive oxygen species sets melanocortin tone and feeding in diet-induced obesity
- Nat Med 17(9):1121-1127 (2011)
Nature Medicine | Letter Peroxisome proliferation–associated control of reactive oxygen species sets melanocortin tone and feeding in diet-induced obesity * Sabrina Diano1, 2, 3, 4 * Zhong-Wu Liu1, 3 * Jin Kwon Jeong1, 2 * Marcelo O Dietrich1, 3, 5 * Hai-Bin Ruan1, 3 * Esther Kim6, 7 * Shigetomo Suyama1, 3 * Kaitlin Kelly1, 2 * Erika Gyengesi1, 2 * Jack L Arbiser8 * Denise D Belsham9 * David A Sarruf10, 11 * Michael W Schwartz10, 11 * Anton M Bennett1, 3, 12 * Marya Shanabrough1, 3 * Charles V Mobbs5 * Xiaoyong Yang1, 3 * Xiao-Bing Gao1, 2, 3 * Tamas L Horvath1, 2, 3, 4 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:1121–1127Year published:(2011)DOI:doi:10.1038/nm.2421Received28 March 2011Accepted15 June 2011Published online28 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Previous studies have proposed roles for hypothalamic reactive oxygen species (ROS) in the modulation of circuit activity of the melanocortin system1, 2. Here we show that suppression of ROS diminishes pro-opiomelanocortin (POMC) cell activation and promotes the activity of neuropeptide Y (NPY)- and agouti-related peptide (AgRP)-co-producing (NPY/AgRP) neurons and feeding, whereas ROS-activates POMC neurons and reduces feeding. The levels of ROS in POMC neurons were positively correlated with those of leptin in lean and ob/ob mice, a relationship that was diminished in diet-induced obese (DIO) mice. High-fat feeding resulted in proliferation of peroxisomes and elevated peroxisome proliferator–activated receptor γ (PPAR-γ) mRNA levels within the hypothalamus. The proliferation of peroxisomes in POMC neurons induced by the PPAR-γ agonist rosiglitazone decreased ROS levels and increased food intake in lean mice on high-fat diet. Conversely, the suppression of peroxisome pr! oliferation by the PPAR antagonist GW9662 increased ROS concentrations and c-fos expression in POMC neurons. Also, it reversed high-fat feeding–triggered elevated NPY/AgRP and low POMC neuronal firing, and resulted in decreased feeding of DIO mice. Finally, central administration of ROS alone increased c-fos and phosphorylated signal transducer and activator of transcription 3 (pStat3) expression in POMC neurons and reduced feeding of DIO mice. These observations unmask a previously unknown hypothalamic cellular process associated with peroxisomes and ROS in the central regulation of energy metabolism in states of leptin resistance. View full text Figures at a glance * Figure 1: Free radicals' effect on the melanocortin system. () Fluorescence double labeling for c-fos (red nuclei) and GFP (green cell bodies) showing colocalization of c-fos with GFP (white arrows) in vehicle- and honokiol-treated mice. Scale bar, 100 μm. Asterisk indicates neurons single labeled for GFP. () Graph showing the percentage of c-fos and NPY-GFP double-labeled neurons compared to vehicle controls as well as the percentage of c-fos/POMC-GFP double labeled cells. *P < 0.05. () Graph showing food intake induced by i.c.v. honokiol treatment. *P < 0.05. () Top, H2O2 significantly (*P < 0.05) depolarized membrane potential of POMC-GFP neurons, an event that was rapidly reversed by washout. Control values indicate firing of POMC neuron when exposed to vehicle. Bottom, H2O2 significantly increased (*P < 0.05) the firing frequency of arcuate nucleus POMC-GFP neurons, an event that was reversed by washout. Control values indicate firing of POMC neuron when exposed to vehicle. () Food intake measurements of mice 1 h, 2 h, 4 h and ! 8 h after H2O2 injections (*P < 0.05). Control mice were injected with equivalent amount of vehicle. () DHE (red fluorescence) in POMC neurons (green fluorescence) of ob/ob, fasted lean, fed lean and DIO mice. () Quantification of DHE in POMC neurons indicated the highest level of ROS production in POMC neurons of DIO and fed lean mice compared to fasted lean and ob/ob mice. *P < 0.05 compared to ob/ob values. #P < 0.05 compared to fasted lean values. There was no significant difference between fed and DIO values. () Leptin was not detectable in ob/ob mice. Leptin levels were significantly higher in fed mice compared to fasted values (#P < 0.05). DIO mice had leptin levels that were significantly higher compared to those in fed ($P < 0.05) and fasted (#P < 0.05) mice. *P < 0.05 compared to ob/ob values. Data are expressed as the mean ± s.e.m. n = 6 for each experimental group. * Figure 2: Peroxisome proliferation in POMC neurons. () Electron micrographs showing a representative section of POMC-GFP perykarya in the arcuate nucleus from fasted lean (top left), ob/ob (bottom left), fed lean (top middle) and DIO (bottom middle) mice. White arrows on the middle images point to peroxisomes. The top and bottom right images are high-power magnifications of peroxisomes from the fed lean and DIO images, respectively. Top left scale bar, 1 μm for left and middle columns. Bottom right scale bar, 500 nm for right column. () Mitochondria number in POMC neurons of fed and DIO mice compared to fasted and ob/ob values. *P < 0.05 compared to ob/ob values. #P < 0.05 compared to fasted values. () Graph showing the number of peroxisomes in POMC neurons in DIO, fed, fasted and ob/ob mice, *P < 0.05 compared to ob/ob values. #P < 0.05 compared to fasted values. $P < 0.05 compared to fed values. () PCR analyses of PPAR-α, PPAR-δ and PPAR-γ mRNA. () Real-time PCR analyses of PPAR-α, PPAR-δ and PPAR-γ and of various ot! her gene transcripts related to PPAR signaling and cellular metabolism in DIO hypothalamus relative to lean control values. LPL, lipoprotein lipase; FASN, fatty acid synthase; LXRa, liver X receptor α; FABP1, fatty acid binding protein 1; CIDEA, cell death–inducing DFFA-like effector a; GK, glucokinase. Results are shown as mean ± s.e.m. The comparison of different groups was carried out using two-tailed unpaired Student's t test. *P < 0.05. () PPAR-γ mRNA expression in the POMC neuronal cell line mHypoA-2/28 with and without pioglitazone treatment (pio). () mRNA expression of the PPAR-γ target Gpd1 in the POMC-expressing hypothalamic cell line after pioglitazone treatment. () mRNA expression of the PPAR-γ target Fabp4 in the AgRP-expressing hypothalamic cell line after pioglitazone treatment. () mRNA expression of the PPAR-γ target (ADFP in the AgRP-expressing hypothalamic cell line after pio treatment. () Real-time PCR analyses of PPAR-α, PPAR-δ and PPAR-γ and ! of various other gene transcripts related to PPAR signaling an! d cellular metabolism in ob/ob hypothalamus relative to wild-type controls. Results are shown as mean ± s.e.m. n = 6 for each experimental group. The comparison of different groups was carried out using two-tailed unpaired Student's t test. *P < 0.05. * Figure 3: Peroxisome proliferation in POMC neurons is associated with altered feeding. (–) Electron micrographs with fluorescence insets of POMC neurons from lean vehicle-treated (top left), lean rosiglitazone-treated (top right), DIO vehicle-treated (bottom left) and DIO GW9662-treated (bottom right) mice. On the electron micrographs, blue arrows point to peroxisomes. On the fluorescence insets, red labeling indicates DHE in green POMC-GFP neurons. Electron micrograph scale bar, 1 μm; inset scale bar, 10 μm. () Graph showing peroxisome number in POMC neurons of lean mice on high-fat diet after rosiglitazone treatment and in DIO mice treated with GW9662, *P < 0.05. () DHE levels in lean mice treated with rosiglitazone and in DIO mice treated with GW9662, *P < 0.05. () Daily food intake measurements of lean mice after rosiglitazone treatment and of DIO mice after GW9662 administration compared to vehicle treated controls. *P < 0.05. () Double immunofluorescence labeling for c-fos (red) and POMC (green) from control DIO (top left), rosigliatzone-treated (top! right), GW9662-treated (bottom left) and H2O2-treated (bottom right) high-fat diet–fed mice. Scale bar, 100 μm. () Bar graphs showing the percentage of c-fos–immunolabeled POMC neurons in the different experimental groups. *P < 0.05 compared to DIO control values; #P < 0.05 compared to rosiglitazone-treated mice. Data are expressed as the mean ± s.e.m. n = 6 for each experimental group. * Figure 4: The effect of PPAR ligands on the melanocortin system. () Firing rate of DIO NPY/AgRP neurons upon vehicle or GW9662 treatment (*P < 0.05). () Firing rate of DIO POMC neurons upon vehicle or GW9662 treatment (*P < 0.05). () Percentage of silent DIO NPY/AgRP neurons upon vehicle or GW9662 treatment. () Percentage of silent DIO POMC neurons upon vehicle or GW9662 treatment. The ratio of silent and active neurons are indicated in the graphs. () Left column indicates daily food intake after 5 d on high-fat diet. Middle column shows mean daily food intake after 5-d treatment with rosiglitazone. Right column indicates daily food intake after 7-d rosiglitazone treatment. Red column on right indicates daily food intake of mice with 7-d rosiglitazone treatment with H2O2 in the last 2 d of the 7-d treatment. *P < 