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• The sequence is dead: long live the genome
  - UNKNOWN 29(6):463 (2011)
  Nature Biotechnology | Editorial The sequence is dead: long live the genome Journal name:Nature BiotechnologyVolume: 29,Page:463Year published:(2011)DOI:doi:10.1038/nbt.1901Published online07 June 2011 To what extent do the effort and resources employed in generating a de novo whole genome sequence merit a high-profile publication? View full text Additional data
• First cystic fibrosis drug advances towards approval
  - UNKNOWN 29(6):465-466 (2011)
  Article preview View full access options Nature Biotechnology | News First cystic fibrosis drug advances towards approval * Cormac Sheridan1Journal name:Nature BiotechnologyVolume: 29,Pages:465–466Year published:(2011)DOI:doi:10.1038/nbt0611-465Published online07 June 2011 AP Photo/The Wichita Eagle, Fernando Salazar A two-year-old child is treated for cystic fibrosis at Via Christi Regional Medical Center-St. Francis Campus, in Wichita, Kansas. Improved management has steadily increased life expectancy for people with cystic fibrosis, but median survival is still 37 years. The first disease-modifying therapy for cystic fibrosis could reach the market next year, following the publication of highly promising data from two phase 3 trials in recent months. Although the drug in question, ivacaftor (VX-770), will address just a small percentage of the cystic fibrosis population initially, it marks the beginning of a new era of small-molecule drugs that could profoundly change the course of therapy for the vast majority of individuals with the disease (Table 1). "Addressing the disease upstream has the potential to impact things that we know about and perhaps even things that we do not know about," says Preston Campbell, executive vice president for medical affairs of the Cystic Fibrosis Foundation (CFF). The majority of individuals with cystic fibrosis will have to wait several more years before disease-modifying drugs become available, however, as efforts to address the most common mutation in cystic fibrosis remain at an early stage. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Dublin * Cormac Sheridan Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar
• Pfizer's JAK inhibitor sails through phase 3 in rheumatoid arthritis
  - UNKNOWN 29(6):467-468 (2011)
  Article preview View full access options Nature Biotechnology | News Pfizer's JAK inhibitor sails through phase 3 in rheumatoid arthritis * Ken Garber1Journal name:Nature BiotechnologyVolume: 29,Pages:467–468Year published:(2011)DOI:doi:10.1038/nbt0611-467Published online07 June 2011 T cells rely on JAK kinases for cytokine signaling through type I and type II cytokine receptors, leading to JAK/STAT phosphorylation and nuclear translocation and gene transcription. Companies are targeting JAKs to treat rheumatoid arthritis and other autoimmune diseases. Incomplete, sporadic Jak inhibition may create a therapeutic window and avoid immunosuppression. Late-stage trial results for Pfizer's first-in-class small-molecule Janus kinase (JAK) inhibitor tofacitinib in rheumatoid arthritis have raised expectations for regulatory approval as early as next year. All five pivotal phase 3 trials met their primary endpoints, the New York–based pharma announced at the end of April. If approved, the oral JAK inhibitor is set to rival biologicals such as Abbott's Humira (adalimumab) that dominate the roughly $13 billion rheumatoid arthritis global market for biologicals. Pfizer's tofacitinib "could be a multibillion [dollar] product for Pfizer," says John Boris, a pharmaceutical analyst for Citigroup Global Markets in New York. Its ultimate fate in the marketplace depends on long-term safety data, although as newer and more specific JAK inhibitors move towards market, Pfizer's current pole position may soon be challenged. JAK inhibition is a novel approach for treating a variety of autoimmune and inflammatory diseases. JAK inhibitors interrupt signaling downstream of a multiplicity of cytokines, rather than blocking one cytokine at a time, as is the case for other biological rheumatoid arthritis treatments such as tumor necrosis factor (TNF) and intlerleukin-6 (IL-6) blockers. "Inhibiting many cytokines—a little bit—in a disease like rheumatoid arthritis, in which many cytokines are found in the synovial tissue and many are implicated in the disease process, might be a useful approach," says David Fox, a rheumatologist at the University of Michigan in Ann Arbor, who was not involved in tofacitinib trials. "You don't throw all your eggs in one basket as you do with a biologic against one particular cytokine." The Pfizer molecule is indeed effective in rheumatoid arthritis. In one phase 3 trial, reported at the 2010 annual meeting of the American College of Rheumatology (ACR), tofacitinib monotherapy achieved a 20% or greater reduction in the number of tender and swollen joints and other parameters (ACR20 criteria) in 66% of participating individuals, including ACR50 and ACR70 responses in 37% and 20% of individuals, respectively. Although trial designs and populations were different, rendering comparisons problematic, these numbers are an improvement over those obtained for Remicade (infliximab), from Centocor, a division of Johnson & Johnson, in its 1999 pivotal rheumatoid arthritis trial. Remicade earned $5.8 billion for Centocor in 2009. Tofacitinib, like other disease-modifying rheumatoid arthritis drugs, has also demonstrated the ability to slow structural joint damage. Pfizer expects to file for tofacitinib registration in the US and Europe by the end of 2011. In the US, a standard 10-month Food and Drug Administration review is likely, said Boris. The product could thus launch by the end of 2012, and Citigroup forecasts $800 million in worldwide sales in 2015. In general, "we don't expect patients who are currently being treated [with biologics] to be taken off therapy and transferred to the oral," Boris said. Fox agrees that adoption will be gradual. "A lot of rheumatologists will probably be cautious and still use what they have experience with, which is TNF inhibitors and some of the newer biologics," he says, "and then reserve the kinase inhibitors for patients who have some sort of difficulty or inadequate response to the biologic." Acceptance would grow over time if tofacitinib proves to be "robust and not too dangerous in clinical practice," he added. Long-term safety is a major issue, because JAK signaling is fundamental to biology, and JAK inhibitors like tofacitinib tread a fine line between therapeutic down-modulation of autoimmunity and outright immunosuppression. (Tofacitinib was first conceived as an immunosuppressant for use in organ transplantation.) The four members of the JAK family—JAKs 1, 2 and 3, and TYK2—play a crucial role in immunity by enabling cytokines, secreted proteins that help orchestrate the immune response, to signal through their receptors. Roughly 60 cytokines signal through type I and type II cytokine receptors, which have no catalytic kinase activity of their own. These receptors instead rely on JAK kinases for receptor phosphorylation, which creates a docking site for STAT transcription factors. Knocking out Jak1 and Jak2 in mice results in perinatal and embryonic lethality, respectively, and Jak3 knockouts have severe combined immunodeficiency. At therapeutic doses, tofacitinib avoids immunosuppression probably because it only partially and transiently blocks its targets. "At the doses being used you inhibit a lot of cytokines, but you don't inhibit them completely," says John O'Shea, an immunologist at the National Institutes of Health in Bethesda, Maryland. Side effects seen so far in the tofacitinib trials include infections (mostly mild to moderate) and minor decreases in neutrophils. Hemoglobin goes down, presumably because JAK2 is important for red blood cell formation, and low-density lipoprotein and high-density lipoprotein both go up. Overall, "I don't think [tofacitinib] looks any more toxic than what you get with injectable therapy," says Boris. A particular concern for JAK inhibitors is cardiac events because there is evidence that JAKs play a protective role in cardiomyocytes (heart muscle cells). In April, an abstract published in advance of the European League Against Rheumatism meeting reported four deaths in one of the studies, but Pfizer said that only one death, from respiratory failure, was reported by the investigator as drug related. Not all phase 3 tofacitinib data had been released as of early May, but the drug doesn't appear to cause heart attacks and strokes. "I don't think there's anything significant... that would lead us to believe that there is any impact on cardiac muscle or myocytes," says Boris. "But again we still have to see the full data set come out." Of other JAK inhibitors in development, the furthest along is INCB28050 from Incyte in Wilmington, Delaware, having completed phase 2a in rheumatoid arthritis (Table 1). The drug has been licensed to Eli Lilly of Indianapolis for inflammatory and autoimmune indications, and so far its efficacy closely mirrors that of tofacitinib. The main difference is that whereas tofacitinib hits JAKs 1, 2 and 3, INCB28050 is specific for JAKs 1 and 2 while sparing 3. "JAK3 inhibition is extra baggage," says Incyte president and CEO Paul Friedman, explaining that as JAK3 is not involved in IL-6 signaling, which is deemed critical, it made little sense to block it. "The reason Pfizer's [compound] does well clinically is certainly due to JAK1 and JAK2 effects." Table 1: Drugs targeting JAK kinases in rheumatoid arthritis Full table Vertex Pharmaceuticals in Cambridge, Massachusetts, and Galapagos, in Mechelen, Belgium, chose even tighter specificity for their JAK inhibitors. Vertex's molecule VX-509, currently in phase 1, is specific for JAK3, largely because JAK3 expression is mainly limited to lymphoid tissues. Galapagos, in contrast, chose to inhibit only JAK1, thus retaining the ability to block signaling downstream of IL-6 while avoiding the anemia that's caused by JAK2 inhibition and any immunosuppressive effects that may come with blocking JAK3. That could create a wider therapeutic window. JAK1 specificity "allows us to move up in dosing to a range where we hope we can extend efficacy without hitting JAK2-related side effects," says Gerben van't Klooster, development project leader for Galapagos's compound, GLPG0634. The compound so far has been tested only in healthy volunteers, and will enter phase 2 in rheumatoid arthritis in the second quarter of 2011. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Ann Arbor, Michigan * Ken Garber Author Details * Ken Garber Search for this author in: * NPG journals * PubMed * Google Scholar
• Stem cell funding resumes
  - UNKNOWN 29(6):468 (2011)
  Article preview View full access options Nature Biotechnology | News Stem cell funding resumes * Laura DeFrancescoJournal name:Nature BiotechnologyVolume: 29,Page:468Year published:(2011)DOI:doi:10.1038/nbt0611-468Published online07 June 2011 Milwaukee Journal-Sentinel/Rapport Syndication Neurons (red) and glial cells (green) derived from human ESCs. US states with bans in place (gold) or pending (red) on research using embryoys or cell products derived from embryos. Source: http://www.hinxtongroup.org/usa_map.html Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Laura DeFrancesco Search for this author in: * NPG journals * PubMed * Google Scholar * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * Research Scientist in Mineral Analysis * Nestle Research Center * Lausanne, Switzerland * Research Scientist, Cellular Immunology * Vertex * Abingdon, Oxfordshire, UK * Early Stage Researcher (12 to 24 Months) Nanophysiology EduGlia ITN * University of Ljubljana, Medical Faculty * Ljubljana, Slovenia, Europe- Slovenia & Germany * Post a free job * More science jobs Open innovation challenges * Chordoma Cancer Cell Lines Needed to Save Lives! Deadline:Jun 13 2011Reward:$10,000 USD The Chordoma Foundation requests cell lines or animal models that can be used for research into ch… * Compounds to Combat Citrus Greening Disease Deadline:Sep 01 2011Reward:$100,000 USD The Seeker, the non-profit Citrus Research and Development Foundation, desires proposals for compo… * Powered by: * More challenges Top content Emailed * Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells Nature Biotechnology 13 Apr 2011 * Selective chemical labeling reveals the genome-wide distribution of 5-hydroxymethylcytosine Nature Biotechnology 12 Dec 2010 * Specification of transplantable astroglial subtypes from human pluripotent stem cells Nature Biotechnology 22 May 2011 * Transgenic Fish: Safe to Eat? Nature Biotechnology 01 Mar 1994 * What to expect at your biotech job interview Nature Biotechnology 01 Dec 2004 View all Downloaded * Monoclonal antibody therapy of cancer Nature Biotechnology 07 Sep 2005 * Full-length transcriptome assembly from RNA-Seq data without a reference genome Nature Biotechnology 15 May 2011 * Continuous release of endostatin from microencapsulated engineered cells for tumor therapy Nature Biotechnology 01 Jan 2001 * Autoantigen discovery with a synthetic human peptidome Nature Biotechnology 22 May 2011 * Specification of transplantable astroglial subtypes from human pluripotent stem cells Nature Biotechnology 22 May 2011 View all Blogged * Next-generation synthetic gene networks Nature Biotechnology 09 Dec 2009 * Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes Nature Biotechnology 23 Jan 2011 * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Fibroblast growth factor 9 delivery during angiogenesis produces durable, vasoresponsive microvessels wrapped by smooth muscle cells Nature Biotechnology 17 Apr 2011 View all * Nature Biotechnology * ISSN: 1087-0156 * EISSN: 1546-1696 * About NPG * Contact NPG * RSS web feeds * Help * Privacy policy * Legal notice * Accessibility statement * Terms * Nature News * Naturejobs * Nature Asia * Nature EducationSearch:Go © 2011 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.partner of AGORA, HINARI, OARE, INASP, CrossRef and COUNTER
• Debate re-ignites on contribution of public research to drug development
  - UNKNOWN 29(6):469-470 (2011)
  Article preview View full access options Nature Biotechnology | News Debate re-ignites on contribution of public research to drug development * Charles Schmidt1Journal name:Nature BiotechnologyVolume: 29,Pages:469–470Year published:(2011)DOI:doi:10.1038/nbt0611-469Published online07 June 2011 c99/c99/ZUMA Press/Newscom Remicade (infliximab), a Johnson & Johnson antibody used to treat rheumatoid arthritis, Crohn's disease and psoriasis was developed by Junming Le and Jan Vilceck at the New York University School of Medicine. Nearly 10% of the 1,541 drugs approved by the US Food and Drug Administration (FDA) since 1990 have their roots in public sector research according to a paper published earlier this year in the New England Journal of Medicine (, 535–541, 2011). The study injects new data into a perennial question: just how much does publicly funded research contribute to drug development? Polarized views have in the past veered from virtually nothing to most of the important discoveries that the pharmaceutical industry relies on for products and profits. As the US National Institutes of Health (NIH) in Bethesda, Maryland, plans the launch a new translational medicine center, the study has re-ignited the debate concerning the role of public institutions in the drug discovery and development process. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Portland, Maine * Charles Schmidt Author Details * Charles Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar
• Supreme setback for pharma
  - UNKNOWN 29(6):470 (2011)
  Article preview View full access options Nature Biotechnology | News Supreme setback for pharma Journal name:Nature BiotechnologyVolume: 29,Page:470Year published:(2011)DOI:doi:10.1038/nbt0611-470aPublished online07 June 2011 In a fraud case closely watched by biotech and pharma companies, the US Supreme Court sided with investors suing a drug maker for not disclosing adverse events to them. In Matrixx Initiatives, Inc. et al. v. James Siracusano et al. investors claimed that Matrixx's failure to disclose adverse events (anosmia, or loss of smell) concerning its blockbuster cold remedy nasal spray Zicam led to investment losses. On March 22, a unanimous Supreme Court declined to adopt a bright-line rule that would protect Matrixx from liability. The company argued it had no duty to disclose because such events were not statistically significant (Nat. Biotechnol., 1142, 2010). However the Court's opinion, written by Justice Sonia Sotomayor, said the absence of statistical data "does not mean that medical experts have no reliable basis for inferring a causal link between a drug and adverse events." She continued, "This is not a case about a handful of anecdotal reports, as Matrixx suggests. M! atrixx received information that plausibly indicated a reliable causal link between Zicam and anosmia. This included information about more than ten patients who had lost their sense of smell after using Zicam. Sotomayor added that the court's ruling did not mean that drug makers must disclose all reports: "[S]omething more is needed, but that something more is not limited to statistical significance and can come from the source, content, and context of the reports." Michael Francisco Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Blue skies ready for investors
