Latest Articles Include:
- March on, not in
- Nat Med 17(5):515 (2011)
Nature Medicine | Editorial March on, not in Journal name:Nature MedicineVolume: 17,Page:515Year published:(2011)DOI:doi:10.1038/nm0511-515Published online05 May 2011 The production setbacks for Genzyme's rare-disease drug Fabrazyme are tragic for the people who need the medicine. But a petition to break the company's patent exclusivity could do far more harm than good. View full text Additional data - Russia pledges $4 billion for Pharma-2020 plan
- Nat Med 17(5):517 (2011)
Nature Medicine | News Russia pledges $4 billion for Pharma-2020 plan * Gary PeachJournal name:Nature MedicineVolume: 17,Page:517Year published:(2011)DOI:doi:10.1038/nm0511-517Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Russia's biomedical industry is woefully underdeveloped, accounting for only 0.2% of the world market. But plans are afoot to change that. Speaking at the opening of a new birth center in Ryazan on 11 March, for example, Prime Minister Vladimir Putin stated that the government wants to boost Russia's presence on the world biopharma stage to 3–5% in the next decade. And he emphasized that the country already possesses the necessary academic and research institutions to achieve that. "We need to come up with measures to stimulate demand for Russia-made biotechnological products and remove barriers that often prevent businesses from working," he said. To that end, Russian leaders announced in March that they have approved 120 billion rubles ($4 billion) for a strategic investment program aimed at developing the country's massively import-dependent pharmaceutical and medical supplies industries. Dubbed Pharma-2020, the program—which was adopted two years ago although financing was only approved by the government last month—will attempt to boost output of local medicines, in gross sales terms, from nearly 25% last year to 50% by 2020. In addition, the program calls for ensuring that 90% of vital medicines are domestically produced, retooling some 160 companies to good manufacturing practice standards, establishing ten research and development centers that will focus on creating innovative products and boosting exports to $100 million. Like nearly all of Russia's state-driven initiatives, Pharma-2020 sets seemingly unattainable targets. Still, some insiders believe it is realistic. "Everyone acknowledges that it's an ambitious program, but, considering the amount of construction work going on right now, and the state funds being allocated, then this task is manageable," says Nikolai Bespalov, an analyst at Pharmexpert, a Moscow-based market research center. Astapkovich Vladimir/Newscom Putin offers a remedy for Russia's lagging biomedical sector. Others have reservations. "I perceive the program as a document and not much more. The strategy is written, the concept approved, but there are more acute problems that could be solved today without strategies and concepts, such as the low level of domestic products in state purchases," says Viktor Dmitriev, director of the Association of Russian Pharmaceutical Manufacturers, based in Moscow. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Gary Peach Search for this author in: * NPG journals * PubMed * Google Scholar - Report backs pending legislation to investigate disease clusters
- Nat Med 17(5):518 (2011)
Nature Medicine | News Report backs pending legislation to investigate disease clusters * Alisa OparJournal name:Nature MedicineVolume: 17,Page:518Year published:(2011)DOI:doi:10.1038/nm0511-518aPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. In Kettleman City, California, a town of 1,620 people, 11 babies were born with severe birth defects in the last three years. Meanwhile, at least 60 men who lived on the Camp Lejeune Marine Corps Base in North Carolina from the late 1950s into the 1980s have developed breast cancer. And residents in Wellington, Ohio are three times more likely to develop multiple sclerosis than in the rest of the country. A new report highlights these and 39 other so-called 'disease clusters'—defined as unusual aggregations, real or perceived, of health events grouped together in time and space—that have been confirmed or are currently being identified by a local, state or federal agency in 13 US states since 1976. The 28 March report from two nonprofit organizations, the Natural Resources Defense Council (NRDC) and the National Disease Clusters Alliance, calls for expanded federal efforts to identify clusters and their causes. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Alisa Opar Search for this author in: * NPG journals * PubMed * Google Scholar - Jackson Laboratory's foray into Florida faces murky future
- Nat Med 17(5):518 (2011)
Nature Medicine | News Jackson Laboratory's foray into Florida faces murky future * Christopher MimsJournal name:Nature MedicineVolume: 17,Page:518Year published:(2011)DOI:doi:10.1038/nm0511-518bPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Lately, it seems as if the Bar Harbor, Maine–based Jackson Laboratory, famous for its research on mammalian genetics, cannot catch a break in its efforts to build a satellite research facility in Florida. Since 2003, the state has heavily recruited biomedical institutions including Scripps, Max Planck, Torrey Pines, Sanford-Burnham and the Vaccine and Gene Therapy Institute (Nat. Med.16, 1066–1069, 2010). But its attempts to engage Jackson Labs have been fraught with delays and setbacks. As recently as the spring of 2010, Jackson Labs was in talks to locate its new branch in Naples, Florida. However, according to Tammie Nemecek, president of the Economic Development Council of Collier County, which includes Naples, that effort fell through when the state failed to fully finance its innovation fund, which would have provided Jackson Labs with $100 million to construct its facility, for two years in a row. Nemecek says that under then-governor Charlie Crist, "you didn't have that leadership at state level where you got the strategy and funding to do it." View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Christopher Mims Search for this author in: * NPG journals * PubMed * Google Scholar - Companies ponder how truly 'personal' medicines can get
- Nat Med 17(5):519 (2011)
Nature Medicine | News Companies ponder how truly 'personal' medicines can get * Monya BakerJournal name:Nature MedicineVolume: 17,Page:519Year published:(2011)DOI:doi:10.1038/nm0511-519Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Cancer drugs such as Herceptin are known as 'personalized medicines' because they are prescribed for subgroups of patients who share specific genetic traits. But truly individualized therapies are represented by treatments such as Provenge, which consists of patients' own cells that have been extracted, exposed to an antigen that trains them to go after prostate cancer and re-infused. The latter category is a tougher nut to crack, yet, ever since the US Food and Drug Administration (FDA) approved Provenge a year ago, cell-based personalized medicine has continued to garner interest. A course of three infusions of the treatment costs $93,000, but demand is still high. Earlier this year, Dendreon, the Seattle-based company that makes Provenge, announced it had received FDA approval to expand the number of production facilities for the product from 12 to 48. Optimists are quick to cite Provenge as the crest of a wave of new therapies. "It has huge implications," says Ronald Levy, a co-founder of Idec Pharmaceuticals (which merged to form Biogen Idec in 2003). "There may be 50 other therapies who hope to follow in the Provenge example." Although Levy, who is now at the Stanford University School of Medicine in California, is buoyant about the future of personalized cell-based therapies, he learned the hard way that some forms of personalized medicine prove too cumbersome to scale up. In the 1980s, he began creating antibodies designed for individual patients with lymphoma. Levy and his colleagues would identify telltale receptors on the wayward lymphocytes for each patient and then produce personalized antibodies designed to attack only his or her cancerous cells. Some 50 patients were treated with antibodies made this way, says Levy. "It worked most of the time, but it became economically unfeasible." So he and his Idec colleagues instead developed the blockbuster rituximab, an antibody that targets a protein found on all B cells, allowing many patients with lymphoma to receive the same drug. Jim Dowdalls/Photo Researchers, Inc. Tailored drugs cost more. Bill Rastetter, a former chief executive at Idec and now a partner at Venrock, a venture-capital firm in Palo Alto, California, says efficacy as well as economics led him to decide against making individualized antibodies. Idec's projected selling price for the personalized antibody approach was $50,000 per course of therapy, with about one in five patients showing remissions longer than those projected from chemotherapy alone. In contrast, about six out of ten patients benefitted from rituximab, he says, at a cost of about $10,000 per treatment course. (Levy notes that the approaches were never tested side by side, so efficacy is hard to compare.) View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar - China's new WHO flu monitoring center seeks to reverse criticism
- Nat Med 17(5):520 (2011)
Nature Medicine | News China's new WHO flu monitoring center seeks to reverse criticism * Hepeng JiaJournal name:Nature MedicineVolume: 17,Page:520Year published:(2011)DOI:doi:10.1038/nm0511-520aPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. BEIJING — China has not always been a world leader when it comes to infectious disease surveillance. Severe acute respiratory syndrome caught the country by surprise in 2003, and, two years later, government officials went into denial after reports surfaced that H5N1 avian influenza had infected people and birds. But since those debacles, China has ramped up its screening efforts, building several infectious-disease institutes and more than 400 labs devoted to flu surveillance and testing, plus adding sentinel equipment to some 550 hospitals. So when H1N1 'swine flu' struck four years later, the world's most populous country was much better prepared. "China has set up the world's largest influenza surveillance network," Yuelong Shu, director of the National Influenza Center, part of the Chinese Center for Disease Control and Prevention (CDC), told Nature Medicine. And now, China can also boast being the first country in the developing world to host a World Health Organization (WHO) Collaborating Center for Reference and Research on Influenza. CNIC The National Influenza Center. Joining other collaborating centers in Australia, Japan, the UK and the US, the Beijing-based National Influenza Center will serve as a regional hub for monitoring and responding to flu outbreaks. The Chinese center will also host research into new antiviral medicines and help provide pandemic preparedness training for medical personnel from across East Asia. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Hepeng Jia Search for this author in: * NPG journals * PubMed * Google Scholar - Qatar proposes national council to direct research efforts
- Nat Med 17(5):520 (2011)
Nature Medicine | News Qatar proposes national council to direct research efforts * Mohammed YahiaJournal name:Nature MedicineVolume: 17,Page:520Year published:(2011)DOI:doi:10.1038/nm0511-520bPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. On 3 April, Qatar unveiled its first National Health Strategy (NHS), which covers the next five years and includes a plan to launch a new national governance body to better manage resources and projects across the various biomedical centers in the small Persian Gulf state. The newly proposed Qatar Medical Research Council (QMRC) will be based in Doha and will be responsible for coordinating research efforts between institutions and communicating the scientific outcomes to policymakers. Currently, most of the scientific work taking place in Qatar is in basic biomedical research, and in 2006 the country committed to raising science funding to 2.8% of its gross domestic product. "Given the generous resources and the unwavering strive to excellence, it is worthwhile considering how to enhance the current elements involved in biomedical science and health research in Qatar," says Momtaz Wassef, a former director of Qatar's Department of Biomedical Research at the Supreme Council of Health who advised on the new NHS plan. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Mohammed Yahia Search for this author in: * NPG journals * PubMed * Google Scholar - Gloomy pharma forecast in Japan downgraded after quake
- Nat Med 17(5):521 (2011)
Nature Medicine | News Gloomy pharma forecast in Japan downgraded after quake * Branwen MorganJournal name:Nature MedicineVolume: 17,Page:521Year published:(2011)DOI:doi:10.1038/nm0511-521aPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Japan is the world's second largest pharmaceutical market after the US on the basis of total revenue. But according to Business Monitor International (BMI), a London-based analyst firm, Japan's overall pharmaceutical market is stagnating, and the devastating 9.0-magnitude earthquake that struck in March has introduced new uncertainties. In its initial review, published in early March, BMI stated that factors such as the government's cuts to subsidies for prescription medicines and its pro-generics stance will conspire to lower annual growth rates going forward. Presently, pharmaceutical expenditure is 1.8% of the country's gross domestic product and BMI predicted that through to 2015 the five-year compound annual growth rate will be 0.7%. However, soon after BMI published these gloomy estimates, they revised them down further. The 11 March earthquake and tsunami that devastated a large part of northeastern Japan led the firm to downgrade its pharmaceutical market forecast to! take into account the impending economic slowdown. "The majority of the report's content is still applicable, but we have lowered our 2020 sales predictions by 1.5% (from $91.7 billion to $90.3 billion), which reflects a change in the country's overall macroeconomics now that the full extent of the recent disaster is known," says Jamie Davies, BMI analyst and report author. "To improve their overall outlook for growth, the country's pharmaceutical companies need to look more to emerging markets, such as China." View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Branwen Morgan Search for this author in: * NPG journals * PubMed * Google Scholar - Experts emphasize need for speed in launch of Australian trials
- Nat Med 17(5):521 (2011)
Nature Medicine | News Experts emphasize need for speed in launch of Australian trials * Branwen MorganJournal name:Nature MedicineVolume: 17,Page:521Year published:(2011)DOI:doi:10.1038/nm0511-521bPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. SYDNEY — This past December, Oprah Winfrey took 300 of her audience members on a much-publicized trip to Australia to celebrate the twenty-fifth anniversary of her US talk show. But it's going to take more than an Oprah endorsement or a catchy advertising campaign for the land Down Under to remain a preferred destination for the conduct of clinical trials. As recently as 2005, the Economist Intelligence Unit conducted a benchmarking study that ranked Australia as the number one location for conducting clinical trials. It was placed ahead of countries such as the US, Japan and India on the basis of its high number of trial sites per capita, high percentage of on-time trial completions and low average trial costs. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Branwen Morgan Search for this author in: * NPG journals * PubMed * Google Scholar - Despite Canadian government woes, neuroscience should win out
- Nat Med 17(5):522 (2011)
Nature Medicine | News Despite Canadian government woes, neuroscience should win out * Hannah HoagJournal name:Nature MedicineVolume: 17,Page:522Year published:(2011)DOI:doi:10.1038/nm0511-522aPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. MONTREAL — When Canada's Conservative government presented its 2011 budget in late March, the fiscal plan didn't contain too many surprises for science funding. Like previous budgets, the proposal offered modest increases to the country's national research agencies and replenished the coffers of Genome Canada, its genomics and proteomics outfit. But the budget also contained a flashy and unprecedented new move: a multimillion-dollar earmark for neuroscience research. Under the Conservatives' proposed scheme, the government would contribute up to C$100 million ($105 million) over several years to the Canada Brain Research Fund, a public-private partnership led by the Brain Canada Foundation in collaboration with the Canadian Association for Neuroscience and Neurological Health Charities Canada (NHCC). The government money would then be matched by funds raised from private sources by Brain Canada to support large, multidisciplinary neuroscience grants, postdoctoral fellowships and training programs. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 Hoag Search for this author in: * NPG journals * PubMed * Google Scholar - NIH faces marching orders on orphan drug shortage
- Nat Med 17(5):522 (2011)
Nature Medicine | News NIH faces marching orders on orphan drug shortage * Elie DolginJournal name:Nature MedicineVolume: 17,Page:522Year published:(2011)DOI:doi:10.1038/nm0511-522bPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Ever since a virus contaminated Genzyme's production plant in Allston, Massachusetts close to two years ago, people with Fabry's disease have faced severe shortages of the medicine they need, Fabrazyme (see editorial on page 515). In August 2010, three people with Fabry's petitioned the US National Institutes of Health (NIH) to step in and demand that Genzyme allow other companies to make the enzyme replacement therapy on the basis of the government's so-called 'march-in' rights. The provision of the Bayh-Dole Act allows funding agencies to override exclusivity rights to intellectual property arising from government-funded research when people's lives are at risk. The NIH denied the request late last year. But, given Genzyme's continuous production delays—the company now says it won't be manufacturing Fabrazyme again until closer to the end of the year—on 5 April the petitioners appealed the original decision. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Companies race to develop first Hedgehog inhibitor cancer drug
- Nat Med 17(5):523 (2011)
Nature Medicine | News Companies race to develop first Hedgehog inhibitor cancer drug * Elie DolginJournal name:Nature MedicineVolume: 17,Page:523Year published:(2011)DOI:doi:10.1038/nm0511-523Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. ORLANDO, FLORIDA — Basal cell carcinoma is the most common form of skin cancer, but in people with a hereditary predisposition to this disease, lesions crop up so fast that they can hardly keep pace with their doctor's appointments. "Surgery can become tedious, and often, because of that, people don't go as often as they should and the [cancerous] areas grow larger," says Maria Michalowski, a former board member of the Basal Cell Carcinoma Nevus Syndrome (BCCNS) Life Support Network who herself suffers from the disease. Yet, judging by trial results reported here last month at the annual meeting of the American Association for Cancer Research (AACR), pharmaceutical options on the horizon may preclude the need for regular surgery. In a phase 2 study of 41 people with BCCNS, a team led by Ervin Epstein from the Children's Hospital Oakland Research Institute in California found that participants taking an experimental Genentech drug called vismodegib developed only four new tumors on average over the course of a year, compared to 24 in subjects on placebo. Plus, subjects taking the drug saw their existing skin lesions shrink dramatically, whereas those on the dummy pill experienced modest growths. "Indeed, there was a tremendous reduction in new lesions," says Epstein. "The people on the drug had no surgeries. The difference was dramatic." Vismodegib, also commonly referred to as GDC-0449, works by inhibiting signaling in the so-called Hedgehog pathway, which regulates cell growth and differentiation. Mutations in this pathway are responsible for some cases of BCCNS as well as a form of brain cancer known as medulloblastoma. And, indeed, vismodegib has also been shown to benefit a young man with the latter disease (N. Engl. J. Med.361, 1173–1178, 2009). USDA Forest Service Cyclopamine's source. But even when no such mutations are present, aberrant Hedgehog signaling can still drive solid tumors, for example by supporting the blood vessels that fuel their growth. That's why Genentech, a San Francisco–based subsidiary of the Swiss pharma giant Roche, is currently testing its drug for nearly 20 other types of cancer. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - New technologies promise to improve blood supply safety
- Nat Med 17(5):524 (2011)
Nature Medicine | News New technologies promise to improve blood supply safety * Michelle PflummJournal name:Nature MedicineVolume: 17,Page:524Year published:(2011)DOI:doi:10.1038/nm0511-524aPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. NEW YORK — Ever since scientists first linked an obscure blood-borne virus to chronic fatigue syndrome two years ago, blood centers around the world have been scrambling to determine whether their collections are safe. With memories of previous blood scares still fresh in the minds of blood bank officials, many collection centers have even gone so far as to bar donations from people with the disease. But it's not just xenotropic murine leukemia virus–related virus (XMRV) that threatens global blood supplies today. Even well-known pathogens such as hepatitis B virus can slip through the cracks of existing screening techniques, leading to contaminated blood products and accidental infections. Newly implemented technologies might change all that. Speaking at the New York Academy of Sciences here in late March, a panel of blood bank officials and infectious disease experts unveiled plans to make blood supplies safer by introducing DNA-based screening tests to improve disease detection. "Our blood supply is safer than it's ever been," Gail Moskowitz, a healthcare consultant who has directed several blood banks in the New York area, said at the 29 March meeting. "But transfusion is still associated with [a] risk of transmission." In most blood banks across the developed world, each unit of blood is screened for a panel of infectious agents, including HIV, hepatitis B and C viruses, leukemia-associated human T-lymphotrophic virus and the syphilis-causing bacterium. Existing serological assays reveal most pathogens in the blood supply. Yet many of the commonly used tests rely on finding antibodies or pathogens circulating in people's blood and can fail in rare cases when recently infected donors have not yet mounted large immune responses or when viral counts remain low. istockphoto Pathogens can go undetected. Aiming to boost pathogen detection rates, over the past decade or so blood banks in the US have introduced new PCR-based techniques that can pick up minute levels of HIV and hepatitis viral RNA in the bloodstream—and the approach seems to have paid off. Reporting in February, a team led by Susan Stramer, executive scientific officer of the American Red Cross in Gaithersburg, Maryland, found that, among close to 4 million blood samples analyzed, a DNA-based assay was more effective than conventional tests at detecting these three viruses in newly infected donors, including those previously vaccinated against hepatitis B (N. Engl. J. Med., 236–247, 2011). Looking beyond HIV and hepatitis, at the March meeting Stramer also reported the results of a 5,000-donor study from Puerto Rico demonstrating that a similar genetic test was ten times more sensitive in detecting Dengue virus than conventional blood assays. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * Michelle Pflumm Search for this author in: * NPG journals * PubMed * Google Scholar - Bangladesh eyes the generic pharma pie
- Nat Med 17(5):524 (2011)
Nature Medicine | News Bangladesh eyes the generic pharma pie * T V PadmaJournal name:Nature MedicineVolume: 17,Page:524Year published:(2011)DOI:doi:10.1038/nm0511-524bPublished online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. China and India, in 2001 and 2005, respectively, amended their patent laws to comply with the World Trade Organization's Trade Related Intellectual Property Rights (TRIPS) agreement, which bans making generic copies of drugs still under patent protection. The move sparked concerns about the affordability of medicines in poor countries. But Bangladesh, categorized among the world's least developed countries (LDCs) according to the UN, hopes to fill the void—at least for the next five years. Under the TRIPS agreement, LDCs can make generic versions of patented drugs until 2016. Bangladesh already has an estimated 350 drug companies, from small domestics to large multinationals, which produce 97% of its domestic demand for medicines. However, to make these medicines for domestic use and export, Bangladesh imports 80% of the active pharmaceutical ingredients (APIs), the chemicals responsible for a drug's action. "That is a weakness, as the imports do not make our pharma industry a fully integrated one," Abdul Muktadir, secretary general of the Dhaka-based Bangladesh Association of Pharmaceutical Industries, told Nature Medicine. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 * T V Padma Search for this author in: * NPG journals * PubMed * Google Scholar - Straight talk with...George Radda
- Nat Med 17(5):525 (2011)
Nature Medicine | News Straight talk with...George Radda * David CyranoskiJournal name:Nature MedicineVolume: 17,Page:525Year published:(2011)DOI:doi:10.1038/nm0511-525Published online05 May 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 Singapore, the fastest growing economy in Asia last year, has enjoyed a decade of free-flowing research funding. Money is still pouring in, but the question remains whether money can buy international-class science, especially after the sudden attachment of strings to grant money starting last fall. Perhaps the best person to answer this question is Sir George Radda (he received his knighthood in 2000). Radda was the chief executive of the UK's Medical Research Council (MRC) from 1996 to 2003. In his final year at the helm of the MRC, he had his first interaction with Singapore's budding biomedical program as a member of the A*STAR Biomedical Sciences International Advisory Council. Shortly thereafter, Radda was asked to help with the next five years' science and technology plan. A pioneer in nuclear magnetic resonance imaging, he became the founding chairman of the Singapore Bioimaging Consortium, traveling to Asia nearly once a month before he moved to Singapore three years ago. In April 2009, he was appointed chairman of the city-state's Biomedical Research Council (BMRC), which coordinates the country's biomedical activities and oversees institutes that comprise the Biopolis, a hub of more than 2,000 researchers and staff. Here he talks with about what's ahead for Singapore. View full text Additional data Author Details * David Cyranoski Search for this author in: * NPG journals * PubMed * Google Scholar - News in brief: Biomedical briefing
- Nat Med 17(5):526-527 (2011)
Nature Medicine | News News in brief: Biomedical briefing Journal name:Nature MedicineVolume: 17,Pages:526–527Year published:(2011)DOI:doi:10.1038/nm0511-526Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. POLICY Major cuts averted The US National Institutes of Health (NIH) was largely spared from funding cuts in an eleventh-hour deal reached last month by Congress and the White House to avoid a government shutdown. Under the compromise agreement, the agency took a $320 million hit for the rest of the 2011 fiscal year—a large chunk, but far less than the $1.6 billion drop proposed by the House in February. John Porter, chairman of Research!America and a former Republican representative, says he's relieved by the budget deal but still laments any cuts to biomedical research. "Science and technology are the economic way forward for our country," he says. See go.nature.com/s3fayd for a full interview with Porter. British budget boost The UK budget for the 2011–2012 fiscal year, announced on 23 March, provided a much-needed boost to British science with a £100 million ($160 million) investment for new research facilities. The government also plans to raise the rate of research and development tax relief for small businesses and to launch a new National Institute for Health Research to fund translational science and streamline clinical trials. Yet, more radical action may be needed for the UK to maintain its competitive edge on the global biopharmaceutical stage, says Imran Khan, director of the London-based Campaign for Science and Engineering. The budget "is really good news, but needs to be viewed in the context of the £1.4 billion cut in capital spending last year." Significant decision The US Supreme Court unanimously ruled in March that drug companies have a duty to notify investors of adverse safety events, even if those findings do not rise to the level of statistical significance. "Given that medical professionals and regulators act on the basis of evidence of causation that is not statistically significant, it stands to reason that in certain cases reasonable investors would as well," Justice Sonia Sotomayor wrote in the 22-page decision. The ruling allows a case of alleged securities fraud to proceed against the Arizona-based over-the-counter pharmaceutical company Matrixx Initiatives. Trial registry opens Seven years after the launch of the much-criticized EudraCT clinical trials database, which was accessible only to regulatory officials (see Nat. Med.10, 555, 2004), researchers, health providers and patients can finally access information about clinical trials conducted in the 27 EU member states, Iceland, Lichtenstein and Norway. On 22 March, the publically accessible EU Clinical Trials Register (https://www.clinicaltrialsregister.eu/) went live with records of protocols, sponsors and locations for more than 5,000 trials. "This is a major step forward," says Gerd Antes, head of the German Cochrane Centre in Freiburg. But, unlike the US's ClinicalTrials.gov, Antes notes that the European portal includes studies testing only drug-based interventions, not medical devices or procedures. Raring to go At a meeting in Bethesda, Maryland last month, the NIH and the European Commission launched a joint, ten-year initiative to develop diagnostic tests for every known rare disease, along with new treatments for 200 of them. The International Rare Disease Research Consortium "is going to mobilize the international community to pool resources and to identify enough patients to do meaningful studies," says Alan Beggs, director of the Manton Center for Orphan Disease Research at Children's Hospital Boston. Organizers plan to announce the first call for research proposals in July, and the next consortium meeting is planned for October in Montreal. BUSINESS Melanoma milestone The first drug to extend overall survival for people with metastatic melanoma won approval from US regulators in March. In phase 3 trials of people with the deadly form of skin cancer, ipilimumab—a monoclonal antibody to be marketed under the brand name Yervoy by New York–based Bristol-Myers Squibb—increased lifespan significantly more than either chemotherapy or an experimental vaccine. "You've got a drug that has now shown it's a consistent winner," says Vernon Sondak, a surgical oncologist at the H. Lee Moffitt Cancer Center in Tampa, Florida. See go.nature.com/o8TFjn for more. Cervical setback The US Food and Drug Administration last month refused to approve the use of Merck's Gardasil in women aged 27 to 45, citing clinical study data showing that the vaccine does not prevent human papillomavirus–related cervical cancer in that age group. The decision "could very well open the door for [GlaxoSmithKline's] Cervarix to have a bigger market share," says the University of Missouri-Kansas City's Diane Harper, a clinical trialist who helped develop both vaccines. Only Cervarix—which is currently approved for those aged 10 to 25 but is being tested in women up to 55 years old—has shown protection against cervical cancer in women with prior viral exposure, as is the case with most sexually active older women, she notes. Paying for Provenge After a long-awaited nine-month review process, the US Centers for Medicare and Medicaid Services announced plans to pay for the expensive prostate cancer vaccine Provenge (sipuleucel-T). In a proposed decision memo released on 30 March, the agency said the $93,000-a-year drug from Seattle-based Dendreon was "reasonable and necessary" for men with advanced prostate tumors. See go.nature.com/kqAGMY for more. PEOPLE New 'World' leader Mukesh Haikerwal The Australian family physician and e-health advocate Mukesh Haikerwal was elected chairman of the World Medical Association (WMA), based in Ferney-Voltaire, France, last month. Haikerwal, a former president of the Australian Medical Association, says that one of his priorities will be to expand the WMA's 97-country membership into more parts of Africa, Asia and Eastern Europe. "If we gain membership from more nations," he says, "I will be pleased." He has cited his survival of a brutal robbery attack in 2008, for which he had to have emergency brain surgery, as having stirred him to work with increased urgency on medical issues. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Auctioning the cure
