Latest Articles Include:
- In this issue
- Nat Biotechnol 29(12):vii-viii (2011)
Nature Biotechnology | In This Issue In this issue Journal name:Nature BiotechnologyVolume: 29,Pages:vii–viiiYear published:(2011)DOI:doi:10.1038/nbt.2068Published online08 December 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Cyborg cells A remarkable array of genetic circuits has been engineered into cells, but optimizing these circuits and ensuring that they are robust to perturbations is notoriously difficult. El-Samad and colleagues show that these difficulties can be overcome using computer-based feedback control to regulate the circuit. Feedback control, which is a cornerstone of many engineering disciplines, involves computing regulatory inputs based on measurements of the output of a system. Working in yeast, the authors modified cells to contain a fluorescent reporter gene whose expression could be turned on and off using light at two different wavelengths. Every 30 minutes, they measured the fluorescence of the reporter using flow cytometry and fed these data to a model-based control algorithm, which the computer used to calculate which pulses of light to apply. This system allowed the authors to drive fluorescence to desired levels from a variety of starting conditions, whereas a simple model that ! did not use feedback failed. Moving regulatory functions outside the cell is a promising paradigm for improved engineering of biological systems. CM Epigenetic memory in human iPS cells Recent studies comparing induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs) have found that the two cell types can be virtually indistinguishable, at least with respect to transcription, epigenetics and differentiation capacity. Yet there have been hints that current reprogramming methods may not be perfect, producing cells that are slightly different from ESCs, the gold standard of pluripotency. Reprogramming can go wrong in several ways—by not erasing all the epigenetic marks characteristic of the starting somatic cell, by not generating all the epigenetic marks of a pluripotent cell or by introducing incorrect epigenetic marks. The persistence of somatic-cell epigenetic marks in iPSCs has been shown previously in mouse cells. Daley and colleagues now explore this question in human cells by comparing reprogramming of umbilical cord blood cells and neonatal keratinocytes. They find that the DNA methylation profiles of iPSCs made from the two cell types! are not equivalent, because of both incomplete erasure of methylation and incorrect de novo methylation. Epigenetic memory correlates with differences in the iPSCs' differentiation potential and cannot always be erased by long-term passaging. KA Single-cell tumor heterogeneity Although it is increasingly recognized that tumors are highly heterogeneous, the mechanisms that generate this diversity and its clinical consequences are not well understood. Quake and colleagues now use high-throughput, single-cell quantitative PCR of normal and cancerous colon tissue to investigate the basis and effects of tumor heterogeneity. The authors first sorted normal human colon epithelial cells according to known differentiation markers and used expression profiles to define subpopulations of cells. Analysis of primary human colon tumors revealed that the tumors also contained distinct cell populations with expression profiles that closely mirror those observed in normal colon epithelium. To rule out a genetic basis of the heterogeneity, Quake and colleagues transplanted single cancer cells into healthy mice. The resulting monoclonal tumors showed a similar subpopulation structure as the parent tumor, suggesting that heterogeneity is at least partly caused by mul! tilineage differentiation programs. Differences in tumor composition also have clinical consequences. Using simple two-gene classifiers based on the gene expression characteristics of the different subpopulations, patients could be divided into high-risk and low-risk groups with hazard ratios comparable to those of microarray-derived multigene expression signatures. ME Modified Bt toxins foil diverse resistance mechanisms The sustained benefits of transgenic crops engineered to produce insecticidal toxins from Bacillus thuringiensis (Bt) are threatened by the emergence of insect resistance. A previously reported structural modification of Bt toxin was developed to counter resistance conferred by mutations in the cadherin gene of a particular pest. Now, working with five major crop pests spanning four families of lepidopterans, Tabashnik and colleagues show that the variant Bt toxin kills pests rendered resistant to native Bt toxins by mutations in genes other than cadherin (e.g., mutations in genes encoding aminopeptidases and ABC transporters). Equally surprising is the observation that the modified toxin doesn't always counter the resistance conferred by mutations in cadherin. In most instances, the modified toxin was more effective against resistant strains than against strains susceptible to unmodified Bt toxin. The findings will provide better insight into the selectivity with which Bt t! oxins kill insect pests and the molecular mechanisms underlying resistance to Bt toxins. PH HiC-flavored cancer rearrangements It has been puzzling that somatic copy-number alterations (SCNAs) in cancer genomes seem to occur at nonrandom locations and with varying frequencies in different cancer types. Two independent computational analyses now provide systematic evidence that the locations where SCNAs occur are influenced by the spatial organization of DNA in the nucleus, DNA replication timing and selective pressures against gain or loss of large genomic regions. Mirny and colleagues analyzed two published data sets: a compendium of copy-number gain and loss events in ~2,800 tumor samples from 26 cancer types and measurements of chromosome conformation in the nucleus collected by the HiC method. By statistically modeling the mutational processes that could form SCNAs, they found that processes that account for nuclear organization and negative selection best explain the SCNA data. De and Michor analyzed the same two data sets along with data on the timing of DNA replication from several human cell! types. Their analyses suggested that the end points of SCNAs tend to be found in regions that are colocalized in the nucleus and replicated at the same time. Taken together, the two studies illustrate strategies for understanding the functional consequences of the higher-order organization of the genome. CM The International Stem Cell Initiative II Human embryonic stem cell (hESC) lines in laboratories around the world have diverse genotypes, were derived by different protocols, are cultured and passaged in various ways, and are studied using a wide array of methods. The International Stem Cell Initiative (ISCI) was formed to promote comparative study of hESC lines and to work toward more standardized and reproducible experimental practices. The first phase of the ISCI project included 59 hESC lines from 17 laboratories worldwide and looked at metrics such as surface antigens, gene expression, imprinting status and microbiological contamination (Nat. Biotechnol. , 803–816, 2007). In ISCI's second phase, Andrews and colleagues now present genetic and epigenetic data on 125 hESC lines from 38 laboratories. Most of the lines were analyzed at early and late passage to identify genetic changes acquired during long-term culture. Understanding the extent of these changes and their functional consequences is important from a! translational perspective as certain genetic aberrations could compromise the cells' safety or efficacy in the clinic. The authors found recurrent genetic changes in chromosomes 1, 12, 17 and 20. A search for genes that might confer a growth advantage during long-term culture led to the minimal amplicon 20q11.2, and, from there, to BCL2L1, the most likely candidate gene identified for driving culture adaptation. KA Patent roundup The Court of Justice of the European Union ruling on October 18 that said an invention involving the destruction of a human embryo cannot be patented has put a question mark over companies working to commercialize cell therapies based on human embryonic stem cells. LM Agricultural microbes have become an attractive target for patenting, but the lack of a consistent global patent regime and increasingly heated debates over microbial ownership rights are barriers to the development of this resource. Kothamasi et al. argue that farmers should benefit (directly or indirectly) from any bioprospecting profits derived from agricultural microbes. MF Recent patent applications in microbiology and crop science. MF Next month in Nature Biotechnology * Solid-phase chromosome conformation capture * Surveying genetic variation in rice * Comparison of human genome sequencing technologies * Optimized filters for SNP calling Additional data - Tilting toward secrecy
- Nat Biotechnol 29(12):1055 (2011)
Nature Biotechnology | Editorial Tilting toward secrecy Journal name:Nature BiotechnologyVolume: 29,Page:1055Year published:(2011)DOI:doi:10.1038/nbt.2075Published online08 December 2011 A European high court ruling on the patentability of inventions related to human embryonic stem cells could promote secrecy and reduce access to data and cell lines. View full text Additional data - European court bans embryonic stem cell patents
- Nat Biotechnol 29(12):1057-1059 (2011)
Article preview View full access options Nature Biotechnology | News European court bans embryonic stem cell patents * Nuala Moran1Journal name:Nature BiotechnologyVolume: 29,Pages:1057–1059Year published:(2011)DOI:doi:10.1038/nbt1211-1057Published online08 December 2011 Martin Oeser/dapd/Apphoto Oliver Brüstle, Director of the Institute of Reconstructive Neurobiology, University of Bonn, holds the patent on neuronal precursor cells at the center of the European court's ruling. The Court of Justice of the European Union (CJEU) ruled on October 18, 2011 that an invention involving the destruction of a human embryo cannot be patented. The judgment is unequivocal, but still far from clear on what the consequences will be for academic researchers and companies working to commercialize cell therapies based on human embryonic stem cells (hESCs). Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * London * Nuala Moran Author Details * Nuala Moran Search for this author in: * NPG journals * PubMed * Google Scholar - Conflicts of interest go online
- Nat Biotechnol 29(12):1058 (2011)
Article preview View full access options Nature Biotechnology | News Conflicts of interest go online * Gunjan SinhaJournal name:Nature BiotechnologyVolume: 29,Page:1058Year published:(2011)DOI:doi:10.1038/nbt1211-1058Published online08 December 2011 EMA transparency In October, the European Medicines Agency (EMA) launched a new database aimed at increasing the transparency of expert advisors' financial ties to industry. The new policy is a reaction to accusations that EMA was not complying with EU legislation that states that members of its scientific committees and expert advisors should not have financial or other interests in the pharma industry that could affect their impartiality. The French government is also considering a law that would fine experts who advise the government on medical treatments up to 30,000 ($40,000) for failing to disclose any conflicts of interest. The US is taking the opposite stand, with the US Food and Drug Administration facing pressure to loosen conflict-of-interest rules (see page 1062). But in Europe, industry and the scientific community largely applaud the new policy. "There is no way other than full transparency," says Richard Bergström, director general of the European Federation of Pharmaceut! ical Industries and Associations. The EMA now requires all advisors, and scientific experts serving on any of EMA's committees (and their families) to declare annually any direct or indirect financial ties to industry or any other conflicts of interest. Previously, declaration forms were only available from the agency by request. Now they will be posted on the EMA's website and can be searched alphabetically or by country. But Bergström is concerned over how restrictions will play out in practice. Industry ties don't necessarily represent a conflict of interest, he says. To become an expert in clinical research, for example, one must have been involved in studies that are usually funded by industry, he adds. The agency will rank committee members into risk categories ranging from 1 to 3, with 3 being the highest risk category applicable to experts with direct financial ties to industry anytime within the past 5 years. Being classified in the highest risk category won't nec! essarily exclude an expert from EMA activities, however, but m! ay severely restrict their participation. Although the risk ranking will not be made publicly available, Tony Mayer, member of the Euroscience Governing Board and specialist in research integrity says, "Assigning risk levels is a reasonable way of managing risk in this situation." The new database lists approximately 5,000 experts, but so far only about half of the entries include declaration-of-interest forms. EMA expects to publish the remaining forms over the next several months. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Gunjan Sinha Search for this author in: * NPG journals * PubMed * Google Scholar - Shutdown by auction