  - UNKNOWN 29(6):470 (2011)
  Article preview View full access options Nature Biotechnology | News Blue skies ready for investors Journal name:Nature BiotechnologyVolume: 29,Page:470Year published:(2011)DOI:doi:10.1038/nbt0611-470bPublished online07 June 2011 Scientists can now apply to a 10 ($14.3) million fund aimed at helping academic researchers package their 'blue sky research' into ideas that appeal to investors. The European Research Council (ERC)—the first pan-European science funding agency—is offering proof of concept (POC) grants of up to 150,000 ($215,200) to allow existing grant holders to demonstrate the commercial potential of their work. The aim, according to the ERC, is to speed the outcomes of research into the marketplace. Investigators awarded POC grants will have 12 months to package their research to make it attractive to venture capitalists or companies looking to in-license technologies. The money can be spent on setting up a company, clarifying intellectual property rights, carrying out market research or validating a technology. However, POC grants are for preparatory work only—not to commercialize an idea or develop a novel technology—leaving it up to grant holders to decide if they want to be i! nvolved in the commercialization of their research. ERC President Helga Nowotny points out that they are "looking at ways to make the ERC more attractive to industry." Nowotny envisages that as the scientific and technological outcomes of ERC research projects, including those supported by POC funding, gain visibility "startup companies will take up results produced by ERC grantees and develop them further towards innovation." The deadline for POC applications is June 15. Nuala Moran Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Chinese vaccine developers gain WHO imprimatur
  - UNKNOWN 29(6):471-472 (2011)
  Article preview View full access options Nature Biotechnology | News Chinese vaccine developers gain WHO imprimatur * Hepeng Jia1 * Karen Carey2Journal name:Nature BiotechnologyVolume: 29,Pages:471–472Year published:(2011)DOI:doi:10.1038/nbt0611-471Published online07 June 2011 James Cavallini/Custom Medical Stock Photo/Newscom China's first export is likely to be a vaccine against Japanese encephalitis, a mosquito-borne disease caused by a flavivirus (pictured). It is the leading cause of viral encephalitis in Asia. China has passed the World Health Organization (WHO)'s vaccine regulatory assessment, an approval that gives local manufacturers a green light to enter the global vaccine market. On March 1, WHO stated that China's State Food and Drug Administration (SFDA) complies with international standards for vaccine regulation. As a result, vaccines coming from China now have the imprimatur of international recognition, both for exports and domestic sales, says Peicheng Liu, a spokesperson of Beijing Sinovac. But keeping up with international standards may erode China's price competitiveness. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Beijing * Hepeng Jia * York, Pennsylvania * Karen Carey Author Details * Hepeng Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Carey Search for this author in: * NPG journals * PubMed * Google Scholar
• GM bananas
  - UNKNOWN 29(6):472 (2011)
  Article preview View full access options Nature Biotechnology | News GM bananas Journal name:Nature BiotechnologyVolume: 29,Page:472Year published:(2011)DOI:doi:10.1038/nbt0611-472Published online07 June 2011 Uganda has launched field trials of its own genetically modified (GM) bananas in an effort to counter a disease that is devastating plantations in the Great Lakes region of Africa. The GM bananas are genetically engineered to resist the Xanthomonas musacearum or BXW, a wilt-causing bacterium that destroys the entire plant. Scientists at the National Banana Research Program in Kampala, led by Wilberforce Tushemereirwe, obtained three banana varieties resistant to BXW by transferring two different sweet pepper (Capsicum annuum) genes into bananas—one encoding the hypersensitivity response–assisting protein and another the plant ferredoxin like protein. Results from the field tests, carried out at the National Agricultural Research Laboratories Institute in Kawanda, are expected by the end of 2011. "The next step is a multilocation field trial that will take a further two years," says Leena Tripathi, a biotechnologist from the International Institute of Tropical Agricul! ture in Nairobi, Kenya, also involved in the project. Support comes from the Gatsby Charitable Foundation, African Agricultural Technology Foundation and USAID. The transgene patent holder, Taiwan's Academia Sinica based in Taipei, issued a royalty-free license for commercial production in sub-Saharan Africa. "Crop scientists in the country are making significant progress for both GM banana and drought-tolerant maize. Parliament should now pass the biosafety law needed to permit an eventual release of these improved varieties to farmers," says Robert Paarlberg, a policy analyst at Wellesley College, Massachusetts. Anna Meldolesi Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Spat over IMI funding and intellectual property
  - UNKNOWN 29(6):473 (2011)
  Article preview View full access options Nature Biotechnology | News Spat over IMI funding and intellectual property * Gunjan SinhaJournal name:Nature BiotechnologyVolume: 29,Page:473Year published:(2011)DOI:doi:10.1038/nbt0611-473aPublished online07 June 2011 Thierry Goorden, EFPIAMAURICIO LIMA/AFP/Getty Images/Newscom Michel Goldman, IMI Executive Director. In March, the Innovative Medicines Initiative (IMI) launched its second wave of eight newly funded research projects with a 172 ($242.7) million budget. The IMI's 2 ($2.8) billion investment is Europe's largest public-private enterprise collaboration aimed at strengthening the continent's "competitiveness and innovativeness". Although purporting to "put small companies first," IMI's agenda has to a large extent been set by large pharmaceutical companies, with the European Commission (EC) providing funding for affiliated academic research (Nat. Biotechnol26, 717–718, 2008). But for many of the academic research institutions participating in the project, complaints are intensifying that IMI skews benefits too much in favor of large industry. Last September, for example, the League of European Research Universities (LERU) published a letter online (http://www.leru.org/index.php/public/news/imi/) complaining that academic institutions participating in IMI projects lose! money and are increasingly reluctant to take part. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Gunjan Sinha Search for this author in: * NPG journals * PubMed * Google Scholar
• Monsanto dips into algae
  - UNKNOWN 29(6):473 (2011)
  Article preview View full access options Nature Biotechnology | News Monsanto dips into algae Journal name:Nature BiotechnologyVolume: 29,Page:473Year published:(2011)DOI:doi:10.1038/nbt0611-473bPublished online07 June 2011 Monsanto acquired a stake in Sapphire Energy, a San Diego–based algae fuel company known for its prominent backers, including Bill Gates's firm Cascade Investment, in Kirkland, Washington, and the Wellcome Trust, in London. Through the deal (figures were not disclosed), the St. Louis agriculture giant gains access to Sapphire's expertise and technology for isolating algal traits that could be applied to agricultural genetic research. Algae share photosynthetic pathways with agricultural plants but their shorter life cycles speed up testing. That should allow the partnership to complete analyses of genetic traits in less than five days, according to Monsanto spokesperson Kelli Powers. "We're interested in gene leads that could ultimately help accelerate our yield-and-stress platform," Powers adds. Because green algae are single-celled, says plant biologist Wim Vermaas of Arizona State University in Tempe, they will be useful for examining traits that shape plant-wide fa! ctors such as photosynthesis or heat resistance—something harder to do in yeast or fungi model systems. Algae are less useful for studying signals that determine the height of a corn stalk or the way its roots develop, as they lack the necessary intracellular machinery. Algae could also be used in livestock feed, Vermaas says. Sapphire, like other algae biofuels firms, is also on the lookout for alternative revenue streams, its CEO Jason Pyle told Biofuels Digest, and Monsanto may offer those. Lucas Laursen Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Bayer's GM rice defeat
  - UNKNOWN 29(6):473 (2011)
  Article preview View full access options Nature Biotechnology | News Bayer's GM rice defeat Journal name:Nature BiotechnologyVolume: 29,Page:473Year published:(2011)DOI:doi:10.1038/nbt0611-473cPublished online07 June 2011 In a lawsuit over genetically modified (GM) modified rice, jury members in an Arkansas circuit court ruled in March in favor of Riceland Foods of Stuttgart, Arkansas, a rice milling and exporting company, and against Bayer CropScience of Research Triangle Park, North Carolina, and Monheim, Germany. The jury recommended that Bayer pay Riceland $136.8 million—$125 million in punitive damages and $11.8 million in compensatory damages—calling Bayer negligent for allowing traces of its genetically engineered Liberty Link, herbicide-tolerant experimental rice to mix with commercial lots of long grain rice in 2006. Back then, Mike Johans, then secretary of the US Department of Agriculture, said: "There are no human health, food safety or environmental concerns associated with this [GM Liberty Link] rice." Nonetheless, Riceland brought suit, claiming "loss of the European Union market," which cost it $380 million in potential sales. Bayer counters that rice then destined! for Europe "accounted for less than 5% of US-grown rice," and "quickly was diverted and sold in other markets." The company, which also points out that the jury-recommended award "exceeds what is permitted by Arkansas law and will therefore be limited to the statutory cap of $1 million," says it will consider whether to appeal after the court issues its final rulings. Meanwhile, Liberty Link rice, which was not commercialized, is no longer being developed. Jeffrey L Fox Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Raising hairs
  - UNKNOWN 29(6):474-476 (2011)
  Nature Biotechnology | News | News Feature Raising hairs * Jill U. Adams1Journal name:Nature BiotechnologyVolume: 29,Pages:474–476Year published:(2011)DOI:doi:10.1038/nbt.1887Published online07 June 2011 Companies are trying to translate the burgeoning science of hair into commercially viable treatments. Jill U. Adams reports. 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 * Albany, New York * Jill U. Adams Competing financial interests The author declares no competing financial interests. Author Details * Jill U. Adams Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• What mergers can do for you
  - UNKNOWN 29(6):477-479 (2011)
  
• Intranasal delivery to the brain
  - UNKNOWN 29(6):480 (2011)
  Article preview View full access options Nature Biotechnology | Opinion and Comment | Correspondence Intranasal delivery to the brain * Robert I Henkin1Journal name:Nature BiotechnologyVolume: 29,Page:480Year published:(2011)DOI:doi:10.1038/nbt.1866Published online07 June 2011 To the Editor: In his News Feature published in the February issue, Michael Eisenstein outlines several alternative methods of drug delivery1 and discusses how several targets in the central nervous system "remain difficult to reach, and the brain presents a particular challenge." Although I agree with Eisenstein that successfully crossing the blood-brain barrier (BBB) after infusion is a substantial hurdle for many biologic therapeutics, particularly as "the mechanism underlying this BBB penetration is poorly understood," I would like to alert readers to a simple and direct approach for delivering drugs into the brain that was not mentioned in the article: intranasal drug delivery2, 3. The use of intranasal delivery to mediate the local, intranasal effects of adrenocorticosteroids and antihistamines has been well documented. But this approach can also exert systemic effects. The nasal mucosal surface has been considered a 'gateway for vaccines'4 and an efficient method for inducing systemic immune responses5. Intranasal drug administration has also been used to deliver peptide hormones to regulate enuresis6 and renal colic7. Several factors are thought to influence drug uptake into the brain by intranasal delivery. First, because the nasal cavity contains a rich vascular bed, intranasal drugs can be readily absorbed by these vessels and enter the systemic circulation. Second, drugs that have entered the circulation can cross the BBB but, as Eisenstein suggests, BBB drug penetration is still poorly understood. And third, it has also been suggested that intranasal delivery can facilitate direct entry into the brain without BBB penetration8—a concept that has been effective in the use of dihydroergotamine treatment of migraine9, theophylline treatment of smell and taste loss10, and insulin treatment of Alzheimer's disease11. To date, the majority of studies investigating this delivery mechanism have looked only at animal models11, but both short- and long-term effects in humans have also been shown. For example, in the case of melanocortin, systemic effects have been observed within minutes of nasal administration. This peptide hormone also reaches the cerebrospinal fluid within minutes and induces long-lasting mediation of fear and anxiety11. In the case of the larger biologic insulin, intranasal administration has not been shown to alter blood insulin or glucose levels but it has been reported to improve attention, memory and cognitive function in patients with Alzheimer's disease11. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Center for Molecular Nutrition and Sensory Disorders, Washington, DC, USA. * Robert I Henkin Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Robert I Henkin Author Details * Robert I Henkin Contact Robert I Henkin Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Quantitative analysis demonstrates most transcription factors require only simple models of specificity
  - UNKNOWN 29(6):480-483 (2011)