- Nat Med 17(5):528-530 (2011)
Nature Medicine | News Auctioning the cure * Cassandra Willyard1Journal name:Nature MedicineVolume: 17,Pages:528–530Year published:(2011)DOI:doi:10.1038/nm0511-528Published online05 May 2011 Intellectual property isn't something you can see or touch. Yet some companies are trying to sell medical patents the same way antiques dealers sell ancient coins or fine art—at live auctions. Will this new model work? explores the weird world of intellectual property auctions. View full text Additional data Affiliations * Cassandra Willyard is a science writer based in Brooklyn, New York. Author Details * Cassandra Willyard Search for this author in: * NPG journals * PubMed * Google Scholar - Restructurally sound
- Nat Med 17(5):531-533 (2011)
Nature Medicine | News Restructurally sound * Brendan Borrell1Journal name:Nature MedicineVolume: 17,Pages:531–533Year published:(2011)DOI:doi:10.1038/nm0511-531Published online05 May 2011 Facing dwindling product pipelines and looming patent cliffs, nearly all of the world's major drugmakers have recently overhauled their research and development activities. asks what difference these efforts have made. View full text Additional data Affiliations * Brendan Borrell is a journalist in Brooklyn, New York. Author Details * Brendan Borrell Search for this author in: * NPG journals * PubMed * Google Scholar - Intramural conflicts of interest warrant scrutiny, too
- Nat Med 17(5):534 (2011)
Nature Medicine | News Intramural conflicts of interest warrant scrutiny, too * Matthew Movsesian1Journal name:Nature MedicineVolume: 17,Page:534Year published:(2011)DOI:doi:10.1038/nm0511-534Published online05 May 2011 Medical school faculty receiving remunerations from industry have financial incentives for promoting the products of the companies paying them. It's no surprise, then, that medical schools require disclosure and management of such relationships. Yet, despite their greater prevalence and more profound influence, the financial incentives offered by medical schools have gone largely unnoticed. A consistent standard for disclosure and management should be applied to both intramural and extramural financial relationships. View full text Additional data Affiliations * Matthew Movsesian is a professor of internal medicine and pharmacology at the University of Utah School of Medicine, Salt Lake City, Utah, USA. Author Details * Matthew Movsesian Search for this author in: * NPG journals * PubMed * Google Scholar - Except for all the others
- Nat Med 17(5):535 (2011)
Nature Medicine | Book Review Except for all the others * Deborah R. Barnbaum1Journal name:Nature MedicineVolume: 17,Page:535Year published:(2011)DOI:doi:10.1038/nm0511-535Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. The Professional Guinea Pig: Big Pharma and the Risky World of Human Subjects Roberto Abadie Duke University Press, 2010 200 pp., paperback, $22.95 ISBN: 9780822348238 Buy this book: USUKJapan Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Phase 1 trials are the first tests of newly developed drugs in humans. They are designed not to test for efficacy or for a positive risk-benefit ratio when treating a particular condition but instead for dosage and toxicity. They are ideally conducted on healthy volunteers, whose responses best indicate whether human beings can tolerate the new medication. However, these trials pose myriad ethical challenges. Informed consent may be illusory at best, as it is difficult to extrapolate from animal models the likelihood or degree of adverse events in human beings. Furthermore, the healthy volunteers in these trials are subjected to a drug from which they derive no benefit, only potential harm. Also, questions persist about the subject selection and the willingness of the volunteers. After all, who would volunteer to be among the first to test a new drug in humans? View full text Author information Affiliations * Deborah R. Barnbaum is in the Department of Philosophy at Kent State University, Kent, Ohio, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Deborah R. Barnbaum Author Details * Deborah R. Barnbaum Contact Deborah R. Barnbaum Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Antitumor properties of histamine in vivo
- Nat Med 17(5):537 (2011)
Nature Medicine | Correspondence Antitumor properties of histamine in vivo * Fredrik B Thoren1, 2 * Johan Aurelius2 * Anna Martner2 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Page:537Year published:(2011)DOI:doi:10.1038/nm0511-537aPublished online05 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. To the Editor: Yang et al.1 recently reported that mice with histamine deficiency due to genetic disruption of histidine decarboxylase (HDC) show impaired myeloid cell differentiation. The absence of histamine formation caused accumulation of immature myeloid cells (IMCs), which was accompanied by an increased susceptibility to chemically induced cancer1. Exogenous histamine reversed the accumulation of tumor-promoting IMCs in Hdc-/- mice, suggesting a potential benefit of histamine-based therapy in cancers1, where IMCs are believed to contribute to an unfavorable course of disease2. Given the effect of exogenous histamine on IMCs, we were surprised that the authors did not discuss the in vivo effects of histamine on cancer development in animals. As seen in Table 1, histamine is an antitumor agent in several histiotypes of experimental cancer3, 4, 5, 6, 7. Table 1: Histamine in experimental cancer Full table The authors also did not mention that histamine has been evaluated in clinical trials in cancer as an inhibitor of immunosuppressive myeloid cells8. In metastatic renal cell carcinoma, the addition of histamine to interleukin-2 immunotherapy was reported to reduce the number of intratumoral macrophages9, implying an effect on the myeloid compartment resembling the results obtained by Yang et al.1. Furthermore, histamine is approved for use in 31 European countries and Israel to prevent relapse in acute myeloid leukemia (AML), a disease characterized by the accumulation of immature myeloid cells. The therapeutic use of histamine in AML aims to reduce myeloid cell–induced immunosuppression of cytotoxic lymphocytes8, 10. In light of these previous findings, and considering the results presented by Yang et al.1, further studies to define mechanisms of relevance to the antitumor properties of histamine in vivo seem highly warranted. 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 * Department of Hematology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden. * Fredrik B Thoren * Cancer Centre Sahlgrenska, University of Gothenburg, Gothenburg, Sweden. * Fredrik B Thoren, * Johan Aurelius & * Anna Martner Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Fredrik B Thoren Author Details * Fredrik B Thoren Contact Fredrik B Thoren Search for this author in: * NPG journals * PubMed * Google Scholar * Johan Aurelius Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Martner Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Reply to Antitumor properties of histamine in vivo
- Nat Med 17(5):537-538 (2011)
Nature Medicine | Correspondence Reply to Antitumor properties of histamine in vivo * Xiang Dong Yang1 * Timothy C Wang1 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:537–538Year published:(2011)DOI:doi:10.1038/nm0511-537bPublished online05 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Yang and Wang reply: We would like to thank Thoren et al.1 for their comments on our recent study2 and for highlighting important previous preclinical and clinical studies on the effects of histamine in cancer. Indeed, we did not have the space to cite the many excellent prior research efforts that have employed histamine or histamine receptor antagonists as antitumor agents. We are grateful to Thoren et al.1 in particular for calling to our attention the fact that histamine has been approved in Europe and Israel for treatment of AML3, 4. Nevertheless, the effects of histamine in cancer models are often paradoxical, and the mechanisms for the antitumor effects of histamine have in the past been unclear. Histamine has multiple physiological roles and targets; we are aware of other earlier literature that suggested direct effects of histamine on cancer cell growth, and a number of preclinical studies have shown antitumor effects of H2 receptor antagonists5, 6. In addition, whereas Martner et al.7 ! suggested that histamine is an inhibitor of immunosuppressive myeloid cells, our studies in mice indicate that the effect is primarily on both CD11b+Ly6G+ and CD11b+Ly6C+ immature myeloid cells (IMCs), and the latter are suggested to be the major suppressor population2, 8. Nevertheless, work from us and them is consistent with the conclusion that histamine seems to reduce the mobilization and circulating numbers of myeloid cells and inhibit progression of some types of cancer9. We agree that further studies are needed to define the effects of histamine in the regulation of myeloid differentiation and maturation, which seems to be central to the promotion of cancer. 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 * Division of Digestive and Liver Diseases, Department of Medicine and Irving Cancer Center, Columbia University, New York, New York, USA. * Xiang Dong Yang & * Timothy C Wang Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Timothy C Wang Author Details * Xiang Dong Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy C Wang Contact Timothy C Wang Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - The B-side story in insulin resistance
- Nat Med 17(5):539-540 (2011)
Nature Medicine | Article B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies * Daniel A Winer1, 2, 7 * Shawn Winer2, 3, 7 * Lei Shen1, 7 * Persis P Wadia4 * Jason Yantha3 * Geoffrey Paltser3 * Hubert Tsui3 * Ping Wu3 * Matthew G Davidson1 * Michael N Alonso1 * Hwei X Leong1 * Alec Glassford5 * Maria Caimol1 * Justin A Kenkel1 * Thomas F Tedder6 * Tracey McLaughlin5 * David B Miklos4 * H-Michael Dosch3 * Edgar G Engleman1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:610–617Year published:(2011)DOI:doi:10.1038/nm.2353Received28 January 2011Accepted04 March 2011Published online17 April 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. 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 Chronic inflammation characterized by T cell and macrophage infiltration of visceral adipose tissue (VAT) is a hallmark of obesity-associated insulin resistance and glucose intolerance. Here we show a fundamental pathogenic role for B cells in the development of these metabolic abnormalities. B cells accumulate in VAT in diet-induced obese (DIO) mice, and DIO mice lacking B cells are protected from disease despite weight gain. B cell effects on glucose metabolism are mechanistically linked to the activation of proinflammatory macrophages and T cells and to the production of pathogenic IgG antibodies. Treatment with a B cell–depleting CD20 antibody attenuates disease, whereas transfer of IgG from DIO mice rapidly induces insulin resistance and glucose intolerance. Moreover, insulin resistance in obese humans is associated with a unique profile of IgG autoantibodies. These results establish the importance of B cells and adaptive immunity in insulin resistance and suggest new! diagnostic and therapeutic modalities for managing the disease. View full text Figures at a glance * Figure 1: B cell and antibody profile in DIO mice. () Left, time course of T cell (T), B cell (B) and macrophage (M) infiltration of VAT after initiation of HFD (two experiments, five mice, *P < 0.05). Middle and right, B cell subsets in VAT in response to 6–12 weeks of HFD in absolute numbers of B cells (*P = 0.005), B1a cells (*P = 0.04), B1b cells, B2 cells (*P = 0.04) and T cells (*P = 0.03) (middle) and in percentages of CD19+ cells (right). Middle and right, three experiments, nine mice. () VAT B cells in absolute numbers (left, *P < 0.05) and as a proportion of CD19+ cells (right, *P < 0.05); three experiments each, nine mice. () Spleen B cell subsets in response to HFD (MZ, marginal zone; FC, follicular cells, *P = 0.01, n = 5). () Spontaneous production of IgM (left, *P = 0.0006) and IgG (right, *P = 0.01) from mouse splenocytes. () Serum antibody concentrations in mice (n = 10): IgA (*P = 0.03) and IgG2c (*P = 0.004). () Antibody subtypes in VAT lysates from mice (*P = 0.0001) (two experiments, five mice). () IgM! (top left) and IgG (bottom left) staining in VAT of DIO mice in regions of few and multiple CLSs (IgM top right; IgG bottom right). Arrows indicate antibody-stained cells. Scale bars, 50 μm (left images) and 25 μm (right images). Error bars in graphs indicate means ± s.e.m. * Figure 2: B cell deficiency modulates glucose metabolism in DIO mice. () Body weights of WT (control) and Bnull mice over time (n = 10 per group). () Relative fat cell diameter of 14- to 18-week-old HFD mice (n = 3). () Ratio of epididymal VAT and SAT pad weights in DIO mice (*P = 0.004, n = 10). (,) Fasting glucose (*P = 0.04, n = 10) () and glucose tolerance test (GTT) () of WT or Bnull mice on NCD or HFD (*P < 0.05, representative GTT from three experiments, n = 10 per group on HFD and two experiments, n = 5 per group on NCD). () Fasting serum insulin concentrations of 16-week-old WT or Bnull mice on NCD or HFD (*P = 0.04, n = 10). () Insulin tolerance test (ITT) in WT or Bnull mice on NCD or HFD (*P < 0.05, n = 5 per group). () Body weight (left), GTT (middle, *P < 0.05, n = 6), and fasting insulin (right, *P = 0.02, n = 6) of DIO Bnull mice 2 weeks after reconstitution with DIO WT B cells (representative of three independent experiments). () Body weight (left), GTT (middle, n = 5), and fasting insulin (right, n = 5) of DIO Bnull mice 2 we! eks after reconstitution with NCD WT B cells (representative of 2 independent experiments). Brackets represent comparison groups for statistics. Error bars on graphs show means ± s.e.m. * Figure 3: B cells influence VAT T cell and macrophage function. () Numbers of cell subsets in VAT of 14- to 18-week-old mice (four experiments, ten mice). () Percentage of VAT macrophages (CD11b+F4/80+Gr-1−) with M1 phenotype (*P = 0.049, three experiments, eight mice). () IFN-γ production from SVC cultures of VAT (three experiments, nine mice, *P = 0.02). () Intracellular IFN-γ staining of CD8+ T cells isolated from VAT (left, four experiments, ten mice, *P = 0.04) and percentage of total VAT CD8+ T cells expressing CD107a (right, *P = 0.02, two experiments, six mice). () TNF-α production from VAT SVC cultures (left, *P = 0.04, two experiments, six mice) and intracellular staining of TNF-α in VAT macrophages (right, two experiments, six mice, *P = 0.02). () CD80 and CD86 expression on VAT macrophages (representative of three experiments, nine mice). () GTT (left), fasting glucose (middle) and fasting insulin (right) of recipient DIO RAG-1null (Rag1−/−) mice 2 weeks after transfer of DIO B cells (n = 10). () CD19+ B cells in VA! T of Bnull mice 2 weeks after reconstitution with DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (three experiments, nine mice). () Weights (left), GTT (middle) and fasting insulin (right) of recipient mice 2 weeks after transfer of DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (*P < 0.05, representative of three experiments, n = 3 per group). () IFN-γ production from VAT SVC cultures (left) and intracellular IFN-γ in VAT CD8+ T cells (middle) and VAT CD4+ T cells (right) isolated from recipient Bnull mice receiving either PBS or DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (*P < 0.05, two experiments, six mice). WT, control. Brackets represent comparison groups for statistics. Error bars in graphs are means ± s.e.m. * Figure 4: HFD IgG induces abnormal glucose metabolism in recipient Bnull mice. () Serum concentration of IgG in Bnull mice 1 week after i.p. IgG injection (n = 3). () Body weights of HFD Bnull recipient mice after IgG transfer (representative of three experiments, n = 4). () GTT (left, *P < 0.05) and fasting insulin (right, *P < 0.05) 1 week after the transfer of IgG into 16-week-old HFD Bnull mice (representative of three experiments, n = 4). () GTT (left) and fasting insulin (right) 4 weeks after the transfer of IgG (representative of two experiments, n = 4). () GTT (left, *P < 0.05) and fasting insulin (right, *P = 0.048) 1 week after the transfer of late or early IgG (n = 5). () Weights (left), GTT (center) and fasting insulin (right) of 6-week-old NCD Bnull mice 1 week after IgG transfer (representative of two experiments, n = 4). () TNF-α from VAT SVC cultures (left, *P = 0.04, two experiments, six mice) and M1 macrophages in HFD Bnull VAT 1 week after HFD IgG transfer (right, *P = 0.007, two experiments, six mice). () GTT (left, *P < 0.05) and ! fasting insulin (right, *P = 0.04) 1 week after the transfer of HFD IgG or HFD F(ab′)2 (n = 5). () TNF-α from HFD Bnull VAT macrophages stimulated in vitro with HFD IgG (*P = 0.007), or HFD F(ab′)2 (n = 3). () GTT (left) and fasting insulin (middle) of HFD Bnull mice 1 week after receiving HFD Ig (n = 5, *P < 0.05). Serum concentration (right) of IgM in HFD Bnull mice 1 week after IgM injection (n = 3). Brackets represent comparison groups for statistics. Error bars show means ± s.e.m. * Figure 5: A CD20-specific B cell-depleting antibody improves obesity-induced glucose abnormalities. () Percentage of CD19+ cells depleted in VAT and spleen ≥8 d after administration of CD20 mAb. (,) Weights of mice () and percentage depletion of IgG and IgM antibody in serum () 28 d after CD20-specific mAb (CD20 mAb) treatment (representative of two experiments, n = 5). (–) Fasting glucose (*P = 0.06) (), GTT (*P < 0.05) () and fasting insulin (*P = 0.04) () in HFD WT mice 28 d after receiving either CD20 mAb or control (IgG2c or PBS) (representative of two experiments, n = 5). (,) IFN-γ (*P = 0.003) and TNF-α (*P = 0.005) production from SVC cultures of VAT isolated from 17-week-old mice treated with CD20-specific mAb at 13 weeks of age (two experiments, eight mice). () Percentage of VAT macrophages expressing TNF-α 4 weeks after treatment with CD20 mAb (*P = 0.01, two experiments, eight mice). Error bars show means ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniel A Winer, * Shawn Winer & * Lei Shen Affiliations * Department of Pathology, Stanford University, Palo Alto, California, USA. * Daniel A Winer, * Lei Shen, * Matthew G Davidson, * Michael N Alonso, * Hwei X Leong, * Maria Caimol, * Justin A Kenkel & * Edgar G Engleman * Department of Laboratory Medicine and Pathobiology, University Health Network, University of Toronto, Toronto, Ontario, Canada. * Daniel A Winer & * Shawn Winer * Neuroscience & Mental Health Program, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * Shawn Winer, * Jason Yantha, * Geoffrey Paltser, * Hubert Tsui, * Ping Wu & * H-Michael Dosch * Department of Medicine, Stanford University, Palo Alto, California, USA. * Persis P Wadia & * David B Miklos * Division of Endocrinology, Stanford University School of Medicine, Palo Alto, California, USA. * Alec Glassford & * Tracey McLaughlin * Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA. * Thomas F Tedder Contributions D.A.W. and S.W. conceived the study, did experimental work and wrote the manuscript. L.S. was involved in experimental work, project planning and manuscript preparation. P.P.W., A.G., T.M. and D.B.M. contributed the human array data. J.Y., G.P., M.G.D., M.N.A., H.T., P.W., H.X.L., J.A.K. and M.C. did experimental work; T.F.T. contributed the CD20-specific mAb and was involved in manuscript preparation. H.M.D. supervised parts of the project and was involved in manuscript preparation; E.G.E. was involved in project planning, financing, supervision, data analysis and manuscript preparation. E.G.E. and H.M.D. are both senior authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Daniel A Winer or * Edgar G Engleman Author Details * Daniel A Winer Contact Daniel A Winer Search for this author in: * NPG journals * PubMed * Google Scholar * Shawn Winer Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Persis P Wadia Search for this author in: * NPG journals * PubMed * Google Scholar * Jason Yantha Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey Paltser Search for this author in: * NPG journals * PubMed * Google Scholar * Hubert Tsui Search for this author in: * NPG journals * PubMed * Google Scholar * Ping Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew G Davidson Search for this author in: * NPG journals * PubMed * Google Scholar * Michael N Alonso Search for this author in: * NPG journals * PubMed * Google Scholar * Hwei X Leong Search for this author in: * NPG journals * PubMed * Google Scholar * Alec Glassford Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Caimol Search for this author in: * NPG journals * PubMed * Google Scholar * Justin A Kenkel Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas F Tedder Search for this author in: * NPG journals * PubMed * Google Scholar * Tracey McLaughlin Search for this author in: * NPG journals * PubMed * Google Scholar * David B Miklos Search for this author in: * NPG journals * PubMed * Google Scholar * H-Michael Dosch Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar G Engleman Contact Edgar G Engleman Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Methods Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Modeling hyperactivity: of mice and men
- Nat Med 17(5):541-542 (2011)
Nature Medicine | Article GIT1 is associated with ADHD in humans and ADHD-like behaviors in mice * Hyejung Won1, 2, 7 * Won Mah1, 2, 7 * Eunjin Kim1 * Jae-Won Kim3 * Eun-Kyoung Hahm1, 2 * Myoung-Hwan Kim1, 2 * Sukhee Cho4 * Jeongjin Kim1 * Hyeran Jang5 * Soo-Churl Cho3 * Boong-Nyun Kim3 * Min-Sup Shin3 * Jinsoo Seo4 * Jaeseung Jeong5 * Se-Young Choi4 * Daesoo Kim1 * Changwon Kang1 * Eunjoon Kim1, 2, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:566–572Year published:(2011)DOI:doi:10.1038/nm.2330Received29 November 2010Accepted11 February 2011Published online17 April 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. 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 Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder that affects ~5% of school-aged children; however, the mechanisms underlying ADHD remain largely unclear. Here we report a previously unidentified association between G protein–coupled receptor kinase–interacting protein-1 (GIT1) and ADHD in humans. An intronic single-nucleotide polymorphism in GIT1, the minor allele of which causes reduced GIT1 expression, shows a strong association with ADHD susceptibility in humans. Git1-deficient mice show ADHD-like phenotypes, with traits including hyperactivity, enhanced electroencephalogram theta rhythms and impaired learning and memory. Hyperactivity in Git1−/− mice is reversed by amphetamine and methylphenidate, psychostimulants commonly used to treat ADHD. In addition, amphetamine normalizes enhanced theta rhythms and impaired memory. GIT1 deficiency in mice leads to decreases in ras-related C3 botulinum toxin substrate-1 (RAC1) signaling and inhibito! ry presynaptic input; furthermore, it shifts the neuronal excitation-inhibition balance in postsynaptic neurons toward excitation. Our study identifies a previously unknown involvement of GIT1 in human ADHD and shows that GIT1 deficiency in mice causes psychostimulant-responsive ADHD-like phenotypes. View full text Figures at a glance * Figure 1: Hyperactivity and impaired memory in Git1−/− mice are normalized by amphetamine treatment. () Genotyping of Git1−/− mice by PCR (top), and undetectable GIT1 proteins in whole brain homogenates from Git1−/− mice (bottom; 6–10 weeks). KO, knockout; WT, wild type. α-tubulin was used as a control. (,) Locomotor activity of WT and Git1−/− mice in an open field. n = 15 (WT), n = 14 (KO); see also Supplementary Table 4. **P < 0.01, ***P < 0.001, NS, not significant; Student's t test. The three asterisks in the top right corner of the panel indicate a significant difference between two genotypes over time, as calculated by repeated-measures analysis of variance (ANOVA) (see Supplementary Table 5 for details of statistical results). () Novel-object recognition in WT and Git1−/− mice. n = 11 (WT), n = 12 (KO). *P < 0.05; Student's t test. () Spatial learning and memory in WT and Git1−/− mice, shown by escape latencies in the Morris water maze (), target quadrant occupancy () and numbers of platform crossings (). n = 16 (WT), n = 19 (KO). *P < 0.05, **! P < 0.01, ***P < 0.001; Student's t test and repeated-measures ANOVA. (,) Effects of amphetamine (amph) and saline (sal; control) on locomotor activities of WT and Git1−/− mice in an open field. The results in were quantified in over a 10–20 min period. n = 9 (sal), n = 6 (amph) for WT; n = 7 (sal), n = 8 (amph) for KO. **P < 0.01, ***P < 0.001, one-way ANOVA. Details of statistical results for (Student's t test) are described in Supplementary Table 5. () Effect of amphetamine (amph) and saline (sal) on novel-object recognition behavior of Git1−/− mice. Saline-treated WT mice were used for comparison. n = 7 (WT sal, KO amph, KO sal). *P < 0.05, **P < 0.01; one-way ANOVA. Error bars indicate means ± s.e.m. * Figure 2: Enhanced theta rhythms in the frontal cortex of Git1−/− mice are reduced by amphetamine. (–) Theta EEG rhythms in the frontal cortex of WT and Git1−/− mice, as shown by representative traces and spectrogram (), theta power (3–10 Hz; 10–20 min) () and number of theta events (), in which a theta event is defined as a group of theta oscillations independent from others. A typical theta range in mice (3–10 Hz) is slightly different from that in humans (4–8 Hz). n = 5 (WT, KO). *P < 0.05, ***P < 0.001; Student's t test and repeated-measures ANOVA. (–) Effects of amphetamine and saline on enhanced theta rhythms in Git1−/− mice. n = 7 (KO sal), n = 6 (KO amph). *P < 0.05, ***P < 0.001; Student's t test and repeated-measures ANOVA. Error bars indicate means ± s.e.m. * Figure 3: Suppressed GIT1-PIX-RAC1-PAK signaling in the Git1−/− brain. () Amounts of PIX proteins (α-PIX and β-PIX) and activity of RAC1 (a downstream effector of PIX) in the WT and Git1−/− brain, as determined by immunoblotting analysis of whole brain homogenates (for PIX) and pull-down analysis of GTP-bound (active) RAC1 with GST–p21 binding domain (GST-PBD). () Amounts of PAK (PAK1 and PAK3) and phosphorylated PAK (pPAK1 and pPAK3) proteins shown by immunoblotting. () Amounts of GIT1-interacting proteins (FAK, MEK, PLC-γ, and liprin-α1) and other synaptic proteins in the WT and Git1−/− brain. n = 6 (WT, KO) for immunoblotting analysis, n = 3 (WT, KO) for pull-down analysis. *P < 0.05, ***P < 0.001; Student's t test. FAK, focal adhesion kinase; MEK, mitogen-activated protein kinase kinase; PLC-γ, phospholipase C-γ; ERK1/2, extracellular-regulated kinase 1 and 2; pERK1/2, phosphorylated ERK1/2; GRIP1, glutamate receptor-interacting protein 1; ERC2, ELKS/Rab6IP2/CAST2; PSD-93/PSD-95, postsynaptic density 93/95; CASK, calcium/cal! modulin-dependent serine protein kinase; NR1, NR2A and NR2B, subunits of NMDA glutamate receptors (also known as GluN1, GluN2A and GluN2B); GluR1 and GluR2, subunits of AMPA glutamate receptors (also known as GluA1 and GluA2). α-tubulin and β-actin were used as controls. Error bars indicate means ± s.e.m. * Figure 4: Reduced inhibitory transmission and elevated excitatory transmission at Git1−/− synapses. () Amplitude and frequency of spontaneous miniature excitatory postsynaptic currents (mEPSCs) in WT and Git1−/− hippocampal CA1 pyramidal neurons. n = 17 cells from three mice for WT and n = 15 cells from three mice for KO. () Frequency and amplitude of miniature inhibitory postsynaptic currents (mIPSCs) in WT and Git1−/− CA1 pyramidal neurons. n = 22 cells from three mice (WT), n = 23 cells from three mice (KO). **P < 0.01; Student's t test. () Amounts of charge transfer for mEPSCs and mIPSCs in WT and Git1−/− mice. n = 17 cells from three mice for WT, n = 15 cells from three mice for KO (mEPSCs), n = 22 cells from three mice for WT, n = 23 cells from three mice for KO (mIPSCs). () Excitatory transmission at WT and Git1−/− SC-CA1 synapses. The initial slopes of field EPSPs were plotted against fiber volley amplitudes. n = 41 slices from ten mice (WT), n = 27 slices from nine mice (KO). ***P < 0.001; Student's t test. () Presynaptic release probabilities at W! T and Git1−/− SC-CA1 synapses, as measured by paired-pulse facilitation ratios (second fEPSP/first fEPSP) at different interstimulus intervals. n = 23 slices from eight mice (WT), n = 16 slices from six mice (KO). Error bars indicate means ± s.e.m. * Figure 5: Reduced presynaptic input at Git1−/− inhibitory synapses. (,) Reduced amounts of inhibitory presynaptic proteins (GAD67 and vGAT) in the CA1 region of the Git1−/− hippocampus, which contrast to normal amounts of the inhibitory postsynaptic protein gephyrin and the presynaptic active zone protein bassoon, which is present at both excitatory and inhibitory synapses. Signals from the somas and processes of Git1−/− slices were normalized to those from WT mice. n = 3 slices from three mice (WT, KO). *P < 0.05; Student's t test. (,) Reduced amounts of parvalbumin (PV), a marker for fast-spiking interneurons, in the Git1−/− hippocampal CA1 region, whereas signals for other interneuron markers, somatostatin (SST), calbindin (CB) and calretinin (CR), were normal. n = 3 slices from three mice (WT, KO). *P < 0.05; Student's t test. (,) Comparable quantities of tyrosine hydroxylase (TH) signals in brain regions of Git1−/− and WT mice, including caudate putamen (CPu), nucleus accumbens (NAc), substantia nigra (SN) and ventral te! gmental area (VTA). n = 3 slices from three mice (WT, KO). Scale bars in and , 20 μm; scale bar in (CPu and NAc), 20 μm; scale bar in (SN and VTA), 63 μm. Error bars indicate means ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hyejung Won & * Won Mah Affiliations * Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. * Hyejung Won, * Won Mah, * Eunjin Kim, * Eun-Kyoung Hahm, * Myoung-Hwan Kim, * Jeongjin Kim, * Daesoo Kim, * Changwon Kang & * Eunjoon Kim * National Creative Research Initiative Center for Synaptogenesis, KAIST, Daejeon, Korea. * Hyejung Won, * Won Mah, * Eun-Kyoung Hahm, * Myoung-Hwan Kim & * Eunjoon Kim * Department of Child and Adolescent Psychiatry, College of Medicine, Seoul National University Hospital, Seoul, Korea. * Jae-Won Kim, * Soo-Churl Cho, * Boong-Nyun Kim & * Min-Sup Shin * Department of Physiology, Seoul National University School of Dentistry, Seoul, Korea. * Sukhee Cho, * Jinsoo Seo & * Se-Young Choi * Department of Bio and Brain Engineering, KAIST, Daejeon, Korea. * Hyeran Jang & * Jaeseung Jeong * Graduate School of Nanoscience and Technology (World Class University), KAIST, Daejeon, Korea. * Eunjoon Kim Contributions Eunjin K. conducted the SNP experiments; J.