0.05 relative to values before rosiglitazone treatment. #P < 0.05 between daily food intake values after 5-d treatment with rosiglitazone. $P < 0.05 between 7-d treatment values. () Left column indicates daily food intake at the ! beginning of GW9662 treatment (day 0). Middle column shows mean food intake after 5-d treatment with GW9662. Right column indicates daily food intake after 7-d GW9662 treatment. Green column on right indicates daily food intake of mice with 7-d GW9662 treatment with honokiol in the last 2 d of the 7-d treatment. *P < 0.05 relative to values before GW9662 treatment. #P < 0.05 between daily food intake values after 5-d treatment with GW9662. $P < 0.05 between 7-d treatment values. () Two-day i.c.v. H2O2 treatment alone resulted in significantly (*P < 0.05) decreased daily food intake of DIO mice compared to vehicle-treated controls. () Photomicrographs of pStat3 (red) and POMC (green) double-immunolabeled hypothalamic sections from leptin-treated DIO mice concurrently treated with vehicle or H2O2 after peripheral leptin injections. P < 0.05 for H2O2-treated mice compared to vehicle-treated controls. Arrows indicate pStat3 labeled nuclei. Arrows indicate pStat3 and POMC co-lab! eled cells. Scale bar, 100 μm. Data are expressed as the mean! ± s.e.m. n = 6 for each experimental group. Author information * Author information * Supplementary information Affiliations * Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale University School of Medicine, New Haven, Connecticut, USA. * Sabrina Diano, * Zhong-Wu Liu, * Jin Kwon Jeong, * Marcelo O Dietrich, * Hai-Bin Ruan, * Shigetomo Suyama, * Kaitlin Kelly, * Erika Gyengesi, * Anton M Bennett, * Marya Shanabrough, * Xiaoyong Yang, * Xiao-Bing Gao & * Tamas L Horvath * Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut, USA. * Sabrina Diano, * Jin Kwon Jeong, * Kaitlin Kelly, * Erika Gyengesi, * Xiao-Bing Gao & * Tamas L Horvath * Section of Comparative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Sabrina Diano, * Zhong-Wu Liu, * Marcelo O Dietrich, * Hai-Bin Ruan, * Shigetomo Suyama, * Anton M Bennett, * Marya Shanabrough, * Xiaoyong Yang, * Xiao-Bing Gao & * Tamas L Horvath * Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USA. * Sabrina Diano & * Tamas L Horvath * Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. * Marcelo O Dietrich & * Charles V Mobbs * Department of Neuroscience, Friedman Brain Institute, Mount Sinai School of Medicine, New York, New York, USA. * Esther Kim * Department of Geriatrics, Friedman Brain Institute, Mount Sinai School of Medicine, New York, New York, USA. * Esther Kim * Department of Dermatology, Emory University School of Medicine, Winship Cancer Institute, Atlanta VA Medical Center, Atlanta, Georgia, USA. * Jack L Arbiser * Department of Physiology, University of Toronto and Division of Cellular and Molecular Biology, Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada. * Denise D Belsham * Diabetes and Obesity Center of Excellence, University of Washington, Seattle, Washington, USA. * David A Sarruf & * Michael W Schwartz * Department of Medicine, University of Washington, Seattle, Washington, USA. * David A Sarruf & * Michael W Schwartz * Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut, USA. * Anton M Bennett Contributions S.D. and T.L.H. developed the conceptual framework of the study, designed the experiments, conducted studies, analyzed data and wrote the paper. Z.-W.L., J.K.J., M.O.D., H.-B.R., E.K., S.S., K.K., E.G., D.A.S. and M.S. conducted experiments. J.L.A. initiated studies with honokiol and provided reagents. C.V.M. designed and supervised in vitro cell signaling studies. M.W.S. designed studies on PPAR-γ–knockout mice. D.D.B. provided POMC and AgRP cell cultures and helped design experiments. A.M.B. provided reagents and advised on signaling aspects of the work. X.Y. and X.-B.G. supervised experiments and analyzed data. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Sabrina Diano or * Tamas L Horvath Author Details * Sabrina Diano Contact Sabrina Diano Search for this author in: * NPG journals * PubMed * Google Scholar * Zhong-Wu Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Jin Kwon Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Marcelo O Dietrich Search for this author in: * NPG journals * PubMed * Google Scholar * Hai-Bin Ruan Search for this author in: * NPG journals * PubMed * Google Scholar * Esther Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Shigetomo Suyama Search for this author in: * NPG journals * PubMed * Google Scholar * Kaitlin Kelly Search for this author in: * NPG journals * PubMed * Google Scholar * Erika Gyengesi Search for this author in: * NPG journals * PubMed * Google Scholar * Jack L Arbiser Search for this author in: * NPG journals * PubMed * Google Scholar * Denise D Belsham Search for this author in: * NPG journals * PubMed * Google Scholar * David A Sarruf Search for this author in: * NPG journals * PubMed * Google Scholar * Michael W Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar * Anton M Bennett Search for this author in: * NPG journals * PubMed * Google Scholar * Marya Shanabrough Search for this author in: * NPG journals * PubMed * Google Scholar * Charles V Mobbs Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoyong Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Bing Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Tamas L Horvath Contact Tamas L Horvath Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–5 Additional data - CD8+ cellular immunity mediates rAd5 vaccine protection against Ebola virus infection of nonhuman primates
- Nat Med 17(9):1128-1131 (2011)
Nature Medicine | Letter CD8+ cellular immunity mediates rAd5 vaccine protection against Ebola virus infection of nonhuman primates * Nancy J Sullivan1 * Lisa Hensley2 * Clement Asiedu1 * Thomas W Geisbert2, 5 * Daphne Stanley1 * Joshua Johnson2 * Anna Honko2 * Gene Olinger2 * Michael Bailey1, 5 * Joan B Geisbert2, 5 * Keith A Reimann3 * Saran Bao1 * Srinivas Rao1 * Mario Roederer1 * Peter B Jahrling4 * Richard A Koup1 * Gary J Nabel1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1128–1131Year published:(2011)DOI:doi:10.1038/nm.2447Received14 February 2011Accepted19 July 2011Published online21 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Vaccine-induced immunity to Ebola virus infection in nonhuman primates (NHPs) is marked by potent antigen-specific cellular and humoral immune responses1, 2; however, the immune mechanism of protection remains unknown. Here we define the immune basis of protection conferred by a highly protective recombinant adenovirus virus serotype 5 (rAd5) encoding Ebola virus glycoprotein (GP)1, 3 in NHPs. Passive transfer of high-titer polyclonal antibodies from vaccinated Ebola virus–immune cynomolgus macaques to naive macaques failed to confer protection against disease, suggesting a limited role of humoral immunity. In contrast, depletion of CD3+ T cells in vivo after vaccination and immediately before challenge eliminated immunity in two vaccinated macaques, indicating a crucial requirement for T cells in this setting. The protective effect was mediated largely by CD8+ cells, as depletion of CD8+ cells in vivo using the cM-T807 monoclonal antibody (mAb), which does not affect CD4+! T cell or humoral immune responses, abrogated protection in four out of five subjects. These findings indicate that CD8+ cells have a major role in rAd5-GP–induced immune protection against Ebola virus infection in NHPs. Understanding the immunologic mechanism of Ebola virus protection will facilitate the development of vaccines for Ebola and related hemorrhagic fever viruses in humans. View full text Figures at a glance * Figure 1: The effect of CD3+ T cell depletion on infectious Ebola virus challenge of vaccinated cynomolgus macaques. () Experimental scheme for immunization, CD3+ T cell depletion and virus challenge. Two Ebola virus–immune cynomolgus macaques received the CD3+ T cell–depleting immunotoxin (Anti-CD3), and one vaccinated cynomolgus macaque received control antibody (None). () Enumeration of CD3+ T cells in vaccinated subjects. Peripheral blood mononuclear cells (PBMCs) collected before antibody administration (before) and 2 d before infectious challenge (after) were immunostained for surface markers and enumerated by flow cytometry as described in the Online Methods. Frequencies are expressed as a percentage of total PBMCs for individual subjects. Gray bars show CD3+ T cells before administration of CRM9-FN18, and black bars show values after depletion. Each bar represents a single measurement per individual NHP. () Kaplan-Meier survival curve for immunized, untreated (No depletion), immunized, CD3+ T cell depleted (Anti-CD3) and unvaccinated (Virus control) cynomolgus macaques after ch! allenge with 1,000 plaque-forming units (PFU) of Zaire Ebola virus by intramuscular injection. * Figure 2: Vaccine-induced immune responses present before immunodepletion. (–) Blood samples were obtained from subjects vaccinated with 1 × 1010 particles of rAd5-GP 2 d