Latest Articles Include:
- Inadequately met needs
- Nat Biotech 29(5):371 (2011)
Nature Biotechnology | Editorial Inadequately met needs Journal name:Nature BiotechnologyVolume: 29,Page:371Year published:(2011)DOI:doi:10.1038/nbt.1880Published online06 May 2011 Innovative drug development is already difficult, but it's particularly difficult in chronic diseases with existing treatments. View full text Additional data - PARP inhibitors stumble in breast cancer
- Nat Biotech 29(5):373-374 (2011)
Nature Biotechnology | News PARP inhibitors stumble in breast cancer * Malini Guha1Journal name:Nature BiotechnologyVolume: 29,Pages:373–374Year published:(2011)DOI:doi:10.1038/nbt0511-373Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. PARP inhibitors as a treatment for cancer. PARP inhibition causes death by synthetic lethality only in tumor cells where DNA repair by homologous recombination (HR) is hampered (e.g., BRCA-negative cells). BER: Base excision repair. In early 2011, one of the most exciting classes of compounds in oncology—inhibitors of the DNA repair enzyme poly-(ADP ribose) polymerase (PARP)—suffered a double blow. In January, Paris-based Sanofi-aventis announced that iniparib (BSI-201), the most advanced PARP inhibitor currently in trials, failed to prolong survival in its first phase 3 trial in metastatic, triple-negative breast cancer (TNBC) despite promising phase 2 trial results (N. Engl. J. Med., 205–214, 2011). Then, the following month, London-based AstraZeneca said that it would not, as it had previously planned, pursue phase 3 development of its PARP inhibitor olaparib (AZD-2281) in hereditary BRCA1- and BRCA 2-associated breast cancer—considered one of its strongest indications. "Everyone was depressed after the phase 3 iniparib trial, but within a very short period of time, there was a resurrection of interest," says Alan D'Andrea, a basic researcher at Dana-Farber Cancer Institute in Boston who focuses on DNA repair. "I actually think the irony of all of this is that the failure of iniparib has lit a fire under companies that have bona fide PARP inhibitors who were sitting low before." To get its hands on iniparib, Sanofi-aventis paid up to $500 million, depending on milestones, in April 2009 when it bought BiPar Sciences, a S. San Francisco–based biotech that is now a subsidiary (Nat. Biotechnol.27, 784–786, 2009). AstraZeneca also shelled out $230 million for the PARP inhibitor olaparib in 2006 when it purchased one of the early pioneers in the field: Kudos Pharmaceuticals of Cambridge, UK. Other large players, including New York–based Pfizer; Merck, of Whitehouse Station, New Jersey; Cephalon, of Frazer, Pennsylvania; and Chicago-based Abbott, also have their own PARP inhibitors in development (Table 1). Table 1: PARP-1 inhibitors in mid- to late-stage trials Full table In 2009, excitement over this class of drugs escalated during the American Society for Clinical Oncology (ASCO) annual meeting in Orlando, Florida, when Sanofi-aventis presented phase 2 results for its front-runner iniparib. The efficacy results were impressive for the small molecule, particularly as the indication was the hard-to-treat TNBC, named such because the cancer cells lack receptors for estrogen and progesterone and do not overexpress human epidermal growth factor receptor 2 (HER-2). The company expected to file a New Drug Application for iniparib's approval in metastatic TNBC in the first half of 2011, but the recent phase 3 failure has put a check on these plans. The company will now meet with regulators to discuss the trial results, but Morgan Stanley analyst Andrew Baum in London believes it is "highly unlikely" that iniparib will be approved without a confirmatory phase 3 trial. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * London * Malini Guha Author Details * Malini Guha Search for this author in: * NPG journals * PubMed * Google Scholar - Human iPSC and ESC translation potential debated
- Nat Biotech 29(5):375-376 (2011)
Nature Biotechnology | News Human iPSC and ESC translation potential debated * Jeffrey L Fox1Journal name:Nature BiotechnologyVolume: 29,Pages:375–376Year published:(2011)DOI:doi:10.1038/nbt0511-375aPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Kuniko Kadoya at ViaCyte ViaCyte's pancreatic islet-like structures (shown above) obtained by transplanting hESC-derived pancreatic progenitors into rodents. The structures contain cells responsive to glucose (blue for insulin, red for somatostatin; and green for glucagon). The first meeting dedicated to charting a road map for pluripotent stem cells to move into the clinic held jointly by the US National Institutes of Health (NIH) and the Food and Drug Administration (FDA) took place in March. Industry, academia, clinical scientists, the FDA and the NIH gathered for a two-day workshop, "Pluripotent Stem Cells in Translation: Early Decisions," in Bethesda, Maryland, to debate challenges and issues in the commercialization of stem cell therapies—whether derived from human embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) (Table 1). View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Washington, DC * Jeffrey L Fox Author Details * Jeffrey L Fox Search for this author in: * NPG journals * PubMed * Google Scholar - Melanoma antibody approved
- Nat Biotech 29(5):375 (2011)
Nature Biotechnology | News Melanoma antibody approved * Ken GarberJournal name:Nature BiotechnologyVolume: 29,Page:375Year published:(2011)DOI:doi:10.1038/nbt0511-375bPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. BMS BMS headquarters in NYC. The March 25 US Food and Drug Administration (FDA) approval of Bristol-Myers Squibb's Yervoy (ipilimumab) for metastatic melanoma was expected, but the breadth of the approval was not. Yervoy, a human monoclonal antibody targeting cytotoxic T-lymphocyte activator-4 (CTLA4) developed by the New York–based company, is the first agent to prolong survival in a phase 3 trial in metastatic melanoma (Nat. Biotechnol.28, 763–764, 2010). The FDA has given the green light for Yervoy to be used in a first-line setting even though the pivotal trial included only individuals who had progressed on other treatments. It was "exactly the right decision," says oncologist Mario Sznol of Yale University in New Haven, Connecticut, as no current first-line treatment improves survival in metastatic melanoma. FDA approval also allows patients who respond initially to Yervoy, but who later relapse, to receive another course of the drug. Sznol expects rapid adoption of the drug by oncologists! , despite a $120,000 wholesale price tag for a single four-infusion course of treatment. "The first thing that has to be on your mind when somebody comes in with metastatic melanoma would be ipilimumab, based on the data," Sznol says. Chris Schott, a pharma analyst at JP Morgan in New York, raised his earlier Yervoy estimates based on the higher-than-expected pricing, and now forecasts sales of $170 million in 2011, growing to $1.25 billion by 2015. Defending the price, Bristol-Myers Squibb spokesperson Sarah Koenig stresses the company's aggressive patient-assistance program. In the US, this "will enable coverage of virtually all, approximately 98%, of uninsured patients," she writes in an e-mail. Another metastatic melanoma drug likely to win approval in the near term is PLX4032 (vemurafenib). PLX4032, a small-molecule inhibitor of mutant BRAF, was developed by the Berkeley, California–based Plexxikon, which was acquired by the Tokyo-based Daiichi Sankyo on Apri! l 4. PLX4032 produces higher response rates than Yervoy and an! undisclosed survival benefit, although virtually all individuals taking the treatment relapse. So the drug probably won't hurt Yervoy sales even in the roughly half of metastatic melanoma patients who qualify for PLX4032, says Sznol, as most will end up taking Yervoy eventually. Plexxikon plans to apply for FDA registration this year. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Ken Garber Search for this author in: * NPG journals * PubMed * Google Scholar - $1.3 billion to translate
- Nat Biotech 29(5):376 (2011)
Nature Biotechnology | News $1.3 billion to translate * Jennifer RohnJournal name:Nature BiotechnologyVolume: 29,Page:376Year published:(2011)DOI:doi:10.1038/nbt0511-376aPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. A £775 million ($1.3 billion) funding boost for National Health Service (NHS)–university partnerships to pursue translational research was announced in March. The UK's National Institute for Health Research (NIHR) will make the largest ever translational research—over five years. The grant scheme builds on the recently unveiled Model Industry Collaborative Research Agreement (MICRA) to broker research collaborations among universities, industry and the NHS. MICRA aims to help industry by identifying suitable clinicians and researchers for collaborations, as well as streamlining the negotiation and contracting process and ensuring that intellectual property can be assigned flexibly. Rob Winder of the BioIndustry Association in London said that even though biotech won't get funded directly by this scheme, it should lubricate their association with academia, help clinical trials start more quickly and otherwise speed up innovation. Industry is "very keen" on the new sc! heme, he added. According to Sally Davies, the UK's Chief Medical Officer and Director General of R&D at the Department of Health, the NIHR's researchers will play a key role in partnering with biotech companies. They already have a track record of liaising successfully with biotech in the fields of liver disease, regenerative medicine and DNA vaccines. The funding will prioritize cancer, heart disease and dementia. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Jennifer Rohn Search for this author in: * NPG journals * PubMed * Google Scholar - Pharma wins vaccine case
- Nat Biotech 29(5):376 (2011)
Nature Biotechnology | News Pharma wins vaccine case * Stephen StraussJournal name:Nature BiotechnologyVolume: 29,Page:376Year published:(2011)DOI:doi:10.1038/nbt0511-376bPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Vaccine makers breathed a sigh of relief after the US Supreme Court ruled on February 22 that the parents of Hannah Bruesewitz, who experienced seizures and developmental problems after receiving a Wyeth vaccine, did not have the right to sue the company in a state court. The Bruesewitzes claimed Hannah's problems began after she received the combined Corynebacterium diphtheria toxoid/Clostridium tetani toxoid/pertussis (DTP) vaccine against diphtheria, tetanus and whooping cough. They brought their petition to a Pennsylvania state court after their case was dismissed by a special Vaccine Court set up by a 1986 Act over fears at the time that lawsuits would force companies to stop making vaccines. The Act says suits cannot be filed against manufacturers if the injury was "unavoidable." In the Bruesewitz v. Wyeth case, the petitioners argued that Wyeth, now owned by New York–based Pfizer, could have put a vaccine with fewer side effects on the market earlier and thus th! eir daughter's injury was avoidable (Nat. Biotechnol.28, 1228, 2010). But the Supreme Court's Justice Antonin Scalia dismissed these claims stating that "drug manufacturers often could trade a little less efficacy for a little more safety, but the safest design is not always the best one." Marion Burton, president of the American Academy of Pediatrics, applauded the decision saying, "The Supreme Court's ruling keeps manufacturers from abandoning the vaccine market." View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Stephen Strauss Search for this author in: * NPG journals * PubMed * Google Scholar - Chinese biotechs wrestle with transparency, cultural hurdles
- Nat Biotech 29(5):377-378 (2011)
Nature Biotechnology | News Chinese biotechs wrestle with transparency, cultural hurdles * Hepeng Jia1 * Feng Tang1 * Brian Orelli2Journal name:Nature BiotechnologyVolume: 29,Pages:377–378Year published:(2011)DOI:doi:10.1038/nbt0511-377Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. AP China's burgeoning economy entices Western entrepreneurs to its biotech hubs in Shanghai (pictured), Guangzhou in Shenzhen, Zhongguancun in Beijing and BioBay in Suzhou. The April announcement by the Chinese government of another $125 billion investment in healthcare signals the country's continuing commitment to improving the health and wellness of its population. Lured by the promise of this rapidly expanding market, Western investors and companies have been increasingly looking for opportunities in Chinese biotechs. But a recent lawsuit resolution between Benda Pharmaceutical, the first company to market a gene therapy, and its US backers, as well as irregularities in financial disclosures of the probiotic maker China Biotics, highlight the potential pitfalls facing investors who make the plunge into the country's fledgling sector. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Beijing * Hepeng Jia & * Feng Tang * San Diego * Brian Orelli Author Details * Hepeng Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Orelli Search for this author in: * NPG journals * PubMed * Google Scholar - OncoTrack tests drugs in virtual people
- Nat Biotech 29(5):378 (2011)
Nature Biotechnology | News OncoTrack tests drugs in virtual people * Markus ElsnerJournal name:Nature BiotechnologyVolume: 29,Page:378Year published:(2011)DOI:doi:10.1038/nbt0511-378Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. A new European consortium will use next-generation biomarkers to build a virtual patient in which to test colon cancer treatments. OncoTrack, announced in March is one of Europe's largest collaborations, a five-year project involving seven academic institutions and 11 industry partners coordinated by the Leverkusen-based Bayer HealthCare Pharmaceuticals and the Max Planck Institute for Molecular Genetics in Berlin. "This is what is really new here—we will have a virtual patient that we can use to try all possible treatments and see what is likely to work," says the academic coordinator of OncoTrack, Hans Lehrach of the Max-Planck Institute for Molecular Genetics. The project will gather large-scale genomic and epigenetic sequences, as well as tumor phenotypic data, from individuals with colon cancer to provide a detailed characterization of each tumor type. The aim is to improve diagnosis and predict an individual's response to therapy. "We know that colon carcinoma ! is a very heterogeneous disease, and we want to provide a comprehensive description of this heterogeneity at a genetic level," says David Henderson, the project's coordinator at Bayer. The genome sequences of primary tumors and metastases for each individual will be compared to their germline genome. The genomic data will be complemented with a characterization of DNA methylation, the transcriptome and various cell physiological parameters. "The inclusion of methylome analysis can be expected to significantly enhance our ability to target a wider spectrum of cancer-specific processes," says Stephan Beck of University College London, who oversees the epigenetics part of OncoTrack. To convert these combined phenotypic and genotypic data into clinically useful information, all experimental results will be fed into a computer model of the patient's cancer cell to help identify signaling pathways that present promising targets for each person's treatment. "We also hope t! hat a better understanding of the biology of colon cancer will! lead to the rational discovery of diagnostic markers in the serum of patients," adds Henderson. In addition, the project aims to develop a series of new cancer cell lines and mouse tumor models from the patient-derived material, which will facilitate preclinical research on tumor biology and experimental therapeutics. The total budget is 25.8 ($37.2) million, which includes 16.1 ($23.3) million from the European Union as part of the Innovative Medicines Initiative and 9.7 ($14) million contributed by industry partners View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Markus Elsner Search for this author in: * NPG journals * PubMed * Google Scholar - Sequencing firms eye pathology labs as next big market opportunity
- Nat Biotech 29(5):379-380 (2011)
Nature Biotechnology | News Sequencing firms eye pathology labs as next big market opportunity * Laura DeFrancesco1 * Nidhi Subbaraman2Journal name:Nature BiotechnologyVolume: 29,Pages:379–380Year published:(2011)DOI:doi:10.1038/nbt0511-379Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Gary Porter/MCT/newscom Six-year-old Nicholas Volker enjoys his meal and recovers his strength at the Children's Hospital of Wisconsin after genome sequencing pinpointed a mutation in the X-linked inhibitor of apoptosis, helping to diagnose and treat his problem. A six-year-old boy's life saved by whole-exome sequencing is a recent example of how sequencing has crossed the threshold into clinical management. The case, reported in March in Genetics in Medicine, baffled physicians at the Medical College of Wisconsin in Milwaukee, Wisconsin. The child was wasting away, despite over 100 surgeries to repair multiple intestinal fistulas. Clinicians could not diagnose the problem, until sequencing revealed the boy had a mutation in XIAP (X-linked inhibitor of apoptosis), which was known to be linked to another pathology remedied by bone marrow transplantation. A transplant saved the boy's life. In February, Richard Lifton, director of the Yale Center for Human Genetics and Genomics in New Haven, published another triumph of genome sequencing over disease. Using exome sequencing on four individuals with a severe form of hypertension, the Yale group identified a mutation in the gene encoding the potassium ion channel KCNJ5 as driving the deve! lopment of aldosterone-producing tumors in the adrenal gland. As DNA sequencing gains credence in pathology laboratories (Table 1), next-generation instrument providers view the trend of solving mystery diseases by sequencing either the total genome or in some cases, all gene coding regions (exome) as an opportunity to expand their customer base beyond the research community. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Pasadena * Laura DeFrancesco * New York * Nidhi Subbaraman Author Details * Laura DeFrancesco Search for this author in: * NPG journals * PubMed * Google Scholar * Nidhi Subbaraman Search for this author in: * NPG journals * PubMed * Google Scholar - Flawed arithmetic on drug development costs
- Nat Biotech 29(5):381 (2011)
Nature Biotechnology | News Flawed arithmetic on drug development costs * Nidhi Subbaraman1Journal name:Nature BiotechnologyVolume: 29,Page:381Year published:(2011)DOI:doi:10.1038/nbt0511-381aPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Associated Press R&D figures contested A study reporting that the median cost of bringing a new drug to market is as low as $59 million has met with considerable controversy. The authors, Donald W. Light of the University of Pennsylvania and the University of Medicine and Dentistry in New Jersey, and Rebecca Warburton, of the University of Victoria in British Columbia, Canada, estimate in the journal BioSocieties (6, 34–50, 2011) that the figures touted by the industry are inflated. Critics retort that although the authors rightly critique the lack of transparency and limited sample of companies used in previous estimates, Light and Warburton wrongly assume that clinical trial and regulatory review times are decreasing and underestimate drug discovery attrition rates and risk. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * New York * Nidhi Subbaraman Author Details * Nidhi Subbaraman Search for this author in: * NPG journals * PubMed * Google Scholar - Around the world in a month