- Nat Biotechnol 29(12):1060 (2011)
Article preview View full access options Nature Biotechnology | News Shutdown by auction * Brian OrelliJournal name:Nature BiotechnologyVolume: 29,Page:1060Year published:(2011)DOI:doi:10.1038/nbt1211-1060aPublished online08 December 2011 Mark Wragg/istockphoto Bids under seal On December 8, sphingosine 1 phosphate receptor (S1P1) agonists, including preclinical and toxicology data, came under the hammer in a sealed bid auction. The small molecules on offer were generated by Lexington, Massachusetts–based EPIX Pharmaceuticals in collaboration with Amgen of Thousand Oaks, California. Earlier, EPIX was forced to shut down operations due to lack of funds. Rather than enter a formal bankruptcy proceeding, the company assigned the S1P1 agonists along with its other assets to Joseph Finn Jr., managing partner at accounting firm Finn, Warnke & Gayton, of Wellesley Hills, Massachusetts, to be offered in a bidding sale. The procedure, available in Massachusetts and other states including California, is well regarded by troubled biotechs and their creditors because it enables companies to quickly wind down operations. For instance, Source Precision Medicine of Boulder, Colorado, and Woburn, Massachusetts–based Prospect Therapeutics went through such auc! tions in the last few years. The process of assigning assets, finding a buyer, vetting creditors and distributing the proceeds can be wrapped up in six months, whereas chapter 7 bankruptcies typically take a year or longer. Finn requests sealed bids, which is faster than an open auction where bidders go back and forth trying to top one another as seen in chapter 7 cases. Typically 50 to 60 companies sign confidentiality agreements with about 10% of those actually making a bid. It is difficult to know whether the one-shot bid process results in higher values. Bids sometimes come in within $100,000 of each other suggesting that's the true value of the asset, although other times Finn said the creditors "catch lightning in a bottle" with one bid substantially higher than the rest. Perhaps more important than speed, the assignment process allows company founders to see where the assets they slaved to develop are headed. "I try to make the process of winding up the company! one that gives them closure of their life's work," Finn sai! d. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Brian Orelli Search for this author in: * NPG journals * PubMed * Google Scholar - GlaxoSmithKline malaria vaccine phase 3 trial heralded
- Nat Biotechnol 29(12):1060-1062 (2011)
Article preview View full access options Nature Biotechnology | News GlaxoSmithKline malaria vaccine phase 3 trial heralded * Simon Franz1Journal name:Nature BiotechnologyVolume: 29,Pages:1060–1062Year published:(2011)DOI:doi:10.1038/nbt1211-1060bPublished online08 December 2011 John-Michael Maas/AP Photo/ A first-generation malaria vaccine that is at least 50% effective could be licensed for use in Africa by 2015. London-based GlaxoSmithKline (GSK) released one-year follow-up data from a phase 3 trial of its malaria vaccine RTS,S (Mosquirix) triggering talk that the world's first vaccine against a protozoan disease could be tantalizingly close to market. It's taken several decades to get to this unprecedented achievement: the protective effect is the highest ever achieved for an malaria vaccine in clinical development (N. Engl. J. Med., 1863–1875, 2011). No one doubts that the RTS,S shot represents a tremendous scientific breakthrough, but opinions remain mixed as to its public-health impact owing to its inability to provide more complete protection against infection. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * London * Simon Franz Author Details * Simon Franz Search for this author in: * NPG journals * PubMed * Google Scholar - Interest groups jostle to influence PDUFA V
- Nat Biotechnol 29(12):1062 (2011)
Article preview View full access options Nature Biotechnology | News Interest groups jostle to influence PDUFA V * Jeffrey L Fox1Journal name:Nature BiotechnologyVolume: 29,Page:1062Year published:(2011)DOI:doi:10.1038/nbt1211-1062Published online08 December 2011 DNY59/istockphoto Upgrading drug approval. A finalized package of formal recommendations for the Prescription Drug User Fee Act V (PDUFA V)—the first came in 1992—is headed to US Congress this January. Its contents reflect many rounds of negotiations involving the US Food and Drug Administration (FDA), industry, medical groups, patient and consumer representatives and the general public. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Washington, DC * Jeffrey L Fox Author Details * Jeffrey L Fox Search for this author in: * NPG journals * PubMed * Google Scholar - Industry continues dabbling with open innovation models
- Nat Biotechnol 29(12):1063-1065 (2011)
Article preview View full access options Nature Biotechnology | News Industry continues dabbling with open innovation models * Cormac Sheridan1Journal name:Nature BiotechnologyVolume: 29,Pages:1063–1065Year published:(2011)DOI:doi:10.1038/nbt1211-1063aPublished online08 December 2011 alandj/istockphoto The United Nations is backing open innovation. The concept of sharing intellectual property to speed up drug discovery is thriving but the model's success is still an open question. On October 26, seven large pharma companies and a biotech firm, Alnylam, announced a collaboration with the World Intellectual Property Organisation (WIPO) to establish WIPO Re:Search. This open innovation initiative lines up United Nations agency WIPO of Geneva with Washington, DC–based BIO Ventures for Global Health, the US National Institutes of Health (NIH), drug companies and academic institutions (Table 1) in an effort to share intellectual property (IP) and resources that can speed drug discovery in 19 neglected tropical diseases, as well as malaria and tuberculosis. A month earlier, the Indianapolis-based Eli Lilly launched its Open Innovation Drug Discovery initiative, an extension of its earlier free web-based screening tool called Phenotypic Drug Discovery Initiative (PD2). And on November 3, Sanford-Burnham Medical Research Institute, of La Jolla, California, became the latest addition to New York–based Pfizer's Centers for Therapeutic Innovation (CTIs), an o! pen innovation network focusing on biologics, which also offers participating investigators access to certain 'select' Pfizer compound libraries. Pharma has been tinkering in open-source collaborations (Nat. Biotechnol.29, 298, 2011) but many in industry remain skeptical. "Let's give it time—but not too much time," says Werner Lanthaler, CEO of Hamburg, Germany–based drug developer Evotec. "It could end up being something where the long-term cost is small but [where the outcome is also] unproductive." Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Affiliations * Dublin, Ireland * Cormac Sheridan Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar - Cold-tolerant trees win
- Nat Biotechnol 29(12):1063 (2011)
Article preview View full access options Nature Biotechnology | News Cold-tolerant trees win * Emily WaltJournal name:Nature BiotechnologyVolume: 29,Page:1063Year published:(2011)DOI:doi:10.1038/nbt1211-1063bPublished online08 December 2011 A lawsuit aimed at halting experimental field trials of genetically modified (GM) trees was tossed out of a US federal court in October, marking the government's first win in a series of similar cases brought by conservation groups. The US District Court for the Southern District of Florida rejected conservation groups' claims that the federal government had failed to adequately review the environmental risks of planting GM cold-tolerant eucalyptus trees before issuing permits for field trials. Tree developer ArborGen in Summerville, South Carolina, gained permits to test the cold-tolerant trees modified to express the C-repeat binding factor (CBF) gene taken from Arabidopsis, on 28 sites in seven southern states and allow flowering on 27 of those sites. The conservation groups, led by the Center for Biological Diversity in Tucson, say they are concerned that the trees will become invasive due to gene flow and seeds escaping the sites, and that eucalyptus plantations will de! plete groundwater and make environments inhospitable for native flora and fauna. The groups alleged that the US Department of Agriculture (USDA) failed to follow statutory procedures for considering the ecological risks. The court, however, found that USDA had fulfilled its regulatory duties outlined in the National Environmental Policy Act. Other environmental assessments performed by the agency—GM glyphosate-tolerant sugar beets and alfalfa—were both found to be deficient by a federal court in 2009 and 2007, respectively. "The eucalyptus decision is a significant reversal for the plaintiffs, given what happened in the sugar beets and alfalfa cases," says Jay Johnson, an attorney with Dorsey & Whitney in Washington, DC. The case may help shift the momentum in the government's favor in future lawsuits likely to be brought by the conservation groups, Johnson says. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Emily Walt Search for this author in: * NPG journals * PubMed * Google Scholar - Around the world in a month
- Nat Biotechnol 29(12):1065 (2011)
Article preview View full access options Nature Biotechnology | News Around the world in a month Journal name:Nature BiotechnologyVolume: 29,Page:1065Year published:(2011)DOI:doi:10.1038/nbt1211-1065Published online08 December 2011 Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Jackson's $1.1 billion makeover
- Nat Biotechnol 29(12):1066 (2011)
Article preview View full access options Nature Biotechnology | News Jackson's $1.1 billion makeover * Jennifer RohnJournal name:Nature BiotechnologyVolume: 29,Page:1066Year published:(2011)DOI:doi:10.1038/nbt1211-1066aPublished online08 December 2011 Connecticut lawmakers passed a $291 million plan to create a state-of-the-art research institute for personalized medicine and systems genomics to be called The Jackson Laboratory for Genomic Medicine. The center, to be erected near the University of Connecticut Health Center campus in Farmington, represents an expansion for the Jackson Laboratory family, which already houses a preeminent mouse genetics facility in Maine and a preclinical testing center in California. Jackson will contribute $809 million towards the project, bringing the total investment to $1.1 billion. The site will occupy 17 acres and is expected to employ 320 people in its first decade—including 30 principal investigators—and more than double its staff in 15 to 20 years. In addition to basic research, the institute plans to commercialize its findings in the area of diagnostics and therapeutics for personalized patient genomics. According to Robert Braun, the associate director of Jackson, having a ph! ysical presence on a medical school campus and direct access to a healthcare system will greatly enhance his organization. What's more, he said, personalized medicine will benefit enormously from Jackson's expertise in mouse genetics, as interpreting the huge and growing mass of human genomic data will require functional studies in model systems. The state's investment is part of an initiative known as Bioscience Connecticut, which seeks to bolster biomedical industry in the region. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Jennifer Rohn Search for this author in: * NPG journals * PubMed * Google Scholar - Near-record drug approvals