before immunodepletion and assessed for serum IgG against Ebola virus GP by ELISA (mean ± s.d.). () and intracellular cytokine staining for GP-specific CD4+ () and CD8+ () T cells as described in the Online Methods. A single measurement is shown per cynomolgus macaque for cellular immune responses. EC90, 90% effective concentration. * Figure 3: CD8+ cell depletion effects on Ebola virus GP vaccine–induced protective immunity. (,) PBMCs were obtained from vaccinated macaques (No depletion) and vaccinated CD8+ cell–depleted macaques (cM-T807), and complete blood cell counts were performed to enumerate circulating CD4+ () and CD8+ () T lymphocytes (mean ± s.e.m.). () Serum levels of the liver enzyme AST after infectious challenge with 1,000 PFU Zaire Ebola virus by intramuscular injection. Vaccinated, untreated macaque is in red, vaccinated, CD8-depleted macaques are in blue and unvaccinated controls are in black. *Denotes AST level exceeding the instrument upper limit of 2,000 international units (IU) l−1. A single measurement is shown for each time point. () Plasma viral load after intramuscular challenge with 1,000 PFU Zaire Ebola virus determined by quantitative RT-PCR as described in the Online Methods. Relative PFU (mean ± s.d.) are genome equivalents derived from the standard curve using Zaire Ebola virus with known PFU by plaque assay. CD8-depleted and unvaccinated control macaques are! shown in blue and black, respectively. () Infectious challenge Kaplan-Meier survival proportions. Vaccinated, undepleted (None, n = 5); vaccinated, CD8-depleted (cM-T807, n = 5); unvaccinated control (Control, n = 2). P value was calculated by log-rank (Mantel-Cox) test. Author information * Author information * Supplementary information Affiliations * Vaccine Research Center, US National Institute of Allergy and Infectious Diseases, US National Institutes of Health, Bethesda, Maryland, USA. * Nancy J Sullivan, * Clement Asiedu, * Daphne Stanley, * Michael Bailey, * Saran Bao, * Srinivas Rao, * Mario Roederer, * Richard A Koup & * Gary J Nabel * United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA. * Lisa Hensley, * Thomas W Geisbert, * Joshua Johnson, * Anna Honko, * Gene Olinger & * Joan B Geisbert * Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. * Keith A Reimann * Integrated Research Facility, US National Institute of Allergy and Infectious Diseases, US National Institutes of Health, Bethesda, Maryland, USA. * Peter B Jahrling * Present addresses: University of Texas Medical Branch, Galveston, Texas, USA (T.W.G. and J.B.G.) and Transformational Medical Technologies, Joint Program Executive Office, US Department of the Army, Fort Belvoir, Virginia, USA (M.B.). * Thomas W Geisbert, * Michael Bailey & * Joan B Geisbert Contributions N.J.S. and G.J.N. conceived of the studies and wrote the manuscript. T.W.G., J.B.G., L.H., J.J., G.O. and A.H. contributed to animal study design, conducted infectious Ebola virus challenge, post-challenge assays and passive antibody administration. N.J.S., M.B., C.A. and D.S. performed vaccine preparation, animal immunization, characterization of vaccine-induced immune responses and immunodepleting antibody administration. K.A.R. provided depleting antibodies and contributed to experimental design. S.B. and S.R. performed immunohistochemistry. M.R., P.B.J. and R.A.K. contributed to experimental design or provided reagents. Competing financial interests N.J.S., G.J.N., T.W.G. and P.B.J. have intellectual property on gene-based vaccines for Ebola. Corresponding author Correspondence to: * Nancy J Sullivan Author Details * Nancy J Sullivan Contact Nancy J Sullivan Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa Hensley Search for this author in: * NPG journals * PubMed * Google Scholar * Clement Asiedu Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas W Geisbert Search for this author in: * NPG journals * PubMed * Google Scholar * Daphne Stanley Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Honko Search for this author in: * NPG journals * PubMed * Google Scholar * Gene Olinger Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Bailey Search for this author in: * NPG journals * PubMed * Google Scholar * Joan B Geisbert Search for this author in: * NPG journals * PubMed * Google Scholar * Keith A Reimann Search for this author in: * NPG journals * PubMed * Google Scholar * Saran Bao Search for this author in: * NPG journals * PubMed * Google Scholar * Srinivas Rao Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Roederer Search for this author in: * NPG journals * PubMed * Google Scholar * Peter B Jahrling Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Koup Search for this author in: * NPG journals * PubMed * Google Scholar * Gary J Nabel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–3 Additional data - Identification of nucleolin as a cellular receptor for human respiratory syncytial virus
- Nat Med 17(9):1132-1135 (2011)
Nature Medicine | Letter Identification of nucleolin as a cellular receptor for human respiratory syncytial virus * Farnoosh Tayyari1, 5 * David Marchant1, 5 * Theo J Moraes2, 3 * Wenming Duan2 * Peter Mastrangelo3 * Richard G Hegele3, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1132–1135Year published:(2011)DOI:doi:10.1038/nm.2444Received28 March 2011Accepted14 June 2011Published online14 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Human respiratory syncytial virus (RSV) causes a large burden of disease worldwide1. There is no effective vaccine or therapy, and the use of passive immunoprophylaxis with RSV-specific antibodies is limited to high-risk patients2, 3, 4, 5. The cellular receptor (or receptors) required for viral entry and replication has yet to be described; its identification will improve understanding of the pathogenesis of infection and provide a target for the development of novel antiviral interventions. Here we show that RSV interacts with host-cell nucleolin via the viral fusion envelope glycoprotein and binds specifically to nucleolin at the apical cell surface in vitro. We observed decreased RSV infection in vitro in neutralization experiments using nucleolin-specific antibodies before viral inoculation, in competition experiments in which virus was incubated with soluble nucleolin before inoculation of cells, and upon RNA interference (RNAi) to silence cellular nucleolin expression! . Transfection of nonpermissive Spodoptera frugiperda Sf9 insect cells with human nucleolin conferred susceptibility to RSV infection. RNAi-mediated knockdown of lung nucleolin was associated with a significant reduction in RSV infection in mice (P = 0.0004), confirming that nucleolin is a functional RSV receptor in vivo. View full text Figures at a glance * Figure 1: RSV VOPBA and coimmunoprecipitation of RSV fusion protein with nucleolin. () RSV VOPBA in different cell types reveals a ~100-kDa signal. RSV A2 was incubated with biotinylated cell surface–enriched protein, separated by SDS-PAGE and transferred to nitrocellulose membranes. Signals were detected with goat RSV-specific antibody (Biodesign). CHO, Chinese hamster ovary cells deficient (pgsA-745) or not deficient (K1) in proteoglycans; MDCK, Madine-Darby canine kidney epithelial cells; HEp-2, human epithelial cells; MW, molecular weight (kDa). β-actin was used as a gel loading control. () VOPBA using indicated RSV isolates and 1HAEo− protein extracts. A2, B and ΔG, laboratory strains of RSV; HLI1 and HLI2, community isolates (type A and B, respectively). Band at ~100 kDa represents nucleolin. () VOPBA on purified nucleolin for different RSV isolates. () Western blots showing Ncl (H-250), RSV fusion protein (F1; NCL-RSV3) and RSV glycoprotein (G; R1600-17) in RSV A2–infected 1HAEo− cells immunoprecipitated (IP) with indicated antibodies, or i! n control lysates without immunoprecipitation (Lysate). () Western blot showing coimmunoprecipitation of indicated proteins as in , using various RSV strains. N, nucleocapsid protein; P, phosphoprotein; M, matrix protein. * Figure 2: RSV and nucleolin colocalized on cell surfaces, and inhibition interventions decreased infection. () Confocal micrographs of polarized 1HAEo− cells exposed to wild-type RSV type A at 4 °C and stained for RSV (green) and nucleolin (Ncl; red). Yellow and arrows indicate RSV-nucleolin colocalization on apical aspect of cell surface; blue, DAPI nuclear staining. () RSV-positive 1HAEo− cells (bearing GFP-tagged RSV A) after 24 h exposure to virus, following incubation of cells with 4 μg nucleolin-specific antibody (anti-Ncl; H-250), or irrelevant isotype-matched antibody (rabbit IgG, Santa Cruz). *P < 0.001; n = 8. By ANOVA, P = 0.0001. () Flow-cytometry results showing percentage of RSV-positive 1HAEo− cells (with GFP-tagged RSV A2) after 24 h exposure; virus was preincubated with nucleolin or transferrin before being added to cells. *P < 0.004; n = 3. By ANOVA, P = 0.0002. () Nucleolin immunoblot of 1HAEo− cells incubated with control siRNA (siDAF), nucleolin siRNA (siNcl) or nucleolin siRNA with 3-nucleotide substitution (siNclΔ3). β-actin was