- Nat Biotech 29(5):381 (2011)
Nature Biotechnology | News Around the world in a month Journal name:Nature BiotechnologyVolume: 29,Page:381Year published:(2011)DOI:doi:10.1038/nbt0511-381bPublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Q1 strong out of the gates
- Nat Biotech 29(5):382 (2011)
Nature Biotechnology | News | Data Page Q1 strong out of the gates * Walter Yang1Journal name:Nature BiotechnologyVolume: 29,Page:382Year published:(2011)DOI:doi:10.1038/nbt.1870Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Excluding partnership monies, biotechs raised $13.2 billion in Q1, the first time since Q1 in 2000, during the genomics bubble, when the industry raised >$13 billion. Deals by Gilead Pharmaceuticals (Foster City, California) as well as specialty pharmas pushed debt financings up to $9.5 billion, nearly five times the amount raised in Q1 last year. Follow-on offerings were also up 33%, but venture money and initial public offerings lagged. Stock market performance Box 1: Stock market performance Full box Global biotech industry financing 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 * Walter Yang is Research Director at BioCentury Author Details * Walter Yang Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Drug pipeline: Q111
- Nat Biotech 29(5):383 (2011)
Nature Biotechnology | News | Data Page Drug pipeline: Q111 * Wayne Peng1Journal name:Nature BiotechnologyVolume: 29,Page:383Year published:(2011)DOI:doi:10.1038/nbt.1871Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Drug approvals picked up speed in the US and Europe in Q1. Registrations included Bristol-Myers Squibb's Yervoy (ipilimumab) for melanoma and Human Genome Sciences' Benlysta (belimumab) for lupus. However, MannKind's inhaled insulin (Afrezza), Abbott Laboratories' ABT-874 (briakinumab) and Protalix BioTherapeutics' Uplyso (taliglucerase) suffered setbacks. Efficacy data are in for several new cell and gene therapies and Baxter's vero cell–derived flu vaccine showed promise in a key US phase 3 trial. US regulatory approvals by drug class Box 1: US regulatory approvals by drug class Full box Notable regulatory approvals (Q1 2011) 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 * Wayne Peng is Emerging Technology Analyst, Nature Publishing Group Author Details * Wayne Peng Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Peering inside Alzheimer's brains
- Nat Biotech 29(5):384-387 (2011)
Nature Biotechnology | News | News Feature Peering inside Alzheimer's brains * Gunjan Sinha1Journal name:Nature BiotechnologyVolume: 29,Pages:384–387Year published:(2011)DOI:doi:10.1038/nbt.1863Published online06 May 2011 Last year's failure of Eli Lilly's drug semagacestat in late-stage clinical trials was the latest in a long line of setbacks for novel Alzheimer's therapies. But advances in imaging and biomarker identification are providing added impetus to ongoing drug development. Gunjan Sinha reports. View full text Additional data Affiliations * Berlin * Gunjan Sinha Author Details * Gunjan Sinha Search for this author in: * NPG journals * PubMed * Google Scholar - Safe and sound
- Nat Biotech 29(5):388-389 (2011)
- Overhauling the reimbursement system for molecular diagnostics
- Nat Biotech 29(5):390-391 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence Overhauling the reimbursement system for molecular diagnostics * Nafees N Malik1Journal name:Nature BiotechnologyVolume: 29,Pages:390–391Year published:(2011)DOI:doi:10.1038/nbt.1869Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. To the Editor: The commentary by Schulman and Tunis1 published last November highlights the lack of clinical utility data (that is, clinical evidence obtained using scientifically robust methods, ideally controlled studies, that shows an improvement in health outcomes in patients) for molecular diagnostic tests as a major barrier to securing coverage for such tests from US healthcare payers. This argument is well supported by recent studies2, 3 that demonstrate that the robustness of clinical data available for biomarker tests is the strongest predictor of gaining coverage. The dilemma is that companies developing molecular diagnostics are deterred from conducting studies to assess clinical utility because such trials are complicated, expensive and time consuming to run and are, moreover, not directly required by the US Food and Drug Administration (FDA) in approving tests. Healthcare insurers, on the other hand, are reluctant to reimburse biomarker tests without evidence of clinical utili! ty as these may prove ineffective in improving patient outcomes and thus be financially wasteful at a time when it has become critical to control rising healthcare costs. Schulman and Tunis1 advocate that coverage with evidence development (CED) programs should be used more widely in the United States as they will allow provisional coverage of new molecular diagnostic tests while clinical utility data are collected to reach a definitive decision regarding coverage. When data that would meet the normal standard for securing coverage are generated (that is, data that demonstrate a test is medically necessary) permanent coverage will follow. This is a worthy initiative with real merit, but I believe that the implementation of CED programs is only a partial solution to healthcare payers' hesitancy in granting coverage to new molecular diagnostics. It is also worth remembering that payer coverage of biomarker tests will become an increasingly important topic as growing numbers of products are approved in the near future—consultants PricewaterhouseCoopers (London) forecast that the US molecular diagnostics market will more than double from $3 bil! lion in 2009 to $7 billion in 2015 (ref. 4). Why is payer coverage of new molecular diagnostics such a key issue? Personalized medicine is expected to transform healthcare. Its success will very much depend upon the development of molecular diagnostic tests that, first, allow drugs to be tailored to individuals, leading to improved drug efficacies and reduced drug side effects, and that, second, allow determination of the genetic susceptibility of an individual to a particular disease, so that strategies to prevent or delay its onset can be instigated. Healthcare payers will also benefit from personalized medicine as it promises to reduce healthcare costs in the long term. If molecular diagnostic tests face overwhelming barriers to securing coverage from healthcare payers then companies will be less likely to develop them, which will in turn seriously impede progress in personalized medicine. I believe that CED programs may have a somewhat limited impact on incentivizing companies to develop new molecular diagnostics. CED programs are applicable to biomarker tests that are already on the market, which have not secured coverage from payers. For a company considering whether to develop a new molecular diagnostic test, CED programs are probably unlikely to sufficiently mitigate the risk that coverage may not be gained after a substantial financial and time investment in bringing the product to the market. What's more, the drive for CED adoption is being spearheaded by Medicare5, 6, 7, which has several potential drawbacks (although Medicare should be praised for showing vision in pursuing CED). Medicare, which is the single largest healthcare payer in the United States, is concerned with medical advances applicable to its coverage population of 39 million senior citizens and 8 million nonelderly people with certain disabilities8. This has three potentially serious repercussions. First, because CED is being led by Medicare, molecular diagnostic tests to guide drug prescribing in diseases affecting younger people may have very few CED programs, resulting in companies being less willing to develop them, as the probability of securing coverage is diminished. Second, Medicare does not cover biomarker tests that predict an individual's genetic predisposition to a specific disease, so that measures to prevent it can be initiated. The motivation to develop such screening tests, which are also more relevant to younger people, will thus be considerably less. This has important implications as disease prevention may be one of the only realistic means by which to control the escalating cost of healthcare in the coming decades. Third, Medicare may justifiably decide not to cover a molecular diagnostic because a CED program demonstrates that it has insufficient clinical utility in the elderly population. However, because Medicare coverage decisions heavily influe! nce other healthcare payers, coverage for that particular test may also be denied in other population groups (for example, younger people) without clinical utility data ever being collected in them (and in whom data may have shown improved health outcomes). Molecular diagnostic test developers face two critical healthcare payer challenges: first, obtaining coverage and, second, securing an appropriate reimbursement payment for new tests. In terms of the challenge of whether a biomarker test will be granted coverage by health insurers, methods should be implemented from the start of test development that would minimize the risk of noncoverage due to insufficient clinical utility data. This approach will provide companies with a greater incentive to develop molecular diagnostics compared with methods, such as CED programs, which are only relevant once a product has been licensed. What pre-approval measures could be implemented to help secure coverage for molecular diagnostic tests as promptly as possible upon regulatory approval? First, companies and healthcare insurers should engage in dialog from the start of product development to ensure that all parties understand what clinical utility data will be necessary to obtain coverage. Second, scientific studies to demonstrate clinical utility and pharmacoeconomic analyses to show cost effectiveness should be conducted alongside regulatory trials (rather than after approval) to allow greater alignment of regulatory and coverage decisions. The FDA and the US Centers for Medicare and Medicaid Services have recently signed a memorandum of understanding to share data, which may be a first step on the road to parallel reviews for regulatory approval and Medicare coverage9. Third, clinical studies completed during the development of a molecular diagnostic test should be published—ideally in high-profile, pee! r-reviewed journals—to help maximize the scientific credibility of the test. In addition to the measures outlined, when molecular diagnostic tests are approved, the FDA should not be unduly hesitant in designating their usage as 'required' before the prescribing of relevant or companion drugs, when high-quality data demonstrate that the test allows personalization of medical therapy for patients, leading to a beneficial impact on drug efficacy or side effects. This would signal the medical necessity of a particular test and so help healthcare insurers reach more rapid decisions regarding coverage. Testing for most biomarkers is currently categorized as 'recommended' or for 'information only'10. In terms of the challenge of securing suitable reimbursement payments for new molecular diagnostics, healthcare payers have traditionally seen diagnostic tests as low price commodities and have not reimbursed them on the basis of the value they generate. This argument is supported by the fact that diagnostic tests account for only ~5% of hospital costs and 2% of Medicare expenditure, but influence 60–70% of all treatment decisions11. Medicare usually pays for new tests at comparable rates to older tests that use similar laboratory technologies, in a practice known as 'cross-walking', rather than at a rate that reflects their innovation, capacity to benefit patients and ability to decrease healthcare costs. Rarely, when a new test has no precedent, payment is set by a process called 'gap-filling', in which Medicare establishes a payment level, using a complicated, unclear and time-consuming assessment process, which is generally considered unsatisfactory. To make matters wo! rse, Medicare's low payments for diagnostic tests are widely used as a benchmark by other healthcare insurers. To incentivize companies to develop novel biomarker tests, an updated reimbursement system is required—one that pays for molecular diagnostics on the basis of the value they create. Traditional diagnostics generally cost <$100, whereas developers of new molecular biomarker tests are often seeking reimbursement at >$1,000. It is imperative that any demand for premium pricing is backed up by robust clinical and pharmacoeconomic data. For example, Genomic Health (Redwood City, CA, USA) gathered the data necessary to allow its Oncotype DX test, which estimates the likelihood of disease recurrence and of chemotherapy benefit in certain types of breast cancer, to be reimbursed at >$3,500 (ref. 12). 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 * Institute of Biotechnology, University of Cambridge, Cambridge, UK. * Nafees N Malik Competing financial interests N.N.M. presently works for GlaxoSmithKline, but the correspondence is based on work done at the University of Cambridge. Corresponding author Correspondence to: * Nafees N Malik Author Details * Nafees N Malik Contact Nafees N Malik Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Interaction databases on the same page
- Nat Biotech 29(5):391-393 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence Interaction databases on the same page * Andrei L Turinsky1 * Sabry Razick2, 3 * Brian Turner1 * Ian M Donaldson2, 4 * Shoshana J Wodak1, 5, 6 * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:391–393Year published:(2011)DOI:doi:10.1038/nbt.1867Published online06 May 2011 To the Editor: Your journal has published standards, such as the HUPO (Human Proteome Organization; Montreal, QC, Canada) Proteomics Standards Initiative–Molecular Interaction (PSI-MI) controlled vocabulary and data structure1, which, together with the continuing efforts of the International Molecular Exchange (IMEx) consortium2, have made it possible to aggregate protein-protein interaction (PPI) data from multiple sources into larger networks amenable to systematic analysis. Although the aggregated data that are available are useful, they are only partially consolidated owing to many outstanding issues. Among these issues are the endemic problem of matching gene and protein identifiers across databases, and varying practices in recording the organism where the interaction has been observed. Databases also tend to use different conventions for representing multiprotein complexes identified by various detection methods. Likewise, high-throughput studies may report raw unprocessed data in! addition to a high-confidence subset of the data, but there is no general agreement between databases on which of these is best fit for redistribution. Two recent reports by our laboratories3, 4 and the iRefWeb interface (http://wodaklab.org/iRefWeb/) have brought these issues to the forefront, making them more transparent to both data 'consumers' and data providers. Here, we briefly summarize our findings and suggest how this increased transparency will raise awareness in end users and incite all stakeholders, which include not only the databases, but also the journals and authors2, 5, to move toward greater standardization of data archiving and curation practices. This will make it possible to focus on the more fundamental challenges of curating and gaining insight from physical interactions between proteins, which should help unravel the complexity of cellular processes and predict disease outcomes. iRefWeb3 is a web resource that consolidates PPI data from ten major public databases (BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID), which each curate and archive PPIs from the scientific literature (references to the individual databases can be found in the Supplementary Note). Previous consolidation efforts have focused primarily on physical protein interactions6, 7, 8, although some projects also integrate additional types of data9. The iRefIndex consolidation procedure behind iRefWeb is one of the most rigorous and thorough to date. It is unique among PPI data integrators in using a well-defined and universal method to assign identifiers to both interaction records and their participants10 (http://irefindex.uio.no/wiki/iRefIndex). The system also records and distributes process-provenance related to this assignment and the data it operates on (for further details on provenance, see Supplementary Note). As a result, the integration method, which i! ncludes isoform normalization, enables data tracking and auditing in a manner that is transparent, reversible, reproducible and universally accessible. These features played a critical role in enabling our studies and allowed us to provide detailed feedback to the source databases. In a follow-up study4, we used iRefWeb to systematically compare the interactions and proteins curated by different databases from the same publication. An interaction and the proteins that form it are two basic descriptors that should ideally be specified unambiguously, and can be readily compared using completely automatic procedures. A total of 15,471 shared publications were analyzed, revealing that, on average, two databases fully agreed on only 42% of the interactions and 62% of the proteins curated from the same publication. Agreement varied for different organisms (Fig. 1a) and different databases (Fig. 1b). back to article Figure 1: Agreement between the information curated by major public protein-interaction databases from shared publications. () Average level of agreement of the interactions (horizontal axis) and the proteins (vertical axis) curated by nine major public databases from shared publications describing experiments in various organism categories. Agreement is measured by a similarity coefficient whose values range from 0 (complete disagreement) to 1 (or 100%) (complete agreement)4. The main organism categories are indicated. For each category, the size of the data point is proportional to the number of instances where two databases curate the same publication, and where at least one database records interactions from the corresponding organism category. The pie charts for the three largest organism categories (yeast (Saccharomyces cerevisiae), human and mouse) illustrate the fraction of theses instances where the two databases record PPIs from different sets of organisms. () Curation overlap across different pairs of databases. Nodes represent individual databases, with the pie charts illustrating the! proportion of shared and unique PPI records in each database. The edge thickness represents the number of instances where the two databases curate the same publication, whereas the edge color represents the average level of agreement (measured as defined above) on recorded interactions, following the color-coded scale. The shared publications between present IMEx members (MINT, DIP, IntAct and MPact) were likely curated before adoption of common curation policies and before these databases agreed to eliminate overlap of curation efforts. 