-W.K., S.-C.C., B.-N.K. and M.-S.S. provided the ADHD and control samples and conducted clinical data analyses; E.-K.H. generated Git1−/− mice; M.-H.K. measured and analyzed minicurrents; S.C., J.S. and S.-Y.C. measured and analyzed evoked synaptic transmission; J.K., H.J. and J.J. contributed to EEG recordings; H.W. and W.M. conducted all the rest of the experiments; D.K., C.K. and Eunjoon K. supervised the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Eunjoon Kim or * Changwon Kang Author Details * Hyejung Won Search for this author in: * NPG journals * PubMed * Google Scholar * Won Mah Search for this author in: * NPG journals * PubMed * Google Scholar * Eunjin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Jae-Won Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Eun-Kyoung Hahm Search for this author in: * NPG journals * PubMed * Google Scholar * Myoung-Hwan Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Sukhee Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Jeongjin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Hyeran Jang Search for this author in: * NPG journals * PubMed * Google Scholar * Soo-Churl Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Boong-Nyun Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Min-Sup Shin Search for this author in: * NPG journals * PubMed * Google Scholar * Jinsoo Seo Search for this author in: * NPG journals * PubMed * Google Scholar * Jaeseung Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Se-Young Choi Search for this author in: * NPG journals * PubMed * Google Scholar * Daesoo Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Changwon Kang Contact Changwon Kang Search for this author in: * NPG journals * PubMed * Google Scholar * Eunjoon Kim Contact Eunjoon Kim Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Methods, Supplementary Figures 1–14 and Supplementary Tables 1–5 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Locking out hepatitis C
- Nat Med 17(5):542-544 (2011)
Nature Medicine | Article EGFR and EphA2 are host factors for hepatitis C virus entry and possible targets for antiviral therapy * Joachim Lupberger1, 2, 13 * Mirjam B Zeisel1, 2, 13 * Fei Xiao1, 2 * Christine Thumann1, 2 * Isabel Fofana1, 2 * Laetitia Zona1, 2 * Christopher Davis3 * Christopher J Mee3 * Marine Turek1, 2 * Sebastian Gorke4 * Cathy Royer1, 2 * Benoit Fischer5 * Muhammad N Zahid1, 2 * Dimitri Lavillette6 * Judith Fresquet6 * François-Loïc Cosset6 * S Michael Rothenberg7 * Thomas Pietschmann8 * Arvind H Patel9 * Patrick Pessaux10 * Michel Doffoël11 * Wolfgang Raffelsberger12 * Olivier Poch12 * Jane A McKeating3 * Laurent Brino5 * Thomas F Baumert1, 2, 11 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:589–595Year published:(2011)DOI:doi:10.1038/nm.2341Received07 December 2010Accepted03 March 2011Published online24 April 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. 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 Hepatitis C virus (HCV) is a major cause of liver disease, but therapeutic options are limited and there are no prevention strategies. Viral entry is the first step of infection and requires the cooperative interaction of several host cell factors. Using a functional RNAi kinase screen, we identified epidermal growth factor receptor and ephrin receptor A2 as host cofactors for HCV entry. Blocking receptor kinase activity by approved inhibitors broadly impaired infection by all major HCV genotypes and viral escape variants in cell culture and in a human liver chimeric mouse model in vivo. The identified receptor tyrosine kinases (RTKs) mediate HCV entry by regulating CD81–claudin-1 co-receptor associations and viral glycoprotein–dependent membrane fusion. These results identify RTKs as previously unknown HCV entry cofactors and show that tyrosine kinase inhibitors have substantial antiviral activity. Inhibition of RTK function may constitute a new approach for prevention ! and treatment of HCV infection. View full text Figures at a glance * Figure 1: EGFR is a cofactor for HCV entry. (,) EGFR mRNA (quantitative RT-PCR analysis) () and protein expression (western blot) () in Huh7.5.1 cells transfected with EGFR-specific individual siRNAs (si1–4). Silencing of CD81 mRNA expression by CD81-specific siRNA served as control. EGFR mRNA (relative to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA) and protein expression compared to cells transfected with control siRNA (siCtrl) is shown. () HCVcc infection in Huh7.5.1 cells transfected with individual siRNAs shown in and . siCtrl and CD81-specific siRNA served as internal controls. Data are expressed as percentage HCVcc infection relative to siCtrl-transfected cells (means ± s.d. from three independent experiments in triplicate). () Entry of HCVpp containing envelope glycoproteins of various isolates14, 39 in Huh7.5.1 cells transfected with si4. Vesicular stomatitis virus (VSV) and measles virus pseudoparticle (pp) entry or cells transfected with CD81-specific siRNA served as controls. Data are expresse! d as percentage pseudoparticle entry relative to siCtrl-transfected cells (means ± s.d. from three independent experiments in triplicate). () HCVpp entry and EGFR protein expression in Huh7.5.1 cells concurrently transfected with EGFR-specific individual si3 and a cDNA encoding RNAi-resistant EGFR (pEGFR-WT)40. () HCVpp entry and EGFR protein expression in PHHs concurrently transduced with lentiviruses expressing shEGFR and wild-type EGFR cDNA (EGFR-WT)40. Data are expressed as percentage HCVpp entry relative to Ctrl cells or as percentage EGFR expression normalized for β-actin expression (means ± s.d. from four independent experiments in triplicate). ***P < 0.0005. * Figure 2: Inhibition of EGFR activation by kinase inhibitors reduces HCV entry and infection. () Effect of erlotinib on HCV entry and infection in Huh7.5.1 cells. HCVcc (Luc-Jc1; J6-JFH1) infection and HCVpp (J6) entry in Huh7.5.1 cells preincubated with the indicated concentrations of erlotinib are shown. Data are expressed as percentage HCVcc infection or HCVpp entry relative to solvent DMSO-treated control cells (means ± s.e.m. from three independent experiments in triplicate). () Northern blot analysis of HCV RNA and GAPDH mRNA in Huh7.5 cells electroporated with RNA from subgenomic HCV JFH1 replicon and incubated with solvent Ctrl, HCV protease inhibitor BILN-2061 or erlotinib (Erl) is shown. Analysis of HCV RNA in cells transfected with replication incompetent HCV RNA (GND, Δ) served as negative control. () Effect of erlotinib on HCVpp and MLVpp entry in HepG2-CD81 cells. The percentage pseudoparticle entry into nonpolarized and polarized HepG2-CD81 cells (generated as previously described15) preincubated with erlotinib (10 μM) is shown (means ± s.d. from t! en independent experiments). () Effect of erlotinib on HCVpp entry into PHHs. The percentage HCVpp entry into PHHs preincubated with erlotinib is shown relative to entry into solvent-treated control cells. IC50 value is expressed as median ± standard error of the median of three independent experiments performed in triplicate. (,) HCVpp entry into PHHs () and HCVcc infection of Huh7.5.1 cells () preincubated with 1 μM erlotinib, gefitinib (Gef), lapatinib (Lap), blebbistatin (Bleb) or wortmannin (Wort) is shown. Cell viability was assessed by MTT assay. Means ± s.d. from three independent experiments in duplicate () or triplicate () are shown. **P < 0.005; ***P < 0.0005. * Figure 3: Modulation of HCV entry by EGFR ligands and an EGFR-specific antibody. () Modulation of EGFR phosphorylation by EGF, erlotinib and EGFR-specific antibody (Anti-EGFR). Phospho-tyrosine (P-Tyr) and phosphorylation of an unrelated kinase (MERTK) served as internal positive and negative controls. (,) Percentage HCVpp entry (HCV-J) into serum-starved Huh7.5.1 cells, polarized HepG2-CD81 cells and PHHs in the presence of EGF () and TGF-α (). () Percentage HCVpp entry into Huh7.5.1, polarized HepG2-CD81 and PHH incubated with EGF or EGF and erlotinib is shown (means ± s.d. from three independent experiments in triplicate). () Flow cytometric analysis of nonpermeabilized PHH binding EGFR-specific or control monoclonal antibody (mAb). () Percentage HCVpp entry into PHHs preincubated with EGFR-specific or control mAb is shown. Viability of cells was assessed by MTT assay. IC50 value is expressed as median ± standard error of the median of three independent experiments in triplicate. () Percentage HCVpp entry into PHHs preincubated with EGF and EGFR-sp! ecific mAb. (,) Intracellular HCV RNA levels in PHHs infected with HCVcc (means ± s.d. from three independent experiments in duplicate) () or serum-derived HCV (one representative experiment) () as measured by quantitative RT-PCR. **P < 0.005; ***P < 0.0005. Unless otherwise indicated, EGFR-specific and control mAbs: 10 μg ml−1; EGF: 1 μg ml−1; erlotinib: 10 μM. * Figure 4: EGFR mediates HCV entry at postbinding steps by promoting CD81-CLDN1 co-receptor interactions and membrane fusion. () Cell surface expression of entry factors in EGFR- or EphA2-silenced Huh7.5.1 cells, as assessed by flow cytometry. SR-BI silencing served as positive control (means ± s.d. from three independent experiments in duplicate). () Western blot analysis of HCV entry factor expression in PKI- or siRNA-treated Huh7.5.1 cells. () Flow cytometric analysis of HCV glycoprotein sE2 binding to Huh7.5.1 cells incubated with EGFR-specific mAb or transfected with siEGFR. SR-BI–specific antibody (Anti–SR-BI) or siSR-BI served as positive controls (means ± s.d. from three independent experiments in duplicate). EGFR-specific and control mAbs: 100 μg ml−1. (,) Percentage HCVcc infection of Huh7.5.1 cells (means ± s.d. from five independent experiments in triplicate) () and percentage HCVpp entry into PHHs (means ± s.d. from three independent experiments in duplicate) () after inhibition of binding and postbinding steps by the indicated compounds (EGFR-specific mAb: 10 and 50 μg ml�! ��1). (,) Time course of HCVcc infection of Huh7.5.1 cells after incubation with erlotinib or the indicated compounds (means ± s.d. from five independent experiments in triplicate) () or EGF at various timepoints during infection (means ± s.d. from three independent experiments in triplicate) () (Supplementary Methods). () FRET of CD81-CLDN1 co-receptor associations in HepG2-CD81 cells incubated with erlotinib or EGFR-specific siRNA (means ± s.e.m. from ten independent experiments). () Percentage viral glycoprotein-dependent fusion of 293T with Huh7 cells incubated with EGF, erlotinib or EGFR-specific siRNA, assessed as previously described25. Means ± s.d. from three independent experiments in triplicate are shown. *P < 0.05; ***P < 0.0005. Unless otherwise indicated, EGFR-specific and control mAbs: 10 μg ml−1; EGF: 1 μg ml−1; erlotinib: 10 μM. * Figure 5: Functional role of EGFR in viral cell-to-cell transmission and spread. () Experimental setup. HCV producer cells cultured with uninfected target cells26 were incubated with siEGFR or PKIs. Cell-free HCV transmission was blocked by an E2-neutralizing antibody (Anti–HCV E2, 25 μg ml−1)26. HCV-infected target cells were quantified by flow cytometry26. () Immunofluorescence analysis of Pi (HCV RNA–electroporated Huh7.5.1 producer cells), T (GFP-expressing Huh7.5 target cells) and Ti (GFP+HCV NS5A+ HCV-infected target cells) cells stained with an HCV non structural protein 5A (NS5A)-specific antibody (red). () Infectivity of Pi-T cell co-cultivation supernatants (cell-free HCV transmission). (,) Quantification of infected Ti cells during erlotinib (10 μM) treatment in the absence (total transmission) and presence (cell-to-cell transmission) of E2-specific antibody by flow cytometry (means ± s.d. from three independent experiments in duplicate). () Effect of PKIs on viral spread. Long-term HCVcc infection of Huh7.5.1 cells incubated with erl! otinib 48 h after infection at the indicated concentrations. Medium with solvent (Ctrl) or PKI was replenished every second day. Cell viability was assessed by MTT test. Means ± s.d. from three independent experiments in triplicate are shown. RLU, relative light units. () EGFR expression in target cells with silenced EGFR expression. Cell surface EGFR expression was analyzed by flow cytometry and target cells were divided in three groups displaying high, medium and low EGFR expression. () HCV infection in GFP-positive target cells expressing EGFR at high, medium and low levels (see ) assessed as described above (means ± s.d. from three independent experiments in triplicate). () Effect of EGFR silencing on viral spread. Long-term analysis of HCVcc infection in Huh7.5.1 cells transfected with EGFR-specific or control siRNA 24 h after infection. Cell viability was assessed by MTT test. Means ± s.d. from three independent experiments in triplicate are shown. *P < 0.05; **P - PPAR-γ action: it's all in your head
- Nat Med 17(5):544-545 (2011)
Nature Medicine | Letter Brain PPAR-γ promotes obesity and is required for the insulin–sensitizing effect of thiazolidinediones * Min Lu1 * David A Sarruf2 * Saswata Talukdar1 * Shweta Sharma1, 3 * Pingping Li1 * Gautam Bandyopadhyay1 * Sarah Nalbandian1 * WuQiang Fan1 * Jiaur R Gayen1, 3 * Sushil K Mahata1, 3 * Nicholas J Webster1, 3 * Michael W Schwartz2 * Jerrold M Olefsky1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:618–622Year published:(2011)DOI:doi:10.1038/nm.2332Received01 December 2010Accepted15 February 2011Published online01 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In adipose tissue, muscle, liver and macrophages, signaling by the nuclear receptor peroxisome proliferator–activated receptor-γ (PPAR-γ) is a determinant of insulin sensitivity and this receptor mediates the insulin–sensitizing effects of thiazolidinediones (TZDs)1, 2, 3, 4. As PPAR-γ is also expressed in neurons5, we generated mice with neuron-specific Pparg knockout (Pparg brain knockout (BKO)) to determine whether neuronal PPAR-γ signaling contributes to either weight gain or insulin sensitivity. During high-fat diet (HFD) feeding, food intake was reduced and energy expenditure increased in Pparg-BKO mice compared to Ppargf/f mice, resulting in reduced weight gain. Pparg-BKO mice also responded better to leptin administration than Ppargf/f mice. When treated with the TZD rosiglitazone, Pparg-BKO mice were resistant to rosiglitazone-induced hyperphagia and weight gain and, relative to rosiglitazone-treated Ppargf/f mice, experienced only a marginal improvement in ! glucose metabolism. Hyperinsulinemic euglycemic clamp studies showed that the increase in hepatic insulin sensitivity induced by rosiglitazone treatment during HFD feeding was completely abolished in Pparg-BKO mice, an effect associated with the failure of rosiglitazone to improve liver insulin receptor signal transduction. We conclude that excess weight gain induced by HFD feeding depends in part on the effect of neuronal PPAR-γ signaling to limit thermogenesis and increase food intake. Neuronal PPAR-γ signaling is also required for the hepatic insulin sensitizing effects of TZDs. View full text Figures at a glance * Figure 1: Neuronal deletion of Pparg in brains of mice. () Quantification of wild-type Pparg mRNA in various brain regions of Ppargf/f mice and Pparg-BKO mice (n = 5 per group). Data shown are the fold induction of gene expression normalized with housekeeping gene and expressed as mean ± s.e.m. () RT-PCR showing WT (wild-type) and KO (smaller PCR product with deletion of exons 3 and 4) Pparg mRNA in various tissues in Ppargf/f and Pparg-BKO mice (n = 3 per group). () Quantification of tissue Pparg mRNA expression (n = 7−15 per group). WAT, white adipose tissue; IP Mac, primary intraperitoneal macrophage. Data shown are the fold induction of gene expression normalized with housekeeping gene and expressed as mean ± s.e.m. *P < 0.05 between the indicated conditions. * Figure 2: Energy balance parameters in Pparg-BKO mice. () Body weight of Ppargf/f and Pparg-BKO mice on either standard chow or HFD. †P < 0.01 between genotypes. () Body composition analysis of Ppargf/f (n = 8) and Pparg-BKO (n = 6) mice at week 5 on HFD. () Ambulatory activity of Ppargf/f (n = 7) and Pparg-BKO (n = 6) mice at week 6 on HFD. AU, arbitrary units. () Average 24-h energy expenditure (EE) in Ppargf/f (n = 8) and Pparg-BKO (n = 6) mice after adjustment for body size differences and 24-h average activity. () Weekly caloric intake of Ppargf/f (n = 12) and Pparg-BKO (n = 11) mice at weeks 1 and 12 on HFD. () Serum leptin concentration in Ppargf/f and Pparg-BKO mice fed either standard chow or HFD (n = 5–9 per group). () Western blot showing acute leptin-stimulated phosphorylation of STAT3 (Tyr705) in hypothalamus. Data shown are quantified ratio of phospho-STAT3 (p-STAT3) / total STAT3 normalized to vehicle (10 mM NaHCO3, pH 7.9) group. All data are means ± s.e.m. Statistical significance between control and Pparg-! BKO mice, or between the indicated conditions: *P < 0.05, †P < 0.01, ‡P < 0.001; NS, not significant. * Figure 3: Effect of rosiglitazone on weight gain and food intake in control and Pparg-BKO mice. () Rosiglitazone-induced weight gain in Ppargf/f (n = 14), Syn1-Cre (n = 6) and Pparg Pparg-BKO (n = 11) mice. Age of mice, start time of HFD and start of HFD and rosiglitazone (rosi) are indicated. () Body weight gain of Ppargf/f and Pparg-BKO mice that were fed HFD for 16 weeks followed by HFD with or without rosiglitazone treatment. Data are shown for weeks 28–34 (n = 6–14 per group). () Weekly caloric intake before and after rosiglitazone treatment in HFD-fed mice showing the effect of rosiglitazone on food intake in Ppargf/f (n = 14) and Pparg-BKO (n = 11) mice. () Measurement of Ucp1 mRNA in epididymal white adipose tissue from Ppargf/f and Pparg-BKO mice. () BAT Ucp1 mRNA expression in Ppargf/f and Pparg-BKO mice after rosiglitazone treatment. () Histochemical image of BAT from Ppargf/f mice and Pparg-BKO mice after rosiglitazone treatment stained with H&E. () Muscle Ucp3 mRNA expression in Ppargf/f and Pparg-BKO mice on HFD with or without rosiglitazone treatment! (n = 5–10 per group). () Liver Ucp3 mRNA expression in Ppargf/f and Pparg-BKO mice on HFD or after rosiglitazone treatment (n = 5–10 per group). In –, data are shown as mean ± s.e.m. In –, all qPCR data shown are the fold induction of gene expression normalized with housekeeping genes (encoding cyclophilin A and RNA polymerase II) and expressed as mean ± s.e.m. Statistical significance between Ppargf/f and Pparg-BKO mice, or between the indicated conditions, *P < 0.05, †P < 0.01. * Figure 4: Neuronal PPAR-γ is required for the full insulin-sensitizing effect of TZD treatment. () Intraperitoneal glucose tolerance tests on Ppargf/f and Pparg-BKO mice on HFD with or without rosiglitazone treatment for 7 weeks (n = 6–12 per group). Statistical significance between values from rosiglitazone-treated Ppargf/f and Pparg Pparg-BKO mice: *P < 0.05 and †P < 0.01. (–) Hyperinsulinemic euglycemic clamp study on Ppargf/f and Pparg-BKO mice fed a HFD with or without rosiglitazone treatment for 8 week (n = 7–12 per group). GIR (), IS-GDR (), basal hepatic glucose production rate (basal HGP) (), insulin-stimulated rate of HGP (), and percentage suppression of HGP by insulin () are shown. () Immunoblotting analysis of insulin-stimulated protein phosphorylation in liver extracts from control and Pparg-BKO mice fed a HFD in the presence or absence of rosiglitazone treatment. GSK3, glycogen synthase kinase-3; ERK1/2, extracellular signal–regulated kinases 1 and 2. CREB, cAMP response element–binding protein. In this experiment, GSK3α/β (S21/9) refers to! serine 21 in GSK3α and serine 9 in GSK3β. () Quantification of relative phosphoprotein levels normalized to respective total kinase protein content or β-tubulin. Data are shown as mean ± s.e.m. () Liver Pck1 mRNA expression in Ppargf/f and Pparg-BKO mice fed a HFD with or without rosiglitazone treatment (n = 5–10 per group). () Liver weight of control and Pparg-BKO mice (n = 10–14 per group) on HFD with or without rosiglitazone treatment. All data shown are as mean ± s.e.m. *P < 0.05, †P < 0.01; NS, not significant. Author information * Author information * Supplementary information Affiliations * Department of Medicine, University of California–San Diego (UCSD), San Diego, California, USA. * Min Lu, * Saswata Talukdar, * Shweta Sharma, * Pingping Li, * Gautam Bandyopadhyay, * Sarah Nalbandian, * WuQiang Fan, * Jiaur R Gayen, * Sushil K Mahata, * Nicholas J Webster & * Jerrold M Olefsky * Diabetes and Obesity Center of Excellence, University of Washington, Seattle, Washington, USA. * David A Sarruf & * Michael W Schwartz * Medical Research Service, Veterans Affairs San Diego Healthcare System, San Diego, California, USA. * Shweta Sharma, * Jiaur R Gayen, * Sushil K Mahata & * Nicholas J Webster Contributions J.M.O., M.L. and M.W.S. designed the study and co-wrote the manuscript. M.L. performed most of the experiments. D.A.S. was responsible for body composition, locomotor activity, indirect calorimetry and leptin sensitivity assays. S.T. conducted most of the qPCR and performed acute insulin stimulation in mice. S.S. and N.J.W. were involved in mouse breeding and performed immunohistochemistry. P.L. was involved in hyperinsulinemic euglycemic clamp and western blotting studies. G.B. measured tissue lipid content. S.N. was involved in metabolic studies in mice. W.F. contributed to western blotting. J.R.G. and S.K.M. were responsible for measurement of cardiac function and catecholamine concentrations. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jerrold M Olefsky or * Michael W Schwartz Author Details * Min Lu Search for this author in: * NPG journals * PubMed * Google Scholar * David A Sarruf Search for this author in: * NPG journals * PubMed * Google Scholar * Saswata Talukdar Search for this author in: * NPG journals * PubMed * Google Scholar * Shweta Sharma Search for this author in: * NPG journals * PubMed * Google Scholar * Pingping Li Search for this author in: * NPG journals * PubMed * Google Scholar * Gautam Bandyopadhyay Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Nalbandian Search for this author in: * NPG journals * PubMed * Google Scholar * WuQiang Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Jiaur R Gayen Search for this author in: * NPG journals * PubMed * Google Scholar * Sushil K Mahata Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Webster Search for this author in: * NPG journals * PubMed * Google Scholar * Michael W Schwartz Contact Michael W Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar * Jerrold M Olefsky Contact Jerrold M Olefsky Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (807K) Supplementary Figures 1–6 and Supplementary Tables 1 and 2 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Window of opportunity for daclizumab
- Nat Med 17(5):545-547 (2011)
Nature Medicine | Article A role for interleukin-2 trans-presentation in dendritic cell–mediated T cell activation in humans, as revealed by daclizumab therapy * Simone C Wuest1 * Jehad H Edwan1 * Jayne F Martin1 * Sungpil Han1, 2 * Justin S A Perry1 * Casandra M Cartagena1 * Eiji Matsuura1 * Dragan Maric3 * Thomas A Waldmann4 * Bibiana Bielekova1, 1 * Affiliations * ContributionsJournal name:Nature MedicineVolume: 17,Pages:604–609Year published:(2011)DOI:doi:10.1038/nm.2365Received04 January 2011Accepted30 March 2011Published online01 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. 