- Nat Biotechnol 29(12):1066 (2011)
Article preview View full access options Nature Biotechnology | News Near-record drug approvals * Jeffrey L. FoxJournal name:Nature BiotechnologyVolume: 29,Page:1066Year published:(2011)DOI:doi:10.1038/nbt1211-1066bPublished online08 December 2011 A near-record 35 innovative drugs were approved by the US Food and Drug Administration (FDA) in the 2011 fiscal year, which ended September 30. The FDA beat other agencies around the world in its approval times, with 24 of those 35 products approved before any other agencies, including the European Medicines Agency, according to an annual performance report under the auspices of the Prescription Drug User Fee Act (PDUFA). "Thirty-five major drug approvals in one year represents a very strong performance, both by industry and by the FDA," says FDA commissioner Margaret Hamburg. That set of 35 includes 10 for treating rare or orphan diseases and 7 new cancer treatments—among them, one for melanoma and another for lung cancer, each of which was approved along with a diagnostic test to identify which patients are most likely to benefit from those treatments. Nearly half of the group was approved under 'priority review', two-thirds within a single review cycle, and three un! der the 'accelerated approval'. "Before the PDUFA program, American patients waited for new drugs long after they were available elsewhere," says Janet Woodcock, director of the FDA Center for Drug Evaluation and Research. "As a result of the user fee program, new drugs are rapidly available to patients in the United States while maintaining our high standards for safety and efficacy." This year's approvals are second only to 2009 when 37 new drugs were approved. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data Author Details * Jeffrey L. Fox Search for this author in: * NPG journals * PubMed * Google Scholar - New startup models emerge as investor landscape shifts
- Nat Biotechnol 29(12):1066-1067 (2011)
Nature Biotechnology | News New startup models emerge as investor landscape shifts * Brady Huggett1Journal name:Nature BiotechnologyVolume: 29,Pages:1066–1067Year published:(2011)DOI:doi:10.1038/nbt1211-1066cPublished online08 December 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Two Cambridge, Massachusetts–based startups are among this year's financings that depart from traditional venture models. In October, neurology startup Sage Therapeutics raised $35 million in a Series A round from a single fund; and three months earlier, Nimbus Discovery attracted financing from a syndicate of three funds, receiving $24 million in a Series A round. The financings exemplify the creative ways by which venture capital (VC) firms are overcoming the challenging timelines and restricted exits for early-stage life sciences investments. Sage Therapeutics was founded by Third Rock Ventures of Boston, which installed ten advisors and provided the entire tranche of Series A funding. The idea was to bring together leaders in the field while also ensuring the financial power needed for growth, avoiding "the old pattern of a $5 million Series A, which is kind of a bridge to nowhere," says Kevin Starr, partner at Third Rock. Nimbus, on the other hand, raised its $24 million from a three-company syndicate, led by Atlas Ventures, and including SR One (the corporate investing arm of GlaxoSmithKline, of London) and Lilly Ventures (the investing arm of Eli Lilly, of Indianapolis). Nimbus uses computational technology in a quest for developing leads against disease targets previously thought undruggable. The company is also partly virtual: it has four programs ongoing but no wet lab on site, and some 60 people working on its projects externally. Both deals came against a backdrop where early-stage startups are finding VC cash difficult to attract, with investors turned away from discovery biomedical science, or at least moving to later-stage deals, deterred by increasingly longer development times (due to a risk averse, safety-conscious US Food and Drug Administration), a public market that is open fitfully to a select type of company (usually at highly discounted prices) and trade sales limited by the number of transactions that the larger companies are capable of. In October, the doomsayers became more strident when Prospect Venture Partners, of Palo Alto, California, returned $150 million to its limited partners, rather than investing them in startup biotech companies. But the picture in early-stage life sciences venture investing looks more complex. The total amount of VC money going into the US biotech sector is consistent with previous years. In fact, according to Arlington, Virginia–based National Venture Capital Association data, VC funding is on pace this year to better both 2009 and 2010, with seed and early-stage money holding up well enough (Fig. 1a). Moreover, Europe has been steady, too (Fig. 1b), though it has been affected by problems with sovereign debt and monetary upheaval in euro-zone countries. Figure 1: VC investment in biotech. () US investments into biotech by year. Figures in parentheses indicate the number of deals done. Source: PricewaterhouseCoopers, National Venture Capital Association MoneyTree Report, with data supplied by Thomson Reuters. () Venture capital investment into European venture-backed biopharmaceutical companies by year. Figures in parentheses indicate number of deals done. Source: Dow Jones VentureSource. * Full size image (56 KB) There is bad news—the number of US-based VC funds has decreased, falling from 1,701 in 2000 to 1,183 in 2010, according to figures from Cambridge, Massachusetts–based financial data provider OnBioVC, and that trend extends to life sciences. As a result, only top-shelf ideas are being funded, and many solid technologies and platforms are being rejected. Bart Bergstein, managing partner and chairman of Forbion Capital Partners in Naarden, The Netherlands, says his "heart bleeds" when he's forced to turn away good ideas. "I tell them, 'You really deserve to find some funding'. But we're picking only the very best we see. It's a shame." What's left is new models, new syndicates and new preferences from risk capital investors, as well as new ways to address ideas coming straight from academia (Box 1). Box 1: Early entry Full box Sofinnova Ventures, which announced in October a $440 million fund dedicated to life sciences, favors an 'asset-based approach' and is looking to invest in later-stage opportunities. An example is Durata Therapeutics, of Morristown, New Jersey, formed in 2009 by Sofinnova and four other VC firms around dalbavancin, an intravenous lipoglycopeptide for acute bacterial skin and skin structure infections that the group acquired from New York–based Pfizer. The syndicate—increased to five firms to provide substantial money upfront—hopes to take the phase 3 compound to approval. Sofinnova typically invests between $15 million and $30 million into its portfolio companies, depending on the stage of involvement, with an investment period of ~2.5–4 years. Startups that have already demonstrated proof of concept are predominantly the ones considered for funding by the French fund. It does, however, invest in early-stage projects when the circumstances are right, says managing partner Jim Healy, ticking off a list of requirements that include a validated target, expressed interest from pharma or the involvement of "highly successful" academic founders. At the opposite end of the spectrum is Atlas Venture, focused almost exclusively on early-stage work. The reasoning, says Bruce Booth, partner at Atlas, of Cambridge, Massachusetts, is that older companies have fully formed DNA, and Atlas "prefers to write the genetic code of the companies we invest in." Thus, Atlas doesn't use a set investing model, but favors "experiments" for its companies that "recognize the changes to the capital markets today." Partially, that means interacting with pharma at both the beginning and the end of the process. At the front end, corporate VC arms have moved into the gap created by the departure of traditional risk investors, and Atlas has taken advantage. Booth says most of Atlas's deals require a three-handed syndicate, and today at least one is going to be a corporate venture arm. Atlas considers pharma on the back end, too, since "almost all our interesting returns have come through M&A [merger & acquisition] deals" or some other structure with pharmaceutical firms, Booth says. That's the case with recently launched Arteaus, also of Cambridge, formed with $18 million from Atlas and OrbiMed Advisors, in New York. The company was formed around a phase 1 antibody to calcitonin gene-related peptide (CGRP) aimed at migraines, in-licensed from Eli Lilly. Arteaus, basically virtual, will collaborate with Lilly to develop the compound through phase 2 proof of concept, at which point Lilly has the option to license it back on prenegotiated terms. Somewhere between Sofinnova and Atlas in terms of philosophy is Forbion Capital Partners. Five years ago, the VC firm would have sought a company with "multiple shots on goal and preferably a platform," says Bergstein. "Nowadays that is more nuanced." Though Bergstein says Forbion "still believes" in platforms, his firm also invests in single areas, and sometimes single assets, as that model is more accommodating to pharma. Today's big buyers won't risk purchasing an entire company to pick up an attractive asset, and Forbion has gone as far as taking a company with two assets and splitting them into separate ventures. Perhaps the most interesting new VC model belongs to Third Rock. Although it also splits its interest—about 50% of portfolio companies are product engines, whereas the other half are single-product (or mechanism) plays or device/diagnostics—every company it backs must have a disruptive technology. "If we can build those kind of companies today, five years from now, these will be the suppliers of new, important products" into specific disease areas, says Starr. The twist is that unlike other investors, which spread risk by funding in syndicates, Third Rock will sometimes provide entire big Series A rounds by itself. This ensures that the vision Third Rock has for the company is the one that will be put in place. "It gets very confusing to have two or three groups trying to form a company at the same time," Starr says. This was the plan for Sage Therapeutics, and also for Blueprint Medicines, which was launched in April to focus on personalized oncology drugs and boosted with a $40 million A round. Third Rock sees anywhere from 500 to 1,000 propositions a year, and launches around six companies annually, typically investing, by itself, $25 million to $30 million in total. But for product engine companies like Blueprint, that will require more in the long run, they find syndicates. If there is an upside to the decreasing number of VC funds interested in early-stage biotech, it's that deal flows for investors are as good as ever, and competition is rarely bad for business. The depressed valuations received by biotechs in the public markets mean that returns are easier to generate, and that should entice wayward venture capitalists back into the game. For investors, at least, the future seems bright. "The returns of 2009, 2011, 2012—in hindsight these will be seen as excellent, vintage years," says Bergstein. Additional data Affiliations * Business editor * Brady Huggett Author Details * Brady Huggett Search for this author in: * NPG journals * PubMed * Google Scholar - Newsmaker: Zafgen