used as a gel loa! ding control. () 1HAEo− cells incubated with indicated siRNAs were inoculated with RSV-GFP and counted by flow cytometry 24 h later (20,000 events counted). ‡P < 0.01; n = 4. By ANOVA, P = 0.0007. NS, no significant difference. Error bars in ,, show s.e.m. * Figure 3: Sf9 cells are made permissive to RSV infection by heterologous expression of human nucleolin. () RSV VOPBA of proteins extracted from HEp-2 cells and Sf9 cells. β-actin was used as a gel loading control. () Sf9 cells were transfected with human nucleolin (Ncl) or pCMV-X6 empty vector (e.v.), and extracted cellular proteins were immunoblotted using anti-Ncl. () Fluorescence confocal microscopy (72 h after transfection) shows nucleolin expression (red) on the cell surface of Ncl-transfected Sf9 cells. Top, immunofluorescence fields; bottom, combined fluorescence and phase-contrast microscopy fields with blue Hoechst nuclear stain. (,) Sf9 cells were transfected with nucleolin or pCMV-X6 empty vector for 3 d and inoculated with RSV A2–GFP, and after 24 h infected cells (assessed by GFP fluorescence) were imaged by confocal microscopy () or counted by flow cytometry (). Top images in show fluorescence microscopy. Bottom, combined phase-contrast and fluorescence images. Bar graphs in show percentage of RSV-infected cells in Ncl-transfected Sf9. *P < 0.001; n = 8; 160,0! 00 cells counted in total. Error bar shows s.e.m. In , arrow indicates mean ± s.e.m. close to zero (0.55 ± 0.20%). * Figure 4: Silencing nucleolin reduces RSV infection in mice. () Nucleolin-targeting (3) and control (3Δ3) RNA oligonucleotides were transfected into mouse NIH 3T3 cells and harvested 4 d after transfection. Cell lysates were immunoblotted for nucleolin (Ncl) and β-actin, and relative quantities were determined by densitometry to assess efficiency of nucleolin knockdown. () Immunohistochemical staining of Ncl (brown) and RSV (red) in formalin-fixed, paraffin-embedded mouse lung tissue. Top, airway epithelium shows Ncl immunostaining (nucleolin-specific rabbit polyclonal; ab22758) on apical aspect of the cell surface (arrowhead), in addition to expected nuclear staining. Use of another Ncl-specific antibody revealed the same pattern of staining (H-250; Supplementary Fig. 12). Bottom, overlap of RSV-Ncl signal in an airway epithelial cell (arrow). RSV-specific rabbit polyclonal antibody (PAB13816) was used. () Quantification of nucleolin staining in apical aspect of airway epithelium 2 d after treatment with siRNA. The percentage of ap! ical Ncl cells is the ratio of airway epithelial cells positive for nucleolin staining over the total number of airway epithelial cells counted per stained mouse lung section, multiplied by 100. By ANOVA, P < 0.0001, *P < 0.001. We counted 123 ± 3 cells per mouse. () Effect of siRNA knockdown of Ncl expression on lung viral titer 2 d after infection. By ANOVA, P = 0.0004. Tukey's MCT was used post hoc to obtain P values between groups. ‡P < 0.01, *P < 0.001; n = 4 and n = 12 per group for experiments in and , respectively. Error bars show s.e.m. NS, no significant difference. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Farnoosh Tayyari & * David Marchant Affiliations * The James Hogg Research Centre, Providence Heart and Lung Institute at St. Paul's Hospital, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada. * Farnoosh Tayyari & * David Marchant * Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * Theo J Moraes & * Wenming Duan * Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. * Theo J Moraes, * Peter Mastrangelo & * Richard G Hegele * Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada. * Richard G Hegele Contributions F.T. conducted VOPBAs, mass spectrometry analysis, neutralization and competition experiments in vitro. D.M. conducted immunoprecipitation, cell culture and virus experiments with mammalian and Sf9 cells. T.J.M. performed mouse dissections and lung fixation. W.D. generated virus for the mouse experiments and did quantitative plaque assays. P.M. assisted with cell culture and mouse experiments, analyzed the data, prepared figures and graphs, and coordinated manuscript writing. R.G.H. did mouse lung histological examination and supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard G Hegele Author Details * Farnoosh Tayyari Search for this author in: * NPG journals * PubMed * Google Scholar * David Marchant Search for this author in: * NPG journals * PubMed * Google Scholar * Theo J Moraes Search for this author in: * NPG journals * PubMed * Google Scholar * Wenming Duan Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Mastrangelo Search for this author in: * NPG journals * PubMed * Google Scholar * Richard G Hegele Contact Richard G Hegele Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–13, Supplementary Table 1 and Supplementary Methods Additional data - Host S-nitrosylation inhibits clostridial small molecule–activated glucosylating toxins
- Nat Med 17(9):1136-1141 (2011)
Nature Medicine | Letter Host S-nitrosylation inhibits clostridial small molecule–activated glucosylating toxins * Tor C Savidge1 * Petri Urvil1 * Numan Oezguen2 * Kausar Ali1 * Aproteem Choudhury1 * Vinay Acharya1 * Irina Pinchuk1 * Alfredo G Torres3 * Robert D English4 * John E Wiktorowicz2, 4 * Michael Loeffelholz5 * Raj Kumar6 * Lianfa Shi7 * Weijia Nie7 * Werner Braun2 * Bo Herman8, 9 * Alfred Hausladen8, 9 * Hanping Feng7 * Jonathan S Stamler8, 9 * Charalabos Pothoulakis10 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1136–1141Year published:(2011)DOI:doi:10.1038/nm.2405Received09 February 2011Accepted20 May 2011Published online21 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The global prevalence of severe Clostridium difficile infection highlights the profound clinical significance of clostridial glucosylating toxins1, 2, 3, 4. Virulence is dependent on the autoactivation of a toxin cysteine protease5, 6, 7, 8, 9, which is promoted by the allosteric cofactor inositol hexakisphosphate (InsP6)10, 11, 12, 13, 14, 15, 16, 17. Host mechanisms that protect against such exotoxins are poorly understood. It is increasingly appreciated that the pleiotropic functions attributed to nitric oxide (NO), including host immunity, are in large part mediated by S-nitrosylation of proteins18, 19. Here we show that C. difficile toxins are S-nitrosylated by the infected host and that S-nitrosylation attenuates virulence by inhibiting toxin self-cleavage and cell entry. Notably, InsP6- and inositol pyrophosphate (InsP7)-induced conformational changes in the toxin enabled host S-nitrosothiols to transnitrosylate the toxin catalytic cysteine, which forms part of a stru! cturally conserved nitrosylation motif. Moreover, treatment with exogenous InsP6 enhanced the therapeutic actions of oral S-nitrosothiols in mouse models of C. difficile infection. Allostery in bacterial proteins has thus been successfully exploited in the evolutionary development of nitrosothiol-based innate immunity and may provide an avenue to new therapeutic approaches. View full text Figures at a glance * Figure 1: C. difficile toxins are S-nitrosylated in vivo. () H&E-stained sections of mouse ileum exposed to vehicle control (left), TcdA (middle) or TcdA-SNO (right) (10 μg toxin for 4 h; scale bar, 50 μm). () TcdA- or TcdA-SNO–induced fluid secretion in mouse ileal loops (10 μg for 4 h) (n = 4 per group; error bars show s.e.m.; P < 0.05 compared with vehicle (veh) control (*) and TcdA (#), respectively; analysis of variance (ANOVA) on ranks). () Tissue GSNO concentrations versus Nos2 mRNA expression levels in mouse ileal loops exposed to TcdA (10 μg) or vehicle for 4 h. () Anti-nitrosocysteine (SNO) immunofluorescence showing epithelial S-nitrosylation in human colitis (right) but not in histologically normal colon (left), where SNOs are largely confined to lamina propria cells (arrows illustrate brush border membrane; SNOs (green); DAPI nuclear counterstain (blue); scale bar, 20 μm). () SNO proteomics showing in situ S-nitrosylated proteins from TcdA-exposed mouse ileum labeled with BODIPY FL maleimide25. MW, molecular wei! ght. Inset, biotin-switch assay using a C-terminus specific antibody to TcdA. () SNO immunofluorescence in Nos3-transfected Caco-2 cells. High SNO-protein levels (green) co-localize with membrane ZO-1 (red). Scale bar, 10 μm. () Top, immunoblot showing eNOS and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression using IR-680 (red) and IR-800 (green) antibody labels, respectively, in vector- and Nos3-transfected Caco-2 cells. Bottom, immunoblots for TcdA-SNO and GAPDH (using IR-680 (red) and IR-800 (green) antibody labels, respectively), after immunoprecipitation with nitrosocysteine-specific antibodies in vector- and Nos3-transfected Caco-2 cells exposed to TcdA. TcdA-exposed cells were also treated with GSH-ethyl ester (GSH-EE) to remove NO groups from proteins (control). () TcdB-induced cell rounding in vector (left) versus Nos3 (right) transfected Caco-2 cells (71.1 ± 14.9% versus 26.5 ± 8.2%, respectively ± s.e.m., n = 3; P < 0.05, Mann-Whitney U test