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 Although previous studies have described CD25 expression and production of interleukin-2 (IL-2) by mature dendritic cells (mDCs), it remains unclear how these molecules participate in the activation of T cells. In search of the mechanisms by which daclizumab, a humanized monoclonal antibody against CD25, inhibits brain inflammation in multiple sclerosis, we observed that although the drug has limited effects on polyclonal T cell activation, it potently inhibits activation of antigen-specific T cells by mDCs. We show that mDCs (and antigen-experienced T cells) secrete IL-2 toward the mDC-T cell interface in an antigen-specific manner, and mDCs 'lend' their CD25 to primed T cells in trans to facilitate early high-affinity IL-2 signaling, which is crucial for subsequent T cell expansion and development of antigen-specific effectors. Our data reveal a previously unknown mechanism for the IL-2 receptor system in DC-mediated activation of T cells. View full text Figures at a glance * Figure 1: Antigen-specific T cell proliferation in DC–T cell cocultures is profoundly inhibited by daclizumab. (,) Carboxyfluorescein diacetate succinimidyl diester (CFSE) proliferation assay: mDCs loaded with Flu-HA (0.5 μg ml−1) () or human brain protein (HBP; 10 μg ml−1) () were cocultured with autologous CFSE-stained T cells in the presence or absence of CD25-blocking antibody control MA-251 (10 μg ml−1) or daclizumab (10 μg ml−1). After 7–10 d, T cell proliferation was assessed by CFSE dilution assay after gating on CD4+ (pink) and CD8+ (blue) T cells. Data are representative of five independent experiments. () Box plots represent group data on antigen-specific CD4+ T cell proliferation with marked group medians (black horizontal line) and means (red horizontal line). ***P < 0.001. Mean values are shown ± s.d. Dac, daclizumab. Abs # is number of proliferating CD4+ T cells normalized between control and Dac conditions by fluorescent beads. () CFSE proliferation assay after polyclonal T cell activation with Dynabeads coated with antibodies to CD3 and CD28 (0.3:1 bea! d to T cell ratio) in the presence or absence of daclizumab. Proliferation was measured by CFSE dilution after 5 d using the same gating strategy (CD4+ T cells in pink, CD8+ T cells in blue). * Figure 2: Selective blockade of CD25 on mDCs abrogates T cell proliferation. () T cell proliferation, analyzed after 5, 7, 9 and 14 d of coculture, of CFSE-stained T cells (Tc) and mDCs in the presence of 20 μg ml−1 control antibody MA-251 (first column) or 20 μg ml−1 daclizumab (second column) added at the beginning of the culture period. Alternatively, mDCs (third column) or CFSE+ T cells (fourth column) were pretreated with 20 μg ml−1 daclizumab for 30 min before coculture. () Events of proliferated CD8+ T cells (top) and CD4+ T cells (bottom) were normalized to allophycocyanin-labeled beads. n = 4; **P < 0.01, ***P < 0.001. Mean values are shown ± s.d. * Figure 3: T cells do not need CD25 expression to proliferate if primed by CD25+ mDCs. () Expression of IL-2R chains on polyclonally activated T cells derived from control individual (left) and subject with CD25 deletion (right). Gray histograms represent appropriate isotype controls. Percentages of positive lymphocytes are shown above the histograms. () Proliferation of CD25− CD4+ (pink; left) and CD8+ (blue; right) T cells derived from an individual with a genetic deletion of CD25 after co-incubation with Flu-HA–loaded, human leukocyte antigen–matched CD25+ mDCs, as measured by CFSE dilution after 7 d. Separate graphs depict percentages and number of T cells normalized between conditions by fluorescent beads (abs #) of CD4+ and CD8+ T cells from four replicates; *P < 0.05. Mean values are shown ± s.d. () Cytokine production (IL-2, IFN-γ and IL-17) by proliferating CD25− CD4+ and CD8+ T cells after coculture with CD25+ mDCs (top) or mDCs pre-treated with daclizumab (bottom). * Figure 4: DCs do not express the β-chain of IL-2R and therefore do not signal in response to IL-2. () Flow cytometry analysis of freshly isolated BDCA-1+ iDCs and mDCs (after 48 h of stimulation) stained for maturation markers CD80, CD83 and MHC-II (top, open histograms) and for IL-2R chains CD25, CD122 and CD132 (bottom, open histograms) or appropriate isotype controls (filled gray histograms). Percentages of surface marker expression are depicted above the histograms. () Flow cytometry analysis of in vitro–generated monocyte-derived iDCs and mDCs stained in an analogous manner to the cells in . (,) STAT5 phosphorylation in response to 50 IU ml−1 of IL-2 () and 200 ng ml−1 of GM-CSF () of fresh uncoagulated whole blood (ex vivo, left), monocyte-derived iDCs (middle) and mDCs (right). Dark gray histograms represent appropriate isotype controls. * Figure 5: mDCs use their surface expression of CD25 to trans-present IL-2 to CD25− T cells (a) Phosphorylation of STAT5 in Flu-HA306–318-specific T cells (TCL) selectively pretreated with daclizumab (DacT) or control Ab (T) and co-incubated with autologous, CD25-expressing mDCs pulsed with 1 μM cognate (Flu-mDC) or noncognate (MBP83–99; MBP-mDC) peptide. At indicated conditions, Flu-mDCs were also pretreated with daclizumab (DacFlu-mDC). Results are depicted as percentages of pStat5-expressing CD4+ T cells ± s.d. () The proportional number of expanded T cells after 5 d of coculture in the same cells and identical conditions as in . Mean values are shown ± s.d. One representative experiment is depicted; all replicates are summarized in Supplementary Figure 6. () The frequency of pStat5+ Flu-HA306–318–specific T cells after 2 h culture with Flu-HA306–318–loaded mDCs (left), daclizumab-pretreated, Flu-HA306–318–loaded mDCs (middle) and MBP83–99 peptide–loaded mDCs (right). MFI, mean fluorescence intensity. () pStat5 phosphorylation, as visualized by Amnis ImageStream in Flu-specific T cells cultured for 2 h with Flu-HA306–318–loaded mDCs (top), daclizumab-pretreated Flu-HA306–318-loaded mDCs (middle) or MBP83–99-loaded mDCs (bottom) in the same cells as in . Scale bars, 10 μm. * Figure 6: mDCs and T cells secrete IL-2 after antigen-specific interactions. () Flow cytometric analysis of IL-2 secretion of mDCs loaded with 1 μM MBP83–99 peptide (MBP-mDC), MBP-specific T cell clones (MBP-TCC (Tc)), cocultures of MBP-mDCs with MBP-specific T cells (MBP-mDC + Tc) and cocultures of mDCs loaded with 1 μM Flu-HA306–318 peptide with MBP-specific T cells (Flu-mDC + Tc). IL-2 was detected after 1 h (top) and 2 h (bottom) of coculture and is plotted against CD25 expression of mDCs and T cells. Percentages of IL-2 secretion by mDCs and T cells are shown in gates. For comparison, mDCs and T cells are presented in the same plot, but they were gated separately on CD11c and CD4 expression. () Independent experiment visualizing secreted IL-2 and surface expression of CD25, CD11c and CD4 by Amnis ImageStream after 2-h coculture of MBP-mDCs with MBP-specific T cells. Top, single MBP-specific CD4+ T cells in the bright field of the microscope with simultaneous expression and/or secretion of fluorescently labeled CD25, CD4 and IL-2. Bottom, c! onjugates of MBP-loaded mDCs with MBP-specific T cells (mDCs highlighted by pink arrows, CD4+ T cells by teal arrows). Scale bars, 10 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA. * Simone C Wuest, * Jehad H Edwan, * Jayne F Martin, * Sungpil Han, * Justin S A Perry, * Casandra M Cartagena, * Eiji Matsuura & * Bibiana Bielekova * School of Medicine, Pusan National University, Yangsan, South Korea. * Sungpil Han * Flow Cytometry Core Facility, NINDS, NIH, Bethesda, Maryland, USA. * Dragan Maric * Metabolism Branch, National Cancer Institute, NIH, Bethesda, Maryland, USA. * Thomas A Waldmann Contributions B.B. developed the concept of the study and supervised the project. B.B. and T.A.W. designed the experiments. S.C.W., J.F.M., S.H., J.S.A.P., C.M.C., D.M., J.E., E.M. and B.B. performed the experiments and analyzed the data. B.B., S.C.W., J.F.M., S.H. and C.M.C. wrote the paper. All authors approved the final version of this paper. Competing financial interests B.B. and T.A.W. are co-inventors on US National Institutes of Health patents related to the use of daclizumab in multiple sclerosis and as such have received patent royalty payments. Author Details * Simone C Wuest Search for this author in: * NPG journals * PubMed * Google Scholar * Jehad H Edwan Search for this author in: * NPG journals * PubMed * Google Scholar * Jayne F Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Sungpil Han Search for this author in: * NPG journals * PubMed * Google Scholar * Justin S A Perry Search for this author in: * NPG journals * PubMed * Google Scholar * Casandra M Cartagena Search for this author in: * NPG journals * PubMed * Google Scholar * Eiji Matsuura Search for this author in: * NPG journals * PubMed * Google Scholar * Dragan Maric Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas A Waldmann Search for this author in: * NPG journals * PubMed * Google Scholar * Bibiana Bielekova Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (729K) Supplementary Figures 1–8 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Shedding LIGHT on severe asthma
- Nat Med 17(5):547-548 (2011)
Nature Medicine | Article The tumor necrosis factor family member LIGHT is a target for asthmatic airway remodeling * Taylor A Doherty1, 2, 5 * Pejman Soroosh1, 5 * Naseem Khorram2 * Satoshi Fukuyama1 * Peter Rosenthal2 * Jae Youn Cho2 * Paula S Norris3 * Heonsik Choi1 * Stefanie Scheu4 * Klaus Pfeffer4 * Bruce L Zuraw2 * Carl F Ware3 * David H Broide2 * Michael Croft1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:596–603Year published:(2011)DOI:doi:10.1038/nm.2356Received23 December 2010Accepted16 March 2011Published online17 April 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. 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 Individuals with chronic asthma show a progressive decline in lung function that is thought to be due to structural remodeling of the airways characterized by subepithelial fibrosis and smooth muscle hyperplasia. Here we show that the tumor necrosis factor (TNF) family member LIGHT is expressed on lung inflammatory cells after allergen exposure. Pharmacological inhibition of LIGHT using a fusion protein between the IgG Fc domain and lymphotoxin β receptor (LTβR) reduces lung fibrosis, smooth muscle hyperplasia and airway hyperresponsiveness in mouse models of chronic asthma, despite having little effect on airway eosinophilia. LIGHT-deficient mice also show a similar impairment in fibrosis and smooth muscle accumulation. Blockade of LIGHT suppresses expression of lung transforming growth factor-β (TGF-β) and interleukin-13 (IL-13), cytokines implicated in remodeling in humans, whereas exogenous administration of LIGHT to the airways induces fibrosis and smooth muscle hyp! erplasia, Thus, LIGHT may be targeted to prevent asthma-related airway remodeling. View full text Figures at a glance * Figure 1: Blockade of LIGHT or LTαβ inhibits airway remodeling and AHR induced by HDM. () Protocol for HDM-induced remodeling. WT mice were given three intranasal (i.n.) challenges with HDM extract, once per week. LTβR-Fc or IgG was given 24 h before each additional intranasal HDM challenge over the next 4 weeks. i.p., intraperitoneal. () Lung sections were stained for Masson's trichrome (top left and middle) and collagen-1 (bottom left and middle) and scored for the extent of fibrosis (top right, n = 54–75 airways per group). Induced total lung collagen was measured (bottom right, pooled from four mice per group, two experiments shown). () Lung sections stained for α-smooth muscle actin (left) and scored for extent of induced peribronchial smooth muscle (right, n = 49–70 airways per group). Induced reflects levels above those detected in mice receiving three intranasal challenges before LTβR-Fc treatment. () Peak airway resistance with increasing doses of methacholine and baseline resistance without methacholine exposure (six or seven mice per group). ! *P < 0.05, **P < 0.005, ***P < 0.001, ****P < 0.0001, means ± s.e.m., Mann-Whitney test. Data are from two or three independent experiments. Scale bars, 100 μm. * Figure 2: LIGHT-deficient mice are resistant to airway remodeling induced by HDM. WT and Tnfsf14−/− mice received HDM intranasally once per week for 3 weeks, then twice per week for 4 weeks. Mice were killed 1 day after the last challenge. () Lung sections stained with Masson's trichrome (top) and collagen-1 (middle) and scoring for fibrosis (bottom left, n = 35–36 airways per group, means ± s.e.m., Mann-Whitney test). Total lung collagen was also measured (bottom right, eight mice per group, means ± s.e.m., Mann-Whitney test). () Peribronchial smooth muscle area (left, n = 34–35 airways per group, means ± s.e.m., Mann-Whitney test) and lung sections stained for α-smooth muscle actin (right). Levels reflect those above lung measurements from naive mice ( and ). () Invasive lung function test and resistance after challenge with 48 mg ml−1 methacholine (means ± s.e.m., Mann-Whitney test from six or seven mice per group). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Scale bars, 100 μm. * Figure 3: LIGHT controls lung TGF-β1 production and accumulation of LAP+ macrophages. Mice were chronically challenged with HDM or OVA. () Free TGF-β1 concentrations assessed in lung homogenates from WT mice treated as in Figure 1 (data from six to eight mice per group; acute signifies levels before immunoglobulin treatment; levels of two naive mice also shown, means ± s.e.m., Mann-Whitney, *P < 0.03); WT and Tnfsf14−/− mice treated as in Figure 2 (data from three or four mice per group, means ± s.e.m., t test, *P < 0.02); WT mice treated as in Supplementary Figure 6 (data from four pooled mice per group run in duplicate, mean ± s.e.m., except single IgG group); WT and Tnfsf14−/− mice treated as in Supplementary Figure 6 (data from four pooled mice per group run in quadruplicate, means ± s.e.m., t test, *P < 0.01). () Lung sections from WT mice in Supplementary Figure 6 stained for LTβR expression. Scale bar, 50 μm. () Lung cells from WT mice in Supplementary Figure 6 analyzed for Mac-3 and CD11c (left), and the gated population evaluated for L! TβR expression (right). Filled histogram indicates isotype staining. () Lung cells from WT and Tnfsf14−/− mice in Supplementary Figure 6 analyzed for Mac-3 and CD11c (top and bottom) and absolute numbers of Mac-3+CD11c+ cells (right, pooled lung cells from four mice per group). () Immunofluorescent staining of lung sections from a representative WT mouse from Figure 1 stained for Mac-3 (red), LAP (green) and DAPI (blue). Scale bar, 50 μm. Image zoom also depicted (bottom). Scale bar, 25 μm. () Gated Mac-3+CD11c+ cells from lungs of mice in Figure 1 and Supplementary Figure 6 analyzed for LAP expression (top), enumeration of total LAP+ macrophages per lung (middle, n = 4 mice per group, means ± s.e.m., two experiments shown for OVA and one for HDM, *P < 0.05, t test) and flow analysis for LAP expression gating on Siglec-F+CD11c+ macrophages (bottom). * Figure 4: LTβR stimulation promotes fibrosis and TGF-β production by lung macrophages. () Lung sections stained with Masson's trichrome (left) and extent of induced peribronchial fibrosis (top right; 44–68 airways per group IgG and anti-LTβR, means ± s.e.m., Mann-Whitney test, *P < 0.05). Scale bar, 100 μm. Mac-3+CD11c+LAP+ cells per lung were enumerated (bottom right, pooled from six mice per group). WT mice were immunized and acutely challenged with OVA over 28 d and then injected with LTβR agonist antibody (anti-LTβR) or rat IgG every 3–4 d for 2 weeks. (,) Analysis of Siglec-F+CD11c+ lung macrophages (, top) from naive mice after stimulation with rat IgG or anti-LTβR and analyzed for surface LAP expression after 2 d (, bottom), TGF-β1 mRNA (, left, ***P < 0.0005) or TGF-β1 protein after HDM was added in the last 8 h of culture (, right, **P < 0.005). Results are triplicates from each group. () Flow cytometry analysis of LAP−Siglec-F+CD11c+Mac-3+ lung macrophages sorted (left) and stimulated with rat IgG or anti-LTβR and analyzed for LAP expr! ession (right). () Flow cytometry analysis of intracellular TGF-β in purified lung macrophages stimulated with recombinant LIGHT, in the presence or absence of ERK inhibitor. Data are representative of at least two experiments. * Figure 5: LIGHT-induced airway remodeling is in part dependent on TGF-β. (,) Lung sections were stained for trichrome (top row, scale bar, 100 μm), collagen-1 (second row, scale bar, 100 μm), α-smooth muscle actin (third row, scale bar, 50 μm) and scored for fibrosis and smooth muscle area (bottom row, 40 airways per group, means ± s.e.m., Mann-Whitney, *P < 0.05, **P < 0.005). WT mice primed with HDM over 3 weeks were treated with intranasal rLIGHT or PBS given eight times over 2 weeks () or pCDNA3 mouse LIGHT plasmid or control plasmid given four times over 2 weeks (). Antibody to TGF-β (anti-TGF) or isotype control IgG was also injected as indicated. Induced reflects levels above those detected in lungs of mice receiving PBS () or control plasmid (). A, airway; BV, blood vessel. * Figure 6: LIGHT promotes IL-13 production by lung eosinophils. () IL-13 content in mice primed and challenged with HDM or OVA. Lung homogenates from mice treated as in Figures 1 and 2 and Supplementary Figure 6 were analyzed (five to seven mice per group from Figure 1, three or four mice per group from Figure 2 and triplicates of pooled samples from four to seven mice per group from Supplementary Figure 6, means ± s.e.m., t test, *P < 0.05, **P < 0.01, ***P < 0.005). () Flow cytometry analysis of sorted granulocytes (>95% eosinophils by cytospin, bottom; scale bar, 50 μm) from WT mice after acute intranasal OVA challenge for LTβR (top middle) and HVEM expression (top right). Isotype control in gray. () Flow cytometry analysis of CD45+CD11c− granulocyte-gated lung eosinophils (top and bottom left) from mice in Supplementary Figure 6 for intracellular IL-13 directly ex vivo (middle right). Siglec‐F+CD11c− eosinophils from mice in Figure 1 (top right) and Figure 2 (bottom right) were stained for IL-13 expression. () Flow cytometry! analysis of BALF (bottom left) and lung cells (top left) and intracellular IL-13 (middle and right) was analyzed in cells gated on forward and side scatter (left, >95% eosinophils). Cells from WT mice immunized and challenged with OVA over 8 days were cultured for 48 h with rLIGHT or medium added during the last 24 h. Data are representative of two independent experiments. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Taylor A Doherty & * Pejman Soroosh Affiliations * Division of Immune Regulation, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. * Taylor A Doherty, * Pejman Soroosh, * Satoshi Fukuyama, * Heonsik Choi & * Michael Croft * Department of Medicine, University of California–San Diego (UCSD), La Jolla, California, USA. * Taylor A Doherty, * Naseem Khorram, * Peter Rosenthal, * Jae Youn Cho, * Bruce L Zuraw & * David H Broide * Institute of Medical Microbiology, Universität Düsseldorf, Düsseldorf, Germany. * Stefanie Scheu & * Klaus Pfeffer * Infectious and Inflammatory Diseases Center, Sanford Burnham Medical Research Institute, La Jolla, California, USA. * Paula S Norris & * Carl F Ware Contributions T.A.D. and P.S. contributed to animal antigen administration, surgery, data collection, analysis and manuscript writing for all studies; S.F. and J.Y.C. contributed to immunostaining and data analysis; N.K. contributed to remodeling and cytokine data collection and analysis; P.R. contributed to airway hyper-responsiveness testing and analysis; P.S.N. produced plasmids, LTβR-F and antibody to LTβR and contributed to experimental design; H.C. contributed to cytokine data collection; S.S. and K.P. developed mutant mice; B.L.Z. contributed to experimental design; C.F.W. contributed to experimental design and reagent production; D.H.B. contributed to experimental design and remodeling data collection; M.C. contributed to experimental design, data analysis and manuscript writing for all studies. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael Croft Author Details * Taylor A Doherty Search for this author in: * NPG journals * PubMed * Google Scholar * Pejman Soroosh Search for this author in: * NPG journals * PubMed * Google Scholar * Naseem Khorram Search for this author in: * NPG journals * PubMed * Google Scholar * Satoshi Fukuyama Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Rosenthal Search for this author in: * NPG journals * PubMed * Google Scholar * Jae Youn Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Paula S Norris Search for this author in: * NPG journals * PubMed * Google Scholar * Heonsik Choi Search for this author in: * NPG journals * PubMed * Google Scholar * Stefanie Scheu Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus Pfeffer Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce L Zuraw Search for this author in: * NPG journals * PubMed * Google Scholar * Carl F Ware Search for this author in: * NPG journals * PubMed * Google Scholar * David H Broide Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Croft Contact Michael Croft Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–10 and Supplementary Methods Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Getting to the bare bones of fertility
- Nat Med 17(5):550-551 (2011)
Nature Medicine | Community Corner Getting to the bare bones of fertility Journal name:Nature MedicineVolume: 17,Pages:550–551Year published:(2011)DOI:doi:10.1038/nm0511-550Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. MedicalRF/Photo Researchers, Inc. Reproductive organs are known to produce hormones that affect bone remodeling, perhaps best illustrated by the fact that ovarian failure leads to bone loss and osteoporosis in post-menopausal women. An intriguing new role for the skeleton has been highlighted by a recent study by Franck Oury et al.1 showing that there is a reciprocal relationship in which bone can regulate fertility. The authors show that, in mice, the bone-derived hormone osteocalcin regulates testosterone production by the testes. Mice lacking osteocalcin show reduced testis weight and sperm counts and also produce less offspring than wild-type controls. This demonstration that the skeleton acts as an endocrine regulator of reproduction might provide insights into the underlying mechanisms involved in male infertility. We asked three experts to comment on how this study enhances our understanding of the relationship between bone and reproduction. Sundeep Khosla The past several decades have witnessed a major evolution in our view of bone. Initially considered a relatively uninteresting scaffold for the rest of the body, bone is now recognized as a fascinating organ that is regulated by several local and systemic factors. More recently, several groups, particularly the laboratory of Gerard Karsenty1, 2, have added another twist to our understanding of the role of bone, showing how the skeleton seems to be a crucial endocrine organ and how osteocalcin, which is produced by bone-forming osteoblast cells, is perhaps its most prominent hormone. Karsenty and his colleagues previously showed that osteocalcin influenced glucose homeostasis2, and the same group has now extended the role of osteocalcin to the regulation of testosterone production and male fertility1. In a convincing series of genetic studies in mice combined with in vitro analyses, these investigators report that osteoblasts, via the secretion of osteocalcin, induce testosterone production by the testis but, interestingly, fail to influence estrogen production by the ovaries1. "Perhaps the most interesting is why the skeleton, via osteocalcin, stimulates hormone production only in the testis, and not the ovary." Whereas these studies open up a number of questions and avenues for further investigation, perhaps the most interesting is why the skeleton, via osteocalcin, stimulates hormone production only in the testis, and not the ovary. A possible answer to this question may lie in the obvious sexual dimorphism of the skeleton in mammals: males have bigger bones than females, and this effect is largely mediated by the effects of testosterone on bone size during growth. 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 Competing financial interests The author declares no competing financial interests. Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Scraping fibrosis: Expressway to the core of fibrosis
- Nat Med 17(5):552-553 (2011)
Nature Medicine | Between Bedside and Bench Scraping fibrosis: Expressway to the core of fibrosis * Wajahat Z Mehal1 * John Iredale2 * Scott L Friedman3 * Affiliations * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:552–553Year published:(2011)DOI:doi:10.1038/nm0511-552Published online05 May 2011 Animal experiments using single organs as models of fibrosis spur therapeutic development toward promising targets, but testing of these therapies in human fibrosis yielded disappointing results and limited efficacy. Finding core pathways relevant in different organs that can become fibrotic will uncover molecules that will prove useful as therapeutic targets in many species, including humans. In 'Bench to Bedside', Scott Friedman, Wajahat Mehal and John Iredale discuss this new paradigm in fibrosis research and its potential as a new drug development approach. In 'Bedside to Bench', Alison Eddy peruses how the protein encoded by UMOD, a gene linked to variable risk for chronic kidney disease and hypertension in humans, may have a role in fibrosis and kidney disease. Uncovering the normal function of UMOD and its gene variants will shed light on the pathogenesis of chronic kidney disease and aid in the discovery of new targets for kidney fibrosis and hypertension. 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 * Wajahat Z. Mehal is in the Section of Digestive Diseases, Yale University, New Haven, Connecticut, USA * John Iredale is at the Medical Research Council Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK * Scott L. Friedman is in the Division of Liver Diseases, Mount Sinai School of Medicine, New York, New York, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Wajahat Z Mehal Author Details * Wajahat Z Mehal Contact Wajahat Z Mehal Search for this author in: * NPG journals * PubMed * Google Scholar * John Iredale Search for this author in: * NPG journals * PubMed * Google Scholar * Scott L Friedman Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Scraping fibrosis: UMODulating renal fibrosis
- Nat Med 17(5):553-555 (2011)
Nature Medicine | Between Bedside and Bench Scraping fibrosis: UMODulating renal fibrosis * Allison A Eddy1Journal name:Nature MedicineVolume: 17,Pages:553–555Year published:(2011)DOI:doi:10.1038/nm0511-553Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Bedside to bench Every medical student learns that the urine sediment may contain a variety of casts—cylindrical structures held together by Tamm-Horsfall protein, a sticky glycoprotein exclusively produced by epithelia of the thick ascending limb of the loop of Henle (TALH) and the early distal tubule that can be released by an unknown sheddase. It was characterized biochemically 60 years ago and shown to be the most abundant mammalian urinary protein, but its multiple proposed functions are still hypothetical. Later on, an immunosuppressive urinary glycoprotein—uromodulin (UMOD)—was isolated from urine of pregnant women, and, in 1987, cDNA sequence analyses determined that Tamm-Horsfall protein and UMOD were identical, and the latter name was adopted1. Interest in UMOD waned until a study in a family of humans identified a mutation in the UMOD gene on chromosome 16p12 as a cause of autosomal dominant chronic tubulointerstitial nephritis with hyperuricemia that leads to chronic kidney disease (CKD)2. Although this condition is rare, additional families with close to 60 distinct UMOD mutations are now known to exist3. Considerable interest in UMOD has resurfaced in the past two years owing to findings in human population studies designed to identify genetic risk factors for CKD. CKD affects more than 10% of the adult population and is associated with a fivefold increased risk of developing cardiovascular diseases such as heart failure, peripheral arterial disease and stroke; those who live long enough develop end-stage kidney disease that requires dialysis or kidney transplant to sustain life. With the goal of elucidating genetic variants that substantially modify the risk of adult-onset CKD, four recently published studies reported single nucleotide polymorphisms (SNPs) in the UMOD gene promoter—the minor T allele at position −3,653 (rs12917707) was associated with a 20% reduction in CKD incidence4; the minor C allele at position −1,550 (rs4293393) was associated with better renal function in both incident people with CKD and controls in the US5, whereas the dominant T allele at this site was associated with increased CKD risk in an Icelandic cohort6 and the minor G allele at position −1,617 (rs13333226) was associated with a lower risk of hypertension and with renal function in a European study7. These recent findings provide a new window of opportunity to probe further into the pathogenesis of CKD, with UMOD as a potential player. Could these minor genetic variants encode functionally altered proteins that modify the universal pathogenetic pathway for progressive CKD, irrespective of the primary etiology—characterized as chronic tubulointerstitial nephritis with interstitial inflammatory cells, matrix-producing myofibroblasts and destroyed renal parenchymal (tubules and pericapillary tubules) as the end result of fibrosis8? Two caveats need consideration. First, either loss of beneficial function of a normal UMOD or gain of harmful function of a variant protein are plausible mechanisms, and, second, UMOD may serve site-specific protective functions that might be selectively altered by a modified protein (Fig. 1). Figure 1: UMOD, normal and genetically determined variants, may actively participate in the pathogenesis of CKD. UMOD is normally expressed by epithelia of the TALH and the early distal tubule (DCT). Loss of protective UMOD activities might impair tubular recovery after injury, alter tubular transport function or enhance calcium crystal deposition. Gain of damaging functions or mislocation of UMOD may promote chronic interstitial fibrosis and irreversible nephron loss due to abnormal intracellular trafficking, leading to stress within the ER, tubular malfunction and eventual death. Abnormal interstitial UMOD deposition may recruit macrophages and myofibroblasts that promote scarring and irreversible kidney damage, the ultimate cause of all CKD. Disruption of the normal permeability barrier created by an apical UMOD gel could contribute to the CKD phenotype. * Full size image (149 KB) An obvious open question is what happens to normal UMOD expression during the initiation and progression phases of CKD. Is protein expression altered, misdirected or both? A better understanding of the function of native UMOD will help address these questions; proposed possibilities include apical membrane activities, such as endocytosis, signaling via interactions with co-receptors, ciliary function and cell polarity; the formation of an apical surface gel-like water permeability barrier; and the inhibition of urinary stones (Fig. 1)3, 9. Limited tissue studies suggest that UMOD may accumulate in the interstitium in damaged kidneys, caused either by 'back-leaking' from the urinary space through damaged tubular cell tight junction or by shedding from the basolateral membrane of tubular cells. Once in the interstitium, UMOD might then promote inflammatory cell recruitment, activation or both, probably through engagement of Toll-like receptor 4, interactions with scavenger rec! eptors or cell surface lectins, and other receptor-ligand–dependent signaling pathways not yet discovered, such as the actions of an unknown specific leukocyte UMOD receptor3, 9, 10, 11. 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 * Allison A. Eddy is in the Department of Pediatrics, University of Washington, Seattle, Washington, USA, and in the Division of Nephrology and at the Tissue & Cells Sciences Research Center, Seattle Children's Research Institute, Seattle, Washington, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Allison A Eddy Author Details * Allison A Eddy Contact Allison A Eddy Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Research Highlights
- Nat Med 17(5):556-557 (2011)