  Nature Biotechnology | Opinion and Comment | Correspondence Quantitative analysis demonstrates most transcription factors require only simple models of specificity * Yue Zhao1 * Gary D Stormo1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:480–483Year published:(2011)DOI:doi:10.1038/nbt.1893Published online07 June 2011 To the Editor: Determining the specificity of transcription factors is an important step in understanding regulatory networks and the effects of genetic variations on those networks. To date, attempts to use position weight matrices (PWMs) to assess the DNA-binding specificity of transcription factors from protein binding microarray (PBM) data have suggested that the energetics of transcription factor–DNA recognition fail to follow simple rules. Here we describe a new method for deriving PWMs from PBMs, BEEML-PBM (Binding Energy Estimation by Maximum Likelihood for PBMs). Using this method, we demonstrate that simple PWMs generally do give good approximations of transcription factor specificity, which are reproducible in PBM experiments. In recent years, several high-throughput approaches have been developed to rapidly and efficiently determine the specificity of transcription factors1. One important issue that arises in the analysis of binding data is the complexity of the specificity model needed. It has important implications for both the characterization of specificity and for the prediction of the consequences of mutations. If the recognition mechanism is simple, then the specificity of a transcription factor can be modeled by a small number of parameters and the effects of mutations are easily predictable. If recognition is complex, then models of transcription factor specificity will require a large number of parameters and the effects of mutations will be difficult to predict. In the worst case, recognition is so complex that no patterns exist and predictions cannot be made. Structurally, transcription factor–DNA interactions are complex with a wide variety of interactions between the protein and DNA making a simple recognition code impossible2. But energetically the situation appears much simpler, with individual base pairs often contributing approximately independently to the total binding energy. Although deviations from strict independence are common, the nonindependent contributions tend to be of smaller magnitude compared with the independent contributions. This allows simple models of interactions, such as PWMs3, to be good approximations to the true binding energies. The physical intuition is that transcription factor–DNA recognition is primarily based on complementarity between the sequence-dependent positioning of hydrogen bond donors and acceptors in the grooves of the double helix and those of the amino acids on the surface of the transcription factor. Because most mutations change the shape of this network of hydrogen bond donor! s and acceptors locally, their effects are also mostly local. PBM is a technique that measures the binding of transcription factors to double-stranded DNA arrays that currently contain all possible 10-nucleotide (nt)-long binding sites and so provides a great deal of information about the specificity of the transcription factor4, 5. In a recent PBM study of mouse transcription factors, Badis et al.6 observed that the energetics of transcription factor–DNA recognition appears to be highly complex: 41 out of the 104 transcription factors studied had clear secondary binding preferences not captured by the primary PWM and 89 out of 104 transcription factors were better represented by a linear combination of multiple PWMs than a single PWM. However, Badis et al.6 used three different methods to obtain PWMs and showed that each method was superior to the others on some data sets, indicating that none of the methods can be optimal at determining the PWM parameters. As noted by Badis et al.6, it is possible that the insufficiency of their PW! Ms is not due to the complexity of transcription factor–DNA recognition, but rather to the algorithms used for parameter estimation. Before abandoning the idea that specificity can be largely explained with simple models, it is critical to assess the fitness of optimal PWMs. In a typical PBM experiment, a purified, epitope-tagged transcription factor is applied to a double-stranded DNA microarray. The degree of binding to each probe on the microarray is quantified by the application of a labeled antibody specific to the epitope tag. In theory, signal intensity of a probe should be directly proportional to the probability of the transcription factor binding to the sequence of that probe. In practice, however, the relationship is not so straightforward owing to a number of factors such as background signal, position effect and influence of flanking sequences. We have found that these factors significantly confound current analysis methods, such as 8-mer enrichment analysis5 used by Badis et al.6 (Supplementary Results, figure S2 and associated text for details). We have taken a different approach. We estimate the position and background effects from the data first, then perform weighted regression to assign parameters to a model of binding energy, explicitly taking these biases into account (Supplementary Methods). This offers several benefits. First, using a model drastically reduces the number of parameters required—a 10-nt-long PWM only requires 30 parameters. This represents a 1,000-fold reduction over 8-mer analysis6, which attempts to estimate transcription factor affinity for all 8-nt-long sequences. Second, having a model of specificity allows us to test hypotheses about the binding mechanism. For example, if the performance of the palindromic model, where the parameters of the half-sites are constrained to equal each other, is comparable to the full model where all parameters are allowed to vary, then it is likely that the transcription factor binds DNA as a homodimer with no interactions between half-sites. An example of! this analysis for yeast transcription factor Pho4 is shown in Supplementary Results, figure S3. Third, all of the data are used to estimate each parameter, improving accuracy. Finally, by using a model to calculate transcription factor binding probability for the entire probe, the influence of flanking sequence that confound the current analysis is explicitly included. Our algorithm, BEEML-PBM extends the existing algorithm BEEML7 to estimate models of transcription factor specificity by weighted regression on PBM data. PBM signal intensity is modeled as a convolution of background effect, position effect and equilibrium binding probability to the probe sequence. Using BEEML-PBM, we find that the simple PWM model of specificity performs very well for most transcription factors. This simplicity has important implications for our understanding of the molecular basis of transcription factor specificity and demonstrates the importance of the analysis method in the interpretation of high-throughput data. Although only PWMs are fitted here, higher order interactions can be easily incorporated into the energy model and their significance can be assessed by standard statistical methods8, 9. We evaluate PWM performance by its ability to predict transcription factor binding preferences on a different PBM design. PBM experiments are performed using two arrays with different probe sequences, but both contain all possible 10-nt-long binding sites. We use the PWM trained on array 1 to predict array 2 probe intensities, and vice versa (Supplementary Methods). Although this gives us confidence that the performance achieved by BEEML-PBM PWMs is not due to overfitting to the training data, the fact that the arrays do not have the same probe sequences means we do not have a direct measure of the reproducibility of variations in probe intensities. For this reason, we conduct our analysis at the level of 8-mer median intensities (the median intensity of all probes containing each 8-nt-long sequence). 8-mer median intensities can be calculated for measured probe intensities of both array designs as well as PWM predicted probe intensities, which allows us to not only compare ! PWM predictions with experimental measurements, but also determine what fraction of reproducible variance of transcription factor binding can be explained by the PWM model. Although 8-mer median intensities are problematic as measures of binding affinity, they serve as a useful measure of how much of the observed sequence-dependent binding variation is experimentally reproducible. In Supplementary Results, we provide several examples of the PWMs obtained by BEEML-PBM and their assessment by various criteria. Here we focus on the finding that a single BEEML-PBM PWM is usually sufficient to provide excellent quantitative descriptions of PBM data. An example of this is shown in Figure 1 for mouse factor Plagl1 (pleomorphic adenoma gene-like 1), where the PWM estimated from replicate 1 performs very well on replicate 2 data (Fig. 1a). In contrast, the primary PWM found by Badis et al.6 does not capture Plagl1 binding specificity (Fig. 1b), leading the authors to the conclusion that multiple PWMs are required. The BEEML-PBM PWM is qualitatively different from the primary PWM identified by Badis et al.6 (Fig. 1c); given the high level of performance achieved by a single BEEML-PBM PWM, it is likely that the need for multiple PWMs identified by Badis et al.6 is due to suboptimal parameterization rather than the complexity of Plagl1 DNA recognition. back to article Figure 1: Plag1 can be modeled well by a single PWM. () BEEML-PBM PWM trained on Plagl1 replicate 1 predicts replicate 2 8-mer median intensities well with R2 = 0.91. () Performance of Plagl1 primary PWM from UniPROBE database10 has only R2 = 0.47. () Comparison of Plagl1 BEEML-PBM PWM with primary PWM from UniPROBE database10. View full text Figures at a glance * Figure 1: Plag1 can be modeled well by a single PWM. () BEEML-PBM PWM trained on Plagl1 replicate 1 predicts replicate 2 8-mer median intensities well with R2 = 0.91. () Performance of Plagl1 primary PWM from UniPROBE database10 has only R2 = 0.47. () Comparison of Plagl1 BEEML-PBM PWM with primary PWM from UniPROBE database10. * Figure 2: A single BEEML-PBM PWM explains the 'secondary motif' phenomenon. () In all but seven cases, BEEML-PBM PWM captured >90% of experimentally reproducible variability. Dashed line marks 90% variability. () A single BEEML-PBM PWM usually outperforms a combination of primary and secondary PWMs from Badis et al.6. () BEEML-PBM PWMs outperforms primary PWMs from UniPROBE database10 for all transcription factors studied by Badis et al.6. The BEEML-PBM PWM from the replicate that gives the best fit is used. Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Washington University Medical School, St. Louis, Missouri, USA. * Yue Zhao & * Gary D Stormo Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gary D Stormo Author Details * Yue Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Gary D Stormo Contact Gary D Stormo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Zip files * Supplementary Note (960K) PDF files * Supplementary Text and Figures (1.9M) Supplementary Methods and Supplementary Results (includes figures 1–6, which are not cited individually) Additional data * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * Group Leader / Senior Research Scientist * Foundation for Liver Research * London, United Kingdom * Research Scientist in Mineral Analysis * Nestle Research Center * Lausanne, Switzerland * Early Stage Researcher (12 to 24 Months) Nanophysiology EduGlia ITN * University of Ljubljana, Medical Faculty * Ljubljana, Slovenia, Europe- Slovenia & Germany * Post a free job * More science jobs Open innovation challenges * Chordoma Cancer Cell Lines Needed to Save Lives! 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• Jury remains out on simple models of transcription factor specificity
  - UNKNOWN 29(6):483-484 (2011)
  Article preview View full access options Nature Biotechnology | Opinion and Comment | Correspondence Jury remains out on simple models of transcription factor specificity * Quaid Morris1, 2, 3 * Martha L Bulyk4, 5, 6, 7 * Timothy R Hughes1, 2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:483–484Year published:(2011)DOI:doi:10.1038/nbt.1892Published online07 June 2011 To the Editor: Zhao and Stormo1 introduce a new method for deriving position weight matrices (PWMs) from protein binding microarrays (BEEML-PBM). Using this method, they challenge a central claim of our 2009 paper2 and conclude "that the widespread phenomenon of secondary binding preference identified by Badis et al. is not supported by our data" and that the PWMs were suboptimally estimated. BEEML-PBM is simple, elegant and corrects for a pronounced positional effect of transcription factor (TF) binding in the PBM assay; however, we do not agree with their overall conclusion and believe that it is based on incomplete and biased analysis of our data. The conclusions of Zhao and Stormo1 are based on comparing the performance of BEEML-PBM PWMs and our methods on held-out data. However, they overestimate the performance of their PWMs and underestimate the performance of our methods. First, their claims of suboptimality of our PWMs are based on results from only one of the three motif finders that we employed, Seed-and-Wobble (SnW). SnW was not developed to predict probe intensities and does not attempt to produce a summary PWM that optimizes performance over all probes in predicting probe intensities. Instead, it was developed for the purpose of summarizing the 8-mer data, seeding with the highest scoring 8-mer, in a compact way for use in visual depiction as sequence logos. In contrast, another of the methods we employed, RankMotif++, is designed to produce summary PWMs and we have previously reported3 that it, like BEEML-PBM, better predicts probe intensities than SnW. So we suspect its performance would be much more competitive. In fact, RankMotif++ is very similar to the BEEML base method4; it fits a PWM model using a regression-like procedure to optimally predict PBM intensity data. RankMotif++ differs from BEEML primarily in that it regresses on a! partial preference ordering of probes inferred from their PBM intensities rather than on their actual intensities themselves. We acknowledge that comparisons with RankMotif++ PWMs would have been difficult because, although the source code for RankMotif++ has been available for 3 years, the PWMs we learned for Badis et al.2 were until recently only available as sequence logos. However, we made the motifs available to Zhao and Stormo1 when we were notified of this oversight and before the final submission of their paper. The motifs are available here: http://the_brain.bwh.harvard.edu/suppl105/. Second, we note that Zhao and Stormo1 use a positional effect model when training their PWMs but do not allow the methods that they are comparing against the same opportunity to correct this bias during training. We propose that this correction is a major cause of BEEML-PBM's success and that both the multiple PWM methods and the 8-mer affinity estimates we employed would greatly benefit from a similar correction, thus restoring our reported gain in performance. For example, the 8-mer median intensities used in their Figure 2a are not corrected for positional biases and this leads to the counterintuitive claim that for the 15–20 (of 41) data points that lie above the diagonal, BEEML-PBM PWMs capture >100% of the replicate reproducibility. A more appropriate comparison would either employ PWMs uncorrected for positional bias (as we did in our original paper) or to compare against similarly corrected 8-mer median intensities. Zhao and Stormo1 do neither and, as such, we beli! eve that their prediction accuracy estimates are inflated. Finally, we note that explaining 90% of the reproducible binding signal is not the same as explaining 100%, and proteins that we and others have confirmed have multiple binding modes do not satisfy Zhao and Stormo's 90% cut-off. For example, we reported that Jundm2 (Jdp2) binds two half-sites with variable spacing between them; this is clearly observed in the top-scoring 8-mers2. This mode of binding is common among other bZIP proteins. Furthermore, Zhao and Stormo1 do not consider the PBM data for Bcl6b, a C2H2 zinc finger for which we obtained two very different PWMs; these are also clearly observed in the top-scoring 8-mers, and, moreover, enrichment for motif matches can be observed in associated ChIP-chip data2. In general, variable spacing in long C2H2 zinc-finger array seems to be common; for example, ChIP-seq for RE1-silencing transcription factor also supports use of partial versus full sites and different spacings5. Single, summary PWMs cannot capture these binding! modes, and it is important to do so, as C2H2 zinc fingers are the most common domain in metazoa, and long arrays of these domains are common in human and mouse genomes. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. * Quaid Morris & * Timothy R Hughes * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Quaid Morris & * Timothy R Hughes * Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. * Quaid Morris * Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Martha L Bulyk * Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Martha L Bulyk * Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School, Boston, Massachusetts, USA. * Martha L Bulyk * Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts, USA. * Martha L Bulyk Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Quaid Morris Author Details * Quaid Morris Contact Quaid Morris Search for this author in: * NPG journals * PubMed * Google Scholar * Martha L Bulyk Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy R Hughes Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• US attitudes toward human embryonic stem cell research
  - UNKNOWN 29(6):484-488 (2011)