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 * Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario, Canada. * Andrei L Turinsky, * Brian Turner & * Shoshana J Wodak * The Biotechnology Centre of Oslo, University of Oslo, Oslo, Norway. * Sabry Razick & * Ian M Donaldson * Biomedical Research Group, Department of Informatics, University of Oslo, Oslo, Norway. * Sabry Razick * Department of Molecular Biosciences, University of Oslo, Oslo, Norway. * Ian M Donaldson * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Shoshana J Wodak * Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada. * Shoshana J Wodak Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Ian M Donaldson or * Shoshana J Wodak Author Details * Andrei L Turinsky Search for this author in: * NPG journals * PubMed * Google Scholar * Sabry Razick Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Turner Search for this author in: * NPG journals * PubMed * Google Scholar * Ian M Donaldson Contact Ian M Donaldson Search for this author in: * NPG journals * PubMed * Google Scholar * Shoshana J Wodak Contact Shoshana J Wodak Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (111Kb) Supplementary Note Additional data * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * TEM Lab / Group Leader, Postdoc, and Operator Positions * Frontier Institute of Science and Technology, Xi'an Jiaotong University * Xi'an, Shaanxi, P.R.China * Bioinformatician (Functional Genomics Production) * European Bioinformatics Institute (EBI) * Cambridge, United Kingdom * Max Planck PhD program: IMPRS for Molecular and Cellular Biology * Max Planck Institute of Immunobiology and Epigenetics, University of Freiburg * Freiburg, Germany * Post a free job * More science jobs Open innovation challenges * Upload Your Compound Libraries! Deadline:Jan 20 2013Reward:See Details As part of our improved Novel Molecules Challenge (NMC) procedure, you may now upload to InnoCenti… * Novel Chemical Derivatives of Bicarbonate Deadline:Jun 02 2011Reward:$20,000 USD The Seeker desires suggestions for novel chemical derivatives of bicarbonate that are water-insolubl… * Powered by: * More challenges Top content Emailed * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Sharing secrets Nature Biotechnology 08 Apr 2011 * Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells Nature Biotechnology 19 Jul 2010 * Amgen spikes interest in live virus vaccines for hard-to-treat cancers Nature Biotechnology 08 Apr 2011 * Label-free quantification of membrane-ligand interactions using backscattering interferometry Nature Biotechnology 13 Mar 2011 View all Downloaded * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells Nature Biotechnology 24 Apr 2011 * Conversion of proteins into biofuels by engineering nitrogen flux Nature Biotechnology 06 Mar 2011 * Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes Nature Biotechnology 20 Mar 2011 * Parallel on-chip gene synthesis and application to optimization of protein expression Nature Biotechnology 24 Apr 2011 View all Blogged * 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 * When less is more Nature Biotechnology 01 Aug 2007 * Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes Nature Biotechnology 20 Mar 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 - PathSeq: software to identify or discover microbes by deep sequencing of human tissue
- Nat Biotech 29(5):393-396 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence PathSeq: software to identify or discover microbes by deep sequencing of human tissue * Aleksandar D Kostic1, 2 * Akinyemi I Ojesina1, 3 * Chandra Sekhar Pedamallu1, 3 * Joonil Jung1, 3 * Roel G W Verhaak1, 3 * Gad Getz1 * Matthew Meyerson1, 2, 3 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:393–396Year published:(2011)DOI:doi:10.1038/nbt.1868Published online06 May 2011 To the Editor: Many human diseases are believed to be caused by undiscovered pathogens1, 2. The advent of next-generation sequencing technology presents an unprecedented opportunity to identify pathogens in hitherto idiopathic diseases. Here we present PathSeq, a highly scalable software tool that performs computational subtraction on high-throughput sequencing data to identify nonhuman nucleic acids that may indicate candidate microbes. PathSeq exhibits high sensitivity and specificity in its ability to discriminate human from nonhuman sequences using both simulated and experimental transcriptome and whole-genome sequencing data. PathSeq is implemented in a cloud computing environment making it readily accessible by the scientific community. Previously, our group and others have developed a computational approach to pathogen discovery, sequence-based computational subtraction3, 4, 5, 6. This method is based on the premise that infected tissues contain both human and microbial nucleic acids and that novel pathogen-derived sequences can be detected after subtracting human sequences. This unbiased approach to pathogen discovery is an advance over targeted PCR or pan-microbial array methods because it requires no sequence information ab initio about the organism being sought (for a recent, in-depth review of pathogen discovery methods, see ref. 2). Even so, performing computational subtraction at any meaningful scale was initially cost prohibitive as this method requires a large number of input sequences, given that any pathogen present is likely to have low nucleic acid representation relative to that of the human host. With the recent development of next-generation sequencing methods7, 8, computational subtraction-based pathogen discovery has now become a viable option. For example, massively parallel pyrosequencing combined with computational subtraction has resulted in the discovery of novel viruses in human disease—Merkel cell polyomavirus in Merkel cell carcinoma9 and a novel Old World arenavirus in a cluster of patients with fatal transplant-associated disease10. Indeed, the past few years have seen steep drops in price and increases in throughput for next-generation sequencing technologies, and these trends are expected to accelerate in the near future7, 8. Even so, this advancement in technology brings with it new computational challenges. Analyzing sequence data using the computational subtraction method is computationally expensive relative to most other next-generation sequencing analyses because it requires subtractive alignments to several large reference databases using loca! l alignment algorithms such as BLAST. PathSeq is a comprehensive computational tool for the analysis of the non-host portion of resequencing data that is capable of detecting the presence of both known and novel pathogens as well as any resident microorganisms. The software runs efficiently on sequence data sets of any size in a scalable and completely reproducible fashion because it is developed on a parallel computing architecture and is implemented in a cloud-computing environment. The PathSeq software package is available for public use in the form of a machine image for cloud computing, which can be launched and monitored using no more than a basic laptop computer. We believe that PathSeq opens the way for a new large-scale effort in pathogen discovery by any researcher with access to deep sequencing data from human tissue. PathSeq's approach begins with a subtractive phase in which input reads are subtracted by alignment to human reference sequences (Fig. 1a). This is followed by an analytic phase in which the remaining reads are aligned to microbial reference sequences and assembled de novo. The input reads are first filtered to remove low-quality, duplicate and repetitive sequences. The initial subtractive alignments are performed using the rapid short-read aligner MAQ11 against five reference human sequence databases, including both genomic DNA and transcriptome references (Supplementary Methods). At the end of each subtractive alignment step, mapped reads are discarded and unmapped reads are subjected to further subtractive analyses. In the final steps, the residual reads are aligned to two additional human reference databases first using the Mega BLAST algorithm and then BLASTN. This identifies alignable reads with additional mismatches and/or short gaps that are not aligned by MAQ. The s! et of reads that remain unmapped after the subtractive phase are candidate nonhuman, pathogen-derived reads. A similar schema may be used for other host organisms by substituting the appropriate reference genome databases. back to article Figure 1: The PathSeq workflow. () Conceptual workflow of the subtractive phase of PathSeq. The size of the read set (orange bars) is proportional to the number of reads at the indicated step in a typical run of the method. The black dots in the bars represent pathogen-derived sequences, which become progressively concentrated. The steps in this conceptual workflow have been reordered for concision (see Supplementary Methods for actual ordering). () Conceptual workflow of the analytic phase of PathSeq. The asterisk indicates the unmapped read-set that is carried over from the subtractive phase. View full text Figures at a glance * Figure 1: The PathSeq workflow. () Conceptual workflow of the subtractive phase of PathSeq. The size of the read set (orange bars) is proportional to the number of reads at the indicated step in a typical run of the method. The black dots in the bars represent pathogen-derived sequences, which become progressively concentrated. The steps in this conceptual workflow have been reordered for concision (see Supplementary Methods for actual ordering). () Conceptual workflow of the analytic phase of PathSeq. The asterisk indicates the unmapped read-set that is carried over from the subtractive phase. * Figure 2: PathSeq performance on simulated and experimental sequence data. () Reads were generated by sampling random 100-mer sequences from a human transcriptome database to produce 20 million reads, and from a set of 12 virus genomes each substitutionally mutated at 12 distinct rates, generating 144,000 reads (Supplementary Fig. 1). The blue bars represent the number of human reads remaining after the indicated step in the PathSeq workflow, and the red squares connected by a line represent the remaining viral reads. () Whole-genome sequencing data from a human ovarian tumor applied to PathSeq. () One lane of total-RNA transcriptome sequencing from HeLa cell lines applied to PathSeq. The inset in shows that the 30,790 reads remaining after the subtractive phase of PathSeq are predominantly composed of HPV18 sequences. 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 * Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA. * Aleksandar D Kostic, * Akinyemi I Ojesina, * Chandra Sekhar Pedamallu, * Joonil Jung, * Roel G W Verhaak, * Gad Getz & * Matthew Meyerson * Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA. * Aleksandar D Kostic & * Matthew Meyerson * Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Akinyemi I Ojesina, * Chandra Sekhar Pedamallu, * Joonil Jung, * Roel G W Verhaak & * Matthew Meyerson Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew Meyerson Author Details * Aleksandar D Kostic Search for this author in: * NPG journals * PubMed * Google Scholar * Akinyemi I Ojesina Search for this author in: * NPG journals * PubMed * Google Scholar * Chandra Sekhar Pedamallu Search for this author in: * NPG journals * PubMed * Google Scholar * Joonil Jung Search for this author in: * NPG journals * PubMed * Google Scholar * Roel G W Verhaak Search for this author in: * NPG journals * PubMed * Google Scholar * Gad Getz Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Meyerson Contact Matthew Meyerson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (471K) Supplementary Figures 1–4, Supplementary Tables 1–4, Supplementary Methods, and Supplementary Data Sets 1–3 Additional data * Journal home * Current issue * For authors * Subscribe * E-alert sign up * RSS feed Science jobs from naturejobs * TEM Lab / Group Leader, Postdoc, and Operator Positions * Frontier Institute of Science and Technology, Xi'an Jiaotong University * Xi'an, Shaanxi, P.R.China * Bioinformatician (Functional Genomics Production) * European Bioinformatics Institute (EBI) * Cambridge, United Kingdom * Copywriter * Indegene Life Systems Pvt. Ltd * Bangalore, Karnataka, India * Post a free job * More science jobs Open innovation challenges * Novel Chemical Derivatives of Bicarbonate Deadline:Jun 02 2011Reward:$20,000 USD The Seeker desires suggestions for novel chemical derivatives of bicarbonate that are water-insolubl… * Quantifying (meth)Acrylate Polymer in Complex Matrices Deadline:May 29 2011Reward:$15,000 USD A sensitive and specific assay for the quantitative detection of acrylate and methacrylate polymers … * Powered by: * More challenges Top content Emailed * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Sharing secrets Nature Biotechnology 08 Apr 2011 * Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells Nature Biotechnology 19 Jul 2010 * Amgen spikes interest in live virus vaccines for hard-to-treat cancers Nature Biotechnology 08 Apr 2011 * Label-free quantification of membrane-ligand interactions using backscattering interferometry Nature Biotechnology 13 Mar 2011 View all Downloaded * Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm Nature Biotechnology 24 Apr 2011 * Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells Nature Biotechnology 24 Apr 2011 * Conversion of proteins into biofuels by engineering nitrogen flux Nature Biotechnology 06 Mar 2011 * Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes Nature Biotechnology 20 Mar 2011 * Parallel on-chip gene synthesis and application to optimization of protein expression Nature Biotechnology 24 Apr 2011 View all Blogged * 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 * When less is more Nature Biotechnology 01 Aug 2007 * Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes Nature Biotechnology 20 Mar 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 - Reforming direct-to-consumer advertising
- Nat Biotech 29(5):397-400 (2011)
Nature Biotechnology | Opinion and Comment | Commentary Reforming direct-to-consumer advertising * Bryan A Liang1, 2 * Tim Mackey2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:397–400Year published:(2011)DOI:doi:10.1038/nbt.1865Published online06 May 2011 Why not exploit direct-to-consumer advertising to facilitate patient education about treatments and improve safety monitoring? 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 * Bryan A. Liang is in the Department of Anesthesiology, San Diego Center for Patient Safety, University of California, San Diego School of Medicine, San Diego, California, USA. * Bryan A. Liang and Tim Mackey are at the Institute of Health Law Studies, California Western School of Law, San Diego, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bryan A Liang Author Details * Bryan A Liang Contact Bryan A Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Tim Mackey Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Mandating race: how the USPTO is forcing race into biotech patents
- Nat Biotech 29(5):401-403 (2011)
Nature Biotechnology | Feature | Patents Mandating race: how the USPTO is forcing race into biotech patents * Jonathan Kahn1Journal name:Nature BiotechnologyVolume: 29,Pages:401–403Year published:(2011)DOI:doi:10.1038/nbt.1864Published online06 May 2011 A recent review of select patent prosecutions before the United States Patent and Trademark Office indicates a troubling dynamic at work. 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 * Jonathan Kahn is at Hamline University School of Law, St. Paul, Minnesota, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Jonathan Kahn Author Details * Jonathan Kahn Contact Jonathan Kahn Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Recent patent applications in biomarkers
- Nat Biotech 29(5):404 (2011)
Nature Biotechnology | Feature | Patents Recent patent applications in biomarkers Journal name:Nature BiotechnologyVolume: 29,Page:404Year published:(2011)DOI:doi:10.1038/nbt.1876Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Building stronger microvessels
- Nat Biotech 29(5):405-406 (2011)
Nature Biotechnology | News and Views Building stronger microvessels * Laura E Niklason1Journal name:Nature BiotechnologyVolume: 29,Pages:405–406Year published:(2011)DOI:doi:10.1038/nbt.1854Published online06 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Therapeutic angiogenesis is enhanced by coaxing smooth muscle cells to wrap around endothelial-cell tubes. 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 * Laura E. Niklason is in the departments of Anesthesiology and Biomedical Engineering, Yale University, New Haven, Connecticut, USA. Competing financial interests The author declares competing financial interests: details accompany the full-text HTML version of the paper at http://www.nature.com/nbt/index.html. Corresponding author Correspondence to: * Laura E Niklason Author Details * Laura E Niklason Contact Laura E Niklason Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Sialidase inhibitors DAMPen sepsis
- Nat Biotech 29(5):406-407 (2011)
Nature Biotechnology | News and Views Sialidase inhibitors DAMPen sepsis * James C Paulson1 * Norihito Kawasaki1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:406–407Year published:(2011)DOI:doi:10.1038/nbt.1859Published online06 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Sepsis can be ameliorated by small-molecule inhibitors of bacterial sialidases. 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 * James C. Paulson and Norihito Kawasaki are in the Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * James C Paulson Author Details * James C Paulson Contact James C Paulson Search for this author in: * NPG journals * PubMed * Google Scholar * Norihito Kawasaki Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Auxin boost for cotton
- Nat Biotech 29(5):407-409 (2011)
Nature Biotechnology | News and Views Auxin boost for cotton * Z Jeffrey Chen1 * Xueying Guan1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:407–409Year published:(2011)DOI:doi:10.1038/nbt.1858Published online06 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Increasing auxin levels at the right time and place during ovule and fiber development improves the yield and quality of cotton fibers. 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 * Z. Jeffrey Chen and Xueying Guan are in the Section of Molecular Cell and Developmental Biology, Center for Computational Biology and Bioinformatics, and Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Z Jeffrey Chen Author Details * Z Jeffrey Chen Contact Z Jeffrey Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Xueying Guan Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - A self-assembling retina
- Nat Biotech 29(5):409 (2011)