Nature Medicine | Research Highlights Research Highlights Journal name:Nature MedicineVolume: 17,Pages:556–557Year published:(2011)DOI:doi:10.1038/nm0511-556Published online05 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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. Neuroscience: Road to retinopathy Melanoma-associated retinopathy (MAR) is a relatively rare vision disorder caused by the presence of autoantibodies in the serum of people with melanoma. A recent study shows that these autoantibodies target the protein transient receptor potential cation channel subfamily M member-1 (TRPM1) in retinal bipolar cells (J. Neurosci., 3962–3967). TRPM1 controls the response of bipolar cells in the retina to light, and mutations in TRPM1 have been identified in people with a disorder called congenital stationary night blindness. Given that melanocytes express TRPM1 and the main characteristic of MAR is also night blindness, Anuradha Dhingra et al. hypothesized that autoantibodies may drive MAR by targeting TRPM1. The researchers showed that autoantibodies in the serum of people with MAR bound cell lines expressing TRPM1 but not control cells, whereas serum from an individual with normal vision could not bind the TRPM1-expressing cells. The MAR serum bound bipolar cells in the retinas of wild-type mice but not mice lacking TRPM1. The authors speculate that the autoantibodies in the serum from people with MAR are probably blocking TRPM1 function, but further experiments are required to determine if and how this may be happening to induce MAR. —EC Autoimmunity: Taking aim at ROR-γt Two groups report on the development of small-molecule inhibitors of T helper type 17 (TH17) cells. The transcription factor retinoic acid receptor–related orphan nuclear receptor-γt (ROR-γt) is expressed in TH17 cells, is required for interleukin-17 (IL-17) production and is crucial in driving autoimmune disease. Dan Littman and his colleagues (Nature doi: 10.1038/nature09978) found that the clinically approved cardiac glycoside digoxin specifically inhibited ROR-γt. Derivatives of digoxin with reduced toxicity in human cells reduced IL-17 production by T cells and the severity of clinical symptoms in a mouse model of multiple sclerosis. Derivatives of the liver X receptor agonist T0901317 also inhibit ROR-γt, according to a study by Thomas P. Burris and his colleagues (Nature doi: 10.1038/nature10075). Treatment of CD4+ T cells with the compound SR1001, which acts as an inverse agonist of both ROR-γt and ROR-a, also suppressed IL-17 expression and delayed the onset and clinical severity of disease in a mouse model of multiple sclerosis. Although several antibody-based strategies aimed at inhibiting IL-17 are being tested in clinical trials, this class of compounds provides a new means of targeting TH17 cells and treating autoimmune disease. —KDS Transplantation: Detecting rejection Tissue biopsies used to detect graft rejection in organ transplant recipients are invasive and expensive and can cause serious complications. Thomas Snyder et al. now report the development of a noninvasive method to monitor the health of heart transplants. (Proc. Natl. Acad. Sci. USA, 6229–6234). The authors confirmed that they could detect DNA from transplanted hearts released by dying cells in recipients' peripheral blood. In female recipients of male hearts, the Y chromosome can be used to identify circulating donor DNA. But detecting donor DNA in sex-matched transplant recipients is much trickier. To get around this problem, the researchers used high-throughput shotgun sequencing to obtain a signature of the single nucleotide polymorphisms in heart donor and recipient DNA. They then used this information to detect changes in the amount of donor-specific DNA in patient plasma that reflected cardiac graft rejection confirmed by biopsy. The technique is not restricted to sex-mismatched grafts and can also be used to monitor treatment response after a rejection episode. Confirming these results in a larger patient cohort and extending the technique to other solid organ transplants may facilitate dynamic monitoring of graft health and early intervention to stem graft rejection. —AF Cancer: Macrophages monitor tumors Signaling through the co-stimulation protein CD40 on antigen-presenting cells (APCs) is thought to activate APCs to directly prime cytotoxic T cells and thereby increase anti-tumor T cell responses. Gregory Beatty et al. now show that an agonist CD40-specific antibody can induce tumor regressions independently of T cells by stimulating macrophages to kill tumor cells directly (Science331, 1612–1616). Out of 21 individuals with pancreatic ductal carcinoma that they treated with the chemotherapeutic gemcitabine and an agonist CD40-specific antibody, four showed tumor regressions. Unexpectedly, tumor biopsies from two of these four individuals showed immune cell infiltrates but no sign of T cells. The researchers confirmed their results in humans using a mouse model of spontaneous pancreatic ductal carcinoma and showed that treatment with the agonist CD40-specific antibody (with or without chemotherapy) induced tumor regressions with no T cell infiltrate. Moreover, tumor shrinkage occurred even after T cell depletion, ruling out a T cell response. Instead, the authors found that macrophages were required for tumor regression and breakdown of the tumor stroma and that macrophages from CD40-specific antibody–treated mice killed tumor cells in vitro. How the macrophages specifically target the tumor is not clear, but the findings suggest that innate immune mechanisms can par! ticipate in tumor immune surveillance and eradication. —AF Infectious diseases: Tolerant TB unmasked Eradication of human tuberculosis with antitubercular drugs has been challenged by emerging populations of resistant bacteria that had been thought to be quiescent. A new study shows that infection of macrophages by Mycobacterium species leads to the development of a replicating and metabolically active population of intracellular bacteria that are multidrug tolerant and can spread to other granuloma lesions (Cell, , 39–53). Using a zebrafish larval model infected with Mycobacterium marinum and cultured human macrophages infected with Mycobacterium tuberculosis, Kristin Adams et al. show that dividing bacteria tolerant to multiple drugs arise within individual macrophages as well as within macrophages in granulomas. Bacterial efflux pumps induced by macrophages boosted bacterial growth and drug tolerance at early stages of tuberculosis infection, which persisted even after dissemination of the bacteria in the granuloma. Mutating the bacterial efflux pumps or treating the bacteria with pharmacological pump inhibitors reduced this macrophage-dependent drug tolerance and attenuated bacterial growth. SPL / Photo Researchers, Inc. These results argue in favor of using inhibitors of bacterial tolerance such as efflux pump inhibitors along with antimicrobial agents to halt Mycobacterium growth and eliminate residual resistant bacteria to shorten treatment times and avoid relapses in people with tuberculosis. —CP View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Medicine for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * 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 - Ablation of Fmrp in adult neural stem cells disrupts hippocampus-dependent learning
- Nat Med 17(5):559-565 (2011)
Nature Medicine | Article Ablation of Fmrp in adult neural stem cells disrupts hippocampus-dependent learning * Weixiang Guo1 * Andrea M Allan1 * Ruiting Zong2 * Li Zhang1 * Eric B Johnson1 * Eric G Schaller1 * Adeline C Murthy1 * Samantha L Goggin1 * Amelia J Eisch3 * Ben A Oostra4 * David L Nelson2 * Peng Jin5 * Xinyu Zhao1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:559–565Year published:(2011)DOI:doi:10.1038/nm.2336Received02 November 2010Accepted23 February 2011Published online24 April 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 Deficiency in fragile X mental retardation protein (FMRP) results in fragile X syndrome (FXS), an inherited form of intellectual disability. Despite extensive research, it is unclear how FMRP deficiency contributes to the cognitive deficits in FXS. Fmrp-null mice show reduced adult hippocampal neurogenesis. As Fmrp is also enriched in mature neurons, we investigated the function of Fmrp expression in neural stem and progenitor cells (aNSCs) and its role in adult neurogenesis. Here we show that ablation of Fmrp in aNSCs by inducible gene recombination leads to reduced hippocampal neurogenesis in vitro and in vivo, as well as markedly impairing hippocampus-dependent learning in mice. Conversely, restoration of Fmrp expression specifically in aNSCs rescues these learning deficits in Fmrp-deficient mice. These data suggest that defective adult neurogenesis may contribute to the learning impairment seen in FXS, and these learning deficits can be rectified by delayed restoration o! f Fmrp specifically in aNSCs. View full text Figures at a glance * Figure 1: Fmrp deletion in Nestin-expressing cells resulted in fewer YFP+ cells in the dentate gyrus. (,) Immunohistological analyses of brain sections from cKO;Cre;YFP mice () and Cre;YFP control mice () 1 d after tamoxifen injection. Red, Fmrp; green, YFP; white, GFAP; blue, DAPI. Left scale bar, 20 μm; right scale bar, 10 μm. (,) Immunohistological analyses of brain sections from cKO;Cre;YFP mice () and Cre;YFP control mice () 56 d after tamoxifen injection. Red, Fmrp; green, YFP; white, NeuN; blue, DAPI. Left scale bar, 20 μm; right scale bar, 10 μm. () Sample images of YFP+ cells in the dentate gyrus 56 d after tamoxifen injection. Green, YFP; blue, DAPI. Scale bar, 50 μm. () Quantification of the number of YFP+ cells in cKO;Cre;YFP and Cre;YFP control mice. GCL, granule cell layer; ML, molecular layer; TAM, tamoxifen. Error bars indicate means ± s.e.m. n = 5 per genotype per time point. * Figure 2: Selective deletion of Fmrp in nestin-expressing cells alters cell proliferation and fate specification of aNSCs. () Schematic diagram showing the cell lineage–specific markers across stages of neurogenesis that were used for fate mapping. (–) Sample confocal images used for fate mapping of YFP+ (green) cells in the dentate gyrus. () Red, GFAP; green, YFP; white, S100β. () Red, DCX; green, YFP; white, Ki67. () Red, NeuN; green, YFP. Scale bars, 20 μm. () Quantitative comparison of the numbers of YFP+GFAP+S100β− type 1 aNSCs in the dentate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. () Quantitative comparison of the number of YFP+DCX−Ki67+ TAP cells in the dentate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. () Quantitative comparison of the number of YFP+DCX+Ki67+ neuroblasts in the dentate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. () Quantitative comparison of the number of YFP+DCX+Ki67− immature neurons in the dentate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. () Quantitative comparison of the number of YFP+NeuN+ mature neurons in the den! tate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. () Quantitative comparison of the number of YFP+S100β+ astrocytes in the dentate gyrus of cKO;Cre;YFP mice and Cre;YFP control mice. Error bars indicate means ± s.e.m. n = 5 per genotype per time point. * Figure 3: Selective deletion of Fmrp in primary aNSCs isolated from adult dentate gyrus results in altered proliferation and differentiation of aNSCs and reduced neurite extension of aNSC-differentiated neurons. (,) Proliferation analysis. () Sample image of aNSCs with (+) or without (−) Cre-GFP retrovirus infection, followed by BrdU pulse labeling and immunocytochemistry analysis. Red, BrdU; green, Cre-GFP; blue, DAPI. Scale bar, 20 μm. () Quantitative comparison of the percentage of BrdU-labeled cells in both Fmrp-cKO cells and wild-type (WT) control cells either without (left) or with (right) Cre-GFP viral infection. (–) Differentiation analysis. () Sample image of differentiated aNSCs with (+) or without (−) Cre-GFP retrovirus infection, analyzed by immunocytochemistry. Red, Tuj1; green, Cre-GFP; white, GFAP; blue, DAPI. Scale bar, 20 μm. (,) Quantitative comparison of the percentage of Tuj1+ neurons () and GFAP+ astrocytes () in both Fmrp-cKO cells and wild-type control cells either without (left) or with (right) Cre-GFP viral infection. () Sample images of neurons differentiated from wild-type and Fmrp-cKO aNSCs infected with Cre-GFP virus. Scale bar, 20 μm. (–) Neu! rite complexity analysis of neurons differentiated from cKO or wild-type aNSCs either with (+) or without (−) Cre-GFP virus infection. () Scholl analysis for dendritic complexity; () neurite length; () number of dendritic nodes (branching points); () number of ends. *P < 0.05; **P < 0.01. On, Fmrp is expressed; Off, Fmrp is not expressed. Error bars indicate means ± s.e.m. n = 3 for all panels except . In , n = 20 neurons per condition from three independent experiments. * Figure 4: Deletion of Fmrp from Nestin-positive aNSCs results in deficits in hippocampus-dependent learning. (,) Context () and tone () trace learning analyses of Fmr1-knockout (KO) mice and wild-type control littermates as determined by the percentage of the time that the animal spent freezing to either training context () or training tone (). (,) Context () and tone () trace learning analyses of cKO;Cre;YFP mice and Cre;YFP control littermates as determined by the percentage of time spent freezing to either training context () or training tone (). () DNMP-RAM analyses of Fmr1-knockout mice and wild-type control littermates as determined by the percentage of correct entry in both separation (Sep) 2 test and separation 4 test. () DNMP-RAM analyses of cKO;Cre;YFP mice and Cre;YFP control littermates as determined by the percentage of correct entry in both separation (Sep) 2 test and separation 4 test. ***P < 0.001. Error bars indicate means ± s.e.m. WT, n = 6; KO, n = 5; cKO;Cre;YFP, n = 7; cKO;YFP control, n = 7. * Figure 5: Restoration of Fmrp in primary aNSCs rescues proliferation and differentiation deficits of Fmrp-deficient aNSCs and neurite extension deficits of aNSC-differentiated neurons. (,) Proliferation analysis. () Sample image of aNSCs with (+) or without (−) Cre-GFP retrovirus infection, followed by BrdU pulse labeling and immunocytochemistry analysis. Red, BrdU; green, Cre-GFP; blue, DAPI. Scale bar, 20 μm. () Quantitative comparison of the percentage of BrdU-labeled cells in Fmrp-cON cells and wild-type control cells without (left) or with (right) Cre-GFP viral infection. (–) Differentiation analysis. () Sample image of differentiated aNSCs with or without Cre-GFP retrovirus infection, analyzed by immunocytochemistry. Red, Tuj1; green, Cre-GFP; white, GFAP; blue, DAPI. Scale bar, 20 μm. (,) Quantitative comparison of the percentage of Tuj1+ neurons () and GFAP+ astrocytes () in Fmrp-cON cells and wild-type control cells without (left) or with (right) Cre-GFP viral infection. () Sample images of neurons differentiated from wild-type and Fmrp-cON aNSCs infected with Cre-GFP virus. Scale bar, 20 μm. (–) Neurite complexity analysis of neurons dif! ferentiated from cON or wild-type aNSCs with (+) or without (−) Cre-GFP virus infection. () Scholl analysis for dendritic complexity; () neurite length; () number of dendritic nodes (branching points); () number of ends. *P < 0.05; **P < 0.01. Error bars indicate means ± s.e.m. n = 3 for all panels except . In , n = 20 neurons per condition from three independent experiments. * Figure 6: Restoration of Fmrp in Nestin-expressing aNSCs and their progeny rescues hippocampus-dependent learning deficits in Fmrp-deficient mice. (,) Immunohistological analyses of brain sections from cON;Cre;YFP mice at 56 d after tamoxifen injection. () Red, Fmrp; green, YFP; white, GFAP; blue, DAPI. () Red, Fmrp; green, YFP; white, NeuN; blue, DAPI. Left scale bar, 20 μm; right scale bar, 10 μm. (,) Context () and tone () trace learning analyses of Cre;YFP mice (which express Fmrp), cON;Cre;YFP mice (with Fmrp restored in nestin-expressing cells) and cON;YFP (no Fmrp) littermates as determined by the percentage time of freezing to training context () or tone (). () DNMP-RAM analyses of Cre;YFP (which express Fmrp), cON;Cre;YFP mice (with Fmrp restored in nestin-expressing cells) and cON;YFP (no Fmrp) littermates as determined by the percentage of correct entry in separation 2 tests and separation 4 tests. Error bars indicate means ± s.e.m. cON;Cre;YFP, n = 6; Cre;YFP control, n = 8; cON;YFP control, n = 6. H, hilar region of the hippocampus. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA. * Weixiang Guo, * Andrea M Allan, * Li Zhang, * Eric B Johnson, * Eric G Schaller, * Adeline C Murthy, * Samantha L Goggin & * Xinyu Zhao * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Ruiting Zong & * David L Nelson * Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Amelia J Eisch * Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands. * Ben A Oostra * Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA. * Peng Jin Contributions W.G. and X.Z. planned the experiments, analyzed data and wrote the manuscript. A.M.A. developed, carried out and analyzed data for behavioral analyses. W.G., L.Z., E.B.J., E.G.S., A.C.M., S.L.G. and A.M.A. carried out all the experiments. P.J. helped with mouse line acquisition and concept development. R.Z., B.A.O. and D.L.N. made Fmr1-cON mice. A.J.E. provided the Nes-CreERT2 and ROSA26-YFP mouse lines and guided histological analysis. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Xinyu Zhao Author Details * Weixiang Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea M Allan Search for this author in: * NPG journals * PubMed * Google Scholar * Ruiting Zong Search for this author in: * NPG journals * PubMed * Google Scholar * Li Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Eric B Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Eric G Schaller Search for this author in: * NPG journals * PubMed * Google Scholar * Adeline C Murthy Search for this author in: * NPG journals * PubMed * Google Scholar * Samantha L Goggin Search for this author in: * NPG journals * PubMed * Google Scholar * Amelia J Eisch Search for this author in: * NPG journals * PubMed * Google Scholar * Ben A Oostra Search for this author in: * NPG journals * PubMed * Google Scholar * David L Nelson Search for this author in: * NPG journals * PubMed * Google Scholar * Peng Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Xinyu Zhao Contact Xinyu Zhao 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 1–8 and Supplementary Methods Additional data - GIT1 is associated with ADHD in humans and ADHD-like behaviors in mice
- Nat Med 17(5):566-572 (2011)
Nature Medicine | Article GIT1 is associated with ADHD in humans and ADHD-like behaviors in mice * Hyejung Won1, 2, 7 * Won Mah1, 2, 7 * Eunjin Kim1 * Jae-Won Kim3 * Eun-Kyoung Hahm1, 2 * Myoung-Hwan Kim1, 2 * Sukhee Cho4 * Jeongjin Kim1 * Hyeran Jang5 * Soo-Churl Cho3 * Boong-Nyun Kim3 * Min-Sup Shin3 * Jinsoo Seo4 * Jaeseung Jeong5 * Se-Young Choi4 * Daesoo Kim1 * Changwon Kang1 * Eunjoon Kim1, 2, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:566–572Year published:(2011)DOI:doi:10.1038/nm.2330Received29 November 2010Accepted11 February 2011Published online17 April 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 Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder that affects ~5% of school-aged children; however, the mechanisms underlying ADHD remain largely unclear. Here we report a previously unidentified association between G protein–coupled receptor kinase–interacting protein-1 (GIT1) and ADHD in humans. An intronic single-nucleotide polymorphism in GIT1, the minor allele of which causes reduced GIT1 expression, shows a strong association with ADHD susceptibility in humans. Git1-deficient mice show ADHD-like phenotypes, with traits including hyperactivity, enhanced electroencephalogram theta rhythms and impaired learning and memory. Hyperactivity in Git1−/− mice is reversed by amphetamine and methylphenidate, psychostimulants commonly used to treat ADHD. In addition, amphetamine normalizes enhanced theta rhythms and impaired memory. GIT1 deficiency in mice leads to decreases in ras-related C3 botulinum toxin substrate-1 (RAC1) signaling and inhibito! ry presynaptic input; furthermore, it shifts the neuronal excitation-inhibition balance in postsynaptic neurons toward excitation. Our study identifies a previously unknown involvement of GIT1 in human ADHD and shows that GIT1 deficiency in mice causes psychostimulant-responsive ADHD-like phenotypes. View full text Figures at a glance * Figure 1: Hyperactivity and impaired memory in Git1−/− mice are normalized by amphetamine treatment. () Genotyping of Git1−/− mice by PCR (top), and undetectable GIT1 proteins in whole brain homogenates from Git1−/− mice (bottom; 6–10 weeks). KO, knockout; WT, wild type. α-tubulin was used as a control. (,) Locomotor activity of WT and Git1−/− mice in an open field. n = 15 (WT), n = 14 (KO); see also Supplementary Table 4. **P < 0.01, ***P < 0.001, NS, not significant; Student's t test. The three asterisks in the top right corner of the panel indicate a significant difference between two genotypes over time, as calculated by repeated-measures analysis of variance (ANOVA) (see Supplementary Table 5 for details of statistical results). () Novel-object recognition in WT and Git1−/− mice. n = 11 (WT), n = 12 (KO). *P < 0.05; Student's t test. () Spatial learning and memory in WT and Git1−/− mice, shown by escape latencies in the Morris water maze (), target quadrant occupancy () and numbers of platform crossings (). n = 16 (WT), n = 19 (KO). *P < 0.05, **! P < 0.01, ***P < 0.001; Student's t test and repeated-measures ANOVA. (,) Effects of amphetamine (amph) and saline (sal; control) on locomotor activities of WT and Git1−/− mice in an open field. The results in were quantified in over a 10–20 min period. n = 9 (sal), n = 6 (amph) for WT; n = 7 (sal), n = 8 (amph) for KO. **P < 0.01, ***P < 0.001, one-way ANOVA. Details of statistical results for (Student's t test) are described in Supplementary Table 5. () Effect of amphetamine (amph) and saline (sal) on novel-object recognition behavior of Git1−/− mice. Saline-treated WT mice were used for comparison. n = 7 (WT sal, KO amph, KO sal). *P < 0.05, **P < 0.01; one-way ANOVA. Error bars indicate means ± s.e.m. * Figure 2: Enhanced theta rhythms in the frontal cortex of Git1−/− mice are reduced by amphetamine. (–) Theta EEG rhythms in the frontal cortex of WT and Git1−/− mice, as shown by representative traces and spectrogram (), theta power (3–10 Hz; 10–20 min) () and number of theta events (), in which a theta event is defined as a group of theta oscillations independent from others. A typical theta range in mice (3–10 Hz) is slightly different from that in humans (4–8 Hz). n = 5 (WT, KO). *P < 0.05, ***P < 0.001; Student's t test and repeated-measures ANOVA. (–) Effects of amphetamine and saline on enhanced theta rhythms in Git1−/− mice. n = 7 (KO sal), n = 6 (KO amph). *P < 0.05, ***P < 0.001; Student's t test and repeated-measures ANOVA. Error bars indicate means ± s.e.m. * Figure 3: Suppressed GIT1-PIX-RAC1-PAK signaling in the Git1−/− brain. () Amounts of PIX proteins (α-PIX and β-PIX) and activity of RAC1 (a downstream effector of PIX) in the WT and Git1−/− brain, as determined by immunoblotting analysis of whole brain homogenates (for PIX) and pull-down analysis of GTP-bound (active) RAC1 with GST–p21 binding domain (GST-PBD). () Amounts of PAK (PAK1 and PAK3) and phosphorylated PAK (pPAK1 and pPAK3) proteins shown by immunoblotting. () Amounts of GIT1-interacting proteins (FAK, MEK, PLC-γ, and liprin-α1) and other synaptic proteins in the WT and Git1−/− brain. n = 6 (WT, KO) for immunoblotting analysis, n = 3 (WT, KO) for pull-down analysis. *P < 0.05, ***P < 0.001; Student's t test. FAK, focal adhesion kinase; MEK, mitogen-activated protein kinase kinase; PLC-γ, phospholipase C-γ; ERK1/2, extracellular-regulated kinase 1 and 2; pERK1/2, phosphorylated ERK1/2; GRIP1, glutamate receptor-interacting protein 1; ERC2, ELKS/Rab6IP2/CAST2; PSD-93/PSD-95, postsynaptic density 93/95; CASK, calcium/cal! modulin-dependent serine protein kinase; NR1, NR2A and NR2B, subunits of NMDA glutamate receptors (also known as GluN1, GluN2A and GluN2B); GluR1 and GluR2, subunits of AMPA glutamate receptors (also known as GluA1 and GluA2). α-tubulin and β-actin were used as controls. Error bars indicate means ± s.e.m. * Figure 4: Reduced inhibitory transmission and elevated excitatory transmission at Git1−/− synapses. () Amplitude and frequency of spontaneous miniature excitatory postsynaptic currents (mEPSCs) in WT and Git1−/− hippocampal CA1 pyramidal neurons. n = 17 cells from three mice for WT and n = 15 cells from three mice for KO. () Frequency and amplitude of miniature inhibitory postsynaptic currents (mIPSCs) in WT and Git1−/− CA1 pyramidal neurons. n = 22 cells from three mice (WT), n = 23 cells from three mice (KO). **P < 0.01; Student's t test. () Amounts of charge transfer for mEPSCs and mIPSCs in WT and Git1−/− mice. n = 17 cells from three mice for WT, n = 15 cells from three mice for KO (mEPSCs), n = 22 cells from three mice for WT, n = 23 cells from three mice for KO (mIPSCs). () Excitatory transmission at WT and Git1−/− SC-CA1 synapses. The initial slopes of field EPSPs were plotted against fiber volley amplitudes. n = 41 slices from ten mice (WT), n = 27 slices from nine mice (KO). ***P < 0.001; Student's t test. () Presynaptic release probabilities at W! T and Git1−/− SC-CA1 synapses, as measured by paired-pulse facilitation ratios (second fEPSP/first fEPSP) at different interstimulus intervals. n = 23 slices from eight mice (WT), n = 16 slices from six mice (KO). Error bars indicate means ± s.e.m. * Figure 5: Reduced presynaptic input at Git1−/− inhibitory synapses. (,) Reduced amounts of inhibitory presynaptic proteins (GAD67 and vGAT) in the CA1 region of the Git1−/− hippocampus, which contrast to normal amounts of the inhibitory postsynaptic protein gephyrin and the presynaptic active zone protein bassoon, which is present at both excitatory and inhibitory synapses. Signals from the somas and processes of Git1−/− slices were normalized to those from WT mice. n = 3 slices from three mice (WT, KO). *P < 0.05; Student's t test. (,) Reduced amounts of parvalbumin (PV), a marker for fast-spiking interneurons, in the Git1−/− hippocampal CA1 region, whereas signals for other interneuron markers, somatostatin (SST), calbindin (CB) and calretinin (CR), were normal. n = 3 slices from three mice (WT, KO). *P < 0.05; Student's t test. (,) Comparable quantities of tyrosine hydroxylase (TH) signals in brain regions of Git1−/− and WT mice, including caudate putamen (CPu), nucleus accumbens (NAc), substantia nigra (SN) and ventral te! gmental area (VTA). n = 3 slices from three mice (WT, KO). Scale bars in and , 20 μm; scale bar in (CPu and NAc), 20 μm; scale bar in (SN and VTA), 63 μm. Error bars indicate means ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hyejung Won & * Won Mah Affiliations * Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. * Hyejung Won, * Won Mah, * Eunjin Kim, * Eun-Kyoung Hahm, * Myoung-Hwan Kim, * Jeongjin Kim, * Daesoo Kim, * Changwon Kang & * Eunjoon Kim * National Creative Research Initiative Center for Synaptogenesis, KAIST, Daejeon, Korea. * Hyejung Won, * Won Mah, * Eun-Kyoung Hahm, * Myoung-Hwan Kim & * Eunjoon Kim * Department of Child and Adolescent Psychiatry, College of Medicine, Seoul National University Hospital, Seoul, Korea. * Jae-Won Kim, * Soo-Churl Cho, * Boong-Nyun Kim & * Min-Sup Shin * Department of Physiology, Seoul National University School of Dentistry, Seoul, Korea. * Sukhee Cho, * Jinsoo Seo & * Se-Young Choi * Department of Bio and Brain Engineering, KAIST, Daejeon, Korea. * Hyeran Jang & * Jaeseung Jeong * Graduate School of Nanoscience and Technology (World Class University), KAIST, Daejeon, Korea. * Eunjoon Kim Contributions Eunjin K. conducted the SNP experiments; J.-W.K., S.-C.C., B.-N.K. and M.-S.S. provided the ADHD and control samples and conducted clinical data analyses; E.-K.H. generated Git1−/− mice; M.-H.K. measured and analyzed minicurrents; S.C., J.S. and S.-Y.C. measured and analyzed evoked synaptic transmission; J.K., H.J. and J.J. contributed to EEG recordings; H.W. and W.M. conducted all the rest of the experiments; D.K., C.K. and Eunjoon K. supervised the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Eunjoon Kim or * Changwon Kang Author Details * Hyejung Won Search for this author in: * NPG journals * PubMed * Google Scholar * Won Mah Search for this author in: * NPG journals * PubMed * Google Scholar * Eunjin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Jae-Won Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Eun-Kyoung Hahm Search for this author in: * NPG journals * PubMed * Google Scholar * Myoung-Hwan Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Sukhee Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Jeongjin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Hyeran Jang Search for this author in: * NPG journals * PubMed * Google Scholar * Soo-Churl Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Boong-Nyun Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Min-Sup Shin Search for this author in: * NPG journals * PubMed * Google Scholar * Jinsoo Seo Search for this author in: * NPG journals * PubMed * Google Scholar * Jaeseung Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Se-Young Choi Search for this author in: * NPG journals * PubMed * Google Scholar * Daesoo Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Changwon Kang Contact Changwon Kang Search for this author in: * NPG journals * PubMed * Google Scholar * Eunjoon Kim Contact Eunjoon Kim Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Methods, Supplementary Figures 1–14 and Supplementary Tables 1–5 Additional data - Epigenetic modulation of the renal β-adrenergic–WNK4 pathway in salt-sensitive hypertension
- Nat Med 17(5):573-580 (2011)