  Nature Biotechnology | Opinion and Comment | Correspondence US attitudes toward human embryonic stem cell research * M D R Evans1 * Jonathan Kelley2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:484–488Year published:(2011)DOI:doi:10.1038/nbt.1891Published online07 June 2011 To the Editor: Although scientifically promising, research using human embryonic stem cells (hESCs) has roused political controversy for nearly two decades, with sharp differences between policies in different nations and, in the United States, repeated changes in policy—the latest of which is the lifting of the ban on US federal funding of hESC work1. US federal funding for research on stem cells derived from nuclear transfer of a patient's own genes, a promising approach sometimes called therapeutic cloning, remains banned2. Arguments for bans on stem cell funding largely turn on general religious and ethical principles, together with unsubstantiated claims about public opinion. The government invited interested parties to comment, but that is not a reliable way to assess public opinion3. Instead, systematic survey research is needed. Here we provide rigorous estimates of the US public's views based on a large, representative national sample survey conducted in 2009 with 2,295 cases (S! upplementary Methods). Ordinary US citizens feel a personal responsibility to make moral judgments on these complex matters, rather than deferring to authorities or institutions. The vast majority say "in deciding whether it is right to allow these treatments," they would follow their own judgment (Supplementary Methods, tables S1 and S2). Only 4% give greater moral weight to the Catholic Church than to themselves. Even among committed churchgoing Catholics, only 21% defer to the church. Only 10% would defer to a government ethics committee. Just a little more persuasive are medical doctors (15%) and the US National Institutes of Health (13%). Previous work suggests that attitudes toward stem cells vary on half a dozen separate dimensions4, 5, 6. We focus on the two most important: the goals envisioned and the source of the cells, using examples that are important in current research. We defer for future analysis the chances of achieving a cure, the number of embryos destroyed, distinctions between treatment and research, and differences between general policy and personal involvement. We first asked about reproductive cloning. The questions asked, the answer options offered and the number of points we assigned to each answer option are given in Box 1. The answer options are scored at equal intervals from 0 points to 100 points, so that they can be analyzed statistically. Basic statistics on these questions are presented in Table 1. Do these questions all measure a single underlying attitude, or several attitudes, or nothing at all? The classic psychometric measurement model requires that the answers to questions on a single factor be highly correlated with each other, have similar correlations with other variables and high loadings on a single factor7, i.e., correlations between each variable and the factor it is designed to measure. Here, attitudes toward cloning animals reflect one underlying factor (see panel A entries in Table 1), but attitudes toward cloning humans reflect a different factor (see panel B entries in Table 1). As in Australia4, the two! factors are distinct (see confirmatory factor loadings in Table 1; exploratory factor analysis in Supplementary Methods, table S8). Thus, changing attitudes toward cloning animals has no necessary implication for attitudes on cloning humans, contrary to 'slippery slope' ethical arguments. back to article Box 1: Questions on reproductive cloning Scientists can now make clones—baby animals that are exact genetic copies of an adult. This is how it works: * BEGIN with a fertilized egg. For humans this is likely to be a spare embryo from an IVF program which would otherwise be thrown away. * REMOVE the original genes. * REPLACE them with genes from the person to be cloned. * GROW the fertilized egg in the lab for a few days into an early embryo, a little ballof cells. * IMPLANT the embryo in the womb of a surrogate mother where it develops into a baby. The baby is the donor's identical twin, except for the difference in age. Do you approve of... Cloning endangered animals? Cloning the best farm animals to improve breeding stock—for example, cloning a superb dairy bull? Cloning a child killed in a traffic accident? Cloning a child that is a copy of its father or mother? The answer options for these questions were: Definitely yes Yes Undecided, mixed feelings No Definitely not We scored these options in equal intervals: Definitely not = 0; No = 25; Undecided, mixed feelings = 50; Yes = 75; Definitely yes = 100. * Box 1 * Next box back to article View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Sociology and Nevada Agricultural Experiment Station, University of Nevada, Reno, Nevada, USA. * M D R Evans * International Survey Canter and Department of Sociology, University of Nevada, Reno, Nevada, USA. * Jonathan Kelley Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * M D R Evans Author Details * M D R Evans Contact M D R Evans Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Kelley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Text and Figures (65K) Supplementary Methods (includes tables, which are not cited individually) Additional data
• Recurrent copy number variations in human induced pluripotent stem cells
  - UNKNOWN 29(6):488-491 (2011)
  Nature Biotechnology | Opinion and Comment | Correspondence Recurrent copy number variations in human induced pluripotent stem cells * Kristen Martins-Taylor1 * Benjamin S Nisler2 * Seth M Taapken2 * Tiwanna Compton1 * Leann Crandall1 * Karen Dyer Montgomery2 * Marc Lalande1 * Ren-He Xu1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:488–491Year published:(2011)DOI:doi:10.1038/nbt.1890Published online07 June 2011 To the Editor: Human induced pluripotent stem cells (hiPSCs) have been shown to acquire genomic and epigenomic aberrations1, 2, 3, 4, 5, 6, 7 that are also frequently observed in human embryonic stem cells (hESCs)7, 8, 9, 10, 11, 12, including recurrent duplications of chromosome 12 (refs. 1,4,7) and 20q11.2 (refs. 3,4). To date, however, no recurrent subchromosomal abnormalities specific to hiPSC lines have been identified. We monitored the genomic stability of 32 hiPSC lines by G-banded karyotyping and high-resolution array-based comparative genomic hybridization (aCGH). Few aneuploidies were detected in hiPSCs by karyotyping after extended periods in culture. However, aCGH analysis identified unique copy number variation (CNV) signatures for hiPSC lines derived from specific sources of parental fibroblasts, including CNVs acquired specifically during culture. Recurrent CNVs at 1q31.3 and 17q21.1 were shared by >25% of hiPSCs. Furthermore, the loss of 8q24.3 was unique to hiPSCs. Of the 32 hiPSC lines, 23 were derived in our laboratory, 8 in the Thomson laboratory13 and 1 in the Daley laboratory14 using different combinations of reprogramming vectors and factors, and from different sources of fibroblasts (dermal, foreskin and lung) (Supplementary Table 1). The lines were cultured and characterized as described (Supplementary Methods and Supplementary Fig. 1). To assay for chromosomal aberrations at early passages, we karyotyped the 23 hiPSC lines derived in our laboratory and their parental fibroblasts by G-banding (Supplementary Table 2). It was difficult to karyotype the cell lines earlier, as it takes several passages to obtain enough cells for this and other quality control tests. The parental fibroblasts had normal karyotypes (Supplementary Table 2). Twenty-one of 23 hiPSC lines (91.3%) had normal karyotypes, whereas the remaining 2 lines (8.7%), HDFa-YK22 and ICF-TK4, had clonal, abnormal karyotypes with structural aberrations (Supplementary Table 2). The karyotype of ICF-TK4 was validated and defined by combined assays as described below (Fig. 1). The chromosomal structural aberrations that occurred in HDFa-YK22 and ICF-TK4 were observed in early passages (passage (p) 5 and p7, respectively), suggesting that these abnormalities were more likely to have developed during reprogramming than during subsequent culture. Analysis! of the nine hiPSC lines derived outside our laboratory13, 14 showed normal karyotypes (Supplementary Table 3). These findings suggest that although most hiPSC lines are karyotypically normal shortly after derivation, in a small fraction of cases they can develop chromosomal structural aberrations during or at early passages after reprogramming. back to article Figure 1: Karyotype of the ICF-TK4 hiPSC line determined by multiple assays. () Karyotype of ICF-TK4 passage 7. Arrow indicates an abnormal chromosome 21. () FISH of a metaphase cell from ICF-TK4 passage 13 with a chromosome 17 painting probe (left) and a probe for the subtelomeric region of the short arm of chromosome 17 (right) indicates that the translocated region was from chromosome 21. 'der' refers to the derivative chromosome or the chromosome with the translocation. () aCGH analysis of ICF-TK4 passage 32 revealed not only the gain at 17p () but also a gain at 21q (). View full text Figures at a glance * Figure 1: Karyotype of the ICF-TK4 hiPSC line determined by multiple assays. () Karyotype of ICF-TK4 passage 7. Arrow indicates an abnormal chromosome 21. () FISH of a metaphase cell from ICF-TK4 passage 13 with a chromosome 17 painting probe (left) and a probe for the subtelomeric region of the short arm of chromosome 17 (right) indicates that the translocated region was from chromosome 21. 'der' refers to the derivative chromosome or the chromosome with the translocation. () aCGH analysis of ICF-TK4 passage 32 revealed not only the gain at 17p () but also a gain at 21q (). * Figure 2: Venn diagrams for total CNVs acquired during prolonged culture of hiPSCs and hESCs. Total number of CNVs at low (dark gray) and high (light gray) passages is displayed for the representative hiPSC line HDFa-TK5 and hESC line WA09. The overlapping areas indicate the number of CNVs that are common between passages. Passage numbers for these representative lines are also shown. * Figure 3: Large chromosomal aberrations >1 Mb in hiPSC samples and analysis of some affected genes. () aCGH ratio plots show loss (depicted in red) of 8q24.3 sequences in four displayed hiPSC samples (Supplementary Table 9). () Real-time PCR shows copy number loss of PUF60 within 8q24.3 in three affected hiPSC samples. () Real-time RT-PCR shows decreased expression of TOP1MT and PUF60 within 8q24.3 in three affected hiPSC samples. Data are displayed as log2 ratio. Accession codes * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Referenced accessions Gene Expression Omnibus * GSE26786 Author information * Accession codes * Author information * Supplementary information Affiliations * University of Connecticut Stem Cell Institute and Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, Connecticut, USA. * Kristen Martins-Taylor, * Tiwanna Compton, * Leann Crandall, * Marc Lalande & * Ren-He Xu * WiCell Cytogenetics Laboratory, WiCell Research Institute, Madison, Wisconsin, USA. * Benjamin S Nisler, * Seth M Taapken & * Karen Dyer Montgomery Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ren-He Xu Author Details * Kristen Martins-Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin S Nisler Search for this author in: * NPG journals * PubMed * Google Scholar * Seth M Taapken Search for this author in: * NPG journals * PubMed * Google Scholar * Tiwanna Compton Search for this author in: * NPG journals * PubMed * Google Scholar * Leann Crandall Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Dyer Montgomery Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Lalande Search for this author in: * NPG journals * PubMed * Google Scholar * Ren-He Xu Contact Ren-He Xu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.6M) Supplementary Methods, Supplementary Figures 1–4 and Supplementary Tables 1–10 Additional data * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * Genomics and Microarray Specialist * PREMAS BIOTECH * Gurgaon, India * Research Scientist, Cellular Immunology * Vertex * Abingdon, Oxfordshire, UK * Group Leader / Senior Research Scientist * Foundation for Liver Research * London, United Kingdom * Post a free job * More science jobs Open innovation challenges * Compounds to Combat Citrus Greening Disease Deadline:Sep 01 2011Reward:$100,000 USD The Seeker, the non-profit Citrus Research and Development Foundation, desires proposals for compo… * Upload Your Compound Libraries! 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• Is silence still golden? Mapping the RNAi patent landscape
  - UNKNOWN 29(6):493-497 (2011)
  Nature Biotechnology | Feature | Patents Is silence still golden? Mapping the RNAi patent landscape * Per Lundin1Journal name:Nature BiotechnologyVolume: 29,Pages:493–497Year published:(2011)DOI:doi:10.1038/nbt.1885Published online07 June 2011 The field of RNA interference has lately seen its fortunes diminish, but is the situation really that precarious? View full text Figures at a glance * Figure 1: Aggregate time trends in PCT applications, US applications and patents, and EPO applications and patents, during 2000–2010. * Figure 2: Top patent assignees. () PCT. () US. () EPO. * Figure 3: Portfolio trends for companies among the total top ten RNAi assignees. * Figure 4: RNAi delivery–related PCT, US and EPO patents and patent applications, in absolute numbers and as percent of total. 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 * Per Lundin is in the life science practice at Albihns.Zacco, Stockholm, Sweden; and the Department of Laboratory Medicine at the Karolinska Institute, Huddinge, Sweden. Competing financial interests The author declares no competing financial interests. Corresponding authors Correspondence to: * Per Lundin or * Per Lundin Author Details * Per Lundin Contact Per Lundin Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Recent patent applications related to zinc fingers
  - UNKNOWN 29(6):498 (2011)
  Article preview View full access options Nature Biotechnology | Feature | Patents Recent patent applications related to zinc fingers Journal name:Nature BiotechnologyVolume: 29,Page:498Year published:(2011)DOI:doi:10.1038/nbt.1896Published online07 June 2011 Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services.
• MicroRNAs and reprogramming
  - UNKNOWN 29(6):499-500 (2011)
  Article preview View full access options Nature Biotechnology | News and Views MicroRNAs and reprogramming * Hao-Ming Chang1 * Richard I Gregory1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:499–500Year published:(2011)DOI:doi:10.1038/nbt.1889Published online07 June 2011 MicroRNAs offer the most efficient method yet devised for reprogramming somatic cells to pluripotent stem cells. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Hao-Ming Chang and Richard I. Gregory are in the Stem Cell Program, Children's Hospital Boston, and the Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard Stem Cell Institute, Boston, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard I Gregory Author Details * Hao-Ming Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Richard I Gregory Contact Richard I Gregory Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Human peptidome display
  - UNKNOWN 29(6):500-502 (2011)
  Article preview View full access options Nature Biotechnology | News and Views Human peptidome display * William H Robinson1 * Lawrence Steinman1 * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:500–502Year published:(2011)DOI:doi:10.1038/nbt.1888Published online07 June 2011 A peptide library representing the entire human proteome is applied to the discovery of autoantigens. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * William H. Robinson is in the Department of Medicine, Division of Immunology and Rheumatology, Stanford University, Stanford, California, USA and the VA Palo Alto Health Care System, Palo Alto, California, USA, and Lawrence Steinman is in the Department of Neurology and Pediatrics, Stanford University, Stanford, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * William H Robinson or * Lawrence Steinman Author Details * William H Robinson Contact William H Robinson Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence Steinman Contact Lawrence Steinman Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Molecular evolution picks up the PACE
  - UNKNOWN 29(6):502-503 (2011)
  Article preview View full access options Nature Biotechnology | News and Views Molecular evolution picks up the PACE * Adam J Meyer1 * Andrew D Ellington1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:502–503Year published:(2011)DOI:doi:10.1038/nbt.1884Published online07 June 2011 Protein variants with improved properties can be rapidly generated by a phage-based system that enables continuous directed evolution. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Adam J. Meyer and Andrew D. Ellington are at the Center for Systems and Synthetic Biology, University of Texas at Austin, Austin, Texas, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andrew D Ellington Author Details * Adam J Meyer Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Ellington Contact Andrew D Ellington Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Research highlights
  - UNKNOWN 29(6):504 (2011)