on ran! ks) (scale bar, 25 μm). () MTT assay for cytotoxicity in vect! or and Nos3-transfected Caco-2 cells exposed to 3.7 nM TcdB for 10 min. Controls included cells treated with GSH-EE and L-NAME (n = 3; error bars show s.e.m.; P < 0.05 compared to vector control– (*) and Nos3- (#) transfected cells, respectively; Mann-Whitney U test on ranks). () Immunoblots showing TcdA-SNO (top; labeled with IR-680) in human stool (n = 8) samples positive for TcdA (bottom; labeled with IR-800). TcdA-SNO was first immunoprecipitated from stool samples with a nitrosocysteine-specific antibody, and samples were then probed for TcdA. Presence of toxin in the stool was independently confirmed by ELISA and immunocytotoxicity assay30 (Supplementary Fig. 4). * Figure 2: Toxin S-nitrosylation is allosterically regulated by inositolphosphate. () N-terminus extended (cyan) CPD model for TcdB (based on 3PA8.pdb9 and 3FZY.pdb11 crystal structures), shows the β-flap (green) abutting the bound allosteric ligand InsP6 (red). () Alignment of GSNO at CPD active site. Surface rendering of the TcdB CPD (3PA8.pdb)9 showing the exposed S-nitrosylation consensus motif Glu743 (red)-Cys698 (yellow)-His653 (blue). GSNO docking shows the SNO group to be aligned with the catalytic Cys. () Crystal structures of TcdA (red)15, Vibrio cholerae RTX toxin (RTXVC; blue)11 and gingipain33 (green). Structurally conserved residues, constituting a nitrosylation motif, are shown in the respective crystal structures. () Assessment of TcdB S-nitrosylation (using SNO-specific (IR800) and TcdB-specific (IR680) antibodies) and effect of InsP6 (100 μM). DTT (1 mM) is used to remove NO from proteins (control). () Assessment of TcdB S-nitrosylation (as in ) for native and Cys698Ser mutant TcdB in the presence and absence of InsP6 cofactor. () Effec! t of GSNO on InsP6-induced TcdB autocleavage. Inset, GelCode Blue staining of unprocessed TcdB (270 kDa) and TcdB cleavage products (207 and 63 kDa) in the presence and absence of GSNO (100 μM) and GSH (100 μM). () Effect of S-nitrosylation on InsP6 binding. Tritiated InsP6 binding to TcdA (Bmax and Kd values are shown in Supplementary Fig. 8a). SNO photolysis is used to eliminate SNO. InsP6 binding to deoxygenated hemoglobin (Hb), which is inhibited by primary amide biotinylation, is provided as a positive control (n = 3; error bars show s.e.m.; *P < 0.05, #P < 0.05, compared to untreated TcdA and hemoglobin, respectively; Mann-Whitney U test on ranks). * Figure 3: A catalytic-site motif for S-nitrosylation. () Effect of InsP6 on S-nitrosylation of the TcdB catalytic site and TcdB catalytic-site mutants (His653Ala, Glu743Ala and Cys698Ser). GSNO (100 μM) and InsP6 (100 μM) were added for 10 min at 37 °C. () Extensive hydrogen bonding within the active site of the N-terminus RTXVC CPD11 (inset, yellow dotted lines) interconnects a catalytic-site S-nitrosylation motif that is conserved in TcdA and TcdB (Supplementary Figs. 7 and 14). The substrate domain (autocleavage site) is shown in turquoise (inset) and in red (top), where it bridges TcdB glucosyltransferase (GT) and cysteine protease (blue) domains, as shown in the combined crystal structure model, described further in Supplementary Figure 13. () Allosteric effect of InsP6 on catalytic activity of native TcdB versus catalytic-site mutants. Top, immunoblot showing autocleavage of native TcdB versus Glu743Ala mutant in the presence of InsP6. Bottom, GelCode Blue staining showing allosteric influence of InsP6 on catalytic act! ivity of native TcdB versus catalytic-site mutants (C698S, E743A and H653A). () Autocleavage of native TcdB versus E743A mutant with increasing InsP6 concentrations (n = 3; error bars show s.e.m. *P < 001 for the lowest significant InsP6 concentration, Mann-Whitney U test for ranks). * Figure 4: GSNO-based therapy for C. difficile infection. () Biotin-switch assay showing increased protein S-nitrosylation in Caco-2 cells treated with GSNO (100 μM for 30 min). () Dose-response curves for GSNO-mediated inhibition of TcdB (3.7 nM; 10 min) in the absence (closed circles) or presence of GSH (1 mM; open circles) and InsP6 (100 μM; filled triangles) (n = 3; error bars show s.e.m.; *P < 0.05 for the lowest significant GSNO concentration, Mann-Whitney U test for ranks). () TcdA induced fluid secretion in mouse ileal loops (10 μg for 4 h) in the presence (and absence) of luminal GSNO (10 mg per kg in 0.1 ml) and/or InsP6 (1 mM) (n > 6 group; error bars show s.e.m.; P < 0.05 compared with vehicle control (*) and TcdA-vehicle (#), respectively; ANOVA on ranks). () TcdA induced Tnf and Il1b mRNA expression in mouse ileal loops in the presence (and absence) of GSNO and/or InsP6 (as in ) (n > 6 group; error bars show s.e.m.; P < 0.05 compared with vehicle control (*) and TcdA-vehicle (#), respectively; ANOVA on ranks). () K! aplan-Meier survival plots of mice orally gavaged with 1 × 106C. difficile VPI10463 and GSNO (10 mg per kg per day); GSNO-InsP6 (10 and 0.25 mg per kg per day), InsP6 (0.25 mg per kg per day) or vancomycin (50 mg per kg per day). GSNO-InsP6 (10 and 0.25 mg per kg per day) was also delivered continuously by intracecal catheter (n = 12 per group). () Mice were orally gavaged with C. difficile VPI10463, and GSNO, GSNO-InsP6, InsP6 or vancomycin was delivered continuously by intracecal catheter (as in ). Survival is shown for day 4 after infection (n = 12; error bars show s.e.m.; P < 0.05 compared with vehicle control (*) and GSNO (#) respectively; ANOVA on ranks). () Schematic diagram of cellular intoxication by C. difficile exotoxins and mechanism for inhibition by host-generated GSNO. Intoxication results from autocleavage and cytosolic entry of the glucosyltransferase domain, whereas inhibition (orthosteric and allosteric) in the host is mediated by S-nitrosylation (red tr! iangles) of the membrane-associated toxin CPD. Author information * Author information * Supplementary information Affiliations * Department of Gastroenterology & Hepatology, University of Texas Medical Branch, Galveston, Texas, USA. * Tor C Savidge, * Petri Urvil, * Kausar Ali, * Aproteem Choudhury, * Vinay Acharya & * Irina Pinchuk * Department of Biochemistry & Molecular Biology, University of Texas Medical Branch, Galveston, Texas, USA. * Numan Oezguen, * John E Wiktorowicz & * Werner Braun * Department of Microbiology & Immunology, University of Texas Medical Branch, Galveston, Texas, USA. * Alfredo G Torres * National Heart, Lung and Blood Institute Proteomics Center, University of Texas Medical Branch, Galveston, Texas, USA. * Robert D English & * John E Wiktorowicz * Department of Pathology, University of Texas Medical Branch, Galveston, Texas, USA. * Michael Loeffelholz * Department of Basic Sciences, The Commonwealth Medical College, Scranton, Pennsylvania, USA. * Raj Kumar * Division of Infectious Diseases, Department of Biomedical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA. * Lianfa Shi, * Weijia Nie & * Hanping Feng * Institute for Transformative Molecular Medicine, Case Western Reserve University and University Hospitals, Cleveland, Ohio, USA. * Bo Herman, * Alfred Hausladen & * Jonathan S Stamler * Department of Medicine, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, Ohio, USA. * Bo Herman, * Alfred Hausladen & * Jonathan S Stamler * Division of Digestive Diseases, University of California–Los Angeles, Los Angeles, California, USA. * Charalabos Pothoulakis Contributions T.C.S. designed the study, performed InsP6, GSNO and cytotoxicity assays, BIACORE analysis, and wrote the paper; P.U. performed the toxin S-nitrosylation and InsP6 binding studies; N.O. and W.B. performed the toxin structural modeling and molecular docking simulations; I.P. performed the SNO immunofluorescence; K.A., A.C. and V.A. performed toxin autocleavage, InsP7 phosphorylation and UDP-glucosylation assays; A.G.T. performed animal toxin studies; R.D.E. performed the mass spectrometry; J.E.W. performed the cysteine saturation labeling studies; M.L. provided clinical specimens; R.K. performed the CD spectral analysis; L.S., W.N. and H.F. developed the toxin mutants, performed InsP6 cleavage and stool cytotoxicity assays and animals studies; B.H., A.H. and J.S.S. performed or oversaw the measurements of GSNO and SNO proteins; J.S.S. assisted with the study design and writing of the paper; C.P. prepared holotoxins, performed animal toxin studies and assisted with study desig! n and manuscript editing. Competing financial interests C.P. is a paid consultant with Merck and Optimer Pharmaceuticals and a paid speaker for the Postgraduate Institute for Medicine. J.S.S. has a small financial interest in N30 Pharma, Adamas Pharma, Vindica LLC, SabrePharm and LifeHealth, early-stage companies in development of nitric oxide–related therapeutics. Corresponding author Correspondence to: * Tor C Savidge Author Details * Tor C Savidge Contact Tor C Savidge Search for this author in: * NPG journals * PubMed * Google Scholar * Petri Urvil Search for this author in: * NPG journals * PubMed * Google Scholar * Numan Oezguen Search for this author