Nature Medicine | Article Epigenetic modulation of the renal β-adrenergic–WNK4 pathway in salt-sensitive hypertension * ShengYu Mu1 * Tatsuo Shimosawa1, 2 * Sayoko Ogura1 * Hong Wang1 * Yuzaburo Uetake1 * Fumiko Kawakami-Mori1 * Takeshi Marumo1 * Yutaka Yatomi2 * David S Geller3 * Hirotoshi Tanaka4 * Toshiro Fujita1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:573–580Year published:(2011)DOI:doi:10.1038/nm.2337Received08 December 2010Accepted28 February 2011Published online17 April 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 How high salt intake increases blood pressure is a key question in the study of hypertension. Salt intake induces increased renal sympathetic activity resulting in sodium retention. However, the mechanisms underlying the sympathetic control of renal sodium excretion remain unclear. In this study, we found that β2-adrenergic receptor (β2AR) stimulation led to decreased transcription of the gene encoding WNK4, a regulator of sodium reabsorption. β2AR stimulation resulted in cyclic AMP-dependent inhibition of histone deacetylase-8 (HDAC8) activity and increased histone acetylation, leading to binding of the glucocorticoid receptor to a negative glucocorticoid−responsive element in the promoter region. In rat models of salt-sensitive hypertension and sympathetic overactivity, salt loading suppressed renal WNK4 expression, activated the Na+-Cl− cotransporter and induced salt-dependent hypertension. These findings implicate the epigenetic modulation of WNK4 transcription in! the development of salt-sensitive hypertension. The renal β2AR-WNK4 pathway may be a therapeutic target for salt-sensitive hypertension. View full text Figures at a glance * Figure 1: Effects of salt loading on blood pressure, renal WNK4 and NCC expression in norepinephrine (NE)-infused C57BL/6j, β1AR-knockout (β1-KO) and β2AR-KO (β2-KO) mice. () Recordings of mean arterial pressure in NE-infused mice on normal-salt (0.3%) diet for 3 d, high-salt (8%) diet for 3 d and low-salt (0.05%) diet for 3 d. Closed circles, averages of mean arterial pressure measured every 30 min by radiotelemetry. () mRNA (left) and protein (right) amounts of renal WNK4 in control (Con), NE- or NE plus propranolol (Pro)-treated mice. The ratio of WNK4 to β-actin mRNA or protein relative to that in control mice is shown. () Effects of salt loading on mean arterial pressure in isoproterenol (Iso)-infused β1-KO and β2-KO mice. () Effects of salt loading (HS) and NE on renal WNK4 mRNA levels in WT, β1-KO and β2-KO mice. () Amounts of NCC protein and phosphorylated NCC protein (p-NCC) in control, NE- or NE plus propranolol (Pro)-treated mice. Quantitative data were normalized using actin as a loading control. () Immunofluorescent micrographs of NCC in the kidney for each of the three groups of mice. Nuclei are stained by DAPI. Data are mea! ns ± s.e.m.; n = 4–6 for each group of mice. *P < 0.01 versus WT or control; #P < 0.01 versus NE or normal salt. NS, not significant. * Figure 2: Effects of isoproterenol (Iso), hydrochlorothiazide (HCTZ) and ICI118551, a β2-specific antagonist, on UNaV, plasma volume (PV) and blood pressure in rats fed a high-salt diet (HS). () Left, effects of acute NCC blockade with intravenous injection of HCTZ (with HCTZ) and ICI 118551on FENa. Right, effect of acute NCC blockade with HCTZ injection on UNaV. () Left, effects of chronic infusion of Iso and HCTZ on daily UNaV and PV in HS rats. Right, percentage change in PV during the Iso infusion, as estimated by changes in hematocrit, in HS + Iso rats and in HS + Iso + HCTZ rats (P < 0.01, paired test). () Effects of HCTZ on salt-induced elevation of mean arterial pressure in Iso-infused rats. () Renal function curve of rats fed normal-salt and high-salt diet and treated or not with Iso or with Iso + HCTZ. Renal function curve was estimated by mean ± s.e.m. of blood pressure and daily UNaV. Data are means ± s.e.m.; n = 4–6 rats for each group. *P < 0.01 versus HS; #P < 0.05 for comparisons indicated in the figure. * Figure 3: Role of glucocorticoid receptor (GR) in Iso-induced WNK4 inhibition and blood pressure elevation. () Effects of Iso, dexamethasone (Dex), RU486 and H89 on WNK4 mRNA levels in mDCT cells in charcoal-stripped medium. () Effects of Iso, Dex and H89 on WNK4 transcription as measured by a luciferase assay in mDCT cells transfected with GR siRNA. Percentage changes of WNK4 transcription compared with control were calculated. () ChIP assay for GR binding to the promoter region of WNK4 containing nGRE in mDCT cells. Cells were treated or not with Dex, Iso or H89 as indicated. Bottom, relative GR binding to nGRE (PCR product / input). (Input and IgG results are shown in Supplementary Fig. 18b.) () Effects of NE and Dex on renal WNK4 mRNA levels in adrenalectomized mice (Adx). () Effects of a high-salt diet on renal WNK4 mRNA levels in Iso-infused WT and GR-knockout (GR-KO) mice. () Effects of a high-salt diet on average of mean arterial pressure measured every 30 min by radiotelemetry in Iso-infused WT and GR-KO mice. Data are means ± s.e.m.; in vitro (–) n = 5 or 6 experiment! s in mDCT cells for each group; in vivo experiments (,) n = 5 or 6 mice for each group. *P < 0.01 versus control or normal salt; #P < 0.01; **P < 0.05 (versus WT + NS in ). * Figure 4: Effects of Iso on nuclear GR protein and histone modulation of WNK4 transcription. () Top, western blotting to detect nuclear GR protein at 60 and 180 min after the indicated treatments in mDCT cells. Bottom, nuclear GR (n-GR) / total GR (t-GR) after 180 min of the indicated treatments. () Effects of Iso, Dex and H89 on acetylation of histone 3 (Ac-H3) and histone 4 (Ac-H4). Acetylated histone to β-actin ratio was calculated. () Quantitative immunoblot analysis of acetylation sites of histones H3 and H4. The level of acetylation of the indicated histone sites relative to that of the control group was calculated. () Chromatin immunoprecipitation (ChIP) assay for the presence of acetylated H3 and H4 in the promoter region of WNK4 containing nGRE in mDCT cells. The ratios of treatment to control PCR product quantities were calculated as relative ChIP. () WNK4 transcription as measured by a luciferase assay using deletion mutants containing 300 bp (–300) or 400 bp (–400) of the WNK4 promoter region in mDCT cells with the indicated treatments. TSA, trichos! tatin A. Percentage changes of WNK4 promoter transcription as compared to control were calculated. Data are means ± s.e.m.; n = 4–6 experiments in mDCT cells for each group. *P < 0.01 versus Con; #P < 0.01. * Figure 5: Effects of Iso treatment on HDAC8 activity and H3 and H4 acetylation in the WNK4 promoter region. () Effects of Iso and Dex on total HDAC (tHDAC) and Ser39 phosphorylated HDAC8 (pHDAC8) levels in mDCT cells. The amounts of phosphorylated HDAC8 as compared to control were calculated. () Effects of Iso and Dex on HDAC8 activity in mDCT cells transfected with plasmids expressing HDAC8 or HDAC8 S39A. () HDAC8 was immunoprecipitated (IP) from extracts of mDCT cells with the indicated treatments and immunoblots (IB) were performed to determine H3 and H4 binding to HDAC8. The cells had been transfected with plasmids expressing WT HDAC8 (left) or HDAC8 S39A (right). Relative binding of HDAC8 to H3 and H4 compared to control group were calculated. () ChIP assay for the presence of acetylated H3 (left) and acetylated H4 (right) in the promoter region of WNK4 containing nGRE in mDCT cells. The cells had been transfected with plasmids expressing WT HDAC or HDAC8 S39A and were treated or not with Dex, ISO or H89 as indicated. (Input and IgG results are shown in Supplementary Fig. 18b! .) () ChIP assay for the binding of GR to the promoter region of WNK4 containing nGRE in mDCT cells which had been transfected with plasmids expressing WT HDAC8 or HDAC8 S39A and treated as indicated. () WNK4 transcription as measured by a luciferase assay in mDCT cells which had been transfected with plasmids expressing WT HDAC8 or HDAC8 S39A or with HDAC8 siRNA and treated as indicated, as compared to control group. Data are means ± s.e.m.; mDCT cells: n = 5 or 6 experiments in mDCT cells for each group. *P < 0.01 versus Con; #P < 0.01. * Figure 6: Renal NE turnover, renal WNK4 expression and mean arterial pressure in DOCA-salt rats and salt-loaded Dahl-S and Dahl-R rats. () Renal NE turnover in SD rats fed a normal-salt or high-salt (HS) diet and DOCA-salt rats (left) and in salt-loaded Dahl-R and Dahl-S rats (right). Renal NE content was measured before and 6 h after addition of α-methyl-tyrosine. () Effects of salt loading and renal denervation (DNx) on renal WNK4 mRNA levels in SD rats. () In DOCA-salt rats, effect of DNx and addition of RU486 or Pro on renal WNK4 mRNA levels () and mean arterial pressure (). () Effect of salt loading on WNK4 mRNA levels (left) and mean arterial pressure (right) 2 and 4 weeks later in Dahl-S rats and Dahl-R rats. () Effects of renal denervation, eplerenone, prazosin or propranolol treatment on renal WNK4 mRNA levels (left) and blood pressure (right) in salt-loaded Dahl-S rats. () Cartoon of a hypothetical mechanism for the development of salt-sensitive hypertension. Salt-induced renal sympathetic overactivity induces renal WNK4 downregulation and leads to sodium retention through NCC activation, thus res! ulting in salt-sensitive hypertension. Sympathetic overactivity leads to β2AR stimulation, HDAC8 phosphorylation and increased histone acetylation in the WNK4 promoter region, resulting in transcriptional modulation dependent on GR binding to nGREs in this region. Data are means ± s.e.m.; n = 4–6 rats in for each group. *P < 0.01 versus NS; #P < 0.01 versus HS; **P < 0.05 versus DOCA-HS. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Nephrology and Endocrinology, University of Tokyo Graduate School of Medicine, Tokyo, Japan. * ShengYu Mu, * Tatsuo Shimosawa, * Sayoko Ogura, * Hong Wang, * Yuzaburo Uetake, * Fumiko Kawakami-Mori, * Takeshi Marumo & * Toshiro Fujita * Department of Clinical Laboratory, University of Tokyo Graduate School of Medicine, Tokyo, Japan. * Tatsuo Shimosawa & * Yutaka Yatomi * West Haven Veterans Affairs Medical Center, Yale University School of Medicine, New Haven, Connecticut, USA. * David S Geller * Division of Clinical Immunology, Advanced Clinical Research Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. * Hirotoshi Tanaka Contributions S.Y.M. carried out both in vitro and in vivo experiments and wrote the manuscript during a PhD course under the direction of T.F. at the University of Tokyo; T.S. carried out in vivo experiments and conducted experiments; S.O., H.W., Y.U., F.K.-M., Y.Y. and T.M. helped with experimental procedures and contributed to data discussion; D.S.G. generated distal nephron-specific glucocorticoid receptor–knockout mice and H.T. provided glucocorticoid receptor plasmids and contributed to data discussion. T.F. designed and directed the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Toshiro Fujita Author Details * ShengYu Mu Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsuo Shimosawa Search for this author in: * NPG journals * PubMed * Google Scholar * Sayoko Ogura Search for this author in: * NPG journals * PubMed * Google Scholar * Hong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yuzaburo Uetake Search for this author in: * NPG journals * PubMed * Google Scholar * Fumiko Kawakami-Mori Search for this author in: * NPG journals * PubMed * Google Scholar * Takeshi Marumo Search for this author in: * NPG journals * PubMed * Google Scholar * Yutaka Yatomi Search for this author in: * NPG journals * PubMed * Google Scholar * David S Geller Search for this author in: * NPG journals * PubMed * Google Scholar * Hirotoshi Tanaka Search for this author in: * NPG journals * PubMed * Google Scholar * Toshiro Fujita Contact Toshiro Fujita 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 1–18 and Supplementary Table 1 Additional data - GDF-15 is an inhibitor of leukocyte integrin activation required for survival after myocardial infarction in mice
- Nat Med 17(5):581-588 (2011)
Nature Medicine | Article GDF-15 is an inhibitor of leukocyte integrin activation required for survival after myocardial infarction in mice * Tibor Kempf1, 2, 9 * Alexander Zarbock3, 4, 9 * Christian Widera1, 2 * Stefan Butz3 * Anika Stadtmann3, 4 * Jan Rossaint3, 4 * Matteo Bolomini-Vittori5 * Mortimer Korf-Klingebiel1, 2 * L Christian Napp1, 2 * Birte Hansen2 * Anna Kanwischer1, 2 * Udo Bavendiek2 * Gernot Beutel6 * Martin Hapke7 * Martin G Sauer7 * Carlo Laudanna5 * Nancy Hogg8 * Dietmar Vestweber3, 9 * Kai C Wollert1, 2, 9 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:581–588Year published:(2011)DOI:doi:10.1038/nm.2354Received01 February 2011Accepted14 March 2011Published online24 April 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 Inflammatory cell recruitment after myocardial infarction needs to be tightly controlled to permit infarct healing while avoiding fatal complications such as cardiac rupture. Growth differentiation factor-15 (GDF-15), a transforming growth factor-β (TGF-β)–related cytokine, is induced in the infarcted heart of mice and humans. We show that coronary artery ligation in Gdf15-deficient mice led to enhanced recruitment of polymorphonuclear leukocytes (PMNs) into the infarcted myocardium and an increased incidence of cardiac rupture. Conversely, infusion of recombinant GDF-15 repressed PMN recruitment after myocardial infarction. In vitro, GDF-15 inhibited PMN adhesion, arrest under flow and transendothelial migration. Mechanistically, GDF-15 counteracted chemokine-triggered conformational activation and clustering of β2 integrins on PMNs by activating the small GTPase Cdc42 and inhibiting activation of the small GTPase Rap1. Intravital microscopy in vivo in Gdf15-deficient ! mice showed that Gdf-15 is required to prevent excessive chemokine-activated leukocyte arrest on the endothelium. Genetic ablation of β2 integrins in myeloid cells rescued the mortality of Gdf15-deficient mice after myocardial infarction. To our knowledge, GDF-15 is the first cytokine identified as an inhibitor of PMN recruitment by direct interference with chemokine signaling and integrin activation. Loss of this anti-inflammatory mechanism leads to fatal cardiac rupture after myocardial infarction. View full text Figures at a glance * Figure 1: Gdf15-knockout mice have an increased rate of fatal cardiac rupture after myocardial infarction. () Gdf-15 mRNA expression levels in the infarcted area and noninfarcted area at the indicated time points after myocardial infarction (MI) in WT mice; control mice were analyzed 24 h after a sham operation; n = 4 or 5 mice; *P < 0.05, ***P < 0.001 versus sham-operated controls. () Gdf-15 mRNA expression levels in the infarcted area of WT, heterozygous (+/−) and homozygous Gdf15-knockout (KO) mice 4 d after a sham operation or MI; n = 3–5 mice; **P < 0.01, ***P < 0.001 versus same-genotype sham. Data are presented as means ± s.e.m. () Cumulative mortality during 6 weeks after MI in WT (n = 26), +/− (n = 72) and KO (n = 36) mice. () Cardiac rupture in a KO heart 7 d after MI (H&E staining). () Percentage of mice with cardiac rupture (1 of 26 WT mice, 4%; 8 of 72 +/− mice, 11%; 11 of 36 KO mice; 31%). * Figure 2: Gdf-15 inhibits myeloid cell recruitment into the infarcted myocardium. () Immunohistochemistry tissue sections illustrating greater infiltration of the infarcted area with Ly-6G+ PMNs and CD68+ monocytes and macrophages in Gdf15-knockout (KO) mice compared with WT mice 4 d after MI. (,) Quantification of Ly-6G+ PMN infiltration () and CD68+ monocyte and macrophage infiltration () of the infarcted area at various time points after MI; n = 5–8 mice; *P < 0.05, **P < 0.01, ***P < 0.001 versus same-genotype sham-operated controls; Anti–Ly-6G, antibody to Ly-6G; anti–CD68, antibody to CD68. () Mortality and survival 6 weeks after MI (the number of dead mice per total number of animals is indicated in each bar); WT and Gdf15-KO mice were injected intraperitoneally with 150 μg of a monoclonal antibody to Gr-1 (anti–Gr-1) or a nonspecific IgG1 antibody (control, Con) 2 d before, during and 4 d after MI. (,) Infiltration of the infarcted area with Ly-6G+ PMNs and CD68+ monocytes and macrophages in WT mice treated with a subcutaneous infusion of! vehicle (0.9% wt/vol NaCl) or recombinant GDF-15 (12 μg d−1) for 1 or 3 d; n = 6–8 mice. () Mmp-9–expressing Ly-6G+ PMNs and CD68+ monocytes and macrophages in the infarcted myocardium of WT mice (PMNs, day 2; monocytes and macrophages, day 7). () Left, Mmp-9 activity in left ventricular myocardium 4 d after a sham operation and in the infarcted area 4 d after MI. Right, representative in-gel zymography with three WT and three Gdf-15 KO samples, each taken from an individual mouse 4 d after MI. () Gelatinase activity in the infarcted area. Data in and are from three to five mice; **P < 0.01, ***P < 0.001 versus same-genotype sham-operated controls. Data are presented as means ± s.e.m. * Figure 3: GDF-15 inhibits myeloid cell adhesion and transendothelial migration under static conditions. (–) Cells were prestimulated with GDF-15 for 20 min. n = 3–6; *P < 0.05, **P < 0.01, ***P < 0.001 versus control. () Adhesion of WT mouse PMNs to bEnd.5 endothelial cell monolayers prestimulated for 16 h with TNF-α (5 nM). () Transendothelial migration (TEM) of WT mouse PMNs through a TNF-α-prestimulated bEnd.5 cell monolayer along a Cxcl1 (40 ng ml−1) chemotactic gradient in a Boyden chamber assay. () Adhesion of WT mouse PMNs to IgG1 or Icam-1-coated tissue culture dishes. () Adhesion of human PMNs to HUVEC monolayers that were prestimulated for 16 h with TNF-α (5 nM). () Transendothelial migration of human PMNs through TNF-α-prestimulated HUVEC monolayers along an fMLP (10 nM) chemotactic gradient. () Adhesion of human PMNs to IgG1 or ICAM-1-coated tissue culture dishes; PMNs were prestimulated for 15 min with CXCL8 (1 ng ml−1). () Transendothelial migration of human monocytes through TNF-α–prestimulated HUVEC monolayers along a CCL2 (6 nM) chemotactic grad! ient. () Adhesion of human monocytes to IgG1 or ICAM-1-coated tissue culture dishes; monocytes were prestimulated for 15 min with CCL2 (20 ng ml−1). Data are presented as means ± s.e.m. * Figure 4: GDF-15 inhibits leukocyte arrest under flow conditions and in vivo. () Leukocyte arrest in untreated WT mice (control) and WT mice pretreated for 15 min with 4 μg recombinant GDF-15 in autoperfused flow chambers coated with P-selectin, Icam-1 and, where indicated, Cxcl1; n = 3 mice. ***P < 0.001 versus control before Cxcl1. () Chemokine-induced leukocyte arrest (cells per mm2) in cremaster muscle postcapillary venules assessed by intravital microscopy in WT mice before (for 1 min) and starting 15 s after intraarterial injection of 600 ng Cxcl1 (for 1 min); 15 min before the experiment, mice received an intra-arterial injection of 4 μg GDF-15; n = 4 or 5 mice; **P < 0.01 versus control before Cxcl1. (–) WT and Gdf15 KO mice received an intrascrotal injection of 50 ng Il-1β or PBS (control) 4 h before assessment of leukocyte rolling flux fraction (), leukocyte adhesion (cells per mm2) () and leukocyte extravasation in postcapillary venules (); n = 4 mice; **P < 0.01 versus same-genotype control. Data are presented as means ± s.e.m. * Figure 5: GDF-15 inhibits β2 integrin activation by activating Cdc42 and deactivating Rap1. () β2 integrin activation assessed by mAb24 binding and flow cytometry in human PMNs incubated for 15 min with CXLC8 (30 ng ml−1) and prestimulated for 20 min with GDF-15 (20 ng ml−1). Isotype control, left. Similar results were obtained in two additional experiments. () Redistribution of surface LFA-1 on WT PMNs stimulated for 90 s with Cxcl1 (100 ng ml−1) and pretreated for 20 min with GDF-15 (20 ng ml−1); n = 3; ***P < 0.001 versus control (Con). () Binding of ICAM-1–coated fluorescent microspheres to human PMNs pretreated for 20 min with GDF-15 (20 ng ml−1) and stimulated for 1 min with CXCL8 (100 ng ml−1); n = 4; ***P < 0.001 versus no CXCL8. () [Ca2+]i in WT mouse PMNs before and after addition of Cxcl1 (100 ng ml−1) with or without GDF-15 (20 ng ml−1), which was added 20 min before Cxcl1 stimulation. Data are mean values from three experiments. () Total Rap1 and GTP-bound Rap1 protein in WT mouse PMNs 30 s after stimulation with Cxcl1 (100 ng ml−1! ) and prestimulation for 20 min with GDF-15 (20 ng ml−1). Maximal (GTP-γS) and minimal (GDP) activation levels of Rap1 are shown at the top. Representative blots from three experiments are shown. () Total Cdc42 and GTP-bound active Cdc42 protein in WT mouse PMNs stimulated with either GDF-15 (20 ng ml−1) for 0, 5, 10 and 20 min or Cxcl1 (100 ng ml−1) for 30 s. Top, quantification of densitometric scans of Cdc42 activation of three similar experiments; values are mean ± s.e.m. () Total Rap1 and GTP-bound Rap1 protein in mouse PMNs 30 s after stimulation with Cxcl1 (100 ng ml−1). Where indicated, PMNs were pretreated for 30 min with 1 μM of Tat-Cdc42-WT or Tat-Cdc42-V12(CA). Representative blots from three experiments are shown. () Total Rap1 and GTP-bound Rap1 protein in mouse PMNs 30 s after stimulation with Cxcl1 (100 ng ml−1) and pretreatment for 30 min with 1 μM of Tat-Cdc42-WT or Tat-Cdc42-N17(DN). Cells were prestimulated for 20 min with GDF-15 (20 ng ml�! ��1). Representative blots from three experiments are shown. (! ) Human PMNs were pretreated for 30 min with 1 μM of Tat-Cdc42-WT, Tat-Cdc42-N17(DN) or Tat-Cdc42-V12(CA) with or without GDF-15. Cells were then perfused for 2 min at 5.94 dyn cm−2 through flow chambers coated with P-selectin and CXCL8 together with mAb24 or an isotype control IgG antibody, and the number of adherent cells per one representative field of view was determined; n = 3. () Human whole blood was pretreated for 30 min with 1 μM of Tat-Cdc42-WT or Tat-Cdc42-N17(DN) with or without GDF-15. Cells were then perfused for 2 min at 5.94 dyn cm−2 through flow chambers coated with P-selectin and CXCL8 together with mAb24 or an isotype control IgG antibody, and the number of adherent cells per one representative field of view was determined; n = 3. Data are presented as means ± s.e.m. * Figure 6: Blockade of leukocyte integrins or deficiency of β2 integrins in myeloid cells rescues the mortality of Gdf15-KO mice after myocardial infarction. (,) Mortality and survival 6 weeks after MI (the number of dead mice per total number of mice is indicated in each bar); () WT and Gdf15-KO mice were injected i.p. with antibodies to LFA-1, Mac-1 and VLA-4 (100 μg each) or a nonspecific IgG2 antibody (control), during and 4 and 8 d after MI surgery; () WT and Gdf15-KO mice were lethally irradiated and transplanted with WT or Cd18-deficient bone marrow. Myocardial infarction was induced 7–8 weeks after bone marrow transplantation (BMTx). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Tibor Kempf, * Alexander Zarbock, * Dietmar Vestweber & * Kai C Wollert Affiliations * Hans Borst Center for Heart and Stem Cell Research, Hannover Medical School, Hannover, Germany. * Tibor Kempf, * Christian Widera, * Mortimer Korf-Klingebiel, * L Christian Napp, * Anna Kanwischer & * Kai C Wollert * Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany. * Tibor Kempf, * Christian Widera, * Mortimer Korf-Klingebiel, * L Christian Napp, * Birte Hansen, * Anna Kanwischer, * Udo Bavendiek & * Kai C Wollert * Max Planck Institute of Molecular Biomedicine, University of Münster, Münster, Germany. * Alexander Zarbock, * Stefan Butz, * Anika Stadtmann, * Jan Rossaint & * Dietmar Vestweber * Department of Anesthesiology and Critical Care Medicine, University of Münster, Münster, Germany. * Alexander Zarbock, * Anika Stadtmann & * Jan Rossaint * Department of Pathology, University of Verona, Verona, Italy. * Matteo Bolomini-Vittori & * Carlo Laudanna * Department of Hematology and Oncology, Hannover Medical School, Hannover, Germany. * Gernot Beutel * Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany. * Martin Hapke & * Martin G Sauer * Cancer Research UK, London Research Institute, London, UK. * Nancy Hogg Contributions T.K. and A.Z. designed and carried out experiments, analyzed the data and contributed to the writing of the manuscript. C.W., S.B., A.S., J.R., M.K.-K., B.H., A.K. and M.H. carried out experiments. M.B.-V., L.C.N., U.B., G.B. and M.G.S. provided key reagents and experimental protocols. C.L. and N.H. provided key reagents and gave conceptual advice. D.V. and K.C.W. designed the study, supervised the experiments and wrote the manuscript. Competing financial interests T.K. and K.C.W. have filed an international patent application with the European Patent Office and have a contract with Roche Diagnostics to develop a GDF-15 assay for cardiovascular applications. Corresponding authors Correspondence to: * Kai C Wollert or * Dietmar Vestweber Author Details * Tibor Kempf Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Zarbock Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Widera Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Butz Search for this author in: * NPG journals * PubMed * Google Scholar * Anika Stadtmann Search for this author in: * NPG journals * PubMed * Google Scholar * Jan Rossaint Search for this author in: * NPG journals * PubMed * Google Scholar * Matteo Bolomini-Vittori Search for this author in: * NPG journals * PubMed * Google Scholar * Mortimer Korf-Klingebiel Search for this author in: * NPG journals * PubMed * Google Scholar * L Christian Napp Search for this author in: * NPG journals * PubMed * Google Scholar * Birte Hansen Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Kanwischer Search for this author in: * NPG journals * PubMed * Google Scholar * Udo Bavendiek Search for this author in: * NPG journals * PubMed * Google Scholar * Gernot Beutel Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Hapke Search for this author in: * NPG journals * PubMed * Google Scholar * Martin G Sauer Search for this author in: * NPG journals * PubMed * Google Scholar * Carlo Laudanna Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy Hogg Search for this author in: * NPG journals * PubMed * Google Scholar * Dietmar Vestweber Contact Dietmar Vestweber Search for this author in: * NPG journals * PubMed * Google Scholar * Kai C Wollert Contact Kai C Wollert Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–11, Supplementary Table 1 and Supplementary Methods Additional data - EGFR and EphA2 are host factors for hepatitis C virus entry and possible targets for antiviral therapy
- Nat Med 17(5):589-595 (2011)
Nature Medicine | Article EGFR and EphA2 are host factors for hepatitis C virus entry and possible targets for antiviral therapy * Joachim Lupberger1, 2, 13 * Mirjam B Zeisel1, 2, 13 * Fei Xiao1, 2 * Christine Thumann1, 2 * Isabel Fofana1, 2 * Laetitia Zona1, 2 * Christopher Davis3 * Christopher J Mee3 * Marine Turek1, 2 * Sebastian Gorke4 * Cathy Royer1, 2 * Benoit Fischer5 * Muhammad N Zahid1, 2 * Dimitri Lavillette6 * Judith Fresquet6 * François-Loïc Cosset6 * S Michael Rothenberg7 * Thomas Pietschmann8 * Arvind H Patel9 * Patrick Pessaux10 * Michel Doffoël11 * Wolfgang Raffelsberger12 * Olivier Poch12 * Jane A McKeating3 * Laurent Brino5 * Thomas F Baumert1, 2, 11 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:589–595Year published:(2011)DOI:doi:10.1038/nm.2341Received07 December 2010Accepted03 March 2011Published online24 April 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 Hepatitis C virus (HCV) is a major cause of liver disease, but therapeutic options are limited and there are no prevention strategies. Viral entry is the first step of infection and requires the cooperative interaction of several host cell factors. Using a functional RNAi kinase screen, we identified epidermal growth factor receptor and ephrin receptor A2 as host cofactors for HCV entry. Blocking receptor kinase activity by approved inhibitors broadly impaired infection by all major HCV genotypes and viral escape variants in cell culture and in a human liver chimeric mouse model in vivo. The identified receptor tyrosine kinases (RTKs) mediate HCV entry by regulating CD81–claudin-1 co-receptor associations and viral glycoprotein–dependent membrane fusion. These results identify RTKs as previously unknown HCV entry cofactors and show that tyrosine kinase inhibitors have substantial antiviral activity. Inhibition of RTK function may constitute a new approach for prevention ! and treatment of HCV infection. View full text Figures at a glance * Figure 1: EGFR is a cofactor for HCV entry. (,) EGFR mRNA (quantitative RT-PCR analysis) () and protein expression (western blot) () in Huh7.5.1 cells transfected with EGFR-specific individual siRNAs (si1–4). Silencing of CD81 mRNA expression by CD81-specific siRNA served as control. EGFR mRNA (relative to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA) and protein expression compared to cells transfected with control siRNA (siCtrl) is shown. () HCVcc infection in Huh7.5.1 cells transfected with individual siRNAs shown in and . siCtrl and CD81-specific siRNA served as internal controls. Data are expressed as percentage HCVcc infection relative to siCtrl-transfected cells (means ± s.d. from three independent experiments in triplicate). () Entry of HCVpp containing envelope glycoproteins of various isolates14, 39 in Huh7.5.1 cells transfected with si4. Vesicular stomatitis virus (VSV) and measles virus pseudoparticle (pp) entry or cells transfected with CD81-specific siRNA served as controls. Data are expresse! d as percentage pseudoparticle entry relative to siCtrl-transfected cells (means ± s.d. from three independent experiments in triplicate). () HCVpp entry and EGFR protein expression in Huh7.5.1 cells concurrently transfected with EGFR-specific individual si3 and a cDNA encoding RNAi-resistant EGFR (pEGFR-WT)40. () HCVpp entry and EGFR protein expression in PHHs concurrently transduced with lentiviruses expressing shEGFR and wild-type EGFR cDNA (EGFR-WT)40. Data are expressed as percentage HCVpp entry relative to Ctrl cells or as percentage EGFR expression normalized for β-actin expression (means ± s.d. from four independent experiments in triplicate). ***P < 0.0005. * Figure 2: Inhibition of EGFR activation by kinase inhibitors reduces HCV entry and infection. () Effect of erlotinib on HCV entry and infection in Huh7.5.1 cells. HCVcc (Luc-Jc1; J6-JFH1) infection and HCVpp (J6) entry in Huh7.5.1 cells preincubated with the indicated concentrations of erlotinib are shown. Data are expressed as percentage HCVcc infection or HCVpp entry relative to solvent DMSO-treated control cells (means ± s.e.m. from three independent experiments in triplicate). () Northern blot analysis of HCV RNA and GAPDH mRNA in Huh7.5 cells electroporated with RNA from subgenomic HCV JFH1 replicon and incubated with solvent Ctrl, HCV protease inhibitor BILN-2061 or erlotinib (Erl) is shown. Analysis of HCV RNA in cells transfected with replication incompetent HCV RNA (GND, Δ) served as negative control. () Effect of erlotinib on HCVpp and MLVpp entry in HepG2-CD81 cells. The percentage pseudoparticle entry into nonpolarized and polarized HepG2-CD81 cells (generated as previously described15) preincubated with erlotinib (10 μM) is shown (means ± s.d. from t! en independent experiments). () Effect of erlotinib on HCVpp entry into PHHs. The percentage HCVpp entry into PHHs preincubated with erlotinib is shown relative to entry into solvent-treated control cells. IC50 value is expressed as median ± standard error of the median of three independent experiments performed in triplicate. (,) HCVpp entry into PHHs () and HCVcc infection of Huh7.5.1 cells () preincubated with 1 μM erlotinib, gefitinib (Gef), lapatinib (Lap), blebbistatin (Bleb) or wortmannin (Wort) is shown. Cell viability was assessed by MTT assay. Means ± s.d. from three independent experiments in duplicate () or triplicate () are shown. **P < 0.005; ***P < 0.0005. * Figure 3: Modulation of HCV entry by EGFR ligands and an EGFR-specific antibody. () Modulation of EGFR phosphorylation by EGF, erlotinib and EGFR-specific antibody (Anti-EGFR). Phospho-tyrosine (P-Tyr) and phosphorylation of an unrelated kinase (MERTK) served as internal positive and negative controls. (,) Percentage HCVpp entry (HCV-J) into serum-starved Huh7.5.1 cells, polarized HepG2-CD81 cells and PHHs in the presence of EGF () and TGF-α (). () Percentage HCVpp entry into Huh7.5.1, polarized HepG2-CD81 and PHH incubated with EGF or EGF and erlotinib is shown (means ± s.d. from three independent experiments in triplicate). () Flow cytometric analysis of nonpermeabilized PHH binding EGFR-specific or control monoclonal antibody (mAb). () Percentage HCVpp entry into PHHs preincubated with EGFR-specific or control mAb is shown. Viability of cells was assessed by MTT assay. IC50 value is expressed as median ± standard error of the median of three independent experiments in triplicate. () Percentage HCVpp entry into PHHs preincubated with EGF and EGFR-sp! ecific mAb. (,) Intracellular HCV RNA levels in PHHs infected with HCVcc (means ± s.d. from three independent experiments in duplicate) () or serum-derived HCV (one representative experiment) () as measured by quantitative RT-PCR. **P < 0.005; ***P < 0.0005. Unless otherwise indicated, EGFR-specific and control mAbs: 10 μg ml−1; EGF: 1 μg ml−1; erlotinib: 10 μM. * Figure 4: EGFR mediates HCV entry at postbinding steps by promoting CD81-CLDN1 co-receptor interactions and membrane fusion. () Cell surface expression of entry factors in EGFR- or EphA2-silenced Huh7.5.1 cells, as assessed by flow cytometry. SR-BI silencing served as positive control (means ± s.d. from three independent experiments in duplicate). () Western blot analysis of HCV entry factor expression in PKI- or siRNA-treated Huh7.5.1 cells. () Flow cytometric analysis of HCV glycoprotein sE2 binding to Huh7.5.1 cells incubated with EGFR-specific mAb or transfected with siEGFR. SR-BI–specific antibody (Anti–SR-BI) or siSR-BI served as positive controls (means ± s.d. from three independent experiments in duplicate). EGFR-specific and control mAbs: 100 μg ml−1. (,) Percentage HCVcc infection of Huh7.5.1 cells (means ± s.d. from five independent experiments in triplicate) () and percentage HCVpp entry into PHHs (means ± s.d. from three independent experiments in duplicate) () after inhibition of binding and postbinding steps by the indicated compounds (EGFR-specific mAb: 10 and 50 μg ml�! ��1). (,) Time course of HCVcc infection of Huh7.5.1 cells after incubation with erlotinib or the indicated compounds (means ± s.d. from five independent experiments in triplicate) () or EGF at various timepoints during infection (means ± s.d. from three independent experiments in triplicate) () (Supplementary Methods). () FRET of CD81-CLDN1 co-receptor associations in HepG2-CD81 cells incubated with erlotinib or EGFR-specific siRNA (means ± s.e.m. from ten independent experiments). () Percentage viral glycoprotein-dependent fusion of 293T with Huh7 cells incubated with EGF, erlotinib or EGFR-specific siRNA, assessed as previously described25. Means ± s.d. from three independent experiments in triplicate are shown. *P < 0.05; ***P < 0.0005. Unless otherwise indicated, EGFR-specific and control mAbs: 10 μg ml−1; EGF: 1 μg ml−1; erlotinib: 10 μM. * Figure 5: Functional role of EGFR in viral cell-to-cell transmission and spread. () Experimental setup. HCV producer cells cultured with uninfected target cells26 were incubated with siEGFR or PKIs. Cell-free HCV transmission was blocked by an E2-neutralizing antibody (Anti–HCV E2, 25 μg ml−1)26. HCV-infected target cells were quantified by flow cytometry26. () Immunofluorescence analysis of Pi (HCV RNA–electroporated Huh7.5.1 producer cells), T (GFP-expressing Huh7.5 target cells) and Ti (GFP+HCV NS5A+ HCV-infected target cells) cells stained with an HCV non structural protein 5A (NS5A)-specific antibody (red). () Infectivity of Pi-T cell co-cultivation supernatants (cell-free HCV transmission). (,) Quantification of infected Ti cells during erlotinib (10 μM) treatment in the absence (total transmission) and presence (cell-to-cell transmission) of E2-specific antibody by flow cytometry (means ± s.d. from three independent experiments in duplicate). () Effect of PKIs on viral spread. Long-term HCVcc infection of Huh7.5.1 cells incubated with erl! otinib 48 h after infection at the indicated concentrations. Medium with solvent (Ctrl) or PKI was replenished every second day. Cell viability was assessed by MTT test. Means ± s.d. from three independent experiments in triplicate are shown. RLU, relative light units. () EGFR expression in target cells with silenced EGFR expression. Cell surface EGFR expression was analyzed by flow cytometry and target cells were divided in three groups displaying high, medium and low EGFR expression. () HCV infection in GFP-positive target cells expressing EGFR at high, medium and low levels (see ) assessed as described above (means ± s.d. from three independent experiments in triplicate). () Effect of EGFR silencing on viral spread. Long-term analysis of HCVcc infection in Huh7.5.1 cells transfected with EGFR-specific or control siRNA 24 h after infection. Cell viability was assessed by MTT test. Means ± s.d. from three independent experiments in triplicate are shown. *P < 0.05; **P - The tumor necrosis factor family member LIGHT is a target for asthmatic airway remodeling