  Article preview View full access options Nature Biotechnology | Research Highlights Research highlights * Kathy Aschheim * Laura DeFrancesco * Markus Elsner * Peter Hare * Craig MakJournal name:Nature BiotechnologyVolume: 29,Page:504Year published:(2011)DOI:doi:10.1038/nbt.1895Published online07 June 2011 Turning mouse tails into liver Liver transplantation is the only remedy for liver failure, but many people in need of a transplant die awaiting a donor. Whereas stem cells of various kinds can be coaxed to form hepatocytes, the protocols are involved and inefficient. Now Huang et al. show that with three transcription factors, immortalized mouse tail tip fibroblasts can be coaxed to transdifferentiate into cells functionally equivalent to hepatocytes (iHep cells). Starting with 14 transcription factors that are important for liver development, the researchers pared it down to three—Gata4, Hnflα and Foxa3. The iHep cells displayed various hepatocyte characteristics in vitro, such as forming tight junctions and expressing E-cadherin. Global gene expression in iHep cells clustered with hepatocyte expression patterns rather than fibroblast patterns. In a mouse model for liver failure, injecting iHep cells into the spleen rescued almost half (5/12) of treated animals, whereas all untreated animals (10/10) d! ied. In the surviving mice, iHep cells made up 5–80% of the liver and the functional hepatocytes were clearly not fusions of transplant and host cells, as shown by chromosome analysis (female iHep cells were transplanted into male recipients). Importantly, because the cells had been immortalized, there was no sign of tumors when iHep cells were transplanted either into the mice prone to liver failure, even several months later, or into nude mice. This promising approach will need to be done with human cells before its therapeutic potential will be known. (Nature published online, doi:10.1038/nature10116, 11 May 2011) LD Unearthing microbiome secrets Just as human physiology is influenced by microbial communities in our gut and skin, the tolerance of plants to root diseases depends on the microbial flora in the soil, according to a new report by Mendes et al. The researchers studied disease-suppressive soils that confer protection against the fungal pathogen Rhizoctonia solani, which afflicts crops, including sugar beet, potato and rice. Mendes et al. used a high-density microarray of 16S ribosomal-DNA oligonucleotides to compare the microbial content of different soils, finding several bacterial taxa associated with disease suppression. Follow-on work in culture pinpointed a strain of bacteria capable of protecting sugar beet seedlings from R. solani. And subsequent transposon mutagenesis of this strain identified a nonribosomal peptide synthetase gene that was required for the bacteria to protect the plants. The gene is part of a pathway predicted to produce a nine-amino-acid chlorinated lipopeptide. This work highligh! ts the potentially complex relationships between plants and the soil microbiome and raises the intriguing possibility of harnessing these relationships for agricultural benefit. (Science published online, doi:10.1126/science.1203980, 5 May 2011) CM SRC and trastuzumab resistance Trastuzumab (Herceptin), a humanized antibody that targets epidermal growth factor receptor 2, is indicated for adjuvant treatment of HER2-overexpressing node-positive or node-negative breast cancer. Many breast cancer patients are, however, unresponsive to initial treatment, and others frequently develop resistance to trastuzumab over time, spurring efforts to identify mechanisms of resistance. Zhang et al. now show that the hyperactivated tyrosine kinase c-SRC is a common node downstream of multiple de novo and acquired trastuzumab-resistance pathways. Increased c-SRC activation correlated with poorer responses to trastuzumab in patients and conferred resistance to the drug in cultured cells. Most notably, an orally available, small-molecule c-SRC inhibitor sensitized five different classes of trastuzumab-resistant cells to the antibody and enabled trastuzumab-mediated elimination of drug-resistant tumors in a mouse xenograft model. These findings suggest that a combinator! ial regimen of c-SRC inhibitor with trastuzumab may be a practical way to overcome multiple modes of trastuzumab resistance in the clinic. (Nat. Med.17, 461–469, 2011) PH iPSCs shed light on schizophrenia Modeling a disease with induced pluripotent stem cells (iPSCs) involves reprogramming somatic cells from a patient, differentiating the iPSCs to a cell type that is affected in the disease and showing that the differentiated cells exhibit essential features of the pathology. This last step—recovering the disease phenotype—is usually the hardest, and the most compelling examples to date have come for monogenic diseases that occur in childhood. A recent paper reports progress in using iPSCs to model a multigenic disease that strikes most often in adolescence. Brennand et al. found that iPSC-derived neurons from individuals with schizophrenia reproduced certain aspects of the disease seen previously in animal models or in human post-mortem neurons (e.g., a reduction in neuronal connectivity and neurite number). However, other known characteristics of the disease were not detected (e.g., impaired synaptic function). Transcriptional profiling of the disease-specific and norma! l neurons showed 596 differentially expressed genes, 25% of which have been linked to schizophrenia. iPSCs derived from patients offer a new approach for drug testing; the authors found that one of five antipsychotic drugs studied mitigated the disease phenotype in vitro. (Nature, 223–225, 2011) KA Lipid-coated nanopores Artificial nanopores have received considerable attention as detectors of single proteins and small molecules, and for high-speed DNA and RNA sequencing. However, low molecular specificity and difficulties in detecting molecules at high translocation speeds have limited their application. Now, Yusko et al. suggest that coating synthetic nanopores with fluid lipid bilayers can help to overcome these problems. Inspired by similar structures in the olfactory system of insects, the authors exposed a silicon nitride matrix with 20–30 nm pores to a suspension of liposomes and showed that the lipids form a bilayer on the silicon nitride surface. Biotinylated lipids acted as specific receptors for target molecules, increasing their concentration on the nanopore surface. When an electric field was applied over the nanopore membrane, the fluid character of the bilayer allowed the biotin-bound molecules to translocate through the nanopores, enabling their detection by a characteristi! c change in the conductance. The high viscosity of the lipid bilayer reduced the translocation speed to permit detection. In addition to offering a potential solution to the selectivity and sensitivity problem, lipid-coated pores were much less prone to clogging caused by unspecific adsorption of protein to the pore. (Nat. Nanotechnol., 253–260, 2011) ME Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Kathy Aschheim Search for this author in: * NPG journals * PubMed * Google Scholar * Laura DeFrancesco Search for this author in: * NPG journals * PubMed * Google Scholar * Markus Elsner Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Hare Search for this author in: * NPG journals * PubMed * Google Scholar * Craig Mak Search for this author in: * NPG journals * PubMed * Google Scholar
• Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell
  - UNKNOWN 29(6):505-511 (2011)
  Nature Biotechnology | Computational Biology | Analysis Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell * Leslie Magtanong1, 2, 5, 6 * Cheuk Hei Ho1, 2, 6 * Sarah L Barker1, 2, 6 * Wei Jiao1, 2 * Anastasia Baryshnikova1, 2 * Sondra Bahr1, 2 * Andrew M Smith1, 2 * Lawrence E Heisler2 * John S Choy3 * Elena Kuzmin1, 2 * Kerry Andrusiak1, 2 * Anna Kobylianski2 * Zhijian Li1, 2 * Michael Costanzo1, 2 * Munira A Basrai3 * Guri Giaever1, 2, 4 * Corey Nislow1, 2 * Brenda Andrews1, 2 * Charles Boone1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:505–511Year published:(2011)DOI:doi:10.1038/nbt.1855Received13 September 2010Accepted25 March 2011Published online15 May 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppress! ion mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell. View full text Figures at a glance * Figure 1: Dosage suppression genetic interaction network for S. cerevisiae. Network diagram of dosage suppression genetic interactions. Genes are represented as nodes and interactions are represented as edges. Colored nodes are sets of genes enriched for Gene Ontology biological processes summarized by the indicated terms. The nodes were distributed using a force-directed layout, such that genes (nodes) that share common dosage suppression interactions form distinct clusters. * Figure 2: Properties of the yeast dosage suppression network. () Frequency of dosage suppression genetic interactions within and across biological processes for the dosage suppression network. The frequency of gene pairs showing dosage suppression interactions was measured for 19 broadly defined functional gene sets11; blue, less than the frequency of random pairs; black, statistically indistinguishable from a random set of gene pairs; yellow, greater than the frequency of random pairs. Dosage suppressor gene function. x axis; query ORF gene function, y axis. The diagonal represents within-process interactions. Red line in color scale, frequency of interactions expected by chance (0.0005). () Scaled square Venn diagram of fraction of dosage suppression gene pairs that also show negative genetic and protein-protein interactions (PPI). Only gene pairs known to be tested for both genetic and physical interactions were considered. Light blue, gene pairs showing dosage suppression interactions only; red, gene pairs showing dosage suppressio! n and negative genetic interactions; dark blue, gene pairs showing dosage suppression and physical interactions; yellow, gene pairs showing dosage suppression, negative genetic and physical interactions. * Figure 3: A genetic requirement for PKA signaling in kinetochore function. () Subset of results of using MoBY-ORF 2.0 to screen for dosage suppressors of NSL1 or DSN1, two components of the MIND kinetochore complex. Arrows link suppressors to the mutated gene. Green, kinetochore function; blue, PKA signaling; red, ribosome biogenesis. () Attenuation of PKA signaling may be required for proper kinetochore function. Fluctuation tests were carried out to measure chromosome loss rates with freshly grown diploid colonies of each genotype as described32. Values are mean ± s.d. n = 3 for BCY1/BCY1 and bcy1Δ/BCY1, n = 2 for ctf19Δ/ctf19Δ. * Figure 4: Decision tree used to categorize dosage suppression interactions. Dosage suppression gene pairs were classified into one of four mechanistic categories. First, gene pairs were analyzed to see if they were annotated with the same Gene Ontology term and/or were also linked by a protein-protein interaction. Gene pairs that did not fit into either of these categories were mechanistically categorized as a chaperone, an RNA processing/protein synthesis dosage suppressor or an unknown dosage suppressor. A chaperone is a dosage suppressor that encodes a gene required for RNA or protein stability, such as a heat-shock protein, that might act to stabilize the mutant query gene product. A dosage suppressor can also participate in RNA processing or protein synthesis. For gene pairs that could otherwise not be classified because their gene products physically interacted but the genes themselves were not functionally related (i), we found additional evidence supporting a functional relationship by examining the primary literature (dotted arrows). Thirte! en percent of all gene pairs could not be classified and fell into a category labeled unknown (ii). * Figure 5: Mechanisms of dosage suppression in yeast. () Complex component. A dosage suppressor can be a gene that encodes a protein that interacts with the mutant gene product and is required for its normal function. At the semipermissive temperature, the mutant protein b predominantly occurs in an unfolded state, probably because the mutation renders the gene product unstable, and it therefore cannot interact with its normal physical partner(s). Overexpression of a dosage suppressor, protein A, increases amount of properly folded mutant protein so that the physical complex can execute its essential function. () Functional relationship. A dosage suppressor can function either upstream or downstream of its respective mutant query allele in the same biological process. In this example, at the semipermissive temperature, the function of the mutant allele b gene product is impaired and cannot transmit information to a downstream effector. A dosage suppressor, encoded by gene A, can act upstream of the mutant allele to activate the! pathway. () Chaperone. A dosage suppressor can affect the amount of the mutant gene product. In this example, the dosage suppressor protein does not normally interact with the mutant gene product. At the semipermissive temperature, the mutant protein b is unfolded, but overexpression of a dosage suppressor, such as a chaperone (protein A), can refold and stabilize the mutant protein, enabling it to carry out its essential function. () RNA processing/protein synthesis. A dosage suppressor can be a gene that acts during transcription or translation. In this example, the dosage suppressor protein A is normally involved in some aspect of transcription. At the semipermissive temperature, transcription of mutant allele b leads to a poor-quality mRNA product that may be translated but probably will be degraded. By increasing some aspect of transcription, however, it might be possible to improve the quality of the mRNA product, which can then be translated instead of degraded, lea! ding to enough functional mutant protein for the cell to be vi! able. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-TABM-1147 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Leslie Magtanong, * Cheuk Hei Ho & * Sarah L Barker Affiliations * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Leslie Magtanong, * Cheuk Hei Ho, * Sarah L Barker, * Wei Jiao, * Anastasia Baryshnikova, * Sondra Bahr, * Andrew M Smith, * Elena Kuzmin, * Kerry Andrusiak, * Zhijian Li, * Michael Costanzo, * Guri Giaever, * Corey Nislow, * Brenda Andrews & * Charles Boone * Banting and Best Department of Medical Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Leslie Magtanong, * Cheuk Hei Ho, * Sarah L Barker, * Wei Jiao, * Anastasia Baryshnikova, * Sondra Bahr, * Andrew M Smith, * Lawrence E Heisler, * Elena Kuzmin, * Kerry Andrusiak, * Anna Kobylianski, * Zhijian Li, * Michael Costanzo, * Guri Giaever, * Corey Nislow, * Brenda Andrews & * Charles Boone * Genetic Branch Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. * John S Choy & * Munira A Basrai * Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada. * Guri Giaever * Present address: Howard Hughes Medical Institute, Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA. * Leslie Magtanong Contributions L.M. was involved in MoBY-ORF construction, carried out experimental analysis and wrote the manuscript; C.H.H. was involved in MoBY-ORF construction, carried out experimental analysis and wrote the manuscript; S.L.B. was involved in MoBY-ORF construction and wrote the manuscript; W.J. and A.B. carried out computational analysis and wrote the manuscript; S.B. was involved in temperature-sensitive strain generation and carried out experimental analysis; A.M.S. and L.E.H. were involved in microarray data analysis; J.S.C. carried out the chromosome loss assays; E.K. and K.A. carried out experimental analysis and edited the manuscript; A.K. carried out experimental analysis; Z.L. was involved in temperature-sensitive strain generation; M.C. wrote the manuscript; M.A.B. edited the manuscript; G.G. and C.N. provided microarray data analysis and edited the manuscript; B.A. wrote the manuscript; C.B. conceived and planned the construction of the MoBY-ORF 2.0 library and wrote the man! uscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Charles Boone or * Brenda Andrews Author Details * Leslie Magtanong Search for this author in: * NPG journals * PubMed * Google Scholar * Cheuk Hei Ho Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah L Barker Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Jiao Search for this author in: * NPG journals * PubMed * Google Scholar * Anastasia Baryshnikova Search for this author in: * NPG journals * PubMed * Google Scholar * Sondra Bahr Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew M Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence E Heisler Search for this author in: * NPG journals * PubMed * Google Scholar * John S Choy Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Kuzmin Search for this author in: * NPG journals * PubMed * Google Scholar * Kerry Andrusiak Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Kobylianski Search for this author in: * NPG journals * PubMed * Google Scholar * Zhijian Li Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Costanzo Search for this author in: * NPG journals * PubMed * Google Scholar * Munira A Basrai Search for this author in: * NPG journals * PubMed * Google Scholar * Guri Giaever Search for this author in: * NPG journals * PubMed * Google Scholar * Corey Nislow Search for this author in: * NPG journals * PubMed * Google Scholar * Brenda Andrews Contact Brenda Andrews Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Boone Contact Charles Boone Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (422K) Dosage suppression interactions curated in the Saccharomyces Genome Database. * Supplementary Table 2 (127K) Dosage suppression interactions identified in this study using the MoBY-ORF 2.0 library. * Supplementary Table 3 (25K) Gene pairs from this study tested for reciprocal suppression interactions. * Supplementary Table 4 (29K) Gene pairs annotated in the Saccharomyces Genome Database that show reciprocal suppression interactions. * Supplementary Table 5 (16K) Significant enrichment of yeast two-hybrid interactions in the dosage suppression interaction network. * Supplementary Table 6 (590K) Restriction digest fragments of MoBY-ORF 2.0 plasmids. * Supplementary Table 7 (348K) Yeast strains used in this study. * Supplementary Table 8 (41K) MoBY-ORF 2.0 plasmids used in this study. Zip files * Supplementary Data (66K) PDF files * Supplementary Text and Figures (713K) Supplementary Figures 1–5 Additional data
• Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants
  - UNKNOWN 29(6):512-520 (2011)
  Nature Biotechnology | Research | Analysis Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants * Dalila Pinto1, 8 * Katayoon Darvishi2, 8 * Xinghua Shi2 * Diana Rajan3 * Diane Rigler3 * Tom Fitzgerald3 * Anath C Lionel1 * Bhooma Thiruvahindrapuram1 * Jeffrey R MacDonald1 * Ryan Mills2 * Aparna Prasad1 * Kristin Noonan2, 4 * Susan Gribble3 * Elena Prigmore3 * Patricia K Donahoe4 * Richard S Smith2 * Ji Hyeon Park2, 7 * Matthew E Hurles3 * Nigel P Carter3 * Charles Lee2 * Stephen W Scherer1, 5 * Lars Feuk6 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:512–520Year published:(2011)DOI:doi:10.1038/nbt.1852Received08 October 2010Accepted22 March 2011Published online08 May 2011Corrected online29 May 2011 Abstract * Abstract * Accession codes * Change history * 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 We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here a! llows independent data evaluation and provides a means to benchmark new algorithms. View full text Figures at a glance * Figure 1: Size distribution of CNV calls. The size distribution for the high-confidence CNV calls (that is, CNV calls made in at least two of three replicates) is shown for all combinations of algorithms (Table 1, CNV analysis tools) and platforms. Each bin represents a different range of CNV lengths and the bars show the percentage of CNVs falling into each size bin. Representative results are shown for one genotyping site only, where the average number of CNVs per sample for that site is given in parentheses. The size distribution is therefore not representative of a sample. Instead, it represents the sizes of CNV calls detected in a total of six samples. Results for all sites and further breakdown into gains-only and losses-only can be found in Supplementary Figure 4. *For Affymetrix 250K-Nsp, dChip detects on average one CNV per sample. Affy, Affymetrix; Ilmn, Illumina; AG, Agilent; BAC, bacterial artificial chromosome; cnvPart, cnvPartition; NG, NimbleGen; PCNV, PennCNV; QSNP, QuantiSNP. * Figure 2: CNV calling reproducibility. (–) Call reproducibility was evaluated by either comparing calls obtained from triplicate experiments (,) or by a comparison to various independent reference data sets (). The percentage of concordant CNV calls between replicates for each combination of array, algorithm and site (). The corresponding average number of CNVs per sample is given in . The results for the lower-resolution arrays can be found in Supplementary Figure 8. () The percentage of high-confidence CNV calls for each set of results that overlaps (minimum of 50% reciprocal overlap) with data from DGV, and references 11 and 41. The DGV data were divided into array-based CNVs and sequence-based CNVs, and for the reference 11 data we independently considered a set of 8,599 validated variants as well as a subset of 4,978 CNVs that were genotyped. The poor performance of the BAC array is explained by the fact that the DGV data set was filtered so that low-resolution studies (including BAC array data) were remov! ed. Site abbreviations: see Table 1 legend. * Figure 3: Reproducibility of CNV breakpoint assignments. The distances between the breakpoints for replicated CNV calls were divided into size bins for each platform, and the proportion of CNVs in each bin are plotted separately for the start (red, left) and end (blue, right) coordinates. The total number of breakpoints is given in parentheses. The data show that high-resolution platforms are highly consistent in the assignment of start and end coordinates for CNVs called across replicate experiments. Affy, Affymetrix; BAC, bacterial artificial chromosome; brkpt, breakpoint; HMS, Harvard Medical School; Ilmn, Illumina; TCAG, The Centre for Applied Genomics; WTSI, Wellcome Trust Sanger Institute. * Figure 4: CNV breakpoint accuracy. (,) The breakpoint accuracy for CNV deletions on each platform was assessed in a comparison to published sequencing data sets of nucleotide-resolution breakpoints compiled from various studies43, 44 (), or detected in the 1000 Genomes Project45, 46 (). Only a subset of platforms is included in this figure, as the lower resolution arrays did not have enough overlapping variants to make the comparison meaningful. In , a total of 3,544 deletion breakpoints for sample NA18517 were collected from the 1000 Genomes Project and compared to the CNVs detected in each of the analyses in this study. Every row in the diagram corresponds to one of the 3,544 deletions and the color indicates whether that deletion was detected in the present study. Each row represents the distance between array versus sequencing-based breakpoints ('left' + 'right' breakpoints for the same event are listed in adjacent rows). Schematic below shows sample-based comparisons between deletion breakpoints obtained! with array versus sequencing methods. Gray means the deletion was not detected, whereas a color on the red-green scale is indicative of the accuracy of detected breakpoints. 1000G, 1000 Genomes Project. Accession codes * Abstract * Accession codes * Change history * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25893 Change history * Abstract * Accession codes * Change history * Author information * Supplementary informationErratum 29 May 2011In the version of this article initially published online, Bhooma Thiruvahindrapuram's name was misspelled. The error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Accession codes * Change history * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Dalila Pinto & * Katayoon Darvishi Affiliations * The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada. * Dalila Pinto, * Anath C Lionel, * Bhooma Thiruvahindrapuram, * Jeffrey R MacDonald, * Aparna Prasad & * Stephen W Scherer * Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Katayoon Darvishi, * Xinghua Shi, * Ryan Mills, * Kristin Noonan, * Richard S Smith, * Ji Hyeon Park & * Charles Lee * Wellcome Trust, Sanger Institute, Hinxton, Cambridge, UK. * Diana Rajan, * Diane Rigler, * Tom Fitzgerald, * Susan Gribble, * Elena Prigmore, * Matthew E Hurles & * Nigel P Carter * Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Kristin Noonan & * Patricia K Donahoe * McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, Canada. * Stephen W Scherer * Department of Immunology, Genetics and Pathology, SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Sweden. * Lars Feuk * Present address: Department of Obstetrics and Gynecology, Pochon CHA University College of Medicine, Seoul, South Korea. * Ji Hyeon Park Contributions D.P., C.L., N.P.C., M.E.H., S.W.S. and L.F. conceived and designed the study. D.P. and L.F. coordinated sample distribution, experiments and analysis. K.D. managed the experiments conceived at the Harvard Medical School and performed the Nexus analysis. R.S.S., D. Rajan, D. Rigler, T.F., J.H.P., K.N., S.G. and E.P. performed the experiments. Data analyses were performed by D.P., K.D., R.S.S., D. Rajan, T.F., A.C.L., B.T., J.R.M., R.M., A.P., K.N., X.S., P.K.D. and L.F. All authors participated in discussions of different parts of the study. D.P., C.L., S.W.S. and L.F. wrote the manuscript. All authors read and approved the manuscript. Competing financial interests The authors declare competing interests. Affymetrix, Agilent, Illumina and Nimblegen provided arrays or reagents for use in this study at substantial discount. The Centre for Applied Genomics (TCAG) routinely provides fee-for-service experimentation using products from Affymetrix, Agilent and Illumina, and is a Core Lab for Affymetrix and Illumina. S.W.S. belongs to the Scientific Advisory Board of Combimatrix Diagnostics. Corresponding author Correspondence to: * Lars Feuk Author Details * Dalila Pinto Search for this author in: * NPG journals * PubMed * Google Scholar * Katayoon Darvishi Search for this author in: * NPG journals * PubMed * Google Scholar * Xinghua Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Diana Rajan Search for this author in: * NPG journals * PubMed * Google Scholar * Diane Rigler Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Fitzgerald Search for this author in: * NPG journals * PubMed * Google Scholar * Anath C Lionel Search for this author in: * NPG journals * PubMed * Google Scholar * Bhooma Thiruvahindrapuram Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey R MacDonald Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan Mills Search for this author in: * NPG journals * PubMed * Google Scholar * Aparna Prasad Search for this author in: * NPG journals * PubMed * Google Scholar * Kristin Noonan Search for this author in: * NPG journals * PubMed * Google Scholar * Susan Gribble Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Prigmore Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia K Donahoe Search for this author in: * NPG journals * PubMed * Google Scholar * Richard S Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Ji Hyeon Park Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew E Hurles Search for this author in: * NPG journals * PubMed * Google Scholar * Nigel P Carter Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen W Scherer Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Feuk Contact Lars Feuk Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Change history * Author information * Supplementary information Excel files * Supplementary Table 3 (16M) List of all CNVs that passed QC. PDF files * Supplementary Text and Figures (4M) Supplementary Methods, Supplementary Tables 1, 2, 4–6, and Supplementary Figs. 1–15 Additional data
• De novo genome sequencing and comparative genomics of date palm (Phoenix dactylifera)
  - UNKNOWN 29(6):521-527 (2011)
  Nature Biotechnology | Research | Article Open De novo genome sequencing and comparative genomics of date palm (Phoenix dactylifera) * Eman K Al-Dous1, 10 * Binu George1, 10 * Maryam E Al-Mahmoud1 * Moneera Y Al-Jaber1 * Hao Wang2 * Yasmeen M Salameh1 * Eman K Al-Azwani1 * Srinivasa Chaluvadi2 * Ana C Pontaroli2, 9 * Jeremy DeBarry2 * Vincent Arondel3 * John Ohlrogge4 * Imad J Saie5 * Khaled M Suliman-Elmeer6 * Jeffrey L Bennetzen2 * Robert R Kruegger7 * Joel A Malek1, 8 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:521–527Year published:(2011)DOI:doi:10.1038/nbt.1860Received17 February 2011Accepted29 March 2011Published online29 May 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Date palm is one of the most economically important woody crops cultivated in the Middle East and North Africa and is a good candidate for improving agricultural yields in arid environments. Nonetheless, long generation times (5–8 years) and dioecy (separate male and female trees) have complicated its cultivation and genetic analysis. To address these issues, we assembled a draft genome for a Khalas variety female date palm, the first publicly available resource of its type for a member of the order Arecales. The ~380 Mb sequence, spanning mainly gene-rich regions, includes >25,000 gene models and is predicted to cover ~90% of genes and ~60% of the genome. Sequencing of eight other cultivars, including females of the Deglet Noor and Medjool varieties and their backcrossed males, identified >3.5 million polymorphic sites, including >10,000 genic copy number variations. A small subset of these polymorphisms can distinguish multiple varieties. We identified a region of the ge! nome linked to gender and found evidence that date palm employs an XY system of gender inheritance. View full text Figures at a glance * Figure 1: Taxonomic tree of selected crops for which genome sequences are available. Date palm is the first member of the order Arecales and the family Arecaceae for which a draft genome sequence is available. Other monocotyledonous plants (class Liliopsida) for which genome sequences are available are mainly grasses (order Poales). The tree was constructed in the Interactive Tree Of Life (http://itol.embl.de/) from taxonomy numbers in NCBI (http://www.ncbi.nlm.nih.gov/Taxonomy/). * Figure 2: Date palm SNP analysis. SNPs were compared between parental alleles of the Khalas reference genome and different varieties. () The distance between parental allele SNPs in Khalas is not normally distributed. The skewed distribution of adjacent SNP distances demonstrates the occurrence of high and low polymorphism islands in the genome. About 49% of SNPs occur within 50 bp of another SNP. This trend was maintained even after removing SNPs likely to be in repetitive regions (KhlsFilter). () Backcrossed varieties of date palm on average show high levels of similarity to their recurrent parent with the number of generations of backcrossing (ranging from backcross 1 to 5 generations) having little effect on similarity levels (error bars are quite small). Intervariety comparisons show significantly more sites with different genotypes. () Principal component analysis (PCA) of sequenced genomes based on 3.5 million polymorphic sites. Khalas and backcrossed variants are essentially on top of each other. DN,! Deglet Noor; Mdjl, Medjool, BC, backcross; AlrF, AlrijalF; Khls, Khalas; Khlt, Khalt. () PCA of sequenced genomes based on 32 decision tree–selected polymorphic sites reveals little loss of discrimination quality with much reduced genotyping required. KhFx, Khalas x Khalas F1. * Figure 3: Analysis of imbalanced sequence count regions (ISCRs) among date palm genomes. Numbers of unique ISCRs remaining in each genome after comparison with other genomes are shown. Only nonbackcrossed genomes were considered to avoid bias from inbreeding. Approximately 7% of ISCRs were unique to any single genome, whereas the majority were observed in at least one other genome. * Figure 4: Enrichment of Gene Ontology categories for genes covered by imbalanced sequence count regions (ISCRs). Gene Ontology categories from genes covered by ISCRs in at least two genomes were analyzed for enrichment. Gene counts in each category were normalized to total gene counts in either the genome or ISCRs. A false discovery rate (FDR) of 0.2 was applied and only categories showing at least twofold enrichment in the ISCRs are reported. * Figure 5: Pedigree and genotype information for gender-discriminating regions. Date palms of known genealogy were genotyped at multiple gender-discriminating regions. () A section of the full pedigree used for linkage analysis showing the complex relationship of the trees. DN, Deglet Noor; Dy, Dayri; Mj, Medjool; BC, backcross; DnPr, initial donor parents. Gray boxes indicate an unknown but theoretically determined genotype. The genotype in each individual is the genotype found at the first gender-discriminating SNP that was genotyped. Segregation of heterozygosity with the male phenotype is clear. () Genotypes from four scaffolds (scales with exons annotated as blue ticks and repeats as red rectangles) with the largest number of male-specific SNPs (MS-SNPs). Genotypes from selected regions (tan rectangles) are presented with their scaffold base pair location above each genotype. F and R indicate on which strand (forward or reverse) primers were designed to amplify the selected region. The number observed (both empirically and theoretically) for each g! ender in each genotype is included. Fem, female; herm., hermaphrodite. Heterozygous SNP calls are shaded gray whereas homozygous calls are shaded blue. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * ACYX00000000 * 9A12F7 * JF313259 * JF313260 * JF313261 * GU183367 * GU183365 * GU183366 * SRA029799 * SRP005625 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Eman K Al-Dous & * Binu George Affiliations * Genomics Core, Weill Cornell Medical College in Qatar, Doha, Qatar. * Eman K Al-Dous, * Binu George, * Maryam E Al-Mahmoud, * Moneera Y Al-Jaber, * Yasmeen M Salameh, * Eman K Al-Azwani & * Joel A Malek * Department of Genetics, University of Georgia, Athens, Georgia, USA. * Hao Wang, * Srinivasa Chaluvadi, * Ana C Pontaroli, * Jeremy DeBarry & * Jeffrey L Bennetzen * Laboratoire de Biogenèse Membranaire, CNRS UMR, Université V. Segalen Bordeaux, Bordeaux, France. * Vincent Arondel * Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA. * John Ohlrogge * Agricultural and Water Research, Ministry of Environment, Doha, Qatar. * Imad J Saie * Biotechnology Centre, Ministry of Environment, Doha, Qatar. * Khaled M Suliman-Elmeer * USDA-ARS National Clonal Germplasm Repository for Citrus & Dates, University of California, Riverside, California, USA. * Robert R Kruegger * Department of Genetic Medicine, Weill Cornell Medical College in Qatar, Doha, Qatar. * Joel A Malek * Present address: EEA Balcarce, Instituto Nacional de Tecnología Agropecuaria, Balcarce, Argentina. * Ana C Pontaroli Contributions E.K.A.