Nature Biotechnology | News and Views A self-assembling retina * Kathy AschheimJournal name:Nature BiotechnologyVolume: 29,Page:409Year published:(2011)DOI:doi:10.1038/nbt.1874Published online06 May 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Unlike the eye of mollusks, the vertebrate eye is oriented upside down1. Light-sensitive cells—the rods and cones—are located in the outermost layer of the neurosensory retina, furthest from the eye chamber and the incoming light. The inner retinal layers, closest to the light, contain the interneurons and ganglia that transmit optical signals to the brain, on the far side of the photoreceptor layer. In a new study2 that marks an exciting advance in the use of embryonic stem cells (ESCs) to mimic development, this distinctive retinal structure has now been shown to emerge spontaneously in vitro starting with little more than mouse ESCs, the growth factor activin/nodal and a bit of extracellular matrix. ESCs have already been differentiated into cells that resemble many of the cell types in the retina, including photoreceptors, neural cells and retinal pigment epithelium (RPE). But how can cells of different types be assembled in vitro into a complex organ such as the eye? For the self-assembling retina, all that was needed were slight modifications of the differentiation conditions. The critical new component was Matrigel or purified extracellular matrix proteins, which were added because they are known to promote the growth of epithelial structure. As Eiraku et al.2 show in time-lapse images taken over 9 days of culture, an in vitro–generated retina begins as a floating aggregate of ESCs. Soon, in the presence of extracellular matrix, the cells begin to express Rx, a marker of the retinal anlage. By day 6, the aggregate becomes a hollow sphere of polarized epithelial cells, and the Rx+ cells self-associate into distinct Rx+ islands (Fig. 1a). By day 7, the Rx+ islands form vesicles that project out from the hollow sphere (Fig. 1b) and that express the retinal marker Pax6 (Fig. 1c). Figure 1: Stages in the generation of a mouse retina in vitro. NR, neural retina; SE, surface ectoderm. * Full size image (89 KB) Over the next three days, the vesicle involutes, creating the double-walled C shape of the developing retina (Fig. 1d). The outer wall expresses markers of RPE (Fig. 1e) and becomes pigmented, similar to the embryonic outer retina, and the inner wall expresses additional makers of the inner, neurosensory retina (Fig. 1d). Overall, the structure bears a striking resemblance to the mouse embryonic optic cup (Fig. 1f). View full text Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Research highlights
- Nat Biotech 29(5):410 (2011)
Nature Biotechnology | Research Highlights Research highlights Journal name:Nature BiotechnologyVolume: 29,Page:410Year published:(2011)DOI:doi:10.1038/nbt.1878Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. TH17 cell differentiation inhibitors T-helper lymphocytes expressing interleukin-17 (TH17 cells) have been implicated in a range of autoimmune diseases, including multiple sclerosis, rheumatoid arthritis, Crohn's disease, psoriasis and lupus. Therapies that target the retinoic acid receptor–related orphan receptors α and γt (RORα and RORγt), which are required for the differentiation of naive CD4+ T cells to TH17 cells, are an attractive alternative to current autoimmune disease treatments that work by general immunosuppression. Until recently, however, the absence of well-characterized ROR ligands has frustrated progress. Two groups now report the feasibility of selectively blocking ROR receptor activity to inhibit TH17 cell differentiation and function. Huh et al. show that digoxin, long used to treat congestive heart failure, and two of its less toxic derivatives all suppress TH17 cell development by antagonizing RORγt activity. Solt et al. derivatize a promiscuous modulator of several nuclear recepto! rs to identify a synthetic suppressor (shown green in inset binding the RORγt ligand-binding domain) of both RORα and RORγt. Both studies use an animal model of multiple sclerosis to demonstrate the ability of the compounds tested to reduce the severity of autoimmune disease in mice. (Nature published online, doi:10.1038/nature09978, 27 March 2011; doi:10.1038/nature10074, 17 April 2011) PH Silencing the silencers Pinning down the functions of microRNA (miRNA) families is tricky because their members are often coexpressed, have redundant function and share a common seed region involved in target recognition. Obad et al. harness this last property to silence multiple family members using antisense oligonucleotides with a single sequence. The authors inhibit miRNAs by transfecting cells or intravenously injecting mice with tiny locked nucleic acids (LNAs), short 7- or 8-mer modified oligos that are complementary to the shared miRNA seed regions. These tiny LNAs are shown to be specific—working only when the seed region, and not other regions of the miRNA, is targeted—and active in cultured HeLa and Huh-7 cells as well as in vivo in many mouse tissues and a mouse model of breast cancer. Experiments using gene expression microarrays, proteomics and luciferase reporter assays suggest that off-target effects are minimal. The advantages of silencing miRNA family members with a single ant! isense oligo, rather than several oligos having different sequences, remain to be demonstrated. (Nat. Genet.43, 371–378, 2011) CM View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm
- Nat Biotech 29(5):411-414 (2011)
Nature Biotechnology | Research | Analysis Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm * Paul Wicks1 * Timothy E Vaughan1 * Michael P Massagli1 * James Heywood1 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:411–414Year published:(2011)DOI:doi:10.1038/nbt.1837Received02 April 2010Accepted10 March 2011Published online24 April 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Patients with serious diseases may experiment with drugs that have not received regulatory approval. Online patient communities structured around quantitative outcome data have the potential to provide an observational environment to monitor such drug usage and its consequences. Here we describe an analysis of data reported on the website PatientsLikeMe by patients with amyotrophic lateral sclerosis (ALS) who experimented with lithium carbonate treatment. To reduce potential bias owing to lack of randomization, we developed an algorithm to match 149 treated patients to multiple controls (447 total) based on the progression of their disease course. At 12 months after treatment, we found no effect of lithium on disease progression. Although observational studies using unblinded data are not a substitute for double-blind randomized control trials, this study reached the same conclusion as subsequent randomized trials, suggesting that data reported by patients over the internet ! may be useful for accelerating clinical discovery and evaluating the effectiveness of drugs already in use. View full text Figures at a glance * Figure 1: Conceptual overview of the online study environment and matching algorithm. () The number of patients choosing to experiment with lithium carbonate peaked in the months after publication of a small clinical trial in Italy. Preliminary negative results from this patient-led study were announced before the first randomized control trial had started recruitment. () Aggregate view of FRS scores for all 348 patients analyzed in this study. These data were publicly available online during the study. () Illustration of disease progression curves of control individuals that are good and poor matches for a particular patient. Both control individuals would be considered comparable by traditional matching criteria. The PatientsLikeMe matching algorithm minimizes the area between the disease progression curves for a target patient and a control. * Figure 2: Results of analyses show no significant effect of lithium carbonate on rate of ALS progression. () Summary of pretreatment disease progression curves for 149 intent-to-treat patients matched by the PatientsLikeMe matching algorithm. Error bars are 1 s.e.m. in each direction. () Intent-to-treat analysis of 149 patients treated with lithium carbonate compared with controls fails to find any significant differences in progression (P > 0.05 at 12 months). Squares represent data from a previous trial7. Error bars are 1 s.e.m. in each direction. Dashed lines indicate the smallest detectable effect (α = 0.05, 80% power). () Full-course analysis of 78 patients treated with lithium carbonate compared with controls fails to find any significant differences in progression (P > 0.05 at 12 months). Dashed lines as in . Author information * Abstract * Author information * Supplementary information Affiliations * Research and Development, PatientsLikeMe Inc., Cambridge, Massachusetts, USA. * Paul Wicks, * Timothy E Vaughan, * Michael P Massagli & * James Heywood Contributions P.W. designed the study, oversaw the project and drafted and revised the manuscript. T.E.V. developed the matching algorithm, analyzed data and revised the manuscript. M.P.M. designed the lithium data capture tool, developed statistical methods and revised the manuscript. J.H. designed the study, designed the ALS web site, developed the matching algorithm and revised the manuscript. Competing financial interests P.W., T.V., M.M. & J.H. are employees of PatientsLikeMe and own shares and/or stock options in the company. Corresponding author Correspondence to: * Paul Wicks Author Details * Paul Wicks Contact Paul Wicks Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy E Vaughan Search for this author in: * NPG journals * PubMed * Google Scholar * Michael P Massagli Search for this author in: * NPG journals * PubMed * Google Scholar * James Heywood Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Zip files * Supplementary Data (2M) Supplementary Data PDF files * Supplementary Text and Figures (1M) Supplementary Figs. 1 and 2 Additional data - Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications
- Nat Biotech 29(5):415-420 (2011)
Nature Biotechnology | Research | Perspective Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications * Pelin Yilmaz1, 2 * Renzo Kottmann1 * Dawn Field3 * Rob Knight4, 5 * James R Cole6, 7 * Linda Amaral-Zettler8 * Jack A Gilbert9, 10, 11 * Ilene Karsch-Mizrachi12 * Anjanette Johnston12 * Guy Cochrane13 * Robert Vaughan13 * Christopher Hunter13 * Joonhong Park14 * Norman Morrison3, 15 * Philippe Rocca-Serra16 * Peter Sterk3 * Manimozhiyan Arumugam17 * Mark Bailey3 * Laura Baumgartner18 * Bruce W Birren19 * Martin J Blaser20 * Vivien Bonazzi21 * Tim Booth3 * Peer Bork17 * Frederic D Bushman22 * Pier Luigi Buttigieg1, 2 * Patrick S G Chain7, 23, 24 * Emily Charlson22 * Elizabeth K Costello4 * Heather Huot-Creasy25 * Peter Dawyndt26 * Todd DeSantis27 * Noah Fierer28 * Jed A Fuhrman29 * Rachel E Gallery30 * Dirk Gevers19 * Richard A Gibbs31, 32 * Inigo San Gil33 * Antonio Gonzalez34 * Jeffrey I Gordon35 * Robert Guralnick28, 36 * Wolfgang Hankeln1, 2 * Sarah Highlander31, 37 * Philip Hugenholtz38 * Janet Jansson23, 39 * Andrew L Kau35 * Scott T Kelley40 * Jerry Kennedy4 * Dan Knights34 * Omry Koren41 * Justin Kuczynski18 * Nikos Kyrpides23 * Robert Larsen4 * Christian L Lauber42 * Teresa Legg28 * Ruth E Ley41 * Catherine A Lozupone4 * Wolfgang Ludwig43 * Donna Lyons42 * Eamonn Maguire16 * Barbara A Methé44 * Folker Meyer10 * Brian Muegge35 * Sara Nakielny4 * Karen E Nelson44 * Diana Nemergut45 * Josh D Neufeld46 * Lindsay K Newbold3 * Anna E Oliver3 * Norman R Pace18 * Giriprakash Palanisamy47 * Jörg Peplies48 * Joseph Petrosino31, 37 * Lita Proctor21 * Elmar Pruesse1, 2 * Christian Quast1 * Jeroen Raes49 * Sujeevan Ratnasingham50 * Jacques Ravel25 * David A Relman51, 52 * Susanna Assunta-Sansone16 * Patrick D Schloss53 * Lynn Schriml25 * Rohini Sinha22 * Michelle I Smith35 * Erica Sodergren54 * Aymé Spor41 * Jesse Stombaugh4 * James M Tiedje7 * Doyle V Ward19 * George M Weinstock54 * Doug Wendel4 * Owen White25 * Andrew Whiteley3 * Andreas Wilke10 * Jennifer R Wortman25 * Tanya Yatsunenko35 * Frank Oliver Glöckner1, 2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:415–420Year published:(2011)DOI:doi:10.1038/nbt.1823Published online06 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 Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The 'environmental packages' apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere. View full text Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRP001108 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany. * Pelin Yilmaz, * Renzo Kottmann, * Pier Luigi Buttigieg, * Wolfgang Hankeln, * Elmar Pruesse, * Christian Quast & * Frank Oliver Glöckner * Jacobs University Bremen gGmbH, Bremen, Germany. * Pelin Yilmaz, * Pier Luigi Buttigieg, * Wolfgang Hankeln, * Elmar Pruesse & * Frank Oliver Glöckner * Natural Environment Research Council Environmental Bioinformatics Centre, Wallington CEH, Oxford, UK. * Dawn Field, * Norman Morrison, * Peter Sterk, * Mark Bailey, * Tim Booth, * Lindsay K Newbold, * Anna E Oliver & * Andrew Whiteley * Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA. * Rob Knight, * Elizabeth K Costello, * Jerry Kennedy, * Robert Larsen, * Catherine A Lozupone, * Sara Nakielny, * Jesse Stombaugh & * Doug Wendel * Howard Hughes Medical Institute, San Francisco, California, USA. * Rob Knight * Ribosomal Database Project, Michigan State University, East Lansing, Michigan, USA. * James R Cole * Center for Microbial Ecology, Michigan State University, East Lansing, Michigan, USA. * James R Cole, * Patrick S G Chain & * James M Tiedje * The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, Massachusetts, USA. * Linda Amaral-Zettler * Plymouth Marine Laboratory, Plymouth, UK. * Jack A Gilbert * Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA. * Jack A Gilbert, * Folker Meyer & * Andreas Wilke * Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA. * Jack A Gilbert * National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. * Ilene Karsch-Mizrachi & * Anjanette Johnston * European Molecular Biology Laboratory (EMBL) Outstation, European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Guy Cochrane, * Robert Vaughan & * Christopher Hunter * WCU Center for Green Metagenomics, School of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea. * Joonhong Park * School of Computer Science, University of Manchester, Manchester, UK. * Norman Morrison * Oxford e-Research Centre, University of Oxford, Oxford, UK. * Philippe Rocca-Serra, * Eamonn Maguire & * Susanna Assunta-Sansone * Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. * Manimozhiyan Arumugam & * Peer Bork * Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, USA. * Laura Baumgartner, * Justin Kuczynski & * Norman R Pace * Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA. * Bruce W Birren, * Dirk Gevers & * Doyle V Ward * Department of Medicine and the Department of Microbiology, New York University Langone Medical Center, New York, New York, USA. * Martin J Blaser * National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA. * Vivien Bonazzi & * Lita Proctor * Department of Microbiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Frederic D Bushman, * Emily Charlson & * Rohini Sinha * DOE Joint Genome Institute, Walnut Creek, California, USA. * Patrick S G Chain, * Janet Jansson & * Nikos Kyrpides * Los Alamos National Laboratory, Bioscience Division, Los Alamos, New Mexico, USA. * Patrick S G Chain * Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Heather Huot-Creasy, * Jacques Ravel, * Lynn Schriml, * Owen White & * Jennifer R Wortman * Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium. * Peter Dawyndt * Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory, Berkeley, California, USA. * Todd DeSantis * Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. * Noah Fierer, * Robert Guralnick & * Teresa Legg * Department of Biological Sciences, University of Southern California, Los Angeles, California, USA. * Jed A Fuhrman * National Ecological Observatory Network, Boulder, Colorado, USA. * Rachel E Gallery * Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA. * Richard A Gibbs, * Sarah Highlander & * Joseph Petrosino * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Richard A Gibbs * Department of Biology, University of New Mexico, LTER Network Office, Albuquerque, New Mexico, USA. * Inigo San Gil * Department of Computer Science, University of Colorado, Boulder, Colorado, USA. * Antonio Gonzalez & * Dan Knights * Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, USA. * Jeffrey I Gordon, * Andrew L Kau, * Brian Muegge, * Michelle I Smith & * Tanya Yatsunenko * University of Colorado Museum of Natural History, University of Colorado, Boulder, Colorado, USA. * Robert Guralnick * Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA. * Sarah Highlander & * Joseph Petrosino * Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia. * Philip Hugenholtz * Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA. * Janet Jansson * Department of Biology, San Diego State University, San Diego, California, USA. * Scott T Kelley * Department of Microbiology, Cornell University, Ithaca, New York, USA. * Omry Koren, * Ruth E Ley & * Aymé Spor * Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA. * Christian L Lauber & * Donna Lyons * Lehrstuhl für Mikrobiologie, Technische Universität München, Freising, Germany. * Wolfgang Ludwig * J. Craig Venter Institute, Rockville, Maryland, USA. * Barbara A Methé & * Karen E Nelson * Department of Environmental Sciences, University of Colorado, Boulder, Colorado, USA. * Diana Nemergut * Department of Biology, University of Waterloo, Ontario, Canada. * Josh D Neufeld * Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. * Giriprakash Palanisamy * Ribocon GmbH, Bremen, Germany. * Jörg Peplies * VIB - Vrije Universiteit Brussel, Brussels, Belgium. * Jeroen Raes * Canadian Centre for DNA Barcoding, Biodiversity Institute of Ontario, University of Guelph, Guelph, Ontario, Canada. * Sujeevan Ratnasingham * Departments of Microbiology and Immunology and Department of Medicine, Stanford University School of Medicine, Stanford, California, USA. * David A Relman * Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA. * David A Relman * Department of Microbiology and Immunology, Ann Arbor, Michigan, USA. * Patrick D Schloss * The Genome Center, Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA. * Erica Sodergren & * George M Weinstock Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Frank Oliver Glöckner Author Details * Pelin Yilmaz Search for this author in: * NPG journals * PubMed * Google Scholar * Renzo Kottmann Search for this author in: * NPG journals * PubMed * Google Scholar * Dawn Field Search for this author in: * NPG journals * PubMed * Google Scholar * Rob Knight Search for this author in: * NPG journals * PubMed * Google Scholar * James R Cole Search for this author in: * NPG journals * PubMed * Google Scholar * Linda Amaral-Zettler Search for this author in: * NPG journals * PubMed * Google Scholar * Jack A Gilbert Search for this author in: * NPG journals * PubMed * Google Scholar * Ilene Karsch-Mizrachi Search for this author in: * NPG journals * PubMed * Google Scholar * Anjanette Johnston Search for this author in: * NPG journals * PubMed * Google Scholar * Guy Cochrane Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Vaughan Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Hunter Search for this author in: * NPG journals * PubMed * Google Scholar * Joonhong Park Search for this author in: * NPG journals * PubMed * Google Scholar * Norman Morrison Search for this author in: * NPG journals * PubMed * Google Scholar * Philippe Rocca-Serra Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Sterk Search for this author in: * NPG journals * PubMed * Google Scholar * Manimozhiyan Arumugam Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Bailey Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Baumgartner Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce W Birren Search for this author in: * NPG journals * PubMed * Google Scholar * Martin J Blaser Search for this author in: * NPG journals * PubMed * Google Scholar * Vivien Bonazzi Search for this author in: * NPG journals * PubMed * Google Scholar * Tim Booth Search for this author in: * NPG journals * PubMed * Google Scholar * Peer Bork Search for this author in: * NPG journals * PubMed * Google Scholar * Frederic D Bushman Search for this author in: * NPG journals * PubMed * Google Scholar * Pier Luigi Buttigieg Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick S G Chain Search for this author in: * NPG journals * PubMed * Google Scholar * Emily Charlson Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth K Costello Search for this author in: * NPG journals * PubMed * Google Scholar * Heather Huot-Creasy Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Dawyndt Search for this author in: * NPG journals * PubMed * Google Scholar * Todd DeSantis Search for this author in: * NPG journals * PubMed * Google Scholar * Noah Fierer Search for this author in: * NPG journals * PubMed * Google Scholar * Jed A Fuhrman Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel E Gallery Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Gevers Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Gibbs Search for this author in: * NPG journals * PubMed * Google Scholar * Inigo San Gil Search for this author in: * NPG journals * PubMed * Google Scholar * Antonio Gonzalez Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey I Gordon Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Guralnick Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Hankeln Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Highlander Search for this author in: * NPG journals * PubMed * Google Scholar * Philip Hugenholtz Search for this author in: * NPG journals * PubMed * Google Scholar * Janet Jansson Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew L Kau Search for this author in: * NPG journals * PubMed * Google Scholar * Scott T Kelley Search for this author in: * NPG journals * PubMed * Google Scholar * Jerry Kennedy Search for this author in: * NPG journals * PubMed * Google Scholar * Dan Knights Search for this author in: * NPG journals * PubMed * Google Scholar * Omry Koren Search for this author in: * NPG journals * PubMed * Google Scholar * Justin Kuczynski Search for this author in: * NPG journals * PubMed * Google Scholar * Nikos Kyrpides Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Larsen Search for this author in: * NPG journals * PubMed * Google Scholar * Christian L Lauber Search for this author in: * NPG journals * PubMed * Google Scholar * Teresa Legg Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth E Ley Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine A Lozupone Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Ludwig Search for this author in: * NPG journals * PubMed * Google Scholar * Donna Lyons Search for this author in: * NPG journals * PubMed * Google Scholar * Eamonn Maguire Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara A Methé Search for this author in: * NPG journals * PubMed * Google Scholar * Folker Meyer Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Muegge Search for this author in: * NPG journals * PubMed * Google Scholar * Sara Nakielny Search for this author in: * NPG journals * PubMed * Google Scholar * Karen E Nelson Search for this author in: * NPG journals * PubMed * Google Scholar * Diana Nemergut Search for this author in: * NPG journals * PubMed * Google Scholar * Josh D Neufeld Search for this author in: * NPG journals * PubMed * Google Scholar * Lindsay K Newbold Search for this author in: * NPG journals * PubMed * Google Scholar * Anna E Oliver Search for this author in: * NPG journals * PubMed * Google Scholar * Norman R Pace Search for this author in: * NPG journals * PubMed * Google Scholar * Giriprakash Palanisamy Search for this author in: * NPG journals * PubMed * Google Scholar * Jörg Peplies Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Petrosino Search for this author in: * NPG journals * PubMed * Google Scholar * Lita Proctor Search for this author in: * NPG journals * PubMed * Google Scholar * Elmar Pruesse Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Quast Search for this author in: * NPG journals * PubMed * Google Scholar * Jeroen Raes Search for this author in: * NPG journals * PubMed * Google Scholar * Sujeevan Ratnasingham Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Ravel Search for this author in: * NPG journals * PubMed * Google Scholar * David A Relman Search for this author in: * NPG journals * PubMed * Google Scholar * Susanna Assunta-Sansone Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick D Schloss Search for this author in: * NPG journals * PubMed * Google Scholar * Lynn Schriml Search for this author in: * NPG journals * PubMed * Google Scholar * Rohini Sinha Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle I Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Erica Sodergren Search for this author in: * NPG journals * PubMed * Google Scholar * Aymé Spor Search for this author in: * NPG journals * PubMed * Google Scholar * Jesse Stombaugh Search for this author in: * NPG journals * PubMed * Google Scholar * James M Tiedje Search for this author in: * NPG journals * PubMed * Google Scholar * Doyle V Ward Search for this author in: * NPG journals * PubMed * Google Scholar * George M Weinstock Search for this author in: * NPG journals * PubMed * Google Scholar * Doug Wendel Search for this author in: * NPG journals * PubMed * Google Scholar * Owen White Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew Whiteley Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Wilke Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer R Wortman Search for this author in: * NPG journals * PubMed * Google Scholar * Tanya Yatsunenko Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Oliver Glöckner Contact Frank Oliver Glöckner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Results 2 (184K) MIMARKS checklist * Supplementary Results 3 (115K) MIMARKS compliant datasets * Supplementary Note (29K) Funding sources PDF files * Supplementary Results 1 (803K) Community led surveys Additional data - Fibroblast growth factor 9 delivery during angiogenesis produces durable, vasoresponsive microvessels wrapped by smooth muscle cells
- Nat Biotech 29(5):421-427 (2011)
Nature Biotechnology | Research | Article Fibroblast growth factor 9 delivery during angiogenesis produces durable, vasoresponsive microvessels wrapped by smooth muscle cells * Matthew J Frontini1, 3 * Zengxuan Nong1 * Robert Gros1, 5 * Maria Drangova1, 4 * Caroline O'Neil1 * Mona N Rahman1 * Oula Akawi1 * Hao Yin1 * Christopher G Ellis4 * J Geoffrey Pickering1, 2, 3, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:421–427Year published:(2011)DOI:doi:10.1038/nbt.1845Received28 January 2011Accepted14 March 2011Published online17 April 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 The therapeutic potential of angiogenic growth factors has not been realized. This may be because formation of endothelial sprouts is not followed by their muscularization into vasoreactive arteries. Using microarray expression analysis, we discovered that fibroblast growth factor 9 (FGF9) was highly upregulated as human vascular smooth muscle cells (SMCs) assemble into layered cords. FGF9 was not angiogenic when mixed with tissue implants or delivered to the ischemic mouse hind limb, but instead orchestrated wrapping of SMCs around neovessels. SMC wrapping in implants was driven by sonic hedgehog–mediated upregulation of PDGFRβ. Computed tomography microangiography and intravital microscopy revealed that microvessels formed in the presence of FGF9 had enhanced capacity to receive flow and were vasoreactive. Moreover, the vessels persisted beyond 1 year, remodeling into multilayered arteries paired with peripheral nerves. This mature physiological competency was attained ! by targeting mesenchymal cells rather than endothelial cells, a finding that could inform strategies for therapeutic angiogenesis and tissue engineering. View full text Figures at a glance * Figure 1: FGF9 is not angiogenic but stimulates recruitment of mural cells, including SMCs, to nascent microvessels. () Photomicrographs of Matrigel constructs embedded with FGF9 or FGF2, harvested 8 d after implantation into C57Bl/6 mice and immunostained for CD31 to identify endothelial cells. () Western blots showing FGF9-induced activation of ERK1/2 in SMCs but not in human aortic endothelial cells (HAEC). Full-length blots are presented in Supplementary Figure 8. () Photomicrographs of Matrigel implants harvested 8 d after implantation into C57Bl/6 mice and double immunostained for CD31 (brown) and smooth muscle (SM) α-actin (red). The percentage of microvessels associated with SM α-actin–expressing (SM α-actin+) cells is significantly higher in gels exposed to both FGF2 and FGF9 (right, *P = 0.001). () Fluorescence microscope images of 200-μm thick sections of Matrigel constructs harvested 8 d after implantation and double immunolabeled for CD31 (red) and SM α-actin (green). The mural coverage of microvessels was determined based on the relative length of microvessels within t! he field of view (n = 10–12 per condition) that were labeled for both CD31 and SM α-actin (right, *P = 0.0003). () Reconstructed confocal microscope images of microvessels, ~25 μm in diameter, within gels embedded with FGF2 (left) and FGF2 plus FGF9 (middle and right). There is circumferential wrapping of the FGF9-exposed microvessel by cells expressing SM α-actin, morphologically confirming the mural cells as SMCs. Orthogonal planes through the reconstructed z-stacks illustrate complete coverage of the endothelial surface by SMCs (right). * Figure 2: FGF9 drives neovascular maturation by PDGFRβ and SHH signaling. () Ethidium bromide–stained gels depicting RT-PCR products of human SMCs stimulated with recombinant FGF9 for 24 h (left) or of human SMCs transduced with cDNA encoding eGFP or FGF9 (upper right). Western blots of primary mouse fibroblasts incubated with either FGF2 or FGF9 and probed for expression of PDGFRβ (right). () Photomicrographs of FGF2 plus FGF9-containing Matrigel constructs mixed with either control IgG or PDGFRβ blocking antibody and double immunolabeled for CD31 (brown) and SM α-actin (red). Quantitative data for the proportion of CD31-positive microvessels that also stain for SM α-actin, in constructs embedded with either FGF2 alone or FGF2 plus FGF9, is depicted in the graph. *P < 0.001 versus respective control IgG; **P < 0.001 versus gel containing FGF2 and control IgG. Scale bar, 25 μm. () Ethidium bromide-stained gel depicting Ptch1 and Shh RT-PCR products amplified from human aortic SMCs transduced with cDNA encoding either eGFP or FGF9. () Shh mR! NA abundance in FGF9-treated SMCs assessed by real time RT-PCR. () Western blots illustrating abrogation of FGF9-induced upregulation of PDGFRβ by 500 nM cyclopamine (Cyc). () Photomicrographs of Matrigel constructs containing FGF2 plus FGF9 and mixed with either DMSO (vehicle) or cyclopamine. After 8 d, constructs were double immunolabeled for CD31 and SM α-actin. The proportion of microvessels invested by SM α-actin–expressing mural cells was significantly inhibited by cyclopamine (graph). *P = 0.02 versus DMSO vehicle. Full-length gels and blots are presented in Supplementary Figure 8. * Figure 3: FGF9 promotes the development of perfusion-competent and vasoreactive microvessels. () Micro-CT angiograms of Matrigel implants harvested after 8 d (top) and 28 d (bottom). Maximum intensity projections are oriented to depict the feeder vessels on the skin and muscle surfaces giving rise to perfusion-competent vessels penetrating the Matrigel. The border of the Matrigel construct and subcutaneous tissue is outlined for the 8-d implants and perfused vessels can be seen to extend deeper into the FGF2/FGF9-embedded gel, with quantitative data shown on the right (*P = 0.04). In the 28-d implants embedded with FGF2 alone (bottom left), vessels can be seen primarily in the adjacent muscle, with an isolated vessel coursing through a cleft within the Matrigel. In the FGF2/FGF9-exposed gel (bottom right), there is an extensive network of relatively straight microvessels penetrating into the gel substrate from both skin and muscle surfaces, approaching each other at the mid-zone of the Matrigel. () Photomicrographs of neovessels in Matrigel implanted 14 d prior, imag! ed live by intravital microscopy after injection of FITC-labeled dextran. Images depict the vascular lumen before and 5 min after subfusion of phenylephrine (PE) and KCl, as indicated. Progressive vasoconstriction is evident in the vessel within the FGF2/FGF9-embedded gels in response to increasing concentration of phenylephrine and after KCl subfusion. () Plot of mean luminal diameter from vessels of three separate mice for each condition, depicting the acute change in vessel diameter in response to phenylephrine and KCl. P < 0.05 for all pairs after 400 s. Additional intravital microscopy images and corresponding movies depicting neovessel reactivity are presented in Supplementary Figure 6 and Supplementary Videos 1 and 2. * Figure 4: FGF9 promotes the development of durable, multilayered microvessels. () Photomicrographs of Matrigel constructs harvested 1 year after implantation and immunostained for SM α-actin. Small diameter (~10 μm) vessels can be seen in the gel originally containing FGF2 (left), whereas vessels in gel originally containing FGF2 and FGF9 are of larger caliber and the media is thicker (right). () Magnified views of a vessel in , right, showing an additional layer of SM α-actin–negative cells outside the SM α-actin–positive cells (left panel, arrow), possibly reflecting an additional primordial layer of the media or a developing adventitia. This vessel also appeared to contain an internal elastic lamina, suggested by the autofluorescence signal with 488 nm excitation (middle panel, arrow) and staining of a near-adjacent thin (2 μm) section with Verhoeff's hematoxylin (right panel, arrow). () Graph illustrating increased microvessel density in gels originally containing FGF2 and FGF9 compared to those exposed to FGF2 alone. This increase was eve! n more prominent for vessels >15 μm in diameter. *P = 0.009 versus FGF2 (all microvessels) and **P = 0.008 versus FGF2 (vessels >15 μm). () Graph showing diversity in vessel sizes after 1 year. Size diversity was evident only in microvessels originally exposed to FGF2 and FGF9. * Figure 5: FGF9 promotes neovessel maturation and functional recovery following hind limb ischemia. () Photomicrographs of the right adductor magnus muscles of 9-month-old mice harvested 10 d after induction of ischemia by partial excision of the right femoral artery. The adductor muscles were subjected to continuous, local infusion of PBS or FGF9 (1 μg/day) for 7 d, beginning the day of surgery, through tubing attached to a subcutaneously implanted osmotic pump. Zinc-fixed sections were double immunostained for CD31 (brown) and SM α-actin (red). Arrows point to vessels invested by smooth muscle α-actin+ cells. () Graphs depicting vessel density and proportion of smooth muscle α-actin+–microvessels in the control and FGF9-infused adductor muscles as well as the regenerating (central nuclei) and necrotic (absent nuclei) areas of the more distal gastrocnemius muscle (P = 0.63, 0.005, 0.026, 0.027, respectively). () Laser Doppler perfusion imaging scans of the mouse right foot before, immediately after, and 15 d after right femoral artery excision. Perfusion ratios are ! depicted in the adjacent graph (*P = 0.001). () Video analysis of mice traversing an illuminated glass walkway. The period of time during which each of the paws contacts the glass is illustrated as a contact duration map. The distance between the blue arrowheads corresponds to 1 second. Arrows denote the lane pertaining to the injured right hind limb. The mean paw contact time per step for each limb, based on five separate experiments each with three replicates, is quantified in the graph below the map (*P = 0.002 versus contralateral, uninjured left hind limb, **P = 0.019 versus PBS-infused injured right hind limb). LF, left front; RF, right front; LH, left hind; RH, right hind. () Depiction of pixel intensity generated by the mouse paws upon contact with the glass floor. Arrows denote contact of the paw from the hind limb subjected to femoral artery ligation. Mean pixel intensity for each limb is depicted in the bar graph (n = 5 experiments, *P = 0.0001 versus contralater! al, uninjured left hind limb, **P = 0.010 versus PBS-infused i! njured right hind limb). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21363 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada and London Health Sciences Centre, London, Canada. * Matthew J Frontini, * Zengxuan Nong, * Robert Gros, * Maria Drangova, * Caroline O'Neil, * Mona N Rahman, * Oula Akawi, * Hao Yin & * J Geoffrey Pickering * Department of Medicine (Cardiology), Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada. * J Geoffrey Pickering * Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada. * Matthew J Frontini & * J Geoffrey Pickering * Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada. * Maria Drangova, * Christopher G Ellis & * J Geoffrey Pickering * Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada. * Robert Gros Contributions M.J.F. undertook experimentation and contributed to the manuscript preparation. Z.N. performed animal surgeries and tissue immunohistochemistry. R.G. contributed to the intravital microscopy experiments and laser Doppler perfusion studies and undertook gait analyses. M.D. undertook micro-CT angiography and its analysis. C.O. and M.N.R. performed the microarray experiments and cell proliferation and migration studies and C.O. also contributed to the laser Doppler flow analyses. O.A. contributed to the telomere analyses. H.Y. undertook the FGFR activation studies and contributed to manuscript preparation. C.G.E. provided technical support and conceptual advice for intravital experiments. J.G.P. conceived and designed the study and prepared the manuscript. Competing financial interests J.G.P., M.J.F. and Z.N. hold a patent relating to the use of FGF9 in the context of the findings in this report. Corresponding author Correspondence to: * J Geoffrey Pickering Author Details * Matthew J Frontini Search for this author in: * NPG journals * PubMed * Google Scholar * Zengxuan Nong Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Gros Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Drangova Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline O'Neil Search for this author in: * NPG journals * PubMed * Google Scholar * Mona N Rahman Search for this author in: * NPG journals * PubMed * Google Scholar * Oula Akawi Search for this author in: * NPG journals * PubMed * Google Scholar * Hao Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher G Ellis Search for this author in: * NPG journals * PubMed * Google Scholar * J Geoffrey Pickering Contact J Geoffrey Pickering Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Vasomotion of control neovessel * Supplementary Video 2 (3M) Vasomotion of FGF9-modified neovessel PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–8 Additional data - Amelioration of sepsis by inhibiting sialidase-mediated disruption of the CD24-SiglecG interaction