- Nat Med 17(5):596-603 (2011)
Nature Medicine | Article The tumor necrosis factor family member LIGHT is a target for asthmatic airway remodeling * Taylor A Doherty1, 2, 5 * Pejman Soroosh1, 5 * Naseem Khorram2 * Satoshi Fukuyama1 * Peter Rosenthal2 * Jae Youn Cho2 * Paula S Norris3 * Heonsik Choi1 * Stefanie Scheu4 * Klaus Pfeffer4 * Bruce L Zuraw2 * Carl F Ware3 * David H Broide2 * Michael Croft1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:596–603Year published:(2011)DOI:doi:10.1038/nm.2356Received23 December 2010Accepted16 March 2011Published online17 April 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 Individuals with chronic asthma show a progressive decline in lung function that is thought to be due to structural remodeling of the airways characterized by subepithelial fibrosis and smooth muscle hyperplasia. Here we show that the tumor necrosis factor (TNF) family member LIGHT is expressed on lung inflammatory cells after allergen exposure. Pharmacological inhibition of LIGHT using a fusion protein between the IgG Fc domain and lymphotoxin β receptor (LTβR) reduces lung fibrosis, smooth muscle hyperplasia and airway hyperresponsiveness in mouse models of chronic asthma, despite having little effect on airway eosinophilia. LIGHT-deficient mice also show a similar impairment in fibrosis and smooth muscle accumulation. Blockade of LIGHT suppresses expression of lung transforming growth factor-β (TGF-β) and interleukin-13 (IL-13), cytokines implicated in remodeling in humans, whereas exogenous administration of LIGHT to the airways induces fibrosis and smooth muscle hyp! erplasia, Thus, LIGHT may be targeted to prevent asthma-related airway remodeling. View full text Figures at a glance * Figure 1: Blockade of LIGHT or LTαβ inhibits airway remodeling and AHR induced by HDM. () Protocol for HDM-induced remodeling. WT mice were given three intranasal (i.n.) challenges with HDM extract, once per week. LTβR-Fc or IgG was given 24 h before each additional intranasal HDM challenge over the next 4 weeks. i.p., intraperitoneal. () Lung sections were stained for Masson's trichrome (top left and middle) and collagen-1 (bottom left and middle) and scored for the extent of fibrosis (top right, n = 54–75 airways per group). Induced total lung collagen was measured (bottom right, pooled from four mice per group, two experiments shown). () Lung sections stained for α-smooth muscle actin (left) and scored for extent of induced peribronchial smooth muscle (right, n = 49–70 airways per group). Induced reflects levels above those detected in mice receiving three intranasal challenges before LTβR-Fc treatment. () Peak airway resistance with increasing doses of methacholine and baseline resistance without methacholine exposure (six or seven mice per group). ! *P < 0.05, **P < 0.005, ***P < 0.001, ****P < 0.0001, means ± s.e.m., Mann-Whitney test. Data are from two or three independent experiments. Scale bars, 100 μm. * Figure 2: LIGHT-deficient mice are resistant to airway remodeling induced by HDM. WT and Tnfsf14−/− mice received HDM intranasally once per week for 3 weeks, then twice per week for 4 weeks. Mice were killed 1 day after the last challenge. () Lung sections stained with Masson's trichrome (top) and collagen-1 (middle) and scoring for fibrosis (bottom left, n = 35–36 airways per group, means ± s.e.m., Mann-Whitney test). Total lung collagen was also measured (bottom right, eight mice per group, means ± s.e.m., Mann-Whitney test). () Peribronchial smooth muscle area (left, n = 34–35 airways per group, means ± s.e.m., Mann-Whitney test) and lung sections stained for α-smooth muscle actin (right). Levels reflect those above lung measurements from naive mice ( and ). () Invasive lung function test and resistance after challenge with 48 mg ml−1 methacholine (means ± s.e.m., Mann-Whitney test from six or seven mice per group). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Scale bars, 100 μm. * Figure 3: LIGHT controls lung TGF-β1 production and accumulation of LAP+ macrophages. Mice were chronically challenged with HDM or OVA. () Free TGF-β1 concentrations assessed in lung homogenates from WT mice treated as in Figure 1 (data from six to eight mice per group; acute signifies levels before immunoglobulin treatment; levels of two naive mice also shown, means ± s.e.m., Mann-Whitney, *P < 0.03); WT and Tnfsf14−/− mice treated as in Figure 2 (data from three or four mice per group, means ± s.e.m., t test, *P < 0.02); WT mice treated as in Supplementary Figure 6 (data from four pooled mice per group run in duplicate, mean ± s.e.m., except single IgG group); WT and Tnfsf14−/− mice treated as in Supplementary Figure 6 (data from four pooled mice per group run in quadruplicate, means ± s.e.m., t test, *P < 0.01). () Lung sections from WT mice in Supplementary Figure 6 stained for LTβR expression. Scale bar, 50 μm. () Lung cells from WT mice in Supplementary Figure 6 analyzed for Mac-3 and CD11c (left), and the gated population evaluated for L! TβR expression (right). Filled histogram indicates isotype staining. () Lung cells from WT and Tnfsf14−/− mice in Supplementary Figure 6 analyzed for Mac-3 and CD11c (top and bottom) and absolute numbers of Mac-3+CD11c+ cells (right, pooled lung cells from four mice per group). () Immunofluorescent staining of lung sections from a representative WT mouse from Figure 1 stained for Mac-3 (red), LAP (green) and DAPI (blue). Scale bar, 50 μm. Image zoom also depicted (bottom). Scale bar, 25 μm. () Gated Mac-3+CD11c+ cells from lungs of mice in Figure 1 and Supplementary Figure 6 analyzed for LAP expression (top), enumeration of total LAP+ macrophages per lung (middle, n = 4 mice per group, means ± s.e.m., two experiments shown for OVA and one for HDM, *P < 0.05, t test) and flow analysis for LAP expression gating on Siglec-F+CD11c+ macrophages (bottom). * Figure 4: LTβR stimulation promotes fibrosis and TGF-β production by lung macrophages. () Lung sections stained with Masson's trichrome (left) and extent of induced peribronchial fibrosis (top right; 44–68 airways per group IgG and anti-LTβR, means ± s.e.m., Mann-Whitney test, *P < 0.05). Scale bar, 100 μm. Mac-3+CD11c+LAP+ cells per lung were enumerated (bottom right, pooled from six mice per group). WT mice were immunized and acutely challenged with OVA over 28 d and then injected with LTβR agonist antibody (anti-LTβR) or rat IgG every 3–4 d for 2 weeks. (,) Analysis of Siglec-F+CD11c+ lung macrophages (, top) from naive mice after stimulation with rat IgG or anti-LTβR and analyzed for surface LAP expression after 2 d (, bottom), TGF-β1 mRNA (, left, ***P < 0.0005) or TGF-β1 protein after HDM was added in the last 8 h of culture (, right, **P < 0.005). Results are triplicates from each group. () Flow cytometry analysis of LAP−Siglec-F+CD11c+Mac-3+ lung macrophages sorted (left) and stimulated with rat IgG or anti-LTβR and analyzed for LAP expr! ession (right). () Flow cytometry analysis of intracellular TGF-β in purified lung macrophages stimulated with recombinant LIGHT, in the presence or absence of ERK inhibitor. Data are representative of at least two experiments. * Figure 5: LIGHT-induced airway remodeling is in part dependent on TGF-β. (,) Lung sections were stained for trichrome (top row, scale bar, 100 μm), collagen-1 (second row, scale bar, 100 μm), α-smooth muscle actin (third row, scale bar, 50 μm) and scored for fibrosis and smooth muscle area (bottom row, 40 airways per group, means ± s.e.m., Mann-Whitney, *P < 0.05, **P < 0.005). WT mice primed with HDM over 3 weeks were treated with intranasal rLIGHT or PBS given eight times over 2 weeks () or pCDNA3 mouse LIGHT plasmid or control plasmid given four times over 2 weeks (). Antibody to TGF-β (anti-TGF) or isotype control IgG was also injected as indicated. Induced reflects levels above those detected in lungs of mice receiving PBS () or control plasmid (). A, airway; BV, blood vessel. * Figure 6: LIGHT promotes IL-13 production by lung eosinophils. () IL-13 content in mice primed and challenged with HDM or OVA. Lung homogenates from mice treated as in Figures 1 and 2 and Supplementary Figure 6 were analyzed (five to seven mice per group from Figure 1, three or four mice per group from Figure 2 and triplicates of pooled samples from four to seven mice per group from Supplementary Figure 6, means ± s.e.m., t test, *P < 0.05, **P < 0.01, ***P < 0.005). () Flow cytometry analysis of sorted granulocytes (>95% eosinophils by cytospin, bottom; scale bar, 50 μm) from WT mice after acute intranasal OVA challenge for LTβR (top middle) and HVEM expression (top right). Isotype control in gray. () Flow cytometry analysis of CD45+CD11c− granulocyte-gated lung eosinophils (top and bottom left) from mice in Supplementary Figure 6 for intracellular IL-13 directly ex vivo (middle right). Siglec‐F+CD11c− eosinophils from mice in Figure 1 (top right) and Figure 2 (bottom right) were stained for IL-13 expression. () Flow cytometry! analysis of BALF (bottom left) and lung cells (top left) and intracellular IL-13 (middle and right) was analyzed in cells gated on forward and side scatter (left, >95% eosinophils). Cells from WT mice immunized and challenged with OVA over 8 days were cultured for 48 h with rLIGHT or medium added during the last 24 h. Data are representative of two independent experiments. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Taylor A Doherty & * Pejman Soroosh Affiliations * Division of Immune Regulation, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. * Taylor A Doherty, * Pejman Soroosh, * Satoshi Fukuyama, * Heonsik Choi & * Michael Croft * Department of Medicine, University of California–San Diego (UCSD), La Jolla, California, USA. * Taylor A Doherty, * Naseem Khorram, * Peter Rosenthal, * Jae Youn Cho, * Bruce L Zuraw & * David H Broide * Institute of Medical Microbiology, Universität Düsseldorf, Düsseldorf, Germany. * Stefanie Scheu & * Klaus Pfeffer * Infectious and Inflammatory Diseases Center, Sanford Burnham Medical Research Institute, La Jolla, California, USA. * Paula S Norris & * Carl F Ware Contributions T.A.D. and P.S. contributed to animal antigen administration, surgery, data collection, analysis and manuscript writing for all studies; S.F. and J.Y.C. contributed to immunostaining and data analysis; N.K. contributed to remodeling and cytokine data collection and analysis; P.R. contributed to airway hyper-responsiveness testing and analysis; P.S.N. produced plasmids, LTβR-F and antibody to LTβR and contributed to experimental design; H.C. contributed to cytokine data collection; S.S. and K.P. developed mutant mice; B.L.Z. contributed to experimental design; C.F.W. contributed to experimental design and reagent production; D.H.B. contributed to experimental design and remodeling data collection; M.C. contributed to experimental design, data analysis and manuscript writing for all studies. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael Croft Author Details * Taylor A Doherty Search for this author in: * NPG journals * PubMed * Google Scholar * Pejman Soroosh Search for this author in: * NPG journals * PubMed * Google Scholar * Naseem Khorram Search for this author in: * NPG journals * PubMed * Google Scholar * Satoshi Fukuyama Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Rosenthal Search for this author in: * NPG journals * PubMed * Google Scholar * Jae Youn Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Paula S Norris Search for this author in: * NPG journals * PubMed * Google Scholar * Heonsik Choi Search for this author in: * NPG journals * PubMed * Google Scholar * Stefanie Scheu Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus Pfeffer Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce L Zuraw Search for this author in: * NPG journals * PubMed * Google Scholar * Carl F Ware Search for this author in: * NPG journals * PubMed * Google Scholar * David H Broide Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Croft Contact Michael Croft Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–10 and Supplementary Methods Additional data - A role for interleukin-2 trans-presentation in dendritic cell–mediated T cell activation in humans, as revealed by daclizumab therapy
- Nat Med 17(5):604-609 (2011)
Nature Medicine | Article A role for interleukin-2 trans-presentation in dendritic cell–mediated T cell activation in humans, as revealed by daclizumab therapy * Simone C Wuest1 * Jehad H Edwan1 * Jayne F Martin1 * Sungpil Han1, 2 * Justin S A Perry1 * Casandra M Cartagena1 * Eiji Matsuura1 * Dragan Maric3 * Thomas A Waldmann4 * Bibiana Bielekova1, 1 * Affiliations * ContributionsJournal name:Nature MedicineVolume: 17,Pages:604–609Year published:(2011)DOI:doi:10.1038/nm.2365Received04 January 2011Accepted30 March 2011Published online01 May 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 Although previous studies have described CD25 expression and production of interleukin-2 (IL-2) by mature dendritic cells (mDCs), it remains unclear how these molecules participate in the activation of T cells. In search of the mechanisms by which daclizumab, a humanized monoclonal antibody against CD25, inhibits brain inflammation in multiple sclerosis, we observed that although the drug has limited effects on polyclonal T cell activation, it potently inhibits activation of antigen-specific T cells by mDCs. We show that mDCs (and antigen-experienced T cells) secrete IL-2 toward the mDC-T cell interface in an antigen-specific manner, and mDCs 'lend' their CD25 to primed T cells in trans to facilitate early high-affinity IL-2 signaling, which is crucial for subsequent T cell expansion and development of antigen-specific effectors. Our data reveal a previously unknown mechanism for the IL-2 receptor system in DC-mediated activation of T cells. View full text Figures at a glance * Figure 1: Antigen-specific T cell proliferation in DC–T cell cocultures is profoundly inhibited by daclizumab. (,) Carboxyfluorescein diacetate succinimidyl diester (CFSE) proliferation assay: mDCs loaded with Flu-HA (0.5 μg ml−1) () or human brain protein (HBP; 10 μg ml−1) () were cocultured with autologous CFSE-stained T cells in the presence or absence of CD25-blocking antibody control MA-251 (10 μg ml−1) or daclizumab (10 μg ml−1). After 7–10 d, T cell proliferation was assessed by CFSE dilution assay after gating on CD4+ (pink) and CD8+ (blue) T cells. Data are representative of five independent experiments. () Box plots represent group data on antigen-specific CD4+ T cell proliferation with marked group medians (black horizontal line) and means (red horizontal line). ***P < 0.001. Mean values are shown ± s.d. Dac, daclizumab. Abs # is number of proliferating CD4+ T cells normalized between control and Dac conditions by fluorescent beads. () CFSE proliferation assay after polyclonal T cell activation with Dynabeads coated with antibodies to CD3 and CD28 (0.3:1 bea! d to T cell ratio) in the presence or absence of daclizumab. Proliferation was measured by CFSE dilution after 5 d using the same gating strategy (CD4+ T cells in pink, CD8+ T cells in blue). * Figure 2: Selective blockade of CD25 on mDCs abrogates T cell proliferation. () T cell proliferation, analyzed after 5, 7, 9 and 14 d of coculture, of CFSE-stained T cells (Tc) and mDCs in the presence of 20 μg ml−1 control antibody MA-251 (first column) or 20 μg ml−1 daclizumab (second column) added at the beginning of the culture period. Alternatively, mDCs (third column) or CFSE+ T cells (fourth column) were pretreated with 20 μg ml−1 daclizumab for 30 min before coculture. () Events of proliferated CD8+ T cells (top) and CD4+ T cells (bottom) were normalized to allophycocyanin-labeled beads. n = 4; **P < 0.01, ***P < 0.001. Mean values are shown ± s.d. * Figure 3: T cells do not need CD25 expression to proliferate if primed by CD25+ mDCs. () Expression of IL-2R chains on polyclonally activated T cells derived from control individual (left) and subject with CD25 deletion (right). Gray histograms represent appropriate isotype controls. Percentages of positive lymphocytes are shown above the histograms. () Proliferation of CD25− CD4+ (pink; left) and CD8+ (blue; right) T cells derived from an individual with a genetic deletion of CD25 after co-incubation with Flu-HA–loaded, human leukocyte antigen–matched CD25+ mDCs, as measured by CFSE dilution after 7 d. Separate graphs depict percentages and number of T cells normalized between conditions by fluorescent beads (abs #) of CD4+ and CD8+ T cells from four replicates; *P < 0.05. Mean values are shown ± s.d. () Cytokine production (IL-2, IFN-γ and IL-17) by proliferating CD25− CD4+ and CD8+ T cells after coculture with CD25+ mDCs (top) or mDCs pre-treated with daclizumab (bottom). * Figure 4: DCs do not express the β-chain of IL-2R and therefore do not signal in response to IL-2. () Flow cytometry analysis of freshly isolated BDCA-1+ iDCs and mDCs (after 48 h of stimulation) stained for maturation markers CD80, CD83 and MHC-II (top, open histograms) and for IL-2R chains CD25, CD122 and CD132 (bottom, open histograms) or appropriate isotype controls (filled gray histograms). Percentages of surface marker expression are depicted above the histograms. () Flow cytometry analysis of in vitro–generated monocyte-derived iDCs and mDCs stained in an analogous manner to the cells in . (,) STAT5 phosphorylation in response to 50 IU ml−1 of IL-2 () and 200 ng ml−1 of GM-CSF () of fresh uncoagulated whole blood (ex vivo, left), monocyte-derived iDCs (middle) and mDCs (right). Dark gray histograms represent appropriate isotype controls. * Figure 5: mDCs use their surface expression of CD25 to trans-present IL-2 to CD25− T cells (a) Phosphorylation of STAT5 in Flu-HA306–318-specific T cells (TCL) selectively pretreated with daclizumab (DacT) or control Ab (T) and co-incubated with autologous, CD25-expressing mDCs pulsed with 1 μM cognate (Flu-mDC) or noncognate (MBP83–99; MBP-mDC) peptide. At indicated conditions, Flu-mDCs were also pretreated with daclizumab (DacFlu-mDC). Results are depicted as percentages of pStat5-expressing CD4+ T cells ± s.d. () The proportional number of expanded T cells after 5 d of coculture in the same cells and identical conditions as in . Mean values are shown ± s.d. One representative experiment is depicted; all replicates are summarized in Supplementary Figure 6. () The frequency of pStat5+ Flu-HA306–318–specific T cells after 2 h culture with Flu-HA306–318–loaded mDCs (left), daclizumab-pretreated, Flu-HA306–318–loaded mDCs (middle) and MBP83–99 peptide–loaded mDCs (right). MFI, mean fluorescence intensity. () pStat5 phosphorylation, as visualized by Amnis ImageStream in Flu-specific T cells cultured for 2 h with Flu-HA306–318–loaded mDCs (top), daclizumab-pretreated Flu-HA306–318-loaded mDCs (middle) or MBP83–99-loaded mDCs (bottom) in the same cells as in . Scale bars, 10 μm. * Figure 6: mDCs and T cells secrete IL-2 after antigen-specific interactions. () Flow cytometric analysis of IL-2 secretion of mDCs loaded with 1 μM MBP83–99 peptide (MBP-mDC), MBP-specific T cell clones (MBP-TCC (Tc)), cocultures of MBP-mDCs with MBP-specific T cells (MBP-mDC + Tc) and cocultures of mDCs loaded with 1 μM Flu-HA306–318 peptide with MBP-specific T cells (Flu-mDC + Tc). IL-2 was detected after 1 h (top) and 2 h (bottom) of coculture and is plotted against CD25 expression of mDCs and T cells. Percentages of IL-2 secretion by mDCs and T cells are shown in gates. For comparison, mDCs and T cells are presented in the same plot, but they were gated separately on CD11c and CD4 expression. () Independent experiment visualizing secreted IL-2 and surface expression of CD25, CD11c and CD4 by Amnis ImageStream after 2-h coculture of MBP-mDCs with MBP-specific T cells. Top, single MBP-specific CD4+ T cells in the bright field of the microscope with simultaneous expression and/or secretion of fluorescently labeled CD25, CD4 and IL-2. Bottom, c! onjugates of MBP-loaded mDCs with MBP-specific T cells (mDCs highlighted by pink arrows, CD4+ T cells by teal arrows). Scale bars, 10 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA. * Simone C Wuest, * Jehad H Edwan, * Jayne F Martin, * Sungpil Han, * Justin S A Perry, * Casandra M Cartagena, * Eiji Matsuura & * Bibiana Bielekova * School of Medicine, Pusan National University, Yangsan, South Korea. * Sungpil Han * Flow Cytometry Core Facility, NINDS, NIH, Bethesda, Maryland, USA. * Dragan Maric * Metabolism Branch, National Cancer Institute, NIH, Bethesda, Maryland, USA. * Thomas A Waldmann Contributions B.B. developed the concept of the study and supervised the project. B.B. and T.A.W. designed the experiments. S.C.W., J.F.M., S.H., J.S.A.P., C.M.C., D.M., J.E., E.M. and B.B. performed the experiments and analyzed the data. B.B., S.C.W., J.F.M., S.H. and C.M.C. wrote the paper. All authors approved the final version of this paper. Competing financial interests B.B. and T.A.W. are co-inventors on US National Institutes of Health patents related to the use of daclizumab in multiple sclerosis and as such have received patent royalty payments. Author Details * Simone C Wuest Search for this author in: * NPG journals * PubMed * Google Scholar * Jehad H Edwan Search for this author in: * NPG journals * PubMed * Google Scholar * Jayne F Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Sungpil Han Search for this author in: * NPG journals * PubMed * Google Scholar * Justin S A Perry Search for this author in: * NPG journals * PubMed * Google Scholar * Casandra M Cartagena Search for this author in: * NPG journals * PubMed * Google Scholar * Eiji Matsuura Search for this author in: * NPG journals * PubMed * Google Scholar * Dragan Maric Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas A Waldmann Search for this author in: * NPG journals * PubMed * Google Scholar * Bibiana Bielekova Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (729K) Supplementary Figures 1–8 Additional data - B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies
- Nat Med 17(5):610-617 (2011)
Nature Medicine | Article B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies * Daniel A Winer1, 2, 7 * Shawn Winer2, 3, 7 * Lei Shen1, 7 * Persis P Wadia4 * Jason Yantha3 * Geoffrey Paltser3 * Hubert Tsui3 * Ping Wu3 * Matthew G Davidson1 * Michael N Alonso1 * Hwei X Leong1 * Alec Glassford5 * Maria Caimol1 * Justin A Kenkel1 * Thomas F Tedder6 * Tracey McLaughlin5 * David B Miklos4 * H-Michael Dosch3 * Edgar G Engleman1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:610–617Year published:(2011)DOI:doi:10.1038/nm.2353Received28 January 2011Accepted04 March 2011Published online17 April 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 Chronic inflammation characterized by T cell and macrophage infiltration of visceral adipose tissue (VAT) is a hallmark of obesity-associated insulin resistance and glucose intolerance. Here we show a fundamental pathogenic role for B cells in the development of these metabolic abnormalities. B cells accumulate in VAT in diet-induced obese (DIO) mice, and DIO mice lacking B cells are protected from disease despite weight gain. B cell effects on glucose metabolism are mechanistically linked to the activation of proinflammatory macrophages and T cells and to the production of pathogenic IgG antibodies. Treatment with a B cell–depleting CD20 antibody attenuates disease, whereas transfer of IgG from DIO mice rapidly induces insulin resistance and glucose intolerance. Moreover, insulin resistance in obese humans is associated with a unique profile of IgG autoantibodies. These results establish the importance of B cells and adaptive immunity in insulin resistance and suggest new! diagnostic and therapeutic modalities for managing the disease. View full text Figures at a glance * Figure 1: B cell and antibody profile in DIO mice. () Left, time course of T cell (T), B cell (B) and macrophage (M) infiltration of VAT after initiation of HFD (two experiments, five mice, *P < 0.05). Middle and right, B cell subsets in VAT in response to 6–12 weeks of HFD in absolute numbers of B cells (*P = 0.005), B1a cells (*P = 0.04), B1b cells, B2 cells (*P = 0.04) and T cells (*P = 0.03) (middle) and in percentages of CD19+ cells (right). Middle and right, three experiments, nine mice. () VAT B cells in absolute numbers (left, *P < 0.05) and as a proportion of CD19+ cells (right, *P < 0.05); three experiments each, nine mice. () Spleen B cell subsets in response to HFD (MZ, marginal zone; FC, follicular cells, *P = 0.01, n = 5). () Spontaneous production of IgM (left, *P = 0.0006) and IgG (right, *P = 0.01) from mouse splenocytes. () Serum antibody concentrations in mice (n = 10): IgA (*P = 0.03) and IgG2c (*P = 0.004). () Antibody subtypes in VAT lysates from mice (*P = 0.0001) (two experiments, five mice). () IgM! (top left) and IgG (bottom left) staining in VAT of DIO mice in regions of few and multiple CLSs (IgM top right; IgG bottom right). Arrows indicate antibody-stained cells. Scale bars, 50 μm (left images) and 25 μm (right images). Error bars in graphs indicate means ± s.e.m. * Figure 2: B cell deficiency modulates glucose metabolism in DIO mice. () Body weights of WT (control) and Bnull mice over time (n = 10 per group). () Relative fat cell diameter of 14- to 18-week-old HFD mice (n = 3). () Ratio of epididymal VAT and SAT pad weights in DIO mice (*P = 0.004, n = 10). (,) Fasting glucose (*P = 0.04, n = 10) () and glucose tolerance test (GTT) () of WT or Bnull mice on NCD or HFD (*P < 0.05, representative GTT from three experiments, n = 10 per group on HFD and two experiments, n = 5 per group on NCD). () Fasting serum insulin concentrations of 16-week-old WT or Bnull mice on NCD or HFD (*P = 0.04, n = 10). () Insulin tolerance test (ITT) in WT or Bnull mice on NCD or HFD (*P < 0.05, n = 5 per group). () Body weight (left), GTT (middle, *P < 0.05, n = 6), and fasting insulin (right, *P = 0.02, n = 6) of DIO Bnull mice 2 weeks after reconstitution with DIO WT B cells (representative of three independent experiments). () Body weight (left), GTT (middle, n = 5), and fasting insulin (right, n = 5) of DIO Bnull mice 2 we! eks after reconstitution with NCD WT B cells (representative of 2 independent experiments). Brackets represent comparison groups for statistics. Error bars on graphs show means ± s.e.m. * Figure 3: B cells influence VAT T cell and macrophage function. () Numbers of cell subsets in VAT of 14- to 18-week-old mice (four experiments, ten mice). () Percentage of VAT macrophages (CD11b+F4/80+Gr-1−) with M1 phenotype (*P = 0.049, three experiments, eight mice). () IFN-γ production from SVC cultures of VAT (three experiments, nine mice, *P = 0.02). () Intracellular IFN-γ staining of CD8+ T cells isolated from VAT (left, four experiments, ten mice, *P = 0.04) and percentage of total VAT CD8+ T cells expressing CD107a (right, *P = 0.02, two experiments, six mice). () TNF-α production from VAT SVC cultures (left, *P = 0.04, two experiments, six mice) and intracellular staining of TNF-α in VAT macrophages (right, two experiments, six mice, *P = 0.02). () CD80 and CD86 expression on VAT macrophages (representative of three experiments, nine mice). () GTT (left), fasting glucose (middle) and fasting insulin (right) of recipient DIO RAG-1null (Rag1−/−) mice 2 weeks after transfer of