in: * NPG journals * PubMed * Google Scholar * Kausar Ali Search for this author in: * NPG journals * PubMed * Google Scholar * Aproteem Choudhury Search for this author in: * NPG journals * PubMed * Google Scholar * Vinay Acharya Search for this author in: * NPG journals * PubMed * Google Scholar * Irina Pinchuk Search for this author in: * NPG journals * PubMed * Google Scholar * Alfredo G Torres Search for this author in: * NPG journals * PubMed * Google Scholar * Robert D English Search for this author in: * NPG journals * PubMed * Google Scholar * John E Wiktorowicz Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Loeffelholz Search for this author in: * NPG journals * PubMed * Google Scholar * Raj Kumar Search for this author in: * NPG journals * PubMed * Google Scholar * Lianfa Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Weijia Nie Search for this author in: * NPG journals * PubMed * Google Scholar * Werner Braun Search for this author in: * NPG journals * PubMed * Google Scholar * Bo Herman Search for this author in: * NPG journals * PubMed * Google Scholar * Alfred Hausladen Search for this author in: * NPG journals * PubMed * Google Scholar * Hanping Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan S Stamler Search for this author in: * NPG journals * PubMed * Google Scholar * Charalabos Pothoulakis Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–15 and Supplementary Methods Additional data - In vivo detection of Staphylococcus aureus endocarditis by targeting pathogen-specific prothrombin activation
- Nat Med 17(9):1142-1146 (2011)
Nature Medicine | Technical Report In vivo detection of Staphylococcus aureus endocarditis by targeting pathogen-specific prothrombin activation * Peter Panizzi1, 2, 9 * Matthias Nahrendorf1, 9 * Jose-Luiz Figueiredo1 * Jennifer Panizzi3 * Brett Marinelli1 * Yoshiko Iwamoto1 * Edmund Keliher1 * Ashoka A Maddur4 * Peter Waterman1 * Heather K Kroh4 * Florian Leuschner1 * Elena Aikawa1 * Filip K Swirski1 * Mikael J Pittet1 * Tilman M Hackeng5 * Pablo Fuentes-Prior6 * Olaf Schneewind7 * Paul E Bock4 * Ralph Weissleder1, 8 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:1142–1146Year published:(2011)DOI:doi:10.1038/nm.2423Received29 October 2010Accepted18 February 2011Published online21 August 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Coagulase-positive Staphylococcus aureus (S. aureus) is the major causal pathogen of acute endocarditis, a rapidly progressing, destructive infection of the heart valves. Bacterial colonization occurs at sites of endothelial damage, where, together with fibrin and platelets, the bacteria initiate the formation of abnormal growths known as vegetations. Here we report that an engineered analog of prothrombin could be used to detect S. aureus in endocarditic vegetations via noninvasive fluorescence or positron emission tomography (PET) imaging. These prothrombin derivatives bound staphylocoagulase and intercalated into growing bacterial vegetations. We also present evidence for bacterial quorum sensing in the regulation of staphylocoagulase expression by S. aureus. Staphylocoagulase expression was limited to the growing edge of mature vegetations, where it was exposed to the host and co-localized with the imaging probe. When endocarditis was induced with an S. aureus strain wit! h genetic deletion of coagulases, survival of mice improved, highlighting the role of staphylocoagulase as a virulence factor. View full text Figures at a glance * Figure 1: Identification of coagulase-positive endocarditis in a mouse model using a fluorescent prothrombin analog. (–) Representative S. aureus vegetation induced in a mouse model of endocarditis. H&E (,) and Gram () staining are shown with 400× total magnification in and . () A schematic representation of vegetation formation in S. aureus endocarditis, including a hypothetical mechanism for probe localization (AF680-ProT, yellow) after endothelial trauma and induction of bacteremia. Staphylocoagulase is shown tethering AF680-ProT to fibrinogen/fibrin via D domain interactions. (top), where fibrinogen (bottom) has a central E domain flanked by two D domains (dark blue). Schematics of AF680-ProT and staphylocoagulase are also shown (bottom). () Ex vivo fluorescence reflectance imaging showing that localization of AF680-ProT is limited to pathogenic S. aureus. Fluorescence images show excised aortas with vegetations (arrowheads) as well as the location of the suture (arrow). () Quantification of fluorescence reflectance imaging data for the controls (either no bacteria (no bact) or coag! ulase-negative (S. epid)) and for three S. aureus strains. TBR, target to background ratio expressed as mean ± s.e.m. *P < 0.01. AU, arbitrary units. (–) Microscopic localization of AF680-ProT correlates with staphylocoagulase-positive immunostaining. Gram stain (), staphylocoagulase (SC) immunohistochemistry (), in situ hybridization against digoxigenin-staphylocoagulase RNA (DIG-SC) () and fluorescence microscopy (). Scale bars, 50 μm. * Figure 2: The mechanism underlying fluorescent prothrombin localization during vegetation formation. () A summary diagram showing the prothrombin binding site at the N-terminal staphylocoagulase domains (D1 and D2) contained in the staphylocoagulase (1–325) fragment, and the staphylocoagulase C-terminal repeats that bind multiple fibrin(ogen) D domains. () Fluorescence (top) and white light (bottom) images of the native gels for the following samples: staphylocoagulase (1–325) (lane 1), fluorescent-labeled prothrombin (lane 2), FragD (lane 3), labeled prothrombin with 1.1-fold staphylocoagulase (1–325) (lane 4), labeled prothrombin with 1.1-fold staphylocoagulase (1–325) and eightfold molar excess FragD (lane 5), labeled prothrombin and FragD (lane 6). () Similar reactions and lanes as except full-length staphylocoagulase (staphylocoagulase (1–660)) was used to confirm formation of a staphylocoagulase (1–660)-prothrombin-FragD ternary complex (lane 5). () Native gel electrophoresis demonstrating that staphylocoagulase binds multiple FragD domains through C-termi! nal repeats on staphylocoagulase. Mixtures of staphylocoagulase (1–660) with increasing molar ratios of FragD are shown in lanes 2–9 (one-, two-, four-, five-, six-, seven-, eight- and tenfold molar excesses of FragD, respectively); samples of staphylocoagulase (1–660) and FragD alone were run in lanes 1 and 10, respectively. () Binding of fragment D to fluorescein-labeled staphylocoagulase pseudorepeat-repeat-1. Titrations of the relative increase in fluorescence (ΔF/Fo) of a 5-(iodoacetamido)fluorescein-labeled cysteine residue attached to the C-terminus of the staphylocoagulase pseudorepeat-repeat-1 peptide at 20 (filled circles), 430 (open circles), and 855 nM (filled triangles) as a function of the total concentration of fragment D ([FragD]o). Solid lines represent the simultaneous least-squares fit of the quadratic binding equation with Kd 36 ± 8 nM, stoichiometric factor 0.77 ± 0.06 mol of fragment D per mol of peptide and a maximum fluorescence change of 8.! 7 ± 0.2%. Inset, SDS-PAGE of molecular mass standards in kDa ! (lane 1), protein-stained gel (4 μg, lane 2) and fluorescence of the same gel (lane 3). * Figure 3: Noninvasive imaging of coagulase-positive S. aureus endocarditis via AF680-ProT. (–) Noninvasive FMT-CT of mice infected with three different pathogenic S. aureus groups (, Tager 104; , Xen8.1; , Xen29) and two control groups injected with either AF680-ProT but no bacteria () or a coagulase-negative S. epidermidis strain (). () Absolute fluorochrome concentration in vegetations quantified by FMT-CT. 2D, two dimensional; 3D, three dimensional. Data are mean ± s.e.m., n = 6 per group, *P < 0.01. * Figure 4: Comparison of AF680-ProT signal in FMT-CT studies of endocarditis in mice infected with S. aureus Newman or coagulase-deficient S. aureus Newman. (,) Noninvasive FMT-CT of mice infected with strain Newman () or Newman rendered genetically deficient in coagulases (Coa) staphylocoagulase and vWbp (). () Absolute fluorochrome concentration in vegetations quantified by FMT-CT. Data are mean ± s.e.m., n = 6 per group,*P < 0.01. () Kaplan-Meier curves comparing the survival of mice infected with Newman or coagulase-deficient Newman, n = 10–13 per group, median survival 2.1 versus 4.3 d, P < 0.0001. * Figure 5: PET-CT imaging of coagulase-positive S. aureus endocarditis with 64Cu-DTPA-ProT. (,) Molecular model of 64Cu-DTPA in the active site of prethrombin 2 (Pre2; orange) bound to staphylocoagulase (1–325) (violet). DTPA atoms are shown as color-coded spheres (carbon, green; oxygen, red; nitrogen, blue; sulfur, yellow; and copper, orange). The two views are related by a 90-degree rotation around the vertical axis. The DTPA moiety was essentially solvent exposed and moved freely within a region spanned by the 60-, 99- and 148-loops on Pre2. (–) Representative CT (, long axis; , short axis) and PET-CT images (,) after injection of 64Cu-DTPA-ProT. The inserted suture is denoted by an arrow, the aortic valve by an asterisk. An arrowhead highlights the vegetation. (–) Autoradiography () and bioluminescence images () of excised aortas from a mouse that received 64Cu-DTPA-ProT and S. aureus Xen8.1 and from a 'no bacteria' control (,). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * AY225090 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Peter Panizzi & * Matthias Nahrendorf Affiliations * Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Peter Panizzi, * Matthias Nahrendorf, * Jose-Luiz Figueiredo, * Brett Marinelli, * Yoshiko Iwamoto, * Edmund Keliher, * Peter Waterman, * Florian Leuschner, * Elena Aikawa, * Filip K Swirski, * Mikael J Pittet & * Ralph Weissleder * Department of Pharmacal Sciences, Harrison School of Pharmacy, Auburn University, Auburn, Alabama, USA. * Peter Panizzi * Nephrology Division, Massachusetts General Hospital, Charlestown, Massachusetts, USA. * Jennifer Panizzi * Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. * Ashoka A Maddur, * Heather K Kroh & * Paul E Bock * Department of Biochemistry, Cardiovascular Research Institute Maastricht, University Maastricht, Maastricht, The Netherlands. * Tilman M Hackeng * Institut de Recerca, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. * Pablo Fuentes-Prior * Department of Microbiology, The University of Chicago, Chicago, Illinois, USA. * Olaf Schneewind * Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. * Ralph Weissleder Contributions P.P. designed experiments, collected and analyzed the data, and wrote the manuscript. J.-L.F. developed the endocarditis model. J.P. conducted the in situ hybridization experiments. B.M. acquired the PET-CT data and fused images from different modalities. Y.I. and E.A. performed the histology experiments. E.K. synthesized, characterized and optimized the PET reporter. F.L., F.K.S. and M.J.P. labeled leukocytes and analyzed data. P.W. performed optical imaging experiments and analyzed data. P.F.-P. modeled the chelator in the PET version of the probe. O.S. made knockout bacteria. P.P., A.A.M., H.K.K., T.M.H. and P.E.B. designed the imaging probe and performed in vitro characterization. M.N. and R.W. designed experiments, supervised the project, developed in vivo imaging strategies and systems, reviewed, analyzed and discussed data, and wrote the manuscript. All authors edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Matthias Nahrendorf or * Ralph Weissleder Author Details * Peter Panizzi Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Nahrendorf Contact Matthias Nahrendorf Search for this author in: * NPG journals * PubMed * Google Scholar * Jose-Luiz Figueiredo Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Panizzi Search for this author in: * NPG journals * PubMed * Google Scholar * Brett Marinelli Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshiko Iwamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Edmund Keliher Search for this author in: * NPG journals * PubMed * Google Scholar * Ashoka A Maddur Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Waterman Search for this author in: * NPG journals * PubMed * Google Scholar * Heather K Kroh Search for this author in: * NPG journals * PubMed * Google Scholar * Florian Leuschner Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Aikawa Search for this author in: * NPG journals * PubMed * Google Scholar * Filip K Swirski Search for this author in: * NPG journals * PubMed * Google Scholar * Mikael J Pittet Search for this author in: * NPG journals * PubMed * Google Scholar * Tilman M Hackeng Search for this author in: * NPG journals * PubMed * Google Scholar * Pablo Fuentes-Prior Search for this author in: * NPG journals * PubMed * Google Scholar * Olaf Schneewind Search for this author in: * NPG journals * PubMed * Google Scholar * Paul E Bock Search for this author in: * NPG journals * PubMed * Google Scholar * Ralph Weissleder Contact Ralph Weissleder Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (918K) Supplementary Figures 1–7, Supplementary Table 1 and Supplementary Methods Additional data - Vascular anastomosis using controlled phase transitions in poloxamer gels
- Nat Med 17(9):1147-1152 (2011)
Nature Medicine | Technical Report Vascular anastomosis using controlled phase transitions in poloxamer gels * Edward I Chang1, 4 * Michael G Galvez1, 4 * Jason P Glotzbach1 * Cynthia D Hamou1 * Samyra El-ftesi1 * C Travis Rappleye1 * Kristin-Maria Sommer2 * Jayakumar Rajadas2 * Oscar J Abilez1, 3 * Gerald G Fuller2 * Michael T Longaker1, 3 * Geoffrey C Gurtner1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:1147–1152Year published:(2011)DOI:doi:10.1038/nm.2424Received18 February 2010Accepted01 March 2011Published online28 August 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Vascular anastomosis is the cornerstone of vascular, cardiovascular and transplant surgery. Most anastomoses are performed with sutures, which are technically challenging and can lead to failure from intimal hyperplasia and foreign body reaction. Numerous alternatives to sutures have been proposed, but none has proven superior, particularly in small or atherosclerotic vessels. We have developed a new method of sutureless and atraumatic vascular anastomosis that uses US Food and Drug Administration (FDA)-approved thermoreversible tri-block polymers to temporarily maintain an open lumen for precise approximation with commercially available glues. We performed end-to-end anastomoses five times more rapidly than we performed hand-sewn controls, and vessels that were too small (<1.0 mm) to sew were successfully reconstructed with this sutureless approach. Imaging of reconstructed rat aorta confirmed equivalent patency, flow and burst strength, and histological analysis demonstrat! ed decreased inflammation and fibrosis at up to 2 years after the procedure. This new technology has potential for improving efficiency and outcomes in the surgical treatment of cardiovascular disease. View full text Figures at a glance * Figure 1: Thermoreversible properties of poloxamer nanogel. () Diagram of the traditional vascular anastomosis procedure. Sutures are placed in collapsed vessel from adventitia through intima on one end, followed by suture placement from intima to adventitia on the other vessel end (left). Sutures are laid down flat and tightened, which approximates the intima, while the lumen is opened to allow placement of sutures (middle). Sutures are then applied circumferentially for the anastomosis, making a complete seal to prevent leakage (right). () Elastic modulus of P407 dissolved in PBS in varying concentrations from 15.0% (wt/vol) to 18.0% (wt/vol) with heating from 10 °C to 40 °C. Increasing poloxamer concentration had a correlative increase in the elastic modulus and a corresponding decrease in the transition temperature. () Graph shows the rapid transition of 16.5% (wt/vol) P407 to a stabilized elastic modulus when heated from 10 °C to 40 °C, with rapid melting to baseline after cooling. () Elastic modulus of P407 with BSA added i! n 0.25% (wt/vol) increments to 1.5%. A formulation of 16.5% (wt/vol) P407 containing 0.25% (wt/vol) BSA was able to initiate phase transition at 30 °C and achieve a maximal elastic modulus of approximately 10,000 Pa at a temperature of 40 °C. () The heated poloxamer easily stabilized an open lumen and allowed precise approximation of the intima (above). Poloxamer extrusion from the tube demonstrated maintenance of luminal shape (middle). Cooling to room temperature resulted in melted poloxamer and subsequent luminal collapse (below). * Figure 2: Thermoreversible poloxamer nanogel and cyanoacrylate glue sutureless anastomoses in vivo. () Schematic representation of anastomosis procedure using the poloxamer nanogel formulation as a temporary intraluminal stent to facilitate a stable, sutureless end-to-end microvascular anastomosis in a rat aorta model. () Intraoperative photographs showing the steps of the procedure. () Graphical representation of temperature measurements taken in the rat abdominal cavity at specific time points during the end-to-end anastomosis procedure. * Figure 3: Poloxamer nanogel anastomoses show long-term patency, flow and equivalent burst strength in vivo. () Comparison of end-to-end anastomosis performed using standard hand-sewn technique versus the sutureless approach. *P < 0.01. () Postoperative CT angiograms performed at 6 weeks confirmed equivalent patent anastomoses using the poloxamer (left) and hand-sewn (right) techniques (arrows point to sites of anastomoses; P > 0.05). () MR angiograms performed at 1 year showing equivalent patency of poloxamer (left) and hand-sewn (right) anastomoses (arrows point to sites of anastomoses; P > 0.05). () Patency in end-to-end anastomoses were assessed between sutureless and hand-sewn anastomoses (*P < 0.001). (,) Ultrasound Doppler studies performed at 6 months after operation (n = 5 per group) showing no significant differences in vessel lumen diameter (P > 0.05) and confirming the patency of all end-to-end anastomoses with similar volumetric flow rates (P > 0.05) when comparing the poloxamer versus the hand-sewn