- Nat Biotechnol 29(12):1068 (2011)
Nature Biotechnology | News Newsmaker: Zafgen * Jennifer Rohn1Journal name:Nature BiotechnologyVolume: 29,Page:1068Year published:(2011)DOI:doi:10.1038/nbt1211-1068Published online08 December 2011 Zafgen hopes that small-molecule targeting of methionine aminopeptidase 2 (MetAP2), an enzyme originally associated with tumor angiogenesis, will lead to a new anti-obesity drug. View full text Additional data Affiliations * London * Jennifer Rohn Author Details * Jennifer Rohn Search for this author in: * NPG journals * PubMed * Google Scholar - Shape shifting
- Nat Biotechnol 29(12):1069-1071 (2011)
Nature Biotechnology | Bioentrepreneur Shape shifting * Bob Baltera1Journal name:Nature BiotechnologyVolume: 29,Pages:1069–1071Year published:(2011)DOI:doi:10.1038/bioe.2011.10Published online08 December 2011 Faced with limited funding and the need to find the most time- and cost-efficient route to proof of concept and relevance, how should you reorganize your company to facilitate development of your most valuable assets? 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 * Bob Baltera, former CEO of Amira Pharmaceuticals, San Diego, California, USA. Corresponding author Correspondence to: * Bob Baltera Author Details * Bob Baltera Contact Bob Baltera Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Generation of the potent anti-malarial drug artemisinin in tobacco
- Nat Biotechnol 29(12):1072-1074 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence Generation of the potent anti-malarial drug artemisinin in tobacco * Moran Farhi1 * Elena Marhevka1 * Julius Ben-Ari1 * Anna Algamas-Dimantov2 * Zhuobin Liang3 * Vardit Zeevi3 * Orit Edelbaum1 * Ben Spitzer-Rimon1 * Hagai Abeliovich2 * Betty Schwartz2 * Tzvi Tzfira3, 4 * Alexander Vainstein1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1072–1074Year published:(2011)DOI:doi:10.1038/nbt.2054Published online08 December 2011 To the Editor: The emergence of multidrug-resistant strains of Plasmodium spp., the etiological agent of malaria, constitutes a major threat to controlling the disease1, 2. Artemisinin, a natural compound from Artemisia annua (sweet wormwood) plants, is highly effective against drug-resistant malaria. Even so, low-cost artemisinin-based drugs are lacking because of the high cost of obtaining natural or chemically synthesized artemisinin1, 2. Martin et al.3 were the first to report the generation of an artemisinin precursor in a microbial system. They engineered Escherichia coli with a synthetic mevalonate pathway from Saccharomyces cerevisiae. Expression of amorphadiene synthase (ADS) from A. annua in this strain allowed production of amorpha-4,11-diene, the sesquiterpene olefin precursor to artemisinin. However, despite extensive effort invested in the past decade in metabolic engineering of artemisinin and its precursors in both microbial and heterologous plant systems2, 3, 4, 5, 6, prod! uction of artemisinin itself has never been achieved. Here we report the metabolic engineering of tobacco to produce artemisinin, generating transgenic plants that express five plant- and yeast-derived genes involved in the mevalonate and artemisinin pathways, all expressed from a single vector. Our experiments demonstrate that artemisinin can be fully biosynthesized in a heterologous (that is, other than A. annua) plant system, such as tobacco. Although the artemisinin levels we have generated in transgenic tobacco are currently lower than those in A. annua, our experimental platform should lead to the design of new routes for the drug's commercial production in heterologous plant systems. View full text Figures at a glance * Figure 1: The pathway and constructs for engineering artemisinin production in tobacco. () Schematic outline of the mevalonate and artemisinin pathways (engineered genes are in red). () Gene constructs assembled to engineer artemisinin production. Arrows indicate genes, boxes indicate promoters and terminators; constructs with either ADS or mtADS are shown. ocs, octopine synthase; nos, nopaline synthase; rbc, rubisco; 35S, cauliflower mosaic virus (CaMV) 35S; HS, hps18.1 promoter; sup, super-promoter; ags, agrocinopine synthase. * Figure 2: Production of artemisinin in engineered tobacco plants. () Extracted ion accurate mass chromatograms (UPLC-HR-MS) for artemisinin (m/z 283.1530) and artemisinin-d3 (m/z 286.1733) in extracts of plants transformed with the ADS (upper panel) or mtADS (middle panel) construct. Lower panel shows artemisinin ion in extracts from ADS/mtADS-lacking transgenic (top chromatogram) and wild-type (bottom chromatogram) control plants. () Artemisinin production in tobacco extracts analyzed by UPLC-MS/MS operated in MRM mode. Extracts from ADS, mtADS and ADS/mtADS-lacking transgenic plants are shown in the upper three panels, respectively. Artemisinin standard is shown in the lower panel. Chromatograms in each panel show artemisinin-specific MRM traces of m/z 283.2–219 and m/z 283.2–265. 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 * Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel. * Moran Farhi, * Elena Marhevka, * Julius Ben-Ari, * Orit Edelbaum, * Ben Spitzer-Rimon & * Alexander Vainstein * Department of Biochemistry, Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel. * Anna Algamas-Dimantov, * Hagai Abeliovich & * Betty Schwartz * Department of Molecular, Cellular and Developmental Biology, The University of Michigan, Ann Arbor, Michigan, USA. * Zhuobin Liang, * Vardit Zeevi & * Tzvi Tzfira * Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel. * Tzvi Tzfira Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alexander Vainstein Author Details * Moran Farhi Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Marhevka Search for this author in: * NPG journals * PubMed * Google Scholar * Julius Ben-Ari Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Algamas-Dimantov Search for this author in: * NPG journals * PubMed * Google Scholar * Zhuobin Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Vardit Zeevi Search for this author in: * NPG journals * PubMed * Google Scholar * Orit Edelbaum Search for this author in: * NPG journals * PubMed * Google Scholar * Ben Spitzer-Rimon Search for this author in: * NPG journals * PubMed * Google Scholar * Hagai Abeliovich Search for this author in: * NPG journals * PubMed * Google Scholar * Betty Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar * Tzvi Tzfira Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Vainstein Contact Alexander Vainstein Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (712K) Supplementary Methods and Supplementary Fig. 1 Additional data - Relative potential of biosynthetic pathways for biofuels and bio-based products
- Nat Biotechnol 29(12):1074-1078 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence Relative potential of biosynthetic pathways for biofuels and bio-based products * Deepak Dugar1 * Gregory Stephanopoulos1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1074–1078Year published:(2011)DOI:doi:10.1038/nbt.2055Published online08 December 2011 To the Editor: Concerns about the sustainability of fossil fuels and global warming1, along with energy security2, have provided a strong incentive recently toward research and development of carbon-neutral alternatives including next-generation biofuels. Currently up to 10% ethanol blends are being used in gasoline with higher biofuel blending targets being used or contemplated by various governments3. The compatibility of higher ethanol blends with existing infrastructure still needs to be validated, hence calls for development of fuel molecules beyond ethanol. Second-generation biofuel molecules are likely to resemble those present in gasoline (that is, C4-C10 branched chain hydrocarbons or derivatives thereof including alcohols) and expected to be compatible with existing infrastructure. Another characteristic of second-generation biofuels is that they are expected to entail lower energy input in downstream purification to fuel-grade specifications compared with ethanol, thus engenderi! ng the possibility of biofuels with higher return on energy input compared with ethanol. View full text Figures at a glance * Figure 1: Model assumptions for the general energy flow in a pathway. Pathways producing excess NADH are termed 'energy surplus' pathways and are inefficient from a yield perspective as not all the substrate's energy is channeled to the product and the need to balance the excess NADH is generally met by expending them in apparently futile pathways or reduced side-product(s) formation leading to diversion of additional substrate from product. Energy-balanced or energy-deficient pathways (that is, those requiring supply of additional reducing equivalents) are generally not expected to lead to side products and can potentially achieve near-maximum yields. Substrate oxidation is assumed to supply these additional reducing equivalents and/or ATP as required by the pathway. Excess ATP produced is generally used for biomass formation or expended in apparently futile pathways. Reducing eq, reducing equivalents (NADH, NADPH, FADH2); fp, futile pathways. * Figure 2: Summary of various pathways for bulk biochemical and/or fuel production. Green compounds have a degree of reduction that is the same as glucose (that is, 4). Compounds in blue are less reduced and those in red are more reduced than glucose. Steps producing and consuming reducing equivalents have been marked with white arrows. Dashed lines represent energy surplus pathways. It can be seen that most pathways can be divided into two phases, namely an oxidation phase leading to an intermediate more oxidized then the substrate followed by a reduction phase where the reducing equivalents generated in a previous phase are used to reach the final product. The (im)balance of the two phases is the source of pathway (in)efficiency. G3P, glyceraldehye-3-phosphate; Pyr, pyruvate; OAA, oxaloacetate; ACP, acyl carrier protein; 3-HB, 3-hydroxybutyrate; PHB, polyhydroxybutyrate; HMG, 3-hydroxy-3-methylglutarate; Asp, aspartate; Thr, threonine. Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Deepak Dugar & * Gregory Stephanopoulos Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gregory Stephanopoulos Author Details * Deepak Dugar Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory Stephanopoulos Contact Gregory Stephanopoulos Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (145K) Supplementary Notes Additional data - Re-evaluating PARP1 inhibitor in cancer
- Nat Biotechnol 29(12):1078-1079 (2011)