- Nat Biotech 29(5):428-435 (2011)
Nature Biotechnology | Research | Article Amelioration of sepsis by inhibiting sialidase-mediated disruption of the CD24-SiglecG interaction * Guo-Yun Chen1 * Xi Chen2 * Samantha King3 * Karen A Cavassani4 * Jiansong Cheng2 * Xincheng Zheng5 * Hongzhi Cao2 * Hai Yu2 * Jingyao Qu2 * Dexing Fang5 * Wei Wu5 * Xue-Feng Bai6 * Jin-Qing Liu6 * Shireen A Woodiga3 * Chong Chen1 * Lei Sun4 * Cory M Hogaboam4 * Steven L Kunkel4 * Pan Zheng1, 4 * Yang Liu1, 7 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:428–435Year published:(2011)DOI:doi:10.1038/nbt.1846Received04 January 2011Accepted16 March 2011Published online10 April 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Suppression of inflammation is critical for effective therapy of many infectious diseases. However, the high rates of mortality caused by sepsis attest to the need to better understand the basis of the inflammatory sequelae of sepsis and to develop new options for its treatment. In mice, inflammatory responses to host danger-associated molecular patterns (DAMPs), but not to microbial pathogen-associated molecular patterns (PAMPs), are repressed by the t interaction of CD24 and SiglecG (SIGLEC10 in human). Here we use an intestinal perforation model of sepsis to show that microbial sialidases target the sialic acid–based recognition of CD24 by SiglecG/10 to exacerbate inflammation. Sialidase inhibitors protect mice against sepsis by a mechanism involving both CD24 and Siglecg, whereas mutation of either gene exacerbates sepsis. Analysis of sialidase-deficient bacterial mutants confirms the key contribution of disrupting sialic acid–based pattern recognition to microbial v! irulence and supports the clinical potential of sialidase inhibition for dampening inflammation caused by infection. View full text Figures at a glance * Figure 1: CD24 and SiglecG protect mice against inflammation and mortality associated with polybacterial sepsis. () Targeted mutations of CD24 or Siglecg increased mortality. Age-matched male mice received antibiotics and CLP using 23G3/4 needles. The mice were observed twice daily for 14 d. Data were analyzed using the Kaplan-Meier method, with statistical significance determined using the log rank test. () Targeted mutation of either CD24 or Siglecg increased the production of the inflammatory cytokines IL-6, MCP-1 and TNFα. Serum samples harvested 12 h or 24 h after CLP were assayed using a cytokine beads array. Data are means ± s.d. (n = 5). (–) Targeted mutation of either the Siglecg or CD24 exacerbates sepsis without increasing bacterial colony-forming units (CFUs) in the bloodstream. 21G needles were used and the CLP mice received no antibiotics. Survival of WT, CD24−/−, Siglecg−/− mice. Data shown are summary of five experiments, each involving ten mice per group (). Bacterial burdens in the blood samples (CFU/ml) harvested at 12 h after CLP (n = 8) (). Elevation of! inflammatory cytokines in mice with targeted mutation of either CD24 or Siglecg at 12 h after CLP (n = 8) (). Inflammatory cytokines in the WT mice 24 h after CLP. Data from mutant CLP mice were not collected due to mortality (). CD24−/− and Siglecg−/− mice exhibit acute organ failures after CLP. Note increased alveolar and interstitial hemorrhage in lung (marked as He in top panels), massive hemorrhage and venous congestion (marked as He in renal medulla and collecting tubules (middle panels)), and focal tubular necrosis with vacuolar degeneration and nuclear pyknosis and karyolysis in kidney (marked by yellow circles) 12 h after CLP (). All data presented have been validated by two to five independent experiments. * Figure 2: Expression of CD24 predominantly on CD11c+ cells protects against sepsis. () CD24−/− mice that expressed CD24 under the control of CD11c promoter, CD24−/−;CD24Cd11ctg. Data shown are FACS profiles depicting the pattern of CD24 expression in the H-2I-Ab+CD11c− and H-2I-Ab+CD11c+ splenocytes of WT, CD24−/− and CD24−/−;CD24Cd11ctg mice. () Expression of CD24 on CD11c+ cells increased mouse survival after CLP. A 23G3/4 needle was used for puncture and no antibiotics were used. CD24−/−;CD24cd11ctg mice and their CD24−/− littermates were subjected to CLP and their survival was monitored. () Transgenic expression of CD24 had no effect on blood bacterial burden at 24 h after CLP (n = 5). () CD24 on CD11c+ cells suppressed production of inflammatory cytokines at 24 h (n = 5). The protection by CD24 against lethality and cytokine production was observed in four independent experiments. CFU data are representative of those from two independent experiments. * Figure 3: The interaction of CD24 with SIGLEC10 depends on sialylation of CD24. () Biotinylated CD24Fc was pretreated with either control buffer (lane 1) or sialidase from Streptococcus pneumoniae (lane 2, specific for cleaving α2-3–linked sialic acids), Clostridium perfringens (lane 3, active for α2-6– or α2-3–linked sialosides) or Vibrio cholerae (lane 4, active for α2-3–, α2-6– or α2-8–linked sialosides) overnight at 37 °C. The SIGLEC10Fc fusion protein was incubated with the digested CD24Fc, and the complex was pulled down with streptavidin beads. The amounts of bead-bound SIGLEC10Fc and CD24Fc were determined by western blot analysis with antibodies specific for either SIGLEC10 or CD24. () Efficient inhibition of CD24-SIGLEC10 interaction by sialosides. SIGLEC10Fc was preincubated with given concentration of either Neu5Acα2-3Lac or Neu5Acα2-6Lac and then added to plate-bound CD24Fc. Amounts of CD24-bound SIGLEC10Fc were measured using biotinylated anti-SIGLEC10 and horseradish peroxidase–labeled streptavidin. () Desialylation! and resialylation of CD24Fc altered its electrophoresis mobility. () Both α2-3 and α2-6 resialylations of CD24 restore SIGLEC10Fc binding. () Sialidase treatment of WT dendritic cells increased their responses to HMGB1 and HSP70. Bone marrow–derived dendritic cells from WT, CD24−/− and Siglecg−/− mice were treated with sialidase before stimulation by either HMGB1 (1 μg/ml) or HSP70 (7 nM). Cytokines in the supernatants were measured by cytokine bead array. () Desialylation of CD24 barely reduced CD24Fc binding to HMGB1. Control IgG1Fc, untreated and desialylated CD24 were co-incubated with HMGB1 (1 μg/ml). Protein A beads were used to pull down Fc. The amounts of HMGB1 associated with CD24Fc were determined by immunoblotting with anti-HMGB1 monoclonal antibody. The data shown are representative of two to five independent experiments. DeSia, desialylation; 2-3 and 2-6ST: α2-3–, α2-6–resialylation, respectively. * Figure 4: Increased circulating sialidase activity and reduction of SIGLEC10 binding of CD24 in CLP mice. () Sialidase activity in the sera of sham-, 100 μg/mouse lipopolysaccharide- or CLP-treated mice. Sera were collected 12 h after treatment (n = 5). () Pretreatment of biotinylated CD24Fc with sera from CLP mice reduced its binding to SIGLEC10Fc. Data were obtained by co-immunoprecipitation with streptavidin-conjugated beads followed by immunoblotting. The top panel shows the amounts of SIGLEC10Fc in the precipitates as determined by western blotting using anti-SIGLEC10 antibodies. The molecular weight shift of CD24Fc in the bottom panel is demonstrated by western blot analysis using horseradish peroxidase–labeled streptavidin. IB, immunoblot; IP, immunoprecipitation. () CLP does not affect CD24 expression in spleen cells. US, unstained. () CLP significantly reduced spleen cell binding to SIGLEC10Fc. Histograms shown on top panels are FACS profiles depicting distribution of CD24 in spleen cells from animals subjected to sham surgery (blue line) or CLP (red line). The bar g! raphs in the bottom panels present means ± s.d. of mean fluorescence intensities (n = 3). The gates used to determine percent positive cells were labeled in the upper panels. SA-PE, phycoerythrin-conjugated streptavidin. (,) CLP reduces spleen-cell binding to WT and mutant SiglecGFc () without affecting total CD24 levels (). The bar graphs in the bottom panels present means ± s.d. of geom-mean fluorescence intensities or percent positive cells (n = 3). αSiglecg, anti-Siglecg sera; αSIGLECG10, anti-SIGLEC10 antibody. () CLP alters the molecular weight distribution of CD24 in the spleen cell lysates, as determined by western blot analysis. () CLP reduces both α2-3– and α2-6–sialylation of spleen cells () and CD11c+ cells (). MAA, fluorescein-Maackia amurensis lectin I, recognizing α2-3–linked terminal sialic acid; SNA, fluorescein-Sambucus nigra (elderberry) bark lectin, recognizing α2-6–linked terminal sialic acid. All data are representative of two to three ! independent experiments. * Figure 5: Sialidase inhibitors protect mice against sepsis. () A mixture of two sialidase inhibitors blocks serum sialidase activity. Sera from CLP mice were mixed with given doses of inhibitors, Neu5Ac2en (AC), Neu5Gc2en (GC) or both (AC + GC) before the assay. The sialidase activity was measured using the Amplex Red Neuraminidase assay kit. Data shown are means ± s.d. of triplicates. () Sialidase inhibitors prolong survival of WT but not CD24−/− and Siglecg−/− mice after CLP (n = 10). The mice received a mixture of AC and GC (100 μg/mouse/injection) after CLP and every 12 h thereafter. () Sialidase inhibitors reduce the levels of multiple inflammatory cytokines. Sera were collected 24 h after CLP to measure cytokine levels. Data shown are means ± s.d. (n = 8). Note that a different scale is used for the y axis for TNFα data. () Sialidase inhibitor protects CLP mice in combination with antibiotics. Data shown are representative of two to five independent experiments. Large 21G needles were used for cecal puncture. Mice i! n – received no antibiotics, whereas some groups in received antibiotics, as marked. * Figure 6: S. pneumoniae sialidases exacerbate sepsis by a CD24− and SiglecG-dependent mechanism. () Characterization of the sialidase activity of WT (D39) and nanA−nanB− mutant (mD39) strains. () Serum sialidase activity of mice 24 h after intraperitoneal infections with 104 CFU of either D39 or mD39. The sialidase activity in and was measured using Amplex Red Neuraminidase Assay Kit. () Bacterial sialidase reduces SIGLEC10Fc binding to spleen cells. Representative FACS profiles are shown in the top panels, whereas summary data are shown in the lower panels (n = 3). () Bacterial sialidases exacerbate sepsis in WT but not mutant mice. Data accumulated from two independent experiments (n = 10) were analyzed using Kaplan Meier survival analysis, and the log rank test was used to calculate P values. () Bacterial sialidases stimulate the production of inflammatory cytokines in WT but not mutant mice. () Deletion of the genes encoding NanA and NanB sialidases does not reduce blood bacterial burden. Data shown represent two to three independent experiments. n = 5 unless ot! herwise specified. Author information * Abstract * Author information * Supplementary information Affiliations * Division of Immunotherapy, Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA. * Guo-Yun Chen, * Chong Chen, * Pan Zheng & * Yang Liu * Department of Chemistry, University of California, Davis, Davis, California, USA. * Xi Chen, * Jiansong Cheng, * Hongzhi Cao, * Hai Yu & * Jingyao Qu * Center for Microbial Pathogenesis, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA. * Samantha King & * Shireen A Woodiga * Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. * Karen A Cavassani, * Lei Sun, * Cory M Hogaboam, * Steven L Kunkel & * Pan Zheng * OncoImmune, Inc., Ann Arbor, Michigan, USA. * Xincheng Zheng, * Dexing Fang & * Wei Wu * Department of Pathology, The Ohio State University, Columbus, Ohio, USA. * Xue-Feng Bai & * Jin-Qing Liu * Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA. * Yang Liu Contributions G.-Y.C., K.A.C., J.C., H.C., H.Y., X.Z., D.F., W.W., J.Q., S.A.W., C.H., C.C. and L.S. performed experiments, C.M.H., and S.L.K. advised on CLP model, X.-F. B. and J.-Q.L. provided transgenic mice, Y.L., P.Z. and G.-Y.C. designed the overall study. X.C. supervised synthesis of sialosides and sialo-modifications of CD24Fc, while S.K. supervised production of mutant bacteria. Y.L., P.Z. and G.-Y.C. wrote the manuscript with input from other authors. Competing financial interests Y.L., P.Z., G.-Y.C., X.C. and S.K. are co-inventors of a pending patent application that has been licensed to OncoImmune, Inc., of which Y.L. and P.Z. are among the co-founders and have equity interest. Corresponding authors Correspondence to: * Yang Liu or * Pan Zheng Author Details * Guo-Yun Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Xi Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Samantha King Search for this author in: * NPG journals * PubMed * Google Scholar * Karen A Cavassani Search for this author in: * NPG journals * PubMed * Google Scholar * Jiansong Cheng Search for this author in: * NPG journals * PubMed * Google Scholar * Xincheng Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Hongzhi Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Hai Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Jingyao Qu Search for this author in: * NPG journals * PubMed * Google Scholar * Dexing Fang Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Xue-Feng Bai Search for this author in: * NPG journals * PubMed * Google Scholar * Jin-Qing Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Shireen A Woodiga Search for this author in: * NPG journals * PubMed * Google Scholar * Chong Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Cory M Hogaboam Search for this author in: * NPG journals * PubMed * Google Scholar * Steven L Kunkel Search for this author in: * NPG journals * PubMed * Google Scholar * Pan Zheng Contact Pan Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Liu Contact Yang Liu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (492K) Supplementary Table 1 and Supplementary Figs. 1–5 Additional data - Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells
- Nat Biotech 29(5):436-442 (2011)
Nature Biotechnology | Research | Article Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells * Michal Rabani1, 2 * Joshua Z Levin1 * Lin Fan1 * Xian Adiconis1 * Raktima Raychowdhury1 * Manuel Garber1 * Andreas Gnirke1 * Chad Nusbaum1 * Nir Hacohen1 * Nir Friedman3, 4, 6 * Ido Amit1, 6 * Aviv Regev1, 5, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:436–442Year published:(2011)DOI:doi:10.1038/nbt.1861Received07 December 2010Accepted01 April 2011Published online24 April 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 Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcri! pts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation. View full text Figures at a glance * Figure 1: Changes in transcription rates during the response of dendritic cells to LPS. () Measuring transcription rates with short metabolic labeling. We used short metabolic labeling (10 min, red lines) and measured the expression of RNA-total (blue) and RNA-4sU (red) for 254 'signature' genes at 13 time points in 15 min intervals (rows) over the first 3 h post-LPS stimulation. () Changes in RNA-4sU levels follow changes in pol-II binding and precede changes in total RNA levels. Shown are example time-course profiles for selected genes for RNA-4sU expression (nCounter, red), RNA-total expression (nCounter, blue) and pol-II binding at the promoter (ChIP, dashed gray). () Distinct temporal clusters of newly transcribed and total RNA. Shown are clusters of expression profiles (nCounter) for 254 'signature' genes (rows) based on RNA-total (left) and RNA-4sU (right) measurements across 13 time points (columns). Cluster I includes the control genes. Cluster numbers (I–VIII) are noted on right; representative member genes are noted on left. Purple, high relative e! xpression; white, mean expression; pink, low relative expression. () Peak transcription precedes peak expression by 15–30 min. Shown are average profiles (y axis) for RNA-4sU (red) and RNA-total (blue) for each cluster at each time point (x axis), ordered by cluster numbers (cluster I topmost; cluster VIII bottommost). The size of each cluster is indicated in parentheses. Pearson correlation coefficient (ρ) of the best time-lag correlation between transcription and expression is indicated on right, with the optimal time lag in parentheses below. * Figure 2: Changes in transcription rate account for most expression changes; changes in degradation rate contribute to 'peaked' responses. () The constant degradation and varying degradation models. A first-degree dynamical model (formula, right) models the expression level of a gene (X; gray) as a function of transcription (α; black) and degradation (β; green) rates. Parameters include an 'impulse' model29, 30 for transcription (black curve) and either a constant function for degradation (constant degradation model, solid green line), or an 'impulse' model (varying degradation model, dashed green line). We fit them to our data (left, RNA-total, blue, and RNA-4sU, red) by optimizing the likelihood function (separately per gene). We compare the model's fit (black and gray curves) to the data (red and blue curves, respectively) and calculate the error. () The constant degradation model fits the majority of genes well. Shown is the distribution of the log likelihood ratios between the constant degradation and varying degradation models. Dashed line indicates the threshold for rejecting constant degradation (P < ! 0.01). () The percent of genes per cluster (numbered as in Fig. 1c) that reject the constant degradation model. () Varying degradation profiles estimated for the 44 genes that reject the constant degradation model. Right: estimated degradation rates (relative rate: purple, high; pink, low) for the 44 genes (rows), clustered into three groups (A–C), across 12 time points (columns; excluding t = 0, which is highly sensitive to noise owing to low RNA levels). Asterisks, known regulators of RNA degradation. Left, mean degradation rate profile per cluster (in parentheses, number of genes in cluster). () Genes with peaked responses reject the constant degradation model. Shown are two example genes. For each, upper row, constant degradation model fit (solid lines) to the data (dashed lines); lower row, varying degradation model fit (solid lines) to the data (dashed lines). Left, expression level; middle, transcription rate; right, degradation rate (estimate only). * Figure 3: Genome-wide analysis of RNA transcription and degradation rates using RNA- and 4sU-Seq. () Experiment overview. We isolated RNA-4sU (after 45 min of 4sU labeling, red) and polyA+ RNA-total (blue) at 1 h intervals (rows) over the first 6 h of the response of dendritic cells to LPS stimulation and used massively parallel sequencing to measure RNA levels. () 4sU-Seq captures a broader representation of transcripts compared to polyA+ RNA-Seq. Shown is the fraction of reads in RNA-4sU-Seq libraries (top) and polyA+ RNA-Seq libraries (bottom), across several annotation categories. Only reads that are mapped to a unique location in the genome are considered. () Distribution of predicted constant mRNA half-lives for the 9,448 genes expressed during the first 6 h of the response to LPS stimulation that do not reject the constant degradation model. Dashed lines distinguish 10 deciles (A–J, 10% increments, 115 transcripts with >200 min half-life are included in the last decile). mRNA half-lives for illustrative genes are denoted in the first and last bins. * Figure 4: Genome-wide analysis of RNA processing rates. () Using 4sU-Seq data to study RNA processing. Sequencing reads in the 4sU-Seq libraries originate from either pre-mRNA (P; purple) or mature mRNA (M; light blue). Whereas mRNA reads map only to exons, the pre-mRNA reads map to both exons and introns. We estimate newly transcribed pre-mRNA expression by the RPKM of a gene's introns alone and overall newly transcribed RNA expression (pre-mRNA + mRNA) by the RPKM of a gene's exons. () An overview of the 'constant degradation and processing' model. The model expands on our constant degradation model (Fig. 2a) by adding a constant processing rate (right, orange). We fit the model parameters to our data (left, mRNA-4sU, dashed red; pre-mRNA-4sU, dashed purple) by optimizing the likelihood function (separately per gene) and using the degradation rates predicted by the constant degradation model. () Distribution of predicted constant processing rates for 2,122 genes with exonic and intronic expression during the first 6 h of the re! sponse to LPS stimulation. Dashed lines distinguish five quintiles (i–v, 20% increments), and transcripts with >30 min half-life are added to the last bin. Pre-mRNA half-lives for illustrative genes are denoted in each bin. () Transcripts with low or high pre-mRNA half-lives are enriched in functional categories, clusters, exon structures or transcript lengths. Shown are the enrichments (P-value, hypergeometric test, gray) of the overlap between the genes in each of the half-life bins in (i–v, columns) and each tested category (rows). Only categories with at least one significant enrichment are shown. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25432 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Nir Friedman, * Ido Amit & * Aviv Regev Affiliations * Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Michal Rabani, * Joshua Z Levin, * Lin Fan, * Xian Adiconis, * Raktima Raychowdhury, * Manuel Garber, * Andreas Gnirke, * Chad Nusbaum, * Nir Hacohen, * Ido Amit & * Aviv Regev * Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Michal Rabani * School of Computer Science, Hebrew University, Jerusalem, Israel. * Nir Friedman * Institute of Life Sciences, Hebrew University, Jerusalem, Israel. * Nir Friedman * Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Aviv Regev Contributions M.R., I.A. and A.R. conceived and designed the study. M.R. and I.A. conducted the experiments. M.R., N.F. and A.R. designed the computational methods. M.R. developed and implemented the computational methods. R.R. made the cell cultures. J.Z.L., X.A., L.F., A.G. and C.N. constructed and sequenced the cDNA libraries. N.H. contributed experimental methods and reagents. M.G. contributed computational methods for RNA-Seq analysis. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Nir Friedman or * Ido Amit or * Aviv Regev Author Details * Michal Rabani Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Z Levin Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Xian Adiconis Search for this author in: * NPG journals * PubMed * Google Scholar * Raktima Raychowdhury Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Garber Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Gnirke Search for this author in: * NPG journals * PubMed * Google Scholar * Chad Nusbaum Search for this author in: * NPG journals * PubMed * Google Scholar * Nir Hacohen Search for this author in: * NPG journals * PubMed * Google Scholar * Nir Friedman Contact Nir Friedman Search for this author in: * NPG journals * PubMed * Google Scholar * Ido Amit Contact Ido Amit Search for this author in: * NPG journals * PubMed * Google Scholar * Aviv Regev Contact Aviv Regev Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (66K) The 254 genes in the 'signature' set. * Supplementary Table 2 (344K) nCounter measurements data. * Supplementary Table 3 (16K) Standard RNA-Seq sequencing libraries statistics. * Supplementary Table 4 (16K) 4sU-Seq sequencing libraries statistics. * Supplementary Table 5 (90K) Functional enrichments in the 8 expression clusters. * Supplementary Table 6 (135K) Functional enrichments in 10 deciles with distinct (fixed) degradation rates and in the group of genes that reject the 'constant degradation' model. PDF files * Supplementary Text and Figures (6M) Supplementary Methods, Supplementary Notes and Supplementary Figs. 1–24 Additional data - Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells
- Nat Biotech 29(5):443-448 (2011)
Nature Biotechnology | Research | Letter Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells * Deepa Subramanyam1, 2 * Samy Lamouille1, 3, 4 * Robert L Judson1, 2, 4 * Jason Y Liu1, 2 * Nathan Bucay1, 2 * Rik Derynck1, 3 * Robert Blelloch1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:443–448Year published:(2011)DOI:doi:10.1038/nbt.1862Received03 November 2010Accepted04 April 2011Published online13 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The embryonic stem cell–specific cell cycle–regulating (ESCC) family of microRNAs (miRNAs) enhances reprogramming of mouse embryonic fibroblasts to induced pluripotent stem cells1. Here we show that the human ESCC miRNA orthologs hsa-miR-302b and hsa-miR-372 promote human somatic cell reprogramming. Furthermore, these miRNAs repress multiple target genes, with downregulation of individual targets only partially recapitulating the total miRNA effects. These targets regulate various cellular processes, including cell cycle, epithelial-mesenchymal transition (EMT), epigenetic regulation and vesicular transport. ESCC miRNAs have a known role in regulating the unique embryonic stem cell cycle2, 3. We show that they also increase the kinetics of mesenchymal-epithelial transition during reprogramming and block TGFβ-induced EMT of human epithelial cells. These results demonstrate that the ESCC miRNAs promote dedifferentiation by acting on multiple downstream pathways. We propos! e that individual miRNAs generally act through numerous pathways that synergize to regulate and enforce cell fate decisions. View full text Figures at a glance * Figure 1: Hsa-miR-302b and/or hsa-miR-372 enhances reprogramming efficiency of human somatic cells. () Fold increase in number of human ESC-like colonies obtained per 15,000 cells compared to mock-transfected cells. Cells infected with 4Y ± miRNA were counted on day 21 after infection, whereas cells infected with 3Y ± miRNA were counted on day 31 after infection. *, a significant difference when compared to mock-transfected; P < 0.05; N = 6. Error bars represent mean ± s.e.m. () Expression of exogenous factors in iPSC lines that were picked and expanded after 3Y or 4Y infection and the indicated miRNA. Expression was determined by RT-qPCR using primers specific to only the exogenous factors. Data were normalized to BJ cells 3 d after retroviral infection with 4Y. () Expression of pluripotency markers in iPSC lines that were picked and expanded after 3Y or 4Y infection and the indicated miRNA. H9 hESCs shown as control. Expression was determined by RT-qPCR. Data were normalized to expression observed in BJ cells. * Figure 2: Hsa-miR-302b and hsa-miR-372 regulate expression of a number of targets that influence reprogramming of human somatic cells. () Heat map showing average expression from three independent experiments of 34 predicted targets of hsa-miR-302b and hsa-miR-372 on day 7 in the process of reprogramming. Expression was determined by qRT-PCR and was first normalized to GAPDH followed by normalization to mock-transfected cells. Statistically significant genes (P < 0.05; ANOVA) are labeled red. () Fold increase in reprogramming of human somatic cells infected with 4Y upon introduction of siRNAs against specific targets. Human ESC-like colonies were counted on day 21 after infection. N = 4. Error bars represent s.d. *, significant difference when compared to mock transfected; P < 0.05; measured by Kruskal-Wallis test. i, siRNA; ROCKi, ROCK inhibitor. () Fold increase in reprogramming of human somatic cells infected with 3Y upon introduction of siRNAs against specific targets. Human ESC-like colonies were counted on day 31 after infection. N = 3. Error bars represent s.d. *, significant difference when compared! to mock-transfected; P < 0.05; measured by Kruskal-Wallis test. i, siRNA; ROCKi, ROCK inhibitor. * Figure 3: Hsa-miR-302b and hsa-miR-372 enhance reprogramming by regulating mesenchymal-epithelial transition. () Western blot showing levels of TβRII from lysates prepared from BJ cells infected with either 4Y or 3Y, or uninfected cells plus the indicated miRNAs. N = 3. () Luciferase analysis of TGFBR2 and RHOC 3′UTRs. Seed matches for ESCC miRNAs in the 3′UTRs along with different mutant constructs are shown in the top panel. Luciferase results after co-transfection with ESCC miRNAs relative to mock transfection are shown in the lower panel after normalization to firefly luciferase values. All data are represented as mean ± s.d. *P < 0.05 by t-test. () RT-qPCR showing relative expression levels of mesenchymal (ZEB1 and SLUG) and epithelial (E-cadherin, CDH1, and occludin, OCLN) markers at day 7 after infection in the process of reprogramming normalized to GAPDH. N = 3. Error bars represent s.e.m. *, significant difference when compared to mock-transfected cells within each group (P < 0.05) by t-test. () Immunocytochemistry performed at different days during the course of repr! ogramming with 4Y or 3Y ± hsa-miR-372. Representative portions of the well are shown in each image. N = 2. Scale bars, 25 μm. * Figure 4: Hsa-miR-302b, hsa-miR-372 and mmu-miR-294 inhibit TGF-β–induced epithelial-mesenchymal transition in human cells. () Western blot showing levels of TGF-β receptors, phospho-SMAD2 and phospho-SMAD3 in HaCaT cells 0–60 min after TGF-β exposure in the presence of miRNA mimics. Cells were transfected with the indicated miRNAs 48 h before TGF-β treatment. miR-294m, mmu-miR-294 seed mutant mimic. Representative blot of N = 2. (,) HaCaT cells were transfected with the indicated miRNAs, then treated or not with TGF-β for 72 h and observed by phase contrast microscopy (), or fixed and subjected to immunostaining for F-actin, E-cadherin and ZO-1 (). N = 2. Scale bars, 100 μm (), 20 μm (). () HaCaT cells were transfected with the indicated miRNAs, then treated with TGF-β for 48 h before lysis and immunoblotting with the indicated antibodies. N = 2. () HaCaT cells were transfected with the indicated miRNAs treated or not with TGF-β for 24 h, before RNA was extracted and analyzed by RT-qPCR. Expression was normalized to RPL19. Representative graph of two independent experiments is shown. E! rror bars represent mean ± s.e.m. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Samy Lamouille & * Robert L Judson Affiliations * Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, California, USA. * Deepa Subramanyam, * Samy Lamouille, * Robert L Judson, * Jason Y Liu, * Nathan Bucay, * Rik Derynck & * Robert Blelloch * Center for Reproductive Sciences and Department of Urology, University of California San Francisco, San Francisco, California, USA. * Deepa Subramanyam, * Robert L Judson, * Jason Y Liu, * Nathan Bucay & * Robert Blelloch * Department of Cell and Tissue Biology, Programs in Cell Biology and Developmental Biology, University of California San Francisco, San Francisco, California, USA. * Samy Lamouille & * Rik Derynck Contributions D.S. did the experiments described in Figures 1, 2 and 3. S.L. and R.L.J. did experiments described in Figure 4. J.Y.L. helped with experiments described in Figure 2a and performed experiments described in 3b. N.B. helped with experiments in Figure 1b,c. D.S. and R.B. wrote the manuscript with help from S.L., R.L.J. and R.D. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Robert Blelloch Author Details * Deepa Subramanyam Search for this author in: * NPG journals * PubMed * Google Scholar * Samy Lamouille Search for this author in: * NPG journals * PubMed * Google Scholar * Robert L Judson Search for this author in: * NPG journals * PubMed * Google Scholar * Jason Y Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Nathan Bucay Search for this author in: * NPG journals * PubMed * Google Scholar * Rik Derynck Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Blelloch Contact Robert Blelloch Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2.8M) Supplementary Tables 1 and 2 and Supplementary Figs. 1–10 Additional data - Parallel on-chip gene synthesis and application to optimization of protein expression
- Nat Biotech 29(5):449-452 (2011)
Nature Biotechnology | Research | Letter Parallel on-chip gene synthesis and application to optimization of protein expression * Jiayuan Quan1, 2, 4 * Ishtiaq Saaem1, 2, 4 * Nicholas Tang1 * Siying Ma1, 2 * Nicolas Negre3 * Hui Gong1 * Kevin P White3 * Jingdong Tian1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:449–452Year published:(2011)DOI:doi:10.1038/nbt.1847Received12 November 2011Accepted17 March 2011Published online24 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Low-cost, high-throughput gene synthesis and precise control of protein expression are of critical importance to synthetic biology and biotechnology1, 2, 3. Here we describe the development of an on-chip gene synthesis technology, which integrates on a single microchip the synthesis of DNA oligonucleotides using inkjet printing, isothermal oligonucleotide amplification and parallel gene assembly. Use of a mismatch-specific endonuclease for error correction results in an error rate of ~0.19 errors per kb. We applied this approach to synthesize pools of thousands of codon-usage variants of lacZα and 74 challenging Drosophila protein antigens, which were then screened for expression in Escherichia coli. In one round of synthesis and screening, we obtained DNA sequences that were expressed at a wide range of levels, from zero to almost 60% of the total cell protein mass. This technology may facilitate systematic investigation of the molecular mechanisms of protein translation a! nd the design, construction and evolution of macromolecular machines, metabolic networks and synthetic cells. View full text Figures at a glance * Figure 1: The integrated on-chip oligo array synthesis, amplification and gene assembly process. Small pools of oligos are synthesized in separate chambers on a plastic DNA microchip using an inkjet DNA microarray synthesizer. The chambers are then filled with a combined amplification and assembly reaction mixture and sealed. In a nicking and strand displacement amplification reaction, a DNA polymerase (Bst large fragment, shown in yellow) extends and displaces the proceeding strand while a nicking endonuclease (Nt.BstNBI, shown in teal) separates the construction oligos from the universal primer (in red) and generates new 3′-ends for extension. After amplification, the free oligos in each chamber are assembled into gene products by polymerase chain assembly. * Figure 2: Expression of synthetic lacZα codon variants in E. coli. () A set of 1,296 E. coli colonies expressing distinct lacZα codon variants sorted by color intensity. Raw images were acquired by scanning an agar plate on the scanning window of a HP Photosmart C7180 Flatbed Scanner. () Bar graph and box plot showing distribution of color intensities of a different set of 1,468 random colonies expressing distinct lacZα codon variants on an agar plate. Owing to the large size of the synthetic codon variant library, the chance of having identical clones on a plate was extremely low, as confirmed by sequencing several hundred blue colonies (data not shown). In the box plot, the expression level of the WT lacZα is marked with a dash line. * Figure 3: Optimization of protein expression. Seventy-four Drosophila transcription factor gene fragments were optimized for production in E. coli by synthesizing ~1,000–1,500 codon variants of each, cloning them in frame with GFP and screening for the colonies with the highest fluorescence. Data for 15 proteins shown here (see Supplementary Fig. 4 for remaining 59). Each pair of lanes shows total cell protein extract of E. coli expressing the wild-type (left lane, WT) and optimized (right lane, Op) clones. The broad bands marked by an arrow represent highly expressed transcription factor–GFP fusion proteins. There was no detectable expression of wild-type transcription factor–GFP fusion proteins as shown in the wild-type lanes. Equal amounts of the total cell protein extracts were separated on NuPage 4–12% gradient gels and stained with EZBlue Gel Staining Reagent. M lanes are Novex Prestained protein standards (Invitrogen). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jiayuan Quan & * Ishtiaq Saaem Affiliations * Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA. * Jiayuan Quan, * Ishtiaq Saaem, * Nicholas Tang, * Siying Ma, * Hui Gong & * Jingdong Tian * Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA. * Jiayuan Quan, * Ishtiaq Saaem, * Siying Ma & * Jingdong Tian * Institute for Genomics and Systems Biology and Department of Human Genetics, The University of Chicago, Chicago, Illinois, USA. * Nicolas Negre & * Kevin P White Contributions J.Q. performed gene library synthesis, expression screen and protein purification experiments. I.S. performed on-chip oligo and gene synthesis experiments. N.T. and J.Q. performed colony imaging and image analysis. S.M., I.S. and J.Q. performed error correction experiments. N.N. and K.P.W. provided wild-type transcription factor sequences and clones. H.G. and J.T. designed gene library sequences. J.T. designed overall strategy and supervised the project. All authors contributed to the manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jingdong Tian Author Details * Jiayuan Quan Search for this author in: * NPG journals * PubMed * Google Scholar * Ishtiaq Saaem Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Siying Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Nicolas Negre Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Gong Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin P White Search for this author in: * NPG journals * PubMed * Google Scholar * Jingdong Tian Contact Jingdong Tian Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (700K) Supplementary Tables 1–3, Supplementary Figs. 1–4, Supplementary Sequences and Supplementary Note Additional data - Spatiotemporal manipulation of auxin biosynthesis in cotton ovule epidermal cells enhances fiber yield and quality
- Nat Biotech 29(5):453-458 (2011)