technique. () Burst strength of native aortas, poloxamer-anastomosed a! ortas and hand-sewn aortas. Data are presented as mean ± s.d. * Figure 4: Poloxamer nanogel anastomoses show less vascular intimal damage. () Schematic and photographic representation of end-to-side microvascular anastomosis performed using the poloxamer formulation to form a stable, sutureless anastomosis in a rat iliac model. () H&E staining of poloxamer (above) and hand-sewn (below) anastomoses tissue sections at 6 weeks after operation showing that the hand-sewn technique caused a greater inflammatory response than did the poloxamer technique (scale bars, 200 μm). () Immunostaining of a vessel wall showing CD68 expression (brown staining with DAB secondary antibody; scale bars, 200 μm). () Quantification of CD68-positive cells showing percentage of CD68-positive cells in the hand-sewn and poloxamer anastomoses. () CD31 immunostaining (with DAPI counterstain) of poloxamer anastomoses showing intact endothelium (scale bars, 200 μm). () H&E histology of poloxamer (above) and hand-sewn (below) anastomoses 1 year after operation showing that the hand-sewn technique also resulted in a greater inflammatory resp! onse compared to the poloxamer technique (scale bars, 200 μm). () Quantification of the inflammatory response at 1 week and 6 weeks after operation. () Modified elastic van Gieson stain at 2 years after operation (200-μm scale bar) demonstrating patent lumen of anastomosis. () Scanning electron microscopy at 1 year after operation showing qualitatively less intimal damage in anastomoses performed with poloxamer (top, with digitally magnified anastomosis site in box) than in anastomoses performed with sutures (bottom, with digitally magnified anastomosis site in box). Scale bars, 200 μm. HPF, high-powered field. Data are presented as mean ± s.d. *P < 0.05. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Edward I Chang & * Michael G Galvez Affiliations * Stanford University School of Medicine, Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford, California, USA. * Edward I Chang, * Michael G Galvez, * Jason P Glotzbach, * Cynthia D Hamou, * Samyra El-ftesi, * C Travis Rappleye, * Oscar J Abilez, * Michael T Longaker & * Geoffrey C Gurtner * Stanford University School of Engineering, Department of Chemical Engineering, Stanford, California, USA. * Kristin-Maria Sommer, * Jayakumar Rajadas & * Gerald G Fuller * Stanford University School of Medicine, Department of Bioengineering, Stanford, California, USA. * Oscar J Abilez & * Michael T Longaker Contributions E.I.C. was responsible for experimental design and data analysis, and wrote the manuscript. M.G.G. designed experiments, analyzed data and wrote the manuscript. J.P.G. analyzed data and wrote the manuscript. C.D.H. designed experiments and analyzed data. S.E. performed imaging studies and analyzed data. C.T.R. and J.R. designed poloxamer experiments and analyzed data. K.-M.S. designed poloxamer experiments. O.J.A. was responsible for burst strength experimental design and data analysis. G.G.F. supervised poloxamer experiments and data analysis. M.T.L. provided ideas and wrote the manuscript. G.C.G. supervised all aspects of this work and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Geoffrey C Gurtner Author Details * Edward I Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Michael G Galvez Search for this author in: * NPG journals * PubMed * Google Scholar * Jason P Glotzbach Search for this author in: * NPG journals * PubMed * Google Scholar * Cynthia D Hamou Search for this author in: * NPG journals * PubMed * Google Scholar * Samyra El-ftesi Search for this author in: * NPG journals * PubMed * Google Scholar * C Travis Rappleye Search for this author in: * NPG journals * PubMed * Google Scholar * Kristin-Maria Sommer Search for this author in: * NPG journals * PubMed * Google Scholar * Jayakumar Rajadas Search for this author in: * NPG journals * PubMed * Google Scholar * Oscar J Abilez Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald G Fuller Search for this author in: * NPG journals * PubMed * Google Scholar * Michael T Longaker Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey C Gurtner Contact Geoffrey C Gurtner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (446K) Supplementary Figure 1 Additional data - Corrigendum: Protective HIV-specific CD8+ T cells evade Treg cell suppression
- Nat Med 17(9):1153 (2011)
Nature Medicine | Corrigendum Corrigendum: Protective HIV-specific CD8+ T cells evade Treg cell suppression * Shokrollah Elahi * Warren L Dinges * Nicholas Lejarcegui * Kerry J Laing * Ann C Collier * David M Koelle * M Juliana McElrath * Helen HortonJournal name:Nature MedicineVolume: 17,Page:1153Year published:(2011)DOI:doi:10.1038/nm0911-1153aPublished online07 September 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Med.17, 989–995 (2011); published online 17 July 2011; corrected after print 7 September 2011 In the version of this article initially published, Figure 3c and Figure 3d were switched. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Shokrollah Elahi Search for this author in: * NPG journals * PubMed * Google Scholar * Warren L Dinges Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Lejarcegui Search for this author in: * NPG journals * PubMed * Google Scholar * Kerry J Laing Search for this author in: * NPG journals * PubMed * Google Scholar * Ann C Collier Search for this author in: * NPG journals * PubMed * Google Scholar * David M Koelle Search for this author in: * NPG journals * PubMed * Google Scholar * M Juliana McElrath Search for this author in: * NPG journals * PubMed * Google Scholar * Helen Horton Search for this author in: * NPG journals * PubMed * Google Scholar - Corrigendum: Treatment of cerebral ischemia by disrupting ischemia-induced interaction of nNOS with PSD-95
- Nat Med 17(9):1153 (2011)
Nature Medicine | Corrigendum Corrigendum: Treatment of cerebral ischemia by disrupting ischemia-induced interaction of nNOS with PSD-95 * Li Zhou * Fei Li * Hai-Bing Xu * Chun-Xia Luo * Hai-Yin Wu * Ming-Mei Zhu * Wei Lu * Xing Ji * Qi-Gang Zhou * Dong-Ya ZhuJournal name:Nature MedicineVolume: 17,Page:1153Year published:(2011)DOI:doi:10.1038/nm0911-1153bPublished online07 September 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Med.16, 1439–1443 (2010); published online 21 November 2010; corrected after print 7 September 2011 In the version of this article initially published, one of the structural details for the compound ZL010 in Figure 3f was incorrect. The R1 of ZL010 should be OCH3, not OH. This error does not affect the interpretation of the data or the conclusions of the paper. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Li Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Fei Li Search for this author in: * NPG journals * PubMed * Google Scholar * Hai-Bing Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Chun-Xia Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Hai-Yin Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Ming-Mei Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Xing Ji Search for this author in: * NPG journals * PubMed * Google Scholar * Qi-Gang Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Dong-Ya Zhu Search for this author in: * NPG journals * PubMed * Google Scholar - Corrigendum: Paraoxonase-1 is a major determinant of clopidogrel efficacy
- Nat Med 17(9):1153 (2011)
Nature Medicine | Corrigendum Corrigendum: Paraoxonase-1 is a major determinant of clopidogrel efficacy * Heleen J Bouman * Edgar Schömig * Jochem W van Werkum * Janna Velder * Christian M Hackeng * Christoph Hirschhäuser * Christopher Waldmann * Hans-Günther Schmalz * Jurriën M ten Berg * Dirk TaubertJournal name:Nature MedicineVolume: 17,Page:1153Year published:(2011)DOI:doi:10.1038/nm0911-1153cPublished online07 September 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Med.17, 110–116 (2011); published online 19 December 2010; corrected online 21 December 2010; corrected after print 7 September 2011 In the first paragraph on page 112, the authors made a typographical error: "constitutes part the active histidine dyad" should have been "is proximate to the active histidine dyad." The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Heleen J Bouman Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar Schömig Search for this author in: * NPG journals * PubMed * Google Scholar * Jochem W van Werkum Search for this author in: * NPG journals * PubMed * Google Scholar * Janna Velder Search for this author in: * NPG journals * PubMed * Google Scholar * Christian M Hackeng Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph Hirschhäuser Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Waldmann Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Günther Schmalz Search for this author in: * NPG journals * PubMed * Google Scholar * Jurriën M ten Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Taubert Search for this author in: * NPG journals * PubMed * Google Scholar
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