DIO B cells (n = 10). () CD19+ B cells in VA! T of Bnull mice 2 weeks after reconstitution with DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (three experiments, nine mice). () Weights (left), GTT (middle) and fasting insulin (right) of recipient mice 2 weeks after transfer of DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (*P < 0.05, representative of three experiments, n = 3 per group). () IFN-γ production from VAT SVC cultures (left) and intracellular IFN-γ in VAT CD8+ T cells (middle) and VAT CD4+ T cells (right) isolated from recipient Bnull mice receiving either PBS or DIO WT, DIO MHC-Inull or DIO MHC-IInull B cells (*P < 0.05, two experiments, six mice). WT, control. Brackets represent comparison groups for statistics. Error bars in graphs are means ± s.e.m. * Figure 4: HFD IgG induces abnormal glucose metabolism in recipient Bnull mice. () Serum concentration of IgG in Bnull mice 1 week after i.p. IgG injection (n = 3). () Body weights of HFD Bnull recipient mice after IgG transfer (representative of three experiments, n = 4). () GTT (left, *P < 0.05) and fasting insulin (right, *P < 0.05) 1 week after the transfer of IgG into 16-week-old HFD Bnull mice (representative of three experiments, n = 4). () GTT (left) and fasting insulin (right) 4 weeks after the transfer of IgG (representative of two experiments, n = 4). () GTT (left, *P < 0.05) and fasting insulin (right, *P = 0.048) 1 week after the transfer of late or early IgG (n = 5). () Weights (left), GTT (center) and fasting insulin (right) of 6-week-old NCD Bnull mice 1 week after IgG transfer (representative of two experiments, n = 4). () TNF-α from VAT SVC cultures (left, *P = 0.04, two experiments, six mice) and M1 macrophages in HFD Bnull VAT 1 week after HFD IgG transfer (right, *P = 0.007, two experiments, six mice). () GTT (left, *P < 0.05) and ! fasting insulin (right, *P = 0.04) 1 week after the transfer of HFD IgG or HFD F(ab′)2 (n = 5). () TNF-α from HFD Bnull VAT macrophages stimulated in vitro with HFD IgG (*P = 0.007), or HFD F(ab′)2 (n = 3). () GTT (left) and fasting insulin (middle) of HFD Bnull mice 1 week after receiving HFD Ig (n = 5, *P < 0.05). Serum concentration (right) of IgM in HFD Bnull mice 1 week after IgM injection (n = 3). Brackets represent comparison groups for statistics. Error bars show means ± s.e.m. * Figure 5: A CD20-specific B cell-depleting antibody improves obesity-induced glucose abnormalities. () Percentage of CD19+ cells depleted in VAT and spleen ≥8 d after administration of CD20 mAb. (,) Weights of mice () and percentage depletion of IgG and IgM antibody in serum () 28 d after CD20-specific mAb (CD20 mAb) treatment (representative of two experiments, n = 5). (–) Fasting glucose (*P = 0.06) (), GTT (*P < 0.05) () and fasting insulin (*P = 0.04) () in HFD WT mice 28 d after receiving either CD20 mAb or control (IgG2c or PBS) (representative of two experiments, n = 5). (,) IFN-γ (*P = 0.003) and TNF-α (*P = 0.005) production from SVC cultures of VAT isolated from 17-week-old mice treated with CD20-specific mAb at 13 weeks of age (two experiments, eight mice). () Percentage of VAT macrophages expressing TNF-α 4 weeks after treatment with CD20 mAb (*P = 0.01, two experiments, eight mice). Error bars show means ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniel A Winer, * Shawn Winer & * Lei Shen Affiliations * Department of Pathology, Stanford University, Palo Alto, California, USA. * Daniel A Winer, * Lei Shen, * Matthew G Davidson, * Michael N Alonso, * Hwei X Leong, * Maria Caimol, * Justin A Kenkel & * Edgar G Engleman * Department of Laboratory Medicine and Pathobiology, University Health Network, University of Toronto, Toronto, Ontario, Canada. * Daniel A Winer & * Shawn Winer * Neuroscience & Mental Health Program, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * Shawn Winer, * Jason Yantha, * Geoffrey Paltser, * Hubert Tsui, * Ping Wu & * H-Michael Dosch * Department of Medicine, Stanford University, Palo Alto, California, USA. * Persis P Wadia & * David B Miklos * Division of Endocrinology, Stanford University School of Medicine, Palo Alto, California, USA. * Alec Glassford & * Tracey McLaughlin * Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA. * Thomas F Tedder Contributions D.A.W. and S.W. conceived the study, did experimental work and wrote the manuscript. L.S. was involved in experimental work, project planning and manuscript preparation. P.P.W., A.G., T.M. and D.B.M. contributed the human array data. J.Y., G.P., M.G.D., M.N.A., H.T., P.W., H.X.L., J.A.K. and M.C. did experimental work; T.F.T. contributed the CD20-specific mAb and was involved in manuscript preparation. H.M.D. supervised parts of the project and was involved in manuscript preparation; E.G.E. was involved in project planning, financing, supervision, data analysis and manuscript preparation. E.G.E. and H.M.D. are both senior authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Daniel A Winer or * Edgar G Engleman Author Details * Daniel A Winer Contact Daniel A Winer Search for this author in: * NPG journals * PubMed * Google Scholar * Shawn Winer Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Persis P Wadia Search for this author in: * NPG journals * PubMed * Google Scholar * Jason Yantha Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey Paltser Search for this author in: * NPG journals * PubMed * Google Scholar * Hubert Tsui Search for this author in: * NPG journals * PubMed * Google Scholar * Ping Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew G Davidson Search for this author in: * NPG journals * PubMed * Google Scholar * Michael N Alonso Search for this author in: * NPG journals * PubMed * Google Scholar * Hwei X Leong Search for this author in: * NPG journals * PubMed * Google Scholar * Alec Glassford Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Caimol Search for this author in: * NPG journals * PubMed * Google Scholar * Justin A Kenkel Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas F Tedder Search for this author in: * NPG journals * PubMed * Google Scholar * Tracey McLaughlin Search for this author in: * NPG journals * PubMed * Google Scholar * David B Miklos Search for this author in: * NPG journals * PubMed * Google Scholar * H-Michael Dosch Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar G Engleman Contact Edgar G Engleman Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Methods Additional data - Brain PPAR-γ promotes obesity and is required for the insulin–sensitizing effect of thiazolidinediones
- Nat Med 17(5):618-622 (2011)
Nature Medicine | Letter Brain PPAR-γ promotes obesity and is required for the insulin–sensitizing effect of thiazolidinediones * Min Lu1 * David A Sarruf2 * Saswata Talukdar1 * Shweta Sharma1, 3 * Pingping Li1 * Gautam Bandyopadhyay1 * Sarah Nalbandian1 * WuQiang Fan1 * Jiaur R Gayen1, 3 * Sushil K Mahata1, 3 * Nicholas J Webster1, 3 * Michael W Schwartz2 * Jerrold M Olefsky1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MedicineVolume: 17,Pages:618–622Year published:(2011)DOI:doi:10.1038/nm.2332Received01 December 2010Accepted15 February 2011Published online01 May 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In adipose tissue, muscle, liver and macrophages, signaling by the nuclear receptor peroxisome proliferator–activated receptor-γ (PPAR-γ) is a determinant of insulin sensitivity and this receptor mediates the insulin–sensitizing effects of thiazolidinediones (TZDs)1, 2, 3, 4. As PPAR-γ is also expressed in neurons5, we generated mice with neuron-specific Pparg knockout (Pparg brain knockout (BKO)) to determine whether neuronal PPAR-γ signaling contributes to either weight gain or insulin sensitivity. During high-fat diet (HFD) feeding, food intake was reduced and energy expenditure increased in Pparg-BKO mice compared to Ppargf/f mice, resulting in reduced weight gain. Pparg-BKO mice also responded better to leptin administration than Ppargf/f mice. When treated with the TZD rosiglitazone, Pparg-BKO mice were resistant to rosiglitazone-induced hyperphagia and weight gain and, relative to rosiglitazone-treated Ppargf/f mice, experienced only a marginal improvement in ! glucose metabolism. Hyperinsulinemic euglycemic clamp studies showed that the increase in hepatic insulin sensitivity induced by rosiglitazone treatment during HFD feeding was completely abolished in Pparg-BKO mice, an effect associated with the failure of rosiglitazone to improve liver insulin receptor signal transduction. We conclude that excess weight gain induced by HFD feeding depends in part on the effect of neuronal PPAR-γ signaling to limit thermogenesis and increase food intake. Neuronal PPAR-γ signaling is also required for the hepatic insulin sensitizing effects of TZDs. View full text Figures at a glance * Figure 1: Neuronal deletion of Pparg in brains of mice. () Quantification of wild-type Pparg mRNA in various brain regions of Ppargf/f mice and Pparg-BKO mice (n = 5 per group). Data shown are the fold induction of gene expression normalized with housekeeping gene and expressed as mean ± s.e.m. () RT-PCR showing WT (wild-type) and KO (smaller PCR product with deletion of exons 3 and 4) Pparg mRNA in various tissues in Ppargf/f and Pparg-BKO mice (n = 3 per group). () Quantification of tissue Pparg mRNA expression (n = 7−15 per group). WAT, white adipose tissue; IP Mac, primary intraperitoneal macrophage. Data shown are the fold induction of gene expression normalized with housekeeping gene and expressed as mean ± s.e.m. *P < 0.05 between the indicated conditions. * Figure 2: Energy balance parameters in Pparg-BKO mice. () Body weight of Ppargf/f and Pparg-BKO mice on either standard chow or HFD. †P < 0.01 between genotypes. () Body composition analysis of Ppargf/f (n = 8) and Pparg-BKO (n = 6) mice at week 5 on HFD. () Ambulatory activity of Ppargf/f (n = 7) and Pparg-BKO (n = 6) mice at week 6 on HFD. AU, arbitrary units. () Average 24-h energy expenditure (EE) in Ppargf/f (n = 8) and Pparg-BKO (n = 6) mice after adjustment for body size differences and 24-h average activity. () Weekly caloric intake of Ppargf/f (n = 12) and Pparg-BKO (n = 11) mice at weeks 1 and 12 on HFD. () Serum leptin concentration in Ppargf/f and Pparg-BKO mice fed either standard chow or HFD (n = 5–9 per group). () Western blot showing acute leptin-stimulated phosphorylation of STAT3 (Tyr705) in hypothalamus. Data shown are quantified ratio of phospho-STAT3 (p-STAT3) / total STAT3 normalized to vehicle (10 mM NaHCO3, pH 7.9) group. All data are means ± s.e.m. Statistical significance between control and Pparg-! BKO mice, or between the indicated conditions: *P < 0.05, †P < 0.01, ‡P < 0.001; NS, not significant. * Figure 3: Effect of rosiglitazone on weight gain and food intake in control and Pparg-BKO mice. () Rosiglitazone-induced weight gain in Ppargf/f (n = 14), Syn1-Cre (n = 6) and Pparg Pparg-BKO (n = 11) mice. Age of mice, start time of HFD and start of HFD and rosiglitazone (rosi) are indicated. () Body weight gain of Ppargf/f and Pparg-BKO mice that were fed HFD for 16 weeks followed by HFD with or without rosiglitazone treatment. Data are shown for weeks 28–34 (n = 6–14 per group). () Weekly caloric intake before and after rosiglitazone treatment in HFD-fed mice showing the effect of rosiglitazone on food intake in Ppargf/f (n = 14) and Pparg-BKO (n = 11) mice. () Measurement of Ucp1 mRNA in epididymal white adipose tissue from Ppargf/f and Pparg-BKO mice. () BAT Ucp1 mRNA expression in Ppargf/f and Pparg-BKO mice after rosiglitazone treatment. () Histochemical image of BAT from Ppargf/f mice and Pparg-BKO mice after rosiglitazone treatment stained with H&E. () Muscle Ucp3 mRNA expression in Ppargf/f and Pparg-BKO mice on HFD with or without rosiglitazone treatment! (n = 5–10 per group). () Liver Ucp3 mRNA expression in Ppargf/f and Pparg-BKO mice on HFD or after rosiglitazone treatment (n = 5–10 per group). In –, data are shown as mean ± s.e.m. In –, all qPCR data shown are the fold induction of gene expression normalized with housekeeping genes (encoding cyclophilin A and RNA polymerase II) and expressed as mean ± s.e.m. Statistical significance between Ppargf/f and Pparg-BKO mice, or between the indicated conditions, *P < 0.05, †P < 0.01. * Figure 4: Neuronal PPAR-γ is required for the full insulin-sensitizing effect of TZD treatment. () Intraperitoneal glucose tolerance tests on Ppargf/f and Pparg-BKO mice on HFD with or without rosiglitazone treatment for 7 weeks (n = 6–12 per group). Statistical significance between values from rosiglitazone-treated Ppargf/f and Pparg Pparg-BKO mice: *P < 0.05 and †P < 0.01. (–) Hyperinsulinemic euglycemic clamp study on Ppargf/f and Pparg-BKO mice fed a HFD with or without rosiglitazone treatment for 8 week (n = 7–12 per group). GIR (), IS-GDR (), basal hepatic glucose production rate (basal HGP) (), insulin-stimulated rate of HGP (), and percentage suppression of HGP by insulin () are shown. () Immunoblotting analysis of insulin-stimulated protein phosphorylation in liver extracts from control and Pparg-BKO mice fed a HFD in the presence or absence of rosiglitazone treatment. GSK3, glycogen synthase kinase-3; ERK1/2, extracellular signal–regulated kinases 1 and 2. CREB, cAMP response element–binding protein. In this experiment, GSK3α/β (S21/9) refers to! serine 21 in GSK3α and serine 9 in GSK3β. () Quantification of relative phosphoprotein levels normalized to respective total kinase protein content or β-tubulin. Data are shown as mean ± s.e.m. () Liver Pck1 mRNA expression in Ppargf/f and Pparg-BKO mice fed a HFD with or without rosiglitazone treatment (n = 5–10 per group). () Liver weight of control and Pparg-BKO mice (n = 10–14 per group) on HFD with or without rosiglitazone treatment. All data shown are as mean ± s.e.m. *P < 0.05, †P < 0.01; NS, not significant. Author information * Author information * Supplementary information Affiliations * Department of Medicine, University of California–San Diego (UCSD), San Diego, California, USA. * Min Lu, * Saswata Talukdar, * Shweta Sharma, * Pingping Li, * Gautam Bandyopadhyay, * Sarah Nalbandian, * WuQiang Fan, * Jiaur R Gayen, * Sushil K Mahata, * Nicholas J Webster & * Jerrold M Olefsky * Diabetes and Obesity Center of Excellence, University of Washington, Seattle, Washington, USA. * David A Sarruf & * Michael W Schwartz * Medical Research Service, Veterans Affairs San Diego Healthcare System, San Diego, California, USA. * Shweta Sharma, * Jiaur R Gayen, * Sushil K Mahata & * Nicholas J Webster Contributions J.M.O., M.L. and M.W.S. designed the study and co-wrote the manuscript. M.L. performed most of the experiments. D.A.S. was responsible for body composition, locomotor activity, indirect calorimetry and leptin sensitivity assays. S.T. conducted most of the qPCR and performed acute insulin stimulation in mice. S.S. and N.J.W. were involved in mouse breeding and performed immunohistochemistry. P.L. was involved in hyperinsulinemic euglycemic clamp and western blotting studies. G.B. measured tissue lipid content. S.N. was involved in metabolic studies in mice. W.F. contributed to western blotting. J.R.G. and S.K.M. were responsible for measurement of cardiac function and catecholamine concentrations. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jerrold M Olefsky or * Michael W Schwartz Author Details * Min Lu Search for this author in: * NPG journals * PubMed * Google Scholar * David A Sarruf Search for this author in: * NPG journals * PubMed * Google Scholar * Saswata Talukdar Search for this author in: * NPG journals * PubMed * Google Scholar * Shweta Sharma Search for this author in: * NPG journals * PubMed * Google Scholar * Pingping Li Search for this author in: * NPG journals * PubMed * Google Scholar * Gautam Bandyopadhyay Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Nalbandian Search for this author in: * NPG journals * PubMed * Google Scholar * WuQiang Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Jiaur R Gayen Search for this author in: * NPG journals * PubMed * Google Scholar * Sushil K Mahata Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Webster Search for this author in: * NPG journals * PubMed * Google Scholar * Michael W Schwartz Contact Michael W Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar * Jerrold M Olefsky Contact Jerrold M Olefsky Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (807K) Supplementary Figures 1–6 and Supplementary Tables 1 and 2 Additional data - A role for central nervous system PPAR-γ in the regulation of energy balance
- Nat Med 17(5):623-626 (2011)
Nature Medicine | Letter A role for central nervous system PPAR-γ in the regulation of energy balance * Karen K Ryan1 * Bailing Li1 * Bernadette E Grayson1 * Emily K Matter1 * Stephen C Woods2 * Randy J Seeley1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MedicineVolume: 17,Pages:623–626Year published:(2011)DOI:doi:10.1038/nm.2349Received30 November 2010Accepted08 March 2011Published online01 May 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 peroxisome proliferator–activated receptor-γ (PPAR-γ) is a nuclear receptor that is activated by lipids to induce the expression of genes involved in lipid and glucose metabolism, thereby converting nutritional signals into metabolic consequences1. PPAR-γ is the target of the thiazolidinedione (TZD) class of insulin-sensitizing drugs, which have been widely prescribed to treat type 2 diabetes mellitus. A common side effect of treatment with TZDs is weight gain2. Here we report a previously unknown role for central nervous system (CNS) PPAR-γ in the regulation of energy balance. We found that both acute and chronic activation of CNS PPAR-γ, by either TZDs or hypothalamic overexpression of a fusion protein consisting of PPAR-γ and the viral transcriptional activator VP16 (VP16–PPAR-γ), led to positive energy balance in rats. Blocking the endogenous activation of CNS PPAR-γ with pharmacological antagonists or reducing its expression with shRNA led to negative en! ergy balance, restored leptin sensitivity in high-fat-diet (HFD)-fed rats and blocked the hyperphagic response to oral TZD treatment. These findings have implications for the widespread clinical use of TZD drugs and for understanding the etiology of diet-induced obesity. View full text Figures at a glance * Figure 1: Activation of hypothalamic PPAR-γ leads to positive energy balance. ,) Twenty-four-hour caloric intake () and weight change () after i3vt rosiglitazone (Rsg) or vehicle (Kruskal-Wallis, Dunn's post hoc). (,) Cumulative food intake () and body fat gain () after bolus infusion of rosiglitazone or vehicle on day 0 (repeated-measures analysis of variance (ANOVA) with Tukey's post hoc). () Representative sections (top, vehicle; bottom, rosiglitazone; left, 10×; right, 20×) showing c-Fos immunoreactivity in the paraventricular nucleus of the hypothalamus at 1 h following i3vt rosiglitazone or vehicle. Scale bars, 100 μm. () Quantification of c-Fos response to 1 μg rosiglitazone or its vehicle i3vt (Mann-Whitney test). (–) Caloric intake (), body weight change () and body fat gain () 4 weeks after overexpression of a constitutively active form of PPAR-γ (VP16–PPAR-γ) or an empty vector control in the medial hypothalamus (repeated-measures ANOVA with Tukey's post hoc, t tests). *P < 0.05, **P < 0.01, ***P < 0.001. Data are represented as m! eans ± s.e.m., n = 3–6 rats per group for the c-Fos experiment; for all other experiments n = 7–12 rats per group. * Figure 2: Activation of CNS PPAR-γ is required for the hyperphagic effect of oral rosiglitazone. (,) Caloric intake () and body weight change () 24 h after 30 mg per kg body weight rosiglitazone or its vehicle by oral gavage (repeated-measures ANOVA with Tukey's post hoc for , t test for ). (,) Body weight gain () and hematocrits () 24 h after 10 mg per kg body weight rosiglitazone or its vehicle by oral gavage (t tests). () Caloric intake 24 h after 10 mg per kg body weight rosiglitazone or its vehicle by oral gavage and 1 μg GW9662 or its vehicle i3vt (ANOVA with Tukey's post hoc). () Caloric intake 24 h after 10 mg per kg body weight rosiglitazone or its vehicle by oral gavage in rats previously infected with a lentivirus expressing an shRNA targeted against PPAR-γ in the medial hypothalamus (repeated-measures ANOVA with Tukey's post hoc). *P < 0.05. Data are represented as means ± s.e.m., n = 5–12 rats per group. * Figure 3: Blocking the activation of CNS PPAR-γ with GW9662 leads to negative energy balance. (,) Twenty-four-hour chow intake after 1 μg GW9662 or vehicle i3vt to ad libitum–fed () or 24-h fasted () rats (t tests). () Caloric intake 24 h after 1 μg GW9662 or vehicle i3vt, to ad libitum–fed rats maintained on either standard chow or HFD (ANOVA with Tukey's post hoc). () Weight change 24 h after 1 μg GW9662 or vehicle i3vt, to ad libitum–fed rats maintained on either standard chow or HFD (t tests). *P < 0.05, **P < 0.01. Data are represented as means ± s.e.m., n = 9–12 rats per group. * Figure 4: Blocking the activation of CNS PPAR-γ with GW9662 leads to improved leptin sensitivity. (,) Body weights (ANOVA) () and () hypothalamic expression of lipoprotein lipase (Lpl) relative to the housekeeping gene L32 (t test), among ad libitum HFD-fed rats receiving a chronic subthreshold i.c.v. dose of GW9662 (3 μg per day) or its vehicle and an acute i.p. dose of leptin (1 mg per kg body weight) or its vehicle. (,) Twenty-four-hour food intake () and body weight change () among rats in and (ANOVA with Tukey's post hoc). *P < 0.05, ***P < 0.001. Data are represented as means ± s.e.m., n = 4–7 rats per group. Author information * Author information * Supplementary information Affiliations * Department of Internal Medicine, Division of Endocrinology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. * Karen K Ryan, * Bailing Li, * Bernadette E Grayson, * Emily K Matter & * Randy J Seeley * Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. * Stephen C Woods Contributions K.K.R. conceptualized, designed, performed and analyzed the experiments and wrote the manuscript. B.E.G. designed and performed the immunohistochemistry experiments. B.L. and E.K.M. cloned the VP16–PPAR-γ into the lentiviral vector and designed, and performed and analyzed the in vitro and gene expression experiments. S.C.W. and R.J.S. conceptualized, designed and analyzed the experiments and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Randy J Seeley Author Details * Karen K Ryan Search for this author in: * NPG journals * PubMed * Google Scholar * Bailing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Bernadette E Grayson Search for this author in: * NPG journals * PubMed * Google Scholar * Emily K Matter Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen C Woods Search for this author in: * NPG journals * PubMed * Google Scholar * Randy J Seeley Contact Randy J Seeley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (287K) Supplementary Figures 1–4 and Supplementary Methods Additional data - Molecular modeling, organ culture and reverse genetics for a newly identified human rhinovirus C
- Nat Med 17(5):627-632 (2011)
Nature Medicine | Technical Report Molecular modeling, organ culture and reverse genetics for a newly identified human rhinovirus C * Yury A Bochkov1 * Ann C Palmenberg2 * Wai-Ming Lee1 * Jennifer A Rathe3 * Svetlana P Amineva1 * Xin Sun4 * Thomas R Pasic5 * Nizar N Jarjour6 * Stephen B Liggett3 * James E Gern1, 6 * Affiliations * Contributions * Corresponding authorJournal name: Nature MedicineVolume: 17,Pages:627–632Year published:(2011)DOI:doi:10.1038/nm.2358Received06 July 2010Accepted01 December 2010Published online10 April 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 recently recognized human rhinovirus species C (HRV-C) is associated with up to half of HRV infections in young children. Here we propagated two HRV-C isolates ex vivo in organ culture of nasal epithelial cells, sequenced a new C15 isolate and developed the first, to our knowledge, reverse genetics system for HRV-C. Using contact points for the known HRV receptors, intercellular adhesion molecule-1 (ICAM-1) and low-density lipoprotein receptor (LDLR), inter- and intraspecies footprint analyses predicted a unique cell attachment site for HRV-Cs. Antibodies directed to binding sites for HRV-A and -B failed to inhibit HRV-C attachment, consistent with the alternative receptor footprint. HRV-A and HRV-B infected HeLa and WisL cells but HRV-C did not. However, HRV-C RNA synthesized in vitro and transfected into both cell types resulted in cytopathic effect and recovery of functional virus, indicating that the viral attachment mechanism is a primary distinguishing feature of HRV! -C. View full text Figures at a glance * Figure 1: Propagation of HRV in mucosal organ cultures. () Viral replication in single sinus organ culture infected with either high or low doses of HRV-A16. () Growth curve of HRV-A1 (triangles) and HRV-A16 (squares) strains in organ cultures after inoculation (1 × 109 vRNA copies per ml) of sinus (solid lines) versus adenoidal (dashed lines) tissue (means ± s.d.). () Serial propagation (72 h) of a clinical isolate (HRV-A78) in three successive sinus organ cultures. () Propagation of HRV-C15 in sinus mucosal organ cultures. Cultured sinus tissue (passage 1) was inoculated with NLF containing HRV-C15, and serially passaged (passages 2–7), resulting in either high (≥2 × 108 vRNA copies per ml) or low viral yields. () Growth curve of HRV-C15 (means, ± s.d.) in sinus organ cultures revealing distinct replication kinetics and viral yields. Organ cultures are designated according to tissue donors (dn). * Figure 2: HRV-C15 localization in sinus mucosa. () Sinus cultures were inoculated with medium alone (left) or HRV-C15 (center and right), and whole mounts of the tissue were analyzed for HRV-C15 RNA by in situ hybridization (purple stain). Scale bars, 1 mm. () Higher magnification view of the areas boxed in panel , showing uninfected cells (left) or cells containing viral RNA (center and right). Scale bars, 0.15 mm. () Sections of mock- (left) or HRV-C–infected (center and right) sinus tissue. Right image is counterstained with eosin (pink). Scale bars, 15 μm. * Figure 3: Neighbor-joining phylogenetic tree based on full-length nucleotide sequences of HRV-A, HRV-B and HRV-C. Complete 5′ and 3′ UTR sequences and the first and second codon positions of the open reading frames were considered (MEGA 4.1 software35). All major nodes are labeled with bootstrap values (% of 1,000 replicates). HRV-A and HRV-B reference strain accession numbers correspond to those published previously1. The HRV-C15 (W10) genome sequenced in this study is shown in bold type. Branch lengths are proportional to nucleotide similarity (p distance). Human enteroviruses (HEV) are included as an outgroup. HRV-C types (followed by strain designations and accession numbers) correspond to the recent classification proposal31. * Figure 4: HRV-C composition at known receptor footprint sites, binding characteristics and drug sensitivity. () WebLogo36 depiction shows the dominant amino-acid compositions at alignment positions with ICAM-1 (HRV-B14 or HRV-A16) or LDLR (HRV-A2) footprint contact residues. Human coxsackievirus A21-only locations are not shown. Positions are labeled either by alignment rank (for example, 205, 206 and so on), or by the structural name of the virus protein residue contributing to the footprint (for example, 16-G-1-148). Compositions were tabulated separately for minor-group (14 strains) or major-group (119 strains) HRV-A and HRA-B and HRV-C (11 strains). Alignment positions identified as compositional matches (circled) or mismatches (all others) to the HRV-C by Pearson or Spearman statistical analyses for each receptor footprint cohort are shown. (–) Inhibition of virus attachment in HeLa cells (), PBE cells () or sinus mucosa () using receptor-blocking antibodies. Viral RNA was quantified in cell lysates and normalized to β-actin (ACTB) expression (means ± s.d., n ≥ 3). *P < ! 0.05 versus medium control. () Inhibition of virus growth in sinus mucosa by WIN56291 (means ± s.d.). * Figure 5: RNA transcripts derived from pC15 clone are infectious. () Cytopathic effects observed 24 h after transfection of WisL and HeLa cells with full-length HRV-A16 or HRV-C15 RNA. Scale bars, 100 μm. () Growth curve analysis of HRV-C15 progeny virus recovered after transfection of WisL cells. () Electron microscopy of concentrated cell lysates obtained 24 h after transfection of WisL cells with HRV-C15 RNA. Scale bar, 25 nm. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. * Yury A Bochkov, * Wai-Ming Lee, * Svetlana P Amineva & * James E Gern * Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Ann C Palmenberg * Cardiopulmonary Genomics Program, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Jennifer A Rathe & * Stephen B Liggett * Laboratory of Genetics, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Xin Sun * Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. * Thomas R Pasic * Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. * Nizar N Jarjour & * James E Gern Contributions Y.A.B. designed and performed experiments, analyzed data and was the principal author of the paper; A.C.P. performed sequence alignments, statistical receptor footprint analysis and contributed to writing; W.-M.L. performed partial sequencing of clinical isolates, constructed pW10-2R, designed antiviral compound experiments and provided purified HRVs; J.A.R. determined the complete genome sequence of HRV-C15; S.P.A. assisted with virus inhibition experiments; X.S. designed and assisted with in situ hybridization experiments; T.R.P. assisted with establishment of the sinus organ culture; N.N.J. and S.B.L. analyzed data and contributed to writing; J.E.G. designed the project, analyzed data and contributed to writing. Competing financial interests J.E.G. has stock options in EraGen BioSciences (Respiratory Multicode PLx Assay). Corresponding author Correspondence to: * Yury A Bochkov Author Details * Yury A Bochkov Contact Yury A Bochkov Search for this author in: * NPG journals * PubMed * Google Scholar * Ann C Palmenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Wai-Ming Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer A Rathe Search for this author in: * NPG journals * PubMed * Google Scholar * Svetlana P Amineva Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas R Pasic Search for this author in: * NPG journals * PubMed * Google Scholar * Nizar N Jarjour Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen B Liggett Search for this author in: * NPG journals * PubMed * Google Scholar * James E Gern Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–3, Supplementary Tables 1–3 and Supplementary Methods Additional data
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