Nature Biotechnology | Research | Letter Spatiotemporal manipulation of auxin biosynthesis in cotton ovule epidermal cells enhances fiber yield and quality * Mi Zhang1, 4 * Xuelian Zheng1, 4 * Shuiqing Song1 * Qiwei Zeng1 * Lei Hou1 * Demou Li1 * Juan Zhao1 * Yuan Wei1 * Xianbi Li1 * Ming Luo1 * Yuehua Xiao1 * Xiaoying Luo1 * Jinfa Zhang2 * Chengbin Xiang3 * Yan Pei1 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:453–458Year published:(2011)DOI:doi:10.1038/nbt.1843Received25 January 2010Accepted14 March 2011Published online10 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The capacity of conventional breeding to simultaneously improve the yield and quality of cotton fiber is limited1. The accumulation of the plant hormone indole-3-acetic acid (IAA) in cotton fiber initials prompted us to investigate the effects of genetically engineering increased IAA levels in the ovule epidermis. Targeted expression of the IAA biosynthetic gene iaaM, driven by the promoter of the petunia MADS box gene Floral Binding protein 7 (FBP7)2, increased IAA levels in the epidermis of cotton ovules at the fiber initiation stage. This substantially increased the number of lint fibers, an effect that was confirmed in a 4-year field trial. The lint percentage of the transgenic cotton, an important component of fiber yield, was consistently higher in our transgenic plants than in nontransgenic controls, resulting in a >15% increase in lint yield. Fiber fineness was also notably improved. View full text Figures at a glance * Figure 1: Cellular localization of IAA in cotton ovules and the effect of an IAA transport inhibitor on fiber initiation. () Visualization of IAA distribution by immunofluorescence. Fluorescent images are labeled FITC and the corresponding overlay images (fluorescence/transmission), overlay. Scales bars, 40 μm. () Visualization of IAA distribution by color development. Scale bars, 40 μm. () Effect of exogenous application of NPA on fiber initiation. ii and iv are magnified frames in i and iii, respectively. Scale bars, 500 μm (i and iii) or 100 μm (ii and iv). WT, wild type; fl, Xuzhou142 fiberless mutant; NPA, NPA was applied on the pedicels of –3-DPA squares of wild-type cotton. * Figure 2: Targeted expression of iaaM driven by FBP7 promoter in the epidermis of ovules increases IAA accumulation in transgenic cotton. () Localization of iaaM mRNA in cotton ovules of FBP7::iaaM cotton, as determined by in situ hybridization. () Histochemical localization of GUS activity in FBP7::GUS transgenic cotton. Stained 0-DPA ovules of FBP7::GUS cotton were observed in paraffin-embedded sections. The magnified image (vi) is from the red square in the original (v). Triangles point to the ovule epidermal layer. Arrows point to the fiber initial (f) or the uninitiated cell (nf). WT, wild type; FM, FBP7::iaaM. Scale bars, 50 μm. () FBP7::iaaM expression pattern in transgenic cotton ovules (with fibers). Bars represent the s.d. of three repeated PCRs. () Endogenous IAA levels in ovules of line FM9 and wild type during fiber initiation. The bar represents the s.d. of three samples. () Cellular localization of IAA distribution in 0-DPA ovules visualized by immunofluorescence. Fluorescent images are marked as FITC and corresponding overlay images (fluorescence/transmission), as overlay. FM, FBP7::iaaM cotto! n treated with DMSO; NPA (FM), FBP7::iaaM cotton treated with NPA; NPA (WT), wild type treated with NPA. Scale bars, 40 μm. * Figure 3: Comparison of fiber initial density, number of mature fibers per seed, and variation of lint percentage and micronaire value over five sampling times in transgenic cotton plants and nontransgenic controls. () Fiber initial densities on the surface of ovules at 0 DPA. Error bars represent s.d. of nine ovules. () Number of mature fibers per seed. Bars represent s.d. of six measurements. Asterisks indicate statistically significant differences between transgenic lines and wild type, as determined by Student's t-test (*, P < 0.05; **, P < 0.01). () Variation of lint percentage over five sampling times in the trials of 2008 and 2009. () Variation of micronaire value over five sampling times in the trials of 2008 and 2009. Bars represent s.d. of three replications. Dotted lines with corresponding colors show the average throughout the harvest season in each year. WT, wild type; FM, FBP7::iaaM; EM, E6::iaaM; Control, segregated, nontransgenic plants derived from FBP7::iaaM cotton. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Mi Zhang & * Xuelian Zheng Affiliations * Key Laboratory of Biotechnology and Crop Quality Improvement of Ministry of Agriculture, Biotechnology Research Center, Southwest University, Chongqing, P.R. China. * Mi Zhang, * Xuelian Zheng, * Shuiqing Song, * Qiwei Zeng, * Lei Hou, * Demou Li, * Juan Zhao, * Yuan Wei, * Xianbi Li, * Ming Luo, * Yuehua Xiao, * Xiaoying Luo & * Yan Pei * Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico, USA. * Jinfa Zhang * School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, P.R. China. * Chengbin Xiang Contributions M.Z. performed field trials, in situ hybridization and quantified IAA content; X.Z. performed greenhouse experiments and quantified IAA content; S.S. performed cotton transformation and field trials; Q.Z. and Y.W. performed cotton transformation; J.Z. and X.L. performed field trials; L.H., D.L., M.L., Y.X. and X.L. performed vector construction and promoter analysis; J.Z. performed field data analysis; C.X. designed the hybridization experiment; Y.P. designed experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yan Pei Author Details * Mi Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Xuelian Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Shuiqing Song Search for this author in: * NPG journals * PubMed * Google Scholar * Qiwei Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Hou Search for this author in: * NPG journals * PubMed * Google Scholar * Demou Li Search for this author in: * NPG journals * PubMed * Google Scholar * Juan Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Yuan Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Xianbi Li Search for this author in: * NPG journals * PubMed * Google Scholar * Ming Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Yuehua Xiao Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoying Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Jinfa Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Chengbin Xiang Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Pei Contact Yan Pei Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (922K) Supplementary Tables 1–6 and Supplementary Figs. 1–10 Additional data - MicroRNA gets down to business
- Nat Biotech 29(5):459 (2011)
Nature Biotechnology | Research | Erratum MicroRNA gets down to business * George S MackJournal name:Nature BiotechnologyVolume: 29,Page:459Year published:(2011)DOI:doi:10.1038/nbt0511-459aPublished online06 May 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.25, 631–638 (2007); published online 7 June 2007; corrected after print 7 December 2010 In the version of the article originally published, reference 8 incorrectly listed Chang, D.Z. as the first author. It should have read Tsuda, N. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * George S Mack Search for this author in: * NPG journals * PubMed * Google Scholar - Can cancer clinical trials be fixed?
- Nat Biotech 29(5):459 (2011)
Nature Biotechnology | Research | Erratum Can cancer clinical trials be fixed? * Malorye AllisonJournal name:Nature BiotechnologyVolume: 29,Page:459Year published:(2011)DOI:doi:10.1038/nbt0511-459bPublished online06 May 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 13–15 (2011); published online 10 January 2010; corrected after print 9 February 2011 In the version of the article originally published, it was stated that "Avastin was studied in combination with Taxol in all these trials, and compared to Taxol alone." It should have read, "Avastin was studied in combination with chemotherapy." In addition, it stated that the FDA had rescinded approval of Avastin for metastatic breast cancer. It should have read, the FDA "recommended removal of the breast cancer indication from the Avastin label..." The errors have been corrected in the HTML and PDF versions of the article. Additional data Author Details * Malorye Allison Search for this author in: * NPG journals * PubMed * Google Scholar - Haplotype-resolved genome sequencing of a Gujarati Indian individual
- Nat Biotech 29(5):459 (2011)
Nature Biotechnology | Research | Erratum Haplotype-resolved genome sequencing of a Gujarati Indian individual * Jacob O Kitzman * Alexandra P MacKenzie * Andrew Adey * Joseph B Hiatt * Rupali P Patwardhan * Peter H Sudmant * Sarah B Ng * Can Alkan * Ruolan Qiu * Evan E Eichler * Jay ShendureJournal name:Nature BiotechnologyVolume: 29,Page:459Year published:(2011)DOI:doi:10.1038/nbt0511-459cPublished online06 May 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 59–63 (2011); published online 19 December 2010; corrected after print 12 April 2011 In the HTML version of this article initially published online, the web link for accession code SRA026360 was incorrect. This error has been corrected in the HTML version of the article. Additional data Author Details * Jacob O Kitzman Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandra P MacKenzie Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew Adey Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph B Hiatt Search for this author in: * NPG journals * PubMed * Google Scholar * Rupali P Patwardhan Search for this author in: * NPG journals * PubMed * Google Scholar * Peter H Sudmant Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah B Ng Search for this author in: * NPG journals * PubMed * Google Scholar * Can Alkan Search for this author in: * NPG journals * PubMed * Google Scholar * Ruolan Qiu Search for this author in: * NPG journals * PubMed * Google Scholar * Evan E Eichler Search for this author in: * NPG journals * PubMed * Google Scholar * Jay Shendure Search for this author in: * NPG journals * PubMed * Google Scholar - Gene therapy finds its niche
- Nat Biotech 29(5):459 (2011)
Nature Biotechnology | Research | Erratum Gene therapy finds its niche * Cormac SheridanJournal name:Nature BiotechnologyVolume: 29,Page:459Year published:(2011)DOI:doi:10.1038/nbt0511-459dPublished online06 May 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 121–128 (2011); published online 7 February 2011; corrected after print 12 April 2011 In the version of the article originally published, Table 1 left off AAV as a section heading. A number of products listed under Adenovirus should have been listed under AAV. Neurologix product NLXP10 was incorrectly listed under the heading of retrovirus, and in phase 2 of development. It should have been listed under the heading AAV, and the phase of development should have read "phase 2 completed." The errors have been corrected in the HTML and PDF versions of the article. Additional data Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar - Chinese hamster ovary cells can produce galactose-α-1, 3-galactose antigens on proteins
- Nat Biotech 29(5):459 (2011)
Nature Biotechnology | Research | Addendum Chinese hamster ovary cells can produce galactose-α-1, 3-galactose antigens on proteins * Carlos J Bosques * Brian E Collins * James W Meador III * Hetal Sarvaiya * Jennifer L Murphy * Guy DelloRusso * Dorota A Bulik * I-Hsuan Hsu * Nathaniel Washburn * Sandra F Sipsey * James R Myette * Rahul Raman * Zachary Shriver * Ram Sasisekharan * Ganesh VenkataramanJournal name:Nature BiotechnologyVolume: 29,Page:459Year published:(2011)DOI:doi:10.1038/nbt0511-459ePublished online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Nat. Biotechnol.28, 1153–1156 (2010); published online 5 November 2010; addendum published after print 12 April 2011 Over the last 20 years, numerous investigations examining the glycosylation of proteins expressed in Chinese hamster ovary (CHO) cells have failed to document the ability of CHO cells to decorate proteins with galactose-α-1,3-galactose (α-Gal) epitopes1, 2, 3, 4, 5, 6. However, the ability of CHO cells to produce the α-Gal epitope in recombinant proteins was reported before7. The mechanism of activation of the α-(1,3)-galactosyltransferase in CHO cells remains unclear. A possible factor may involve the transfection process, as similar activation has been documented with other glycosyltransferases8. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Carlos J Bosques Search for this author in: * NPG journals * PubMed * Google Scholar * Brian E Collins Search for this author in: * NPG journals * PubMed * Google Scholar * James W Meador III Search for this author in: * NPG journals * PubMed * Google Scholar * Hetal Sarvaiya Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer L Murphy Search for this author in: * NPG journals * PubMed * Google Scholar * Guy DelloRusso Search for this author in: * NPG journals * PubMed * Google Scholar * Dorota A Bulik Search for this author in: * NPG journals * PubMed * Google Scholar * I-Hsuan Hsu Search for this author in: * NPG journals * PubMed * Google Scholar * Nathaniel Washburn Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra F Sipsey Search for this author in: * NPG journals * PubMed * Google Scholar * James R Myette Search for this author in: * NPG journals * PubMed * Google Scholar * Rahul Raman Search for this author in: * NPG journals * PubMed * Google Scholar * Zachary Shriver Search for this author in: * NPG journals * PubMed * Google Scholar * Ram Sasisekharan Search for this author in: * NPG journals * PubMed * Google Scholar * Ganesh Venkataraman Search for this author in: * NPG journals * PubMed * Google Scholar - First-quarter biotech job picture
- Nat Biotech 29(5):460 (2011)
Nature Biotechnology | Careers and Recruitment First-quarter biotech job picture * Michael Francisco1Journal name:Nature BiotechnologyVolume: 29,Page:460Year published:(2011)DOI:doi:10.1038/nbt.1872Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Michael Francisco is a Senior Editor at Nature Biotechnology. Author Details * Michael Francisco Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - People
- Nat Biotech 29(5):462 (2011)
Nature Biotechnology | Careers and Recruitment | People People Journal name:Nature BiotechnologyVolume: 29,Page:462Year published:(2011)DOI:doi:10.1038/nbt.1875Published online06 May 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Pepscan Therapeutics (Lelystad, The Netherlands) has named (below, right) as chief business officer. He joins Pepscan from Octoplus, where he held the position of senior manager, business development, responsible for licensing, partnering and alliance management activities. "We are delighted to bring Michiel Lodder on board to help us exploit the enormous potential of Pepscan's pioneering CLIPS platform for the discovery and development of therapeutic peptides and antibodies," says Wim Mol, CEO of Pepscan. "Pepscan has recently established a number of research collaboration and licensing agreements with major pharma and biotech companies, and Michiel's business savvy and deal-making experience make him ideal to further expand our partnership base." "I'm very excited to join the company at this pivotal stage," states Lodder. "The potential of the CLIPS technology to significantly improve therapeutic peptide leads or generate antibodies against intractable targets such as G protein–coupled receptors (GPCRs) and ion channels is far reaching. Through successful partnering, Pepscan can play an important role in the advancement of innovative peptide and antibody therapeutics." Aegerion Pharmaceuticals (Cambridge, MA, USA) has announced the appointment of to its board of directors and as chair of the compensation committee. Barer is the former CEO and current chairman of Celgene. has joined Affymax (Palo Alto, CA, USA) as vice president of medical affairs. He was previously vice president of clinical research for Proteon Therapeutics. Enlight Biosciences (Boston) has announced as its new CEO. She is an 18-year veteran at Roche and was most recently global head of emerging science and technologies in the company's partnering group in Basel, Switzerland. Enlight's founding CEO will continue his service on Enlight's board of directors. Epigenomics (Berlin) has appointed as CEO of its US subsidiary based in Seattle. He was most recently CEO of OpGen and was previously senior vice president for the molecular diagnostics division of Affymetrix. Novavax (Rockville, MD, USA) has named chairman as CEO, succeeding , who had served as CEO for the past 6 years. Erck joined the company in 2010 after serving as CEO at Iomai from 2000 to 2008. He will remain a member of the Novavax board, and current board member , former president of R&D at MedImmune, will become chairman. Regulus Therapeutics (La Jolla, CA, USA) has named as its chief scientific officer. Gibson has more than 17 years of pharmaceutical drug development experience, most recently serving as chief scientific officer and oncology therapeutic area head of Pfizer's oncology research unit. He also held leadership roles on Pfizer's oncology business unit leadership team and on the global leadership team of Pfizer's pharmatherapeutics organization. In conjunction with an investment of $30 million in bluebird bio (Cambridge, MA, USA) by ARCH Venture Partners, has joined bluebird bio's board of directors. Gillis is a managing director at ARCH. Previously he was a founder and CEO of Corixa and a founder, chief scientific officer and CEO of Immunex. Roche (Nutley, NJ, USA) has announced that has joined the company as vice president, translational medicine–virology in pharmaceutical research and early development (pRED). She previously served as chief medical officer and senior vice president of global medical affairs at Valeant Pharmaceuticals. Privately held Symphogen (Copenhagen) has announced the appointment of as chief scientific/medical officer. He joins the company from Enzon, where he served as chief scientific officer and president of R&D. Symphogen also named , formerly executive vice president of business operations and chief business officer at Roxro Pharma, as chief business officer. The board of directors of Oxygen Biotherapeutics (Morrisville, NC, USA) has accepted the resignation of president and CEO . He will complete his term on the board of directors but will not seek re-election during the annual shareholder meeting to be held in September. To ensure a smooth transition, Kiral and the company have entered into a 2-year consulting agreement. has joined Mersana Therapeutics (Cambridge, MA, USA) as executive vice president and COO. He previously led Forest Laboratories' M&A activities as a senior member of the business development team. will step down next year as president and CEO of The Hastings Center (Garrison, NY, USA). He came to the bioethics research center as president in 1999 from Case Western Reserve University, where he was director of the Center for Biomedical Ethics in the School of Medicine and the Susan E. Watson Professor of Bioethics. He plans to continue his work on Hastings Center research projects and remain a Hastings Center Fellow. Agennix (Heidelberg, Germany) has named as vice president, clinical development, a newly created position. Simonson previously held senior positions at AstraZeneca including senior director, clinical research, infection therapeutic area. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Biotechnology for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Rent this article from DeepDyve * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
No comments:
Post a Comment