-D. extracted genomic DNA, created libraries, sequenced the genome and assisted with the manuscript writing. B.G. conducted SNP, CNV and annotation analysis. M.E.A.-M. genotyped gender-discriminating regions. E.K.A.-A. and Y.M.S. assisted in genome sequencing, conducted qPCR validation of CNVs and helped write the manuscript. M.Y.A.-J. cloned, sequenced and analyzed sequences from standard sequencing technology for comparison to the next generation data. I.J.S. and K.M.S.-E. maintained the tree tissue culture and cultivar data on the sequenced trees. H.W., S.C., A.C.P., J.D. and J.L.B. constructed the fosmid library, sequenced date palm fosmids, provided transposable element annotation and generated comparative analyses and genome size predictions. J.O. and V.A. constructed EST libraries and provided DNA sequence from ESTs. H.W. and J.L.B. also helped write the manuscript. R.R.K. maintained and provided the date palm genetic resource including the pedigree information ! and assisted in phenotyping of the date palms. J.A.M. conceived and planned the project, created libraries, analyzed for gender-specific regions, assembled and annotated the genome and wrote the manuscript. Competing financial interests J.A.M. has been named on a patent application by Weill Cornell Medical College in Qatar with regard to date palm gender-specific markers. Corresponding author Correspondence to: * Joel A Malek Author Details * Eman K Al-Dous Search for this author in: * NPG journals * PubMed * Google Scholar * Binu George Search for this author in: * NPG journals * PubMed * Google Scholar * Maryam E Al-Mahmoud Search for this author in: * NPG journals * PubMed * Google Scholar * Moneera Y Al-Jaber Search for this author in: * NPG journals * PubMed * Google Scholar * Hao Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yasmeen M Salameh Search for this author in: * NPG journals * PubMed * Google Scholar * Eman K Al-Azwani Search for this author in: * NPG journals * PubMed * Google Scholar * Srinivasa Chaluvadi Search for this author in: * NPG journals * PubMed * Google Scholar * Ana C Pontaroli Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy DeBarry Search for this author in: * NPG journals * PubMed * Google Scholar * Vincent Arondel Search for this author in: * NPG journals * PubMed * Google Scholar * John Ohlrogge Search for this author in: * NPG journals * PubMed * Google Scholar * Imad J Saie Search for this author in: * NPG journals * PubMed * Google Scholar * Khaled M Suliman-Elmeer Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey L Bennetzen Search for this author in: * NPG journals * PubMed * Google Scholar * Robert R Kruegger Search for this author in: * NPG journals * PubMed * Google Scholar * Joel A Malek Contact Joel A Malek Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 5 (2M) ISCRs overlapping genes in 4 genomes compared to Khalas PDF files * Supplementary Text and Figures (807K) Supplementary Tables 1–4,6,7, Supplementary Methods, Supplementary Notes and Supplementary Figs. 1–5 Creative Commons Attribution-Noncommercial-Share Alike license This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial-ShareAlike license (http://creativecommons.org/licenses/by-nc-sa/3.0/), which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation, and derivative works must be licensed under the same or similar license. Additional data
• Specification of transplantable astroglial subtypes from human pluripotent stem cells
  - UNKNOWN 29(6):528-534 (2011)
  Nature Biotechnology | Research | Article Specification of transplantable astroglial subtypes from human pluripotent stem cells * Robert Krencik1, 2 * Jason P Weick2, 6 * Yan Liu3, 6 * Zhi-Jian Zhang2 * Su-Chun Zhang1, 2, 3, 4, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:528–534Year published:(2011)DOI:doi:10.1038/nbt.1877Received10 January 2011Accepted20 April 2011Published online22 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 Human pluripotent stem cells (hPSCs) have been differentiated efficiently to neuronal cell types. However, directed differentiation of hPSCs to astrocytes and astroglial subtypes remains elusive. In this study, hPSCs were directed to nearly uniform populations of immature astrocytes (>90% S100β+ and GFAP+) in large quantities. The immature human astrocytes exhibit similar gene expression patterns as primary astrocytes, display functional properties such as glutamate uptake and promotion of synaptogenesis, and become mature astrocytes by forming connections with blood vessels after transplantation into the mouse brain. Furthermore, hPSC-derived neuroepithelia, patterned to rostral-caudal and dorsal-ventral identities with the same morphogens used for neuronal subtype specification, generate immature astrocytes that express distinct homeodomain transcription factors and display phenotypic differences of different astroglial subtypes. These human astroglial progenitors and imm! ature astrocytes will be useful for studying astrocytes in brain development and function, understanding the roles of astrocytes in disease processes and developing novel treatments for neurological disorders. View full text Figures at a glance * Figure 1: Differentiation of astroglia from hPSCs. () HPSCs were first differentiated to early neuroepithelial cells (NE) in the absence of exogenous growth factors for 10 d, followed by patterning with morphogens between days 10 and 21. The neural/astroglial progenitors were expanded in the presence of EGF and FGF2, during which progenitors were differentiated for 7 d with CNTF every 30 d for characterization with cell type–specific markers. RA, retinoic acid. () At day 180, immature astrocytes display a stellate morphology, express S100β in both the cytoplasm and nuclei, and express GFAP in a filamentous pattern throughout the cytoplasm. Nuclei are indicated by Hoechst (Ho) staining. () Temporal course comparison of S100β (120 d, P = 0.0055) and GFAP (120 d, P = 0.001; 180 d, P = 0.0066) expression of retinoic acid– and FGF8-specified astroglia (from three separate passages of the H9 hESC line) among total cells. () hESC-astroglia cells express Aldh1L1, but not NG2, in contrast to mouse primary astrocytes. () Western! blotting analysis confirms the expression of GFAP and GLT-1 in day 180 astroglia (RA and FGF8, as in ). () Subsets of astroglia express A2B5, whereas the majority of immature astrocytes express CD44 by 90 d. () NFIA is not expressed in early neuroepithelial cells at day 11, but it begins to be expressed in a small number of progenitors with concomitant down-regulation of PAX6 at day 30 (arrows). By day 180, all cells express NFIA. () Incorporation of BrdU by retinoic acid– and FGF8-specified astroglial progenitors at 60 d (n = 6, P = 0.0043) demonstrates differential proliferation of subtypes. By 180 d, BrdU uptake is not different between groups, and is completely absent in cells after removal of growth factors and addition of CNTF. Scale bars, 50 μM. Data are represented as mean ± s.e.m. *, P < 0.05. * Figure 2: Astroglial subtypes express region-specific proteins. () Differential treatment with patterning molecules (retinoic acid, FGF8 or SHH) from days 10–21 generates cells with distinct expression of homeodomain transcription factors, which is maintained as cells differentiate from neural progenitors (NP) to immature astrocytes. () At day 60, FGF8- but not retinoic acid– specified S100β+ astroglia express OTX2 in nuclei. () Retinoic acid– but not FGF8- specified S100β+ astroglia express HOXB4. GFAP+ immature astrocytes continue to express () OTX2 and () HOXB4 at day 180. () Quantification of regional marker expression of day 120 GFAP+ immature astrocytes (FGF8-specified; OTX2 = 92.1% ± 2.5, HOXB4 = 0. GFAP+ retinoic acid–specified; OTX2 = 3.2% ± 1.3, HOXB4 = 97.6% ± 2.1). () Quantification of day 30 ventralized astroglial progenitors (-SHH; NKX2.1 = 0. +SHH; NKX2.1 = 82.6% ± 7.0). () Day 60 S100β+ astroglia differentiated from SHH-ventralized neural progenitors express NKX2.1. Scale bar, 50 μM. * Figure 3: Functional characteristics of hPSC-derived immature astrocytes. () Immature astrocytes were analyzed by whole-cell patch clamping. (i–iii) Voltage steps (clamped at −70 mV and stepped from −50 to +50 mV at 10 mV increments for 500 ms) induced outward currents in red fluorescent–labeled 4-month astroglia, which significantly decreased in the presence of neurons for 2 weeks (n = 10 for both groups). Action potentials could not be elicited (inset in i). () (i–ii) The inward current response by AMPA was blocked with CNQX and AP5, and the L-glutamate response was partially reduced. (iii) L-Glutamate–induced inward current was reduced by glutamate transporter inhibitors DHK and SOS. () Kinetics of cellular uptake of L-glutamate (starting at 50 μM) was measured in the absence or presence of PDC and Na+ and normalized to μg of protein (n = 3 for each group). () Immature astrocytes propagate calcium waves to adjacent cells upon mechanical stimulation. Calcium wave propagation was measured for anterior and posterior immature astrocyt! es (20 s; retinoic acid = 59.2 μm ± 3.2, FGF8 = 76.0 μm ± 3.1, P = 0.0196. 30 s; retinoic acid = 62.4 μm ± 3.8, FGF8 = 91.4 μm ± 7.8, P = 0.0288, n = 3 (arrowheads) for all groups), both of which were inhibited by 2-APB (20 s p values; retinoic acid = 0.0144, FGF8 = 0.0104. 30 s p values; retinoic acid = 0.0147, FGF8 = 0.0197). () Co-culturing of hESC-derived neurons and immature astrocytes for 3 weeks results in an increased presence of Synapsin 1+ puncta (Syn, n = 3, P = 0.0119). Scale bars, 50 μm. Error bars, s.e.m. *, P < 0.05. * Figure 4: HPSC-derived astroglia retain their identity in vivo. () Illustration of intraventricular transplantation of hESC-derived astroglia and the resulting position of grafted cells. () One hundred days after transplantation, both retinoic acid–specified (n = 3) and FGF8-specified (n = 4) human cells (hNUC+ = red) are present in ventricular areas (outlined with dashed lines), and express GFAP. Arrows indicate the human cells magnified in the insets. () Grafted, retinoic acid–specified human astrocytes (hNUC+ = blue) in the corpus callosum (outlined with dashed lines) express HOXB4 (red, 65/65). In contrast, all of the FGF8-specified hNUC+/GFAP+ cells express OTX2 (red, 52/52). () Illustration of hippocampal transplantation. () Human astrocytes (red) survive and express GFAP but not βIII-tubulin () 6 weeks after transplantation to the adult hippocampus (n = 4 for each group). () Six months after transplantation of day 21 hESC-derived neural progenitors, human astrocytes (white arrow; hNUC+/GFAP+ shown in upper inset) extend proce! sses onto endogenous blood vessels (outlined with dashes) with end feet (yellow arrow; shown in the lower inset on a single plane). () Mouse and human astrocytes exhibit distinct phenotypes, including process length and blood vessel association. Scale bars, 50 μm. * Figure 5: Hypothesis of astroglial subtype specification. The regional identity (anterior-posterior, dorsal-ventral) of astrocytes is determined when early neuroepithelial cells (NE, neural stem cells) are patterned to regional progenitors by morphogens such as retinoic acid and SHH. The regionalized progenitors first give rise to subclasses of neurons and then, during gliogenesis, generate regional-specific astrocytes with functionally distinct characteristics. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jason P Weick & * Yan Liu Affiliations * Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin, USA. * Robert Krencik & * Su-Chun Zhang * Waisman Center, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Robert Krencik, * Jason P Weick, * Zhi-Jian Zhang & * Su-Chun Zhang * Department of Human Anatomy and Histology, Fudan University Shanghai Medical School, Shanghai, China. * Yan Liu & * Su-Chun Zhang * Department of Neuroscience, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Su-Chun Zhang * Department of Neurology, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Su-Chun Zhang Contributions R.K. and S.-C.Z. designed the experiments and wrote the manuscript. R.K., J.P.W., Y.L. and Z.-J.Z. performed the experiments. R.K., J.P.W., Y.L., Z.-.J.Z. and S.-C.Z. analyzed the data. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Su-Chun Zhang Author Details * Robert Krencik Search for this author in: * NPG journals * PubMed * Google Scholar * Jason P Weick Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhi-Jian Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Su-Chun Zhang Contact Su-Chun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Movie 1 (2M) Representative example of calcium wave propagation in FGF8-specified astroglia. * Supplementary Movie 2 (616K) Z series of human astrocyte image in Fig. 4g stained for GFAP (green), human nuclei (red), and total nuclei (blue). PDF files * Supplementary Text and Figures (804K) Supplementary Tables 1 and 2 and Supplementary Figures 1–4 Additional data
• Autoantigen discovery with a synthetic human peptidome
  - UNKNOWN 29(6):535-541 (2011)
  Nature Biotechnology | Research | Article Autoantigen discovery with a synthetic human peptidome * H Benjamin Larman1, 2, 3 * Zhenming Zhao3, 8 * Uri Laserson1, 4, 5 * Mamie Z Li3 * Alberto Ciccia3 * M Angelica Martinez Gakidis3 * George M Church5 * Santosh Kesari6 * Emily M LeProust7 * Nicole L Solimini3 * Stephen J Elledge3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:535–541Year published:(2011)DOI:doi:10.1038/nbt.1856Received15 November 2010Accepted28 March 2011Published online22 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 Immune responses targeting self-proteins (autoantigens) can lead to a variety of autoimmune diseases. Identification of these antigens is important for both diagnostic and therapeutic reasons. However, current approaches to characterize autoantigens have, in most cases, met only with limited success. Here we present a synthetic representation of the complete human proteome, the T7 peptidome phage display library (T7-Pep), and demonstrate its application to autoantigen discovery. T7-Pep is composed of >413,000 36-residue, overlapping peptides that cover all open reading frames in the human genome, and can be analyzed using high-throughput DNA sequencing. We developed a phage immunoprecipitation sequencing (PhIP-Seq) methodology to identify known and previously unreported autoantibodies contained in the spinal fluid of three individuals with paraneoplastic neurological syndromes. We also show how T7-Pep can be used more generally to identify peptide-protein interactions, sugge! sting the broader utility of our approach for proteomic research. View full text Figures at a glance * Figure 1: Construction and characterization of T7-Pep and the PhIP-Seq methodology. () The T7-Pep library is made from 413,611 DNA sequences encoding 36-amino acid peptide that span 24,239 unique ORFs from build 35.1 of the human genome. Each peptide overlaps its neighbors by seven amino acids on each side. () The DNA sequences from are printed as 140-mer oligos on releasable DNA microarrays. () After oligo release, the DNA is PCR-amplified and cloned into a FLAG-expressing derivative of the T7Select 10-3b mid-copy phage display system. (). The T7-Pep library is mixed with patient samples containing autoantibodies. (). Antibodies and bound phage are captured on magnetic beads coated with Protein A and G. (). DNA from the immunoprecipitated phage is recovered. (). Library inserts are PCR-amplified with sequencing adapters. A single nucleotide change (arrow) is introduced for multiplex analysis. () Results of plaque sequencing of 71 phage from T7-Pep Pool 1 and T7-CPep Pool 1. () Histogram showing results from Illumina sequencing of the T7-Pep library. Sevent! y-eight percent of the total area lies between the vertical red lines at 10 and 100 reads, demonstrating the relative uniformity of the library. Representation of each subpool in the T7-Pep library (inset) compared to expected (horizontal red line). aa, amino acid; nt, nucleotide; mt, mutation. * Figure 2: Statistical analysis of PhIP-Seq data. () Comparison of sequencing reads from T7-Pep input library and from patient A immunoprecipitated (IP) phage (Pearson coefficient = 0.435; P≅ 0). Highlighted are all clones with an input abundance of 50 reads (red), and all clones with an input abundance of 100 reads (blue). The target of the SAPK4 control antibody is in green. () Histogram of sequencing reads from the data highlighted in with corresponding colors. The curves are fit with a generalized Poisson (GP) distribution. Pmf, probability mass function of the corresponding GP distribution. x, number of immunoprecipitated clones' sequencing reads. () Poisson distribution parameters lambda and theta for each input abundance, calculated as previously described13. Lambda is regressed to its average value (black dashed line), and theta is linearly regressed (red dashed curve). () Comparison of clone enrichment significances (as −log10P-value) from two independent PhIP-Seq experiments using cerebrospinal fluid from pati! ent A. Red dashed line shows the cutoff for considering a clone to be significantly enriched, and the SAPK4 control antibody target is in green. * Figure 3: Validation of PhIP-Seq candidates. () Western blot with cerebrospinal fluid (CSF) from patient A, staining for full-length TGIF2LX-GFP expressed in 293T cells by transient transfection. Bands corresponding to TGIF2LX-GFP are denoted by an arrow. (For full-length blots, see Supplementary Fig. 9.) () ClustalW alignment of the seven significantly enriched hypothetical protein LOC26080 peptides, and the nine-element MEME-generated recognition motif. () Western blot with cerebrospinal fluid from patient B, staining for indicated full-length proteins expressed in 293T cells by transient transfection. () Bar graph of −log10P-values of enrichment for the indicated TGIF2LX peptides in the three patients. () Immunoprecipitation (IP) of the GAD65-GFP from by CSF from patient B (but not patient A). () Western blot with cerebrospinal fluid from patient C, staining for indicated full-length proteins expressed in 293T cells by transient transfection. () Phage lysates from candidate T7 clones were spotted directly onto nit! rocellulose membranes, which were subsequently immunoblotted with patient cerebrospinal fluid. * Figure 4: PhIP-Seq can identify peptide-protein interactions. GST-RPA2 was used to precipitate phage from the T7-Pep library on magnetic glutathione beads. −Log10P-values of enrichment were calculated using the generalized Poisson method. Clones are arranged in increasing input abundance from left to right. The experiment identified two of the known RPA2 binding partners SMARCAL1 (P < 10−14) and UNG2 (P < 10−5), shown in red. Author information * Abstract * Author information * Supplementary information Affiliations * Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA. * H Benjamin Larman & * Uri Laserson * Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * H Benjamin Larman * Department of Genetics, Harvard University Medical School, and Division of Genetics, Howard Hughes Medical Institute, Brigham and Women's Hospital, Boston, Massachusetts, USA. * H Benjamin Larman, * Zhenming Zhao, * Mamie Z Li, * Alberto Ciccia, * M Angelica Martinez Gakidis, * Nicole L Solimini & * Stephen J Elledge * Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Uri Laserson * Department of Genetics, Harvard University Medical School, Boston, Massachusetts, USA. * Uri Laserson & * George M Church * Division of Neuro-Oncology, Department of Neurosciences, University of California, San Diego, Moores Cancer Center, La Jolla, California, USA. * Santosh Kesari * Agilent Technologies, Genomics, Santa Clara, California, USA. * Emily M LeProust * Present address: Biogen Idec, Cambridge, Massachusetts, USA. * Zhenming Zhao Contributions S.J.E. conceived the project, which was supervised by N.L.S. and S.J.E. Z.Z. designed the DNA sequences for synthesis. Oligo libraries were constructed by E.M.L. Cloning was performed by M.Z.L., M.A.M.G. and N.L.S. The T7-Pep, T7-NPep, and T7-CPep phage libraries were constructed by N.L.S. and characterized by N.L.S. and H.B.L. The PhIP-Seq protocol was developed and implemented by H.B.L. Clinical evaluations and patient sample acquisitions were performed by S.K. Statistical analysis of PhIP-Seq data was conceived by U.L. under the supervision of G.M.C. and implemented by H.B.L. PhIP-Seq candidates were confirmed by H.B.L. The RPA2 experiment was performed by A.C. The manuscript was prepared by H.B.L. and edited by N.L.S. and S.J.E. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Nicole L Solimini or * Stephen J Elledge Author Details * H Benjamin Larman Search for this author in: * NPG journals * PubMed * Google Scholar * Zhenming Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Uri Laserson Search for this author in: * NPG journals * PubMed * Google Scholar * Mamie Z Li Search for this author in: * NPG journals * PubMed * Google Scholar * Alberto Ciccia Search for this author in: * NPG journals * PubMed * Google Scholar * M Angelica Martinez Gakidis Search for this author in: * NPG journals * PubMed * Google Scholar * George M Church Search for this author in: * NPG journals * PubMed * Google Scholar * Santosh Kesari Search for this author in: * NPG journals * PubMed * Google Scholar * Emily M LeProust Search for this author in: * NPG journals * PubMed * Google Scholar * Nicole L Solimini Contact Nicole L Solimini Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Elledge Contact Stephen J Elledge Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Tables 1–3 and Supplementary Figs. 1–9 Additional data
• Global gene disruption in human cells to assign genes to phenotypes by deep sequencing
  - UNKNOWN 29(6):542-546 (2011)
  Nature Biotechnology | Research | Letter Global gene disruption in human cells to assign genes to phenotypes by deep sequencing * Jan E Carette1, 4, 5 * Carla P Guimaraes1, 5 * Irene Wuethrich1, 5 * Vincent A Blomen1, 4, 5 * Malini Varadarajan1 * Chong Sun1 * George Bell1 * Bingbing Yuan1 * Markus K Muellner2 * Sebastian M Nijman2 * Hidde L Ploegh1, 3 * Thijn R Brummelkamp1, 4 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:542–546Year published:(2011)DOI:doi:10.1038/nbt.1857Received15 November 2010Accepted24 March 2011Published online29 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 Insertional mutagenesis in a haploid background can disrupt gene function1. We extend our earlier work by using a retroviral gene-trap vector to generate insertions in >98% of the genes expressed in a human cancer cell line that is haploid for all but one of its chromosomes. We apply phenotypic interrogation via tag sequencing (PhITSeq) to examine millions of mutant alleles through selection and parallel sequencing. Analysis of pools of cells, rather than individual clones1 enables rapid assessment of the spectrum of genes involved in the phenotypes under study. This facilitates comparative screens as illustrated here for the family of cytolethal distending toxins (CDTs). CDTs are virulence factors secreted by a variety of pathogenic Gram-negative bacteria responsible for tissue damage at distinct anatomical sites2. We identify 743 mutations distributed over 12 human genes important for intoxication by four different CDTs. Although related CDTs may share host factors, they a! lso exploit unique host factors to yield a profile characteristic for each CDT. View full text Figures at a glance * Figure 1: Phenotypic interrogation via tag sequencing (PhITSeq). () Approximately 100 million near-haploid KBM7 cells were infected with gene-trap vectors and expanded without selection. Short DNA sequences flanking the inserted gene-trap vectors were amplified and sequenced in parallel and aligned to the human genome. Insertion sites were identified in genes that were expressed and nonexpressed in KBM7 cells. The population of 100 million cells was used to select several thousand clones for particular phenotypes. Selected clones were expanded and used for parallel sequencing for insertion sites. A proximity index, calculated for each insertion site, corresponds to the calculated inverse of the average distance between a specific insertion and its immediate upstream and downstream insertions. () Mutagenized cells were selected with ABT-737 and insertion sites were mapped in the selected populations. N, number of insertions found in each gene. () Immunoblot analysis of BAX and NOXA protein expression in clonally derived cell lines that con! tain gene-trap insertions in corresponding genes. () Insertions in the HBEGF locus in the unselected mutagenized pool and in a cell population selected after exposure to diphtheria toxin. Gene-trap insertions in the same transcriptional orientation as the gene (sense) are depicted in green and in the antisense orientation are drawn in red. Note that selection against HBEGF function enriches for sense orientation insertions in introns but not in exons. * Figure 2: Host factors used by different CDTs. () CDTs are tripartite protein toxins that show the highest sequence conservation in the catalytic CdtB-subunit and lower conservation in the cell binding CdtA and CdtC subunits. Sequence conservation of the four CDTs used in this study is depicted using the H. ducreyi CTD crystal structure11. () CDTs are secreted by pathogenic bacteria that infect and colonize the human body at different anatomical locations such as the oral cavity (A. actinomycetemcomitans) digestive tract (C. jejuni and E. coli) and genitalia (H. ducreyi). () CDTs from different bacterial species induce a G2/M cell cycle arrest in both HeLa cells and KBM7 cells. () PhITSeq screens performed with CDTs secreted by different bacteria. The y axis represents the proximity index calculated for each insertion. The x axis represents the chromosomes in which each insertion is located. N indicates the number of insertions found in each gene. * Figure 3: Genes linked to different phenotypes. () Gene-trap insertions identified in loci essential for CDT intoxication. A color code distinguishes gene-trap insertions that were enriched using distinct CDTs: green, A. actinomycetemcomitans; red, E. coli; blue, C. jejuni; yellow, H. ducreyi. () Loci linked to 12 separate phenotypes. Cells were exposed to small-molecule inhibitors of the Bcl2-family (ABT-737), Chk1-kinase activity (AZD7762), Bcr-Abl-activity (imatinib), or DNA methylation (decitabine). Additional phenotypic selections were done using biological agents such as TRAIL, CDTs, diphtheria toxin, ricin toxin and reovirus. Gene-trap insertions in exonic sequences or in the sense orientation of genes were counted per individual selection. The enrichment P-value was calculated for each gene locus by comparing this number to the number of insertions identified in the same locus within the unselected cell population. Each screen resulted in a distinct set of one to eight genes with high significance. These include a! lready known entry factors used by pathogens such as the entry receptor for diphtheria toxin (HBEGF), the reovirus receptor (F11R) and an enzyme involved in carbohydrate synthesis required for ricin entry (MGAT2). It also includes downstream effectors of kinases, for example. CDC25A in a screen with a Chk1 inhibitor or PTPN1 and PTPN12 identified by Bcr-abl inhibition using imatinib. Strong resistance against decitabine was observed in cells containing mutations in deoxycytidine kinase (DCK), the rate-limiting kinase for activation of several nucleoside analogs20. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jan E Carette, * Carla P Guimaraes, * Irene Wuethrich & * Vincent A Blomen Affiliations * Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA. * Jan E Carette, * Carla P Guimaraes, * Irene Wuethrich, * Vincent A Blomen, * Malini Varadarajan, * Chong Sun, * George Bell, * Bingbing Yuan, * Hidde L Ploegh & * Thijn R Brummelkamp * Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. * Markus K Muellner & * Sebastian M Nijman * Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Hidde L Ploegh * Present addresses: Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA (J.E.C.) and The Netherlands Cancer Institute, Amsterdam, The Netherlands (V.A.B. and T.R.B.). * Jan E Carette, * Vincent A Blomen & * Thijn R Brummelkamp Contributions J.E.C., C.P.G., I.W., V.A.B., M.V., C.S., M.K.M., S.M.N. and T.R.B. designed and performed experiments. B.Y., G.B., J.E.C., V.A.B. and T.R.B. were involved in the bioinformatics. J.E.C., C.P.G., I.W., V.A.B., H.L.P. and T.R.B. wrote the manuscript. Competing financial interests J.E.C. and T.R.B. are named inventors on a patent application on technology described in this manuscript. S.M.N. and T.R.B. are co-founders of an early-stage startup company involved in haploid genetic approaches. Corresponding authors Correspondence to: * Hidde L Ploegh or * Thijn R Brummelkamp Author Details * Jan E Carette Search for this author in: * NPG journals * PubMed * Google Scholar * Carla P Guimaraes Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Wuethrich Search for this author in: * NPG journals * PubMed * Google Scholar * Vincent A Blomen Search for this author in: * NPG journals * PubMed * Google Scholar * Malini Varadarajan Search for this author in: * NPG journals * PubMed * Google Scholar * Chong Sun Search for this author in: * NPG journals * PubMed * Google Scholar * George Bell Search for this author in: * NPG journals * PubMed * Google Scholar * Bingbing Yuan Search for this author in: * NPG journals * PubMed * Google Scholar * Markus K Muellner Search for this author in: * NPG journals * PubMed * Google Scholar * Sebastian M Nijman Search for this author in: * NPG journals * PubMed * Google Scholar * Hidde L Ploegh Contact Hidde L Ploegh Search for this author in: * NPG journals * PubMed * Google Scholar * Thijn R Brummelkamp Contact Thijn R Brummelkamp Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Other * Supplementary Table 1 (36M) Supplementary Table 1 * Supplementary Table 2 (117M) Supplementary Table 2 * Supplementary Table 3 (312K) Supplementary Table 3 * Supplementary Table 4 (108K) Supplementary Table 4 * Supplementary Table 5 (92K) Supplementary Table 5 * Supplementary Table 6 (272K) Supplementary Table 6 * Supplementary Table 7 (120K) Supplementary Table 7 * Supplementary Table 8 (72K) Supplementary Table 8 * Supplementary Table 9 (192K) Supplementary Table 9 * Supplementary Table 10 (140K) Supplementary Table 10 * Supplementary Table 11 (144K) Supplementary Table 11 * Supplementary Table 12 (104K) Supplementary Table 12 * Supplementary Table 13 (160K) Supplementary Table 13 * Supplementary Table 14 (84K) Supplementary Table 14 * Supplementary Table 15 (116K) Supplementary Table 15 * Supplementary Table 16 (328K) Supplementary Table 16 * Supplementary Table 17 (60K) Supplementary Table 17 PDF files * Supplementary Text and Figures (2M) Supplementary Figs. 1–3 Additional data
• Assessing the impact of China's Thousand Talents Program on life sciences innovation
  - UNKNOWN 29(6):547-548 (2011)
  Nature Biotechnology | Careers and Recruitment Assessing the impact of China's Thousand Talents Program on life sciences innovation * Erik Lundh1Journal name:Nature BiotechnologyVolume: 29,Pages:547–548Year published:(2011)DOI:doi:10.1038/nbt.1894Published online07 June 2011 How will China's push into the life sciences affect US organizations? 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 * Erik Lundh is at J. Robert Scott, San Francisco, California, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Erik Lundh Author Details * Erik Lundh Contact Erik Lundh Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• People
  - UNKNOWN 29(6):550 (2011)
  Article preview View full access options Nature Biotechnology | Careers and Recruitment | People People Journal name:Nature BiotechnologyVolume: 29,Page:550Year published:(2011)DOI:doi:10.1038/nbt.1872Published online07 June 2011 Personalized cancer diagnostics company Foundation Medicine (Cambridge, MA, USA) has announced the appointment of (right) as president and CEO. He succeeds founding CEO , who will remain on the company's board of directors and will assume the position of chairman. Most recently, Pellini held the position of president and COO of Clarient. Before that, he served as vice president, life sciences at Safeguard Scientifics, executive vice president and COO at Lakewood Pathology Associates and president and CEO of Genomics Collaborative. "The rapid advancements in genomics and growth in the number of available targeted therapies provide ever-expanding opportunities for more effective, individualized treatment of cancer patients," says Pellini. "Foundation Medicine's approach, which uses next-generation sequencing to analyze relevant genomic information in cancer tissue, represents a potential breakthrough in cancer diagnostics. I look forward to working with the Foundation Medicine team to bring this important technology to the clinic in order to benefit physicians and their patients." (right) has resigned his position as the director of the US National Institute of Food and Agriculture "to spend more time with his wife, his children and his grandchildren," according to a memo released by the US Department of Agriculture. Beachy was the founding president of the Donald Danforth Plant Science Center and is a member of the US National Academy of Sciences and Science Academy, among others, and is a recipient of the prestigious Wolf Prize in Agriculture. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data