Article preview View full access options Nature Biotechnology | Opinion and Comment | Correspondence Re-evaluating PARP1 inhibitor in cancer * Alexei Tulin1Journal name:Nature BiotechnologyVolume: 29,Pages:1078–1079Year published:(2011)DOI:doi:10.1038/nbt.2058Published online08 December 2011 To the Editor: A News story in the May issue by Malini Guha discusses recent clinical setbacks of poly(ADP-ribose) polymerase 1 (PARP1) inhibitors for the treatment of certain cancers1. Agreeing with the article's conclusion that it is too early to discount the effectiveness of PARP1 inhibitors in cancer treatments, I outline below three principal problems regarding our understanding of PARP1 function and the role of PARP1 inhibitors in limiting tumor growth that could hamper their progress in the clinic. The first problem proceeds from the underlying assumption that the main role of PARP proteins in cancer cells is controlling the DNA repair pathway. The direct evidence supporting this view is the ability of PARP1 to bind damaged DNA and to become activated upon binding2. PARP1 also interacts with a subset of DNA repair enzymes2 and Parp1 null mutant mice show a substantial level of genomic instability3. On the basis of these data, a widely accepted model1 postulates that cells accumulate DNA breaks upon inhibition of PARP1. Such a model, however, does not accord with the observation that no increase in DNA breaks is detected in cancer cells treated with a PARP1 inhibitor4. Therefore, the purported role of PARP1 in DNA repair seems tangential to tumor proliferation. After DNA-dependent activation, PARP1 immediately becomes automodified and loses contact with DNA5. A likely purpose of this chain of molecular events is to remove PARP1 from chromatin and terminate PARP1-depende! nt processes, thereby facilitating access of DNA repair enzymes to DNA. The specific distribution of PARP1 protein in chromatin presents additional evidence against the notion that the main PARP1 function is to repair DNA. PARP1 is predominantly localized within the promoter regions of protein-encoding genes6 and accumulates in nucleoli7 as well as in telomeres8, 9. These are very peculiar sites of localization for a protein that purportedly controls a general DNA repair pathway. Furthermore, the PARP1 protein has been shown to regulate transcription as well as chromatin decondensation coupled with transcription10, 11, 12 and in regulating nuclear factor kB (NF-kB)-dependent cellular responses11, 13. As NF-kB signaling is important for tumor growth14, PARP1 inhibitors might be effective in preventing this stage of progression, particularly when combined with drugs that act at other steps in the overall process. PARP1 controls expression of a chaperone protein heat shock protein 70 (HSP70)11, 15, which also contributes to tumor cell survival and ! resistance to therapy16. These functions correspond well with sites of PARP1 accumulation and may be crucial for survival and proliferation of tumor cells. Reexamining PARP1 functions in tumor cells and targeting pathways other than DNA repair should improve the theoretical framework surrounding PARP1 inhibitors in cancer research. The second obstacle to effective PARP1-targeted cancer treatments is the assumption that the sensitivity of cancer cells to inhibitors is specifically linked to the presence of BRCA mutations17. It is now clear that not all tumor cells carrying BRCA1/2 mutations are sensitive to PARP1 inhibitors, whereas some cancer cells that have mutations other than BRCA1/2 are sensitive to them. It appears that the best biomarker of PARP1 inhibitor sensitivity is not a particular mutation but a pre-existing high level of poly(ADP-ribose) in cancer cells4. Previously, it was shown that the main target of poly(ADP-ribose) in a living cell is PARP1 protein itself18, 19. This auto-poly(ADP-ribosyl) ation leads to PARP1 auto-inactivation and removal of PARP1 from chromatin5. Therefore, the accumulation of poly(ADP-ribose) reflects not only a high level of PARP1 protein activity, but also a high level of PARP1 protein inactivation and the deficit of PARP1 protein in a cell18. The inactivation ! of PARP1 by auto-poly(ADP-ribosyl)ation may explain the sensitivity of cancer cells with a high level of poly(ADP-ribose) to PARP1 inhibitors. Such cells simply have a very small pool of functional PARP1, thereby allowing an inhibitor to quickly titrate away the remainder of PARP1 and block its activity (Fig. 1), abolishing PARP1-dependent transcription and other pathways. The mechanism of this high level of poly(ADP-ribose) accumulation is still to be discovered, and may open new gateways to PARP1-based cancer therapy. Figure 1: Model explaining the differences in PARP1 inhibitor effectiveness between inhibitor-sensitive and inhibitor-insensitive cancer cells. * Full size image (126 KB) The third pitfall to PARP1-based cancer treatments concerns the strategies deployed for designing PARP1 inhibitors. The majority of PARP1 inhibitors have been designed to compete with NAD for a binding site on the PARP1 molecule. This strategy resulted mainly in discovery of nucleotide-like PARP1 inhibitors that may target not only PARPs, but also other enzymatic pathways involving NAD and nucleotides as co-factors. Using such inhibitors affects multiple NAD/nucleotide-dependent enzymatic pathways, which results in secondary toxic effects proceeding from the inactivation of other pathways, whereas the efficiency against the PARP1 pathway specifically is diminished. A possible strategy to bypass this pitfall is to design inhibitors by targeting other binding sites on the PARP1 protein. Our research20, 21 has shown that interaction with core histones plays a crucial role in PARP1 protein regulation in vivo. Moreover, histones control PARP1 activation in both transcriptional an! d DNA-repair pathways21. Therefore, identifying small molecule inhibitors that attack PARP1 interaction with histones22 should yield compounds inhibiting PARP1 pathways with high specificity. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA. * Alexei Tulin Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Alexei Tulin Author Details * Alexei Tulin Contact Alexei Tulin Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Access to human embryonic stem cell lines
- Nat Biotechnol 29(12):1079-1081 (2011)
Nature Biotechnology | Opinion and Comment | Correspondence Access to human embryonic stem cell lines * Aaron D Levine1Journal name:Nature BiotechnologyVolume: 29,Pages:1079–1081Year published:(2011)DOI:doi:10.1038/nbt.2029Published online08 December 2011 To the Editor: The promise of human embryonic stem cell (hESC) science for basic research, drug discovery and cell-based therapies depends on a wide range of scientists acquiring existing hESC lines or deriving their own lines—in short, gaining access to the field's key research tool. Historically, norms of scientific behavior have supported sharing of research materials, yet concerns have emerged that this behavior is in decline. Among the factors potentially complicating access to hESC lines is an intellectual property environment that features both broad patents on hESCs assigned to the Wisconsin Alumni Research Foundation (WARF; Madison, WI, USA) and numerous narrower patents claiming specific hESC-related techniques1. Access is also complicated by the ethical controversy associated with hESC science, the field's political salience and its commercial potential. Responding to this environment, the interdisciplinary Hinxton Group (Baltimore) has called for new information and data shar! ing hubs to facilitate stem cell research, echoing concerns voiced since hESCs were first derived2. 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 * School of Public Policy & Institute of Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA. * Aaron D Levine Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Aaron D Levine Author Details * Aaron D Levine Contact Aaron D Levine Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (120K) Supplementary Methods, Supplementary Tables 1–3 Additional data - Sarbanes-Oxley overburdens biotech companies
- Nat Biotechnol 29(12):1081-1082 (2011)
Article preview View full access options Nature Biotechnology | Opinion and Comment | Correspondence Sarbanes-Oxley overburdens biotech companies * Mark Kessel1Journal name:Nature BiotechnologyVolume: 29,Pages:1081–1082Year published:(2011)DOI:doi:10.1038/nbt.2059Published online08 December 2011 To the Editor: ©Boston Globe Eric Lander of the Broad Institute and MIT, one of 26 thought leaders, industrialists and entrepreneurs that make up the President's Council on Jobs and Competitiveness, which supports amendment of SOX regulations. As small and mid-cap biotech companies are seeing their market values decline due to the continued disruption in the financial markets, the cost of compliance with US governance regulations instituted over the past decade, particularly those regulations related to the 2002 Sarbanes-Oxley (SOX) legislation, continues to increase at an unabated pace. These regulations not only are financially burdensome, but also impair the ability of biotech companies to raise much-needed capital. The bottom line is that SOX has not produced the governance improvements that offset the burden placed on the biotech sector. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Mark Kessel is at Sagent Advisors Inc., New York, New York, USA and Symphony Capital LLC, New York, New York, USA. Competing financial interests The author declares no competing financial interests. Author Details * Mark Kessel Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - What's fueling the biotech engine—2010 to 2011
- Nat Biotechnol 29(12):1083-1089 (2011)
Nature Biotechnology | Feature What's fueling the biotech engine—2010 to 2011 * Saurabh Aggarwal1Journal name:Nature BiotechnologyVolume: 29,Pages:1083–1089Year published:(2011)DOI:doi:10.1038/nbt.2060Published online08 December 2011 In the past year, biologics sector sales grew by single digits, driven by monoclonal antibodies and insulin products. New product launches are showing mixed results and are facing rising challenges from changes to reimbursement policies. View full text Figures at a glance * Figure 1: Growth trends in the United States biotech market for biologic drugs (2006–2010). () Total sales and growth rate trends. () Quarterly sales growth for biologic drugs (2006–Q2 2011). * Figure 2: Top nine categories of biologic drugs in terms of US sales in 2010. The pie chart shows US sales of these drug categories. The table shows the growth rates of the categories between 2009 and 2010. The red boxes indicate the major categories showing the fastest growth rate during that period. For therapeutic enzymes, their manufacturers do not break out the US sales, so their sales were estimated assuming 20–30% of worldwide sales were generated in the United States. * Figure 3: Top companies comprising the majority of sales of biologic drugs in 2010. The pie chart shows the fraction of total biotech sales of the top 13 companies. The table shows the annual growth rates of the top ten companies. Red boxes indicate companies that had biologics sales growth of >10%. For the purpose of this analysis, Rituxan US sales have been split equally between Genentech and Biogen Idec; Erbitux US sales were split 40/60 between Lilly and Bristol-Myers Squibb. J&J, Johnson & Johnson; BMS, Bristol-Myers Squibb. * Figure 4: Trends in US sales of mAbs. () 2010 sales for US markets for mAbs ($ billions). 'Others' includes all mAbs with sales <$300 million per year. () Trends in US sales show Remicade leading, Humira rising and Herceptin lagging. * Figure 5: Trends in US sales of recombinant hormones. () 2010 sales in US market for recombinant hormones ($ billions). () Trends in US sales show Lantus, Levemir and Novolog rising. 'Others' includes all hormones with sales of <$200 million per year. * Figure 6: Trends in US sales of growth factors. () 2010 sales in US market for growth factors ($ billions). () Trends in US sales show Neulasta leading and Neupogen and Aranesp lagging. * Figure 7: US sales of cytokines and therapeutic enzymes ($ billions). () Cytokines sales, showing the top four brands. () Therapeutic enzyme sales, showing the top six brands. * Figure 8: US sales of recombinant vaccines, blood factors and anticoagulants ($ billions). () Recombinant vaccines, where there are new launches, are showing more fragmentation. () Blood factors are dominated by two brands. 'Others' includes all hormones with sales of <$200 million per year. () The recombinant anticoagulants market is dominated by two brands. 'Others' includes all hormones with sales of <$50 million per year. Author information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Saurabh Aggarwal is at Novel Health Strategies LLC, Bethesda, Maryland, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Saurabh Aggarwal Author Details * Saurabh Aggarwal Contact Saurabh Aggarwal Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Agricultural microbial resources: private property or global commons?
- Nat Biotechnol 29(12):1091-1093 (2011)
Nature Biotechnology | Feature | Patents Agricultural microbial resources: private property or global commons? * David Kothamasi1 * Matthew Spurlock2 * E Toby Kiers3 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1091–1093Year published:(2011)DOI:doi:10.1038/nbt.2056Published online08 December 2011 Agricultural microbes have become an attractive target for patenting, but the lack of a consistent global patent regime and increasingly heated debates over microbial ownership rights are barriers to the development of this resource. 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 * David Kothamasi is in the Laboratory of Soil Biology and Microbial Ecology, Department of Environmental Studies, University of Delhi, India. * Matthew Spurlock is a JD candidate at Harvard Law School, Cambridge, Massachusetts, USA. * E. Toby Kiers is at the Department of Ecological Science, Vrije Universiteit, Amsterdam, The Netherlands. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * E Toby Kiers Author Details * David Kothamasi Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Spurlock Search for this author in: * NPG journals * PubMed * Google Scholar * E Toby Kiers Contact E Toby Kiers Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Recent patent applications related to agricultural microbes
- Nat Biotechnol 29(12):1094 (2011)
Article preview View full access options Nature Biotechnology | Feature | Patents Recent patent applications related to agricultural microbes Journal name:Nature BiotechnologyVolume: 29,Page:1094Year published:(2011)DOI:doi:10.1038/nbt.2066Published online08 December 2011 Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Dissecting cancer heterogeneity
- Nat Biotechnol 29(12):1095-1096 (2011)
Article preview View full access options Nature Biotechnology | News and Views Dissecting cancer heterogeneity * David Dornan1 * Jeff Settleman1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1095–1096Year published:(2011)DOI:doi:10.1038/nbt.2063Published online08 December 2011 Transcriptional profiling of single cells in colon tumors may enable prediction of patient outcomes. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * David Dornan and Jeff Settleman are at Genentech, Molecular Diagnostics and Cancer Cell Biology, San Francisco, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jeff Settleman Author Details * David Dornan Search for this author in: * NPG journals * PubMed * Google Scholar * Jeff Settleman Contact Jeff Settleman Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Genomic rearrangement in three dimensions
- Nat Biotechnol 29(12):1096-1098 (2011)
Article preview View full access options Nature Biotechnology | News and Views Genomic rearrangement in three dimensions * PJ Hastings1 * Susan M Rosenberg1, 2 * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:1096–1098Year published:(2011)DOI:doi:10.1038/nbt.2064Published online08 December 2011 Two studies illuminate why genome rearrangements in cancer cells occur where they do. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Molecular and Human Genetics at Baylor College of Medicine, Houston, Texas, USA. * PJ Hastings & * Susan M Rosenberg * Departments of Biochemistry and Molecular Biology, Molecular Virology and Microbiology, and the Dan L. Duncan Cancer Center at Baylor College of Medicine, Houston, Texas, USA. * Susan M Rosenberg Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * PJ Hastings or * Susan M Rosenberg Author Details * PJ Hastings Contact PJ Hastings Search for this author in: * NPG journals * PubMed * Google Scholar * Susan M Rosenberg Contact Susan M Rosenberg Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - The new landscape of protein ubiquitination
- Nat Biotechnol 29(12):1098-1100 (2011)
Article preview View full access options Nature Biotechnology | News and Views The new landscape of protein ubiquitination * Guoqiang Xu1 * Samie R Jaffrey1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1098–1100Year published:(2011)DOI:doi:10.1038/nbt.2061Published online08 December 2011 Proteome-wide identification of ubiquitination events reveals their functional classes and identifies substrates for ubiquitin ligases. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Guoqiang Xu and Samie R. Jaffrey are at the Department of Pharmacology, Weill Medical College of Cornell University, New York, New York, USA. Competing financial interests Cornell University and the authors may be entitled to royalties on the sale of certain antibodies that can be used for ubiquitin proteomic research. Corresponding author Correspondence to: * Samie R Jaffrey Author Details * Guoqiang Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Samie R Jaffrey Contact Samie R Jaffrey Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes
- Nat Biotechnol 29(12):1103-1108 (2011)
Nature Biotechnology | Computational Biology | Analysis DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes * Subhajyoti De1, 2 * Franziska Michor1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:1103–1108Year published:(2011)DOI:doi:10.1038/nbt.2030Received13 May 2011Accepted05 October 2011Published online20 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Somatic copy-number alterations (SCNA) are a hallmark of many cancer types, but the mechanistic basis underlying their genome-wide patterns remains incompletely understood. Here we integrate data on DNA replication timing, long-range interactions between genomic material, and 331,724 SCNAs from 2,792 cancer samples classified into 26 cancer types. We report that genomic regions of similar replication timing are clustered spatially in the nucleus, that the two boundaries of SCNAs tend to be found in such regions, and that regions replicated early and late display distinct patterns of frequencies of SCNA boundaries, SCNA size and a preference for deletions over insertions. We show that long-range interaction and replication timing data alone can identify a significant proportion of SCNAs in an independent test data set. We propose a model for the generation of SCNAs in cancer, suggesting that data on spatial proximity of regions replicating at the same time can be used to pred! ict the mutational landscapes of cancer genomes. View full text Figures at a glance * Figure 1: Long-range DNA interactions and the distribution of SNCAs with regard to replication timing zones. () Organization of genomic DNA with long-range interactions between distant replication timing zones can increase the risk of interference between adjacent replication forks, leading to genomic alterations. (–) The two boundaries of SCNAs are significantly more likely to reside in genomic regions with the same replication timing than that expected by chance for all SCNAs (), somatic copy number amplifications (SCNA-Amplifications) (), somatic copy number deletions (SCNA-Deletions) () and SCNAs that overlap with known cancer genes listed in the Cancer Gene Census (SCNACGC) (). Supplementary Module 3 provides separate analyses of SCNA amplifications and deletions as identified by GISTIC. The absolute number (n) of observed and expected cases is provided below each bar. *, P = 1.21 × 10−5; **, P = 1.98 × 10−5; ***, P = 3.76 × 10−6. * Figure 2: SCNA frequencies vary between different replication timing zones. (–) Observed (black) and expected (gray) proportions of SCNA boundaries in early-, mid- and late-replication timing zones. The absolute number (n) of observed and expected cases is shown inside each bar. Similar graphs are shown for somatic copy number amplifications (SCNA-Amplifications) () and somatic copy number deletions (SCNA-Deletions) (). The triangle reflects the direction of enrichment. () SNP-chip log2 ratios indicating SCNAs with boundaries in genomic regions within early- and late-replication timing zones. The dashed line, drawn along the median of late-replication timing data points, serves to highlight the difference with early-replication timing data points. () SNP-chip log2 ratios indicating SCNAs with boundaries in genomic regions within early- and late-replication timing zones, which overlap with known cancer genes listed in the Cancer Gene Census (SCNACGC). Supplementary Module 6 provides the contingency tables for SCNA amplifications and deletions as id! entified by GISTIC. The dashed line serves the same purpose as in . () Distribution frequencies of SCNA boundaries near early-, mid- and late-replication transition zones, after dividing the transition zones into 10-kb nonoverlapping windows. () Distribution of frequencies of SCNACGC boundaries near early-, mid- and late-replication transition zones, after dividing the transition zones into 10-kb nonoverlapping windows. Replication transition is continuous. *, P < 2.21 × 10−16; **, P = 1.59 × 10−4; ***, P < 2.2 × 10−16; ****, P = 1.42 × 10−8. * Figure 3: Genome-wide distributions of long-range interactions and SCNAs. () Pairs of genomic regions that were at least 5 Mb apart on the same chromosome were classified into four categories depending on whether they share no HiC reads, ≥1, >5 or >10 HiC reads. We estimated the proportion of region pairs that also harbor the two boundaries of one or more SCNAs. () Same as , but only considering those SCNAs that overlap with known cancer genes. Supplementary Module 7 provides the information for SCNA amplifications and deletions as identified by GISTIC. () We classified large SCNAs (>5 Mb, black), and those that overlap with known cancer genes (gray) into four groups depending on the number of HiC reads that link the two boundary regions of the SCNAs. () Distributions of SCNAs and long-range interactions around BCL6, a cancer gene on human chromosome 3. The HiC reads that link two boundary regions of SCNAs are shown in red. () Pie chart showing the proportion of ovarian cancer SCNAs whose boundaries were predicted using replication timing and Hi! C data alone. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Subhajyoti De & * Franziska Michor * Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. * Subhajyoti De & * Franziska Michor Contributions S.D. and F.M. designed the experiments and wrote the paper. S.D. performed the analysis. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Subhajyoti De or * Franziska Michor Author Details * Subhajyoti De Contact Subhajyoti De Search for this author in: * NPG journals * PubMed * Google Scholar * Franziska Michor Contact Franziska Michor Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (938K) Supplementary Modules 1–10 Additional data - High order chromatin architecture shapes the landscape of chromosomal alterations in cancer
- Nat Biotechnol 29(12):1109-1113 (2011)
Nature Biotechnology | Computational Biology | Analysis High order chromatin architecture shapes the landscape of chromosomal alterations in cancer * Geoff Fudenberg1 * Gad Getz2 * Matthew Meyerson2, 3, 4, 5 * Leonid A Mirny2, 6, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1109–1113Year published:(2011)DOI:doi:10.1038/nbt.2049Received09 September 2011Accepted21 October 2011Published online20 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The accumulation of data on structural variation in cancer genomes provides an opportunity to better understand the mechanisms of genomic alterations and the forces of selection that act upon these alterations in cancer. Here we test evidence supporting the influence of two major forces, spatial chromosome structure and purifying (or negative) selection, on the landscape of somatic copy-number alterations (SCNAs) in cancer1. Using a maximum likelihood approach, we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule model2, 3. This analysis suggests that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and that purifying selection, as well as positive selection, influences SCNAs during somatic evolution of cancer cells. View full text Figures at a glance * Figure 1: 3D proximity as mechanism for SCNA formation. () Model of how chromosomal architecture and selection can influence observed patterns of SCNAs. First, spatial proximity of the loop ends makes an SCNA more likely to occur after DNA damage (yellow lightning bolts) and repair. Next, forces of positive selection and purifying selection act on SCNAs that have arisen (deletions (blue) or amplifications (red)), leading to their ultimate fixation or loss. Observed SCNAs in cancer thus reflect both mutational and selective forces. Inset illustrates looping in a simulated fractal globule architecture. Two contact points are highlighted by spheres and represent potential end points of SCNAs. () SCNA length distribution for 60,580 less-recurrent SCNAs (39,071 amplifications and 21,509 deletions) mapped in 3,131 cancer specimens from 26 histological types1. Squares show mean number of amplification (red) or deletion (blue) SCNAs after binning at 100 kb resolution (and then averaged over logarithmic intervals). Light magenta lines sho! w ~1/L distributions. Gray line shows the best fit for purifying selection (equation (4) with a uniform mutation rate). Thick dark purple line shows best fit for deletions for FG+sel. () The mean number of contacts between two loci distance L apart on a chromosome at 100 kb resolution. Contacts are obtained from intrachromosomal interactions of 22 human chromosomes characterized by the HiC method (human cell line GM06690)2. Shaded area shows range from 5th and 95th percentiles for number of counts in a 100-kb bin at a given distance. The mean number of contacts is shown by blue line. Light magenta line shows ~1/L scaling also observed in the fractal globule model of chromatin architecture. Blue dashed line provides a baseline for contact frequency obtained as interchromosomal contacts in the same data set. * Figure 2: Heatmaps for chromosome 17 at 1 Mb resolution. () SCNA heatmap. The heatmap value for site (i,j) is the number of SCNAs starting at genomic location i (vertical axis) and ending at location j (horizontal axis) on the same chromosome. Chromosome band structure from UCSC browser shown on the left side. () HiC heatmap. Site (i,j) has the number of reported interactions between genomic locations i and j at Mb resolution. HiC domain structure is shown on the left side. Domains were determined by thresholding the HiC eigenvector (as in ref. 2, white represents open domains, dark gray represents closed domains). () Permuted SCNA heatmap. As in , but after randomly permuting SCNA locations while keeping SCNA lengths fixed. Visually, the true SCNA heatmap is similar to HiC (Pearson's r = 0.55, P < 0.001, see Supplementary Table 1 for other chromosomes), displaying a 'domain' style organization. Cartoons above the heatmaps illustrate how mapped HiC fragments and SCNA end points can be converted into interactions between genomic lo! cations i and j. Because our statistical analysis did not consider inter-arm SCNAs, SCNAs with end points near centromeres or telomeres, and SCNAs <1 Mb, these areas of the heatmaps are grayed out. * Figure 3: Selecting a model of SCNA formation based on its consistency with the 24,310 observed SCNAs that do not span highly recurrent SCNA regions listed in reference 1. Each model (Uniform, HiC and FG) was considered without (−) and with (+) the influences of purifying selection. The HiC model assumes mutation rates proportional to experimentally measured contact probabilities, whereas the FG model assumes mutation rates proportional to mean contact probability in a fractal globule architecture (~1/L). Left y axis shows BIC-corrected log-likelihood ratio for each model versus the Uniform model. Right y axis shows the same data as a fold difference in likelihood per cancer specimen (sample) versus the Uniform model. Error bars were obtained by bootstrapping: squares represent the median values, bar ends represent the 5th and 95th percentiles. * Figure 4: Permutation analysis of the relationship between SCNAs and megabase-level structure of HiC chromosomal interactions. () Histogram of log-likelihood ratios over all 22 autosomes for randomly permuted SCNAs given HiC versus observed SCNAs given HiC. Observed SCNAs (blue arrow) are fit better by HiC contact probability (P < 0.001). Permutations are performed by shuffling SCNA locations while keeping SCNA lengths fixed. () Distributions of the same log-likelihood ratios for individual chromosomes (versus their corresponding observed SCNA, blue horizontal line). Squares represent median values, error bars respective represent range from 5th to 25th percentile and 75th to 95th percentile. Author information * Abstract * Author information * Supplementary information Affiliations * Harvard University, Program in Biophysics, Boston, Massachusetts, USA. * Geoff Fudenberg * The Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA. * Gad Getz, * Matthew Meyerson & * Leonid A Mirny * Harvard Medical School, Boston, Massachusetts, USA. * Matthew Meyerson * Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Matthew Meyerson * Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Matthew Meyerson * Harvard-MIT, Division of Health Sciences and Technology, Cambridge, Massachusetts, USA. * Leonid A Mirny * Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Leonid A Mirny Contributions G.F. designed the study, performed data analysis and wrote the paper; G.G. and M.M. provided expertise in SCNA analysis and developed the manuscript; L.A.M. designed the study, performed initial data analysis and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Leonid A Mirny Author Details * Geoff Fudenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Gad Getz Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Meyerson Search for this author in: * NPG journals * PubMed * Google Scholar * Leonid A Mirny Contact Leonid A Mirny Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–9 and Supplementary Table 1 Additional data - In silico feedback for in vivo regulation of a gene expression circuit
- Nat Biotechnol 29(12):1114-1116 (2011)
Nature Biotechnology | Research | Brief Communications In silico feedback for in vivo regulation of a gene expression circuit * Andreas Milias-Argeitis1, 4 * Sean Summers1, 4 * Jacob Stewart-Ornstein2, 4 * Ignacio Zuleta2 * David Pincus2 * Hana El-Samad2 * Mustafa Khammash3 * John Lygeros1 * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:1114–1116Year published:(2011)DOI:doi:10.1038/nbt.2018Received01 July 2011Accepted27 September 2011Published online06 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible. View full text Figures at a glance * Figure 1: Characterization of the light-switched system. () Light-switchable gene system based on PhyB-PIF3 interaction. Transformed cells grown in darkness and incubated with the chromophore phycocyanobilin (PCB) synthesize both PhyB(Pr)-GBD and PIF3-GAD fusion proteins. Because PIF3 interacts only with the activated form of PhyB (Pfr), the Gal1 target gene is initially off. Upon exposure to red light, PhyB is rapidly converted into its active Pfr form and binds the PIF3 moiety of PIF3-GAD. The transcription activation domain of Gal4 is therefore recruited to the promoter and induces transcription of the target gene. Exposure to far-red light switches off gene expression by rapidly converting PhyB into its inactive Pr form, causing its dissociation from PIF3-GAD. () The expression of YFP driven by the Gal1 promoter can be repeatedly switched on and off using a train of red (R) and far-red (FR) pulses. The trains of light pulses can serve as a control input, and the amount of YFP plays the role of a controlled output. () Experimen! tal dynamics of cell fluorescence in response to a red pulse followed by a far-red pulse. All pulses have 1-min duration. YFP flow cytometry measurements (squares) were taken every 30 min. Each set of matched colored arrow and output squares and curve represent a distinct experiment in which the far-red input was applied at different times. Spontaneous transition of PhyB Pfr to Pr takes place in the dark (a phenomenon known as 'dark reversion') resulting in dissociation of PIF3 from PhyB. Consequently, cell fluorescence reaches a peak and then declines, as mRNA decays over time. Gray squares and line correspond to a control experiment with chromophore addition and no light exposure. () Simulation of the response to the same input as in . The model reproduces several essential features of the experimental responses, including peak times and decay dynamics. Slight differences between simulated and experimental responses are due to nonlinear effects and delays that are not cap! tured by the model. () Reversibility of the PhyB-PIF3 interact! ion. The system does not lose its responsiveness to light over several on-off cycles. () Simulation results for the same input as in . () Response to multiple red pulses. Multiple applications of red light drive the system to higher expression levels than a single red pulse. () Simulation results for multiple- compared with single-pulse responses. * Figure 2: In silico feedback achieves robust regulation of gene expression fold change. () In silico feedback control scheme for the light-activated gene system. () Regulation of average YFP fluorescence to sevenfold over a 7-h period using in silico feedback (orange). A pre-computed light pulse train that achieves set point regulation when applied to the mathematical model (gray) did not achieve the desired fold induction when applied in open loop to the biological construct (green). In contrast, closed-loop feedback control achieves the desired fold induction. OL and CL denotes open- and closed-loop control, respectively. () Regulation of average YFP fluorescence to fourfold above basal over a 7-h period. Open- and closed-loop pulse trains determined as in . () Regulation of average YFP to fivefold above basal over a 7-h period, starting from a randomly perturbed culture. Closed-loop control achieves the desired set point, irrespective of the initial conditions of the system. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Andreas Milias-Argeitis, * Sean Summers & * Jacob Stewart-Ornstein Affiliations * Department of Electrical Engineering, ETH Zurich, Automatic Control Laboratory, Zurich, Switzerland. * Andreas Milias-Argeitis, * Sean Summers & * John Lygeros * Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, USA. * Jacob Stewart-Ornstein, * Ignacio Zuleta, * David Pincus & * Hana El-Samad * Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. * Mustafa Khammash Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Hana El-Samad or * Mustafa Khammash Author Details * Andreas Milias-Argeitis Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Summers Search for this author in: * NPG journals * PubMed * Google Scholar * Jacob Stewart-Ornstein Search for this author in: * NPG journals * PubMed * Google Scholar * Ignacio Zuleta Search for this author in: * NPG journals * PubMed * Google Scholar * David Pincus Search for this author in: * NPG journals * PubMed * Google Scholar * Hana El-Samad Contact Hana El-Samad Search for this author in: * NPG journals * PubMed * Google Scholar * Mustafa Khammash Contact Mustafa Khammash Search for this author in: * NPG journals * PubMed * Google Scholar * John Lygeros Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (688K) Supplementary Methods and Supplementary Figures 1–4 Additional data - Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells
- Nat Biotechnol 29(12):1117-1119 (2011)
Nature Biotechnology | Research | Brief Communications Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells * Kitai Kim1, 2, 3, 9, 10 * Rui Zhao1, 2, 3, 10 * Akiko Doi4, 10 * Kitwa Ng1, 3, 10 * Juli Unternaehrer1, 2, 3 * Patrick Cahan1, 2, 3 * Huo Hongguang1, 3 * Yuin-Han Loh1, 2, 3 * Martin J Aryee5 * M William Lensch1, 2, 3 * Hu Li6 * James J Collins6, 7 * Andrew P Feinberg4 * George Q Daley1, 2, 3, 8 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:1117–1119Year published:(2011)DOI:doi:10.1038/nbt.2052Received17 May 2011Accepted28 October 2011Published online27 November 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We compared bona fide human induced pluripotent stem cells (iPSCs) derived from umbilical cord blood (CB) cells and neonatal keratinocytes (K). As a consequence of both incomplete erasure of tissue-specific methylation and aberrant de novo methylation, CB-iPSCs and K-iPSCs were distinct in genome-wide DNA methylation profiles and differentiation potential. Extended passage of some iPSC clones in culture did not improve their epigenetic resemblance to embryonic stem cells, implying that some human iPSCs retain a residual 'epigenetic memory' of their tissue of origin. View full text Figures at a glance * Figure 1: Derivation and differentiation of iPSCs from neonatal umbilical cord blood cells and foreskin fibroblasts. () Experimental schema. () Q-PCR of the keratinocyte marker K14 in day-6 embryoid bodies from CB-iPSCs (n = 6) and K-iPSCs (n = 7). Gene expression was normalized to actin, and shown as fold-difference relative to CB-iPSCs. () Numbers of keratinocytes differentiated from CB-iPSCs (n = 6) and K-iPSCs (n = 7). () Numbers of hematopoietic colony–forming cells in day-14 embryoid bodies differentiated from CB-iPSCs (n = 6) and K-iPSCs (n = 7). Error bar = s.d. G, granulocyte progenitor; M, macrophage progenitor; GM, granulocyte-macrophage progenitor; GEMM, granulocyte-erythrocyte-macrophage-megakaryocyte progenitor. * Figure 2: Analysis of methylation in CB-iPSCs, K-iPSCs, ESCs and somatic cells. () Numbers of differentially methylated regions (DMRs) between CB-iPSCs, K-iPSCs, ESCs, umbilical cord blood cells and cultured keratinocytes. DMRs were defined by an area cutoff of 2.0. CBC, cord bloods cells. () Cluster dendrogram analysis using the top 1,000 most variable probes across all samples. (,) Gene enrichment analysis of DMRs. Blue histograms represent a probability distribution of the number of genes predicted to overlap DMRs by chance. Red vertical lines indicate the observed number of genes that overlap DMRs. () Genes differentially methylated between CB-iPSCs and K-iPSCs are enriched in DMR-associated genes (genes both differentially expressed and methylated between cord blood cells and keratinocytes). () Genes highly expressed in keratinocytes are enriched in DMRs that are both hypermethylated in K-iPSCs relative to CB-iPSCs and are located in gene bodies rather than promoters. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE27224 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Kitai Kim, * Rui Zhao, * Akiko Doi & * Kitwa Ng Affiliations * Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Children's Hospital Boston and Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Kitai Kim, * Rui Zhao, * Kitwa Ng, * Juli Unternaehrer, * Patrick Cahan, * Huo Hongguang, * Yuin-Han Loh, * M William Lensch & * George Q Daley * Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Kitai Kim, * Rui Zhao, * Juli Unternaehrer, * Patrick Cahan, * Yuin-Han Loh, * M William Lensch & * George Q Daley * Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. * Kitai Kim, * Rui Zhao, * Kitwa Ng, * Juli Unternaehrer, * Patrick Cahan, * Huo Hongguang, * Yuin-Han Loh, * M William Lensch & * George Q Daley * Center for Epigenetics and Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Akiko Doi & * Andrew P Feinberg * Oncology Department, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA. * Martin J Aryee * Department of Biomedical Engineering and Center for BioDynamics, Boston University, Boston, Massachusetts, USA. * Hu Li & * James J Collins * Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA.; Howard Hughes Medical Institute. * James J Collins * Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * George Q Daley * Present address: Department of Cancer Biology and Genetics, Center for Cell Engineering, Sloan-Kettering Institute, New York, New York, USA. * Kitai Kim Contributions K.K., R.Z., K.N. and G.Q.D. conceived the experimental plan. K.K., R.Z., A.D., K.N., J.U., H.H., M.W.L., Y.-H.L. and H.L. performed the experiments. K.K., A.D., P.C. and M.J.A. performed data analysis. A.D., M.J.A. and A.P.F. performed CHARM and guided analysis of methylation. K.K., R.Z., A.D., K.N., J.U., P.C., J.J.C., M.W.L., A.P.F. and G.Q.D. wrote the manuscript. Competing financial interests G.Q.D. is a member of the scientific advisory boards of iPierian, Verastem, Epizyme, Solasia, MPM Capital and Johnson & Johnson. Corresponding authors Correspondence to: * George Q Daley or * Andrew P Feinberg Author Details * Kitai Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Akiko Doi Search for this author in: * NPG journals * PubMed * Google Scholar * Kitwa Ng Search for this author in: * NPG journals * PubMed * Google Scholar * Juli Unternaehrer Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Cahan Search for this author in: * NPG journals * PubMed * Google Scholar * Huo Hongguang Search for this author in: * NPG journals * PubMed * Google Scholar * Yuin-Han Loh Search for this author in: * NPG journals * PubMed * Google Scholar * Martin J Aryee Search for this author in: * NPG journals * PubMed * Google Scholar * M William Lensch Search for this author in: * NPG journals * PubMed * Google Scholar * Hu Li Search for this author in: * NPG journals * PubMed * Google Scholar * James J Collins Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew P Feinberg Contact Andrew P Feinberg Search for this author in: * NPG journals * PubMed * Google Scholar * George Q Daley Contact George Q Daley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1–6, Supplementary Methods and Supplementary Figures 1–15 Additional data - Single-cell dissection of transcriptional heterogeneity in human colon tumors
- Nat Biotechnol 29(12):1120-1127 (2011)
Nature Biotechnology | Research | Article Single-cell dissection of transcriptional heterogeneity in human colon tumors * Piero Dalerba1, 2, 9 * Tomer Kalisky3, 9 * Debashis Sahoo1, 9 * Pradeep S Rajendran1 * Michael E Rothenberg1, 4 * Anne A Leyrat3 * Sopheak Sim1 * Jennifer Okamoto3, 5 * Darius M Johnston1, 3, 5 * Dalong Qian1 * Maider Zabala1 * Janet Bueno6 * Norma F Neff3 * Jianbin Wang3 * Andrew A Shelton7 * Brendan Visser7 * Shigeo Hisamori1 * Yohei Shimono1 * Marc van de Wetering8 * Hans Clevers8 * Michael F Clarke1, 2, 9 * Stephen R Quake3, 5, 9 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 29,Pages:1120–1127Year published:(2011)DOI:doi:10.1038/nbt.2038Received02 May 2011Accepted12 October 2011Published online13 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Cancer is often viewed as a caricature of normal developmental processes, but the extent to which its cellular heterogeneity truly recapitulates multilineage differentiation processes of normal tissues remains unknown. Here we implement single-cell PCR gene-expression analysis to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single (n = 1) cell, we demonstrate that the transcriptional diversity of cancer tissues is largely explained by in vivo multilineage differentiation and not only by clonal genetic heterogeneity. Finally, we show that the different gene-expression programs linked to multilineage differentiation are strongly associated with patient survival. We develop two-gene classifier systems (KR! T20 versus CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard ratios superior to those of pathological grade and comparable to those of microarray-derived multigene expression signatures. View full text Figures at a glance * Figure 1: Single-cell PCR gene-expression analysis of human normal colon epithelium. (–) Immunohistochemistry of normal human colon epithelium, stained for MUC2 (), labeling goblet cells, MKI67 (), labeling proliferating cells, KRT20 () and CEACAM1 (), preferentially labeling top-of-the-crypt cells. (,) Flow cytometry sorting strategy for top-of-the-crypt and bottom-of-the-crypt epithelial cells. () Colon epithelial cells, both CD44neg and CD44+, were separated from stromal cells based on their EpCAM+ phenotype. () Bottom-of-the-crypt epithelial cells were defined as EpCAMhigh/CD44+ (, P12 blue sort gate) and top-of-the-crypt epithelial cells as EpCAM+/CD44−/CD66ahigh (, P11 orange sort gate). () Hierarchical clustering of single-cell PCR gene-expression analysis data visualized distinct cell populations, including enterocyte-like cells (CA1+/SLC26A3+ and GUCA2B+), goblet-like cells (MUC2+/TFF3high) and two compartments defined by gene-expression profiles reminiscent of more immature progenitors (OLFM4+/CA2high and LGR5+/ASCL2+). (,) Principal component ! analysis of single-cell PCR gene-expression data visualized different cell types and different gene families. Different cell types were characterized by different scores along the two main principal components (PC1 and PC2) (). Different gene families were characterized by different contributions to the two main principal components. To allow comparisons between hierarchical clustering and PCA results, we displayed each cell or gene in PCA plots with the color corresponding to the cell type or gene family it was assigned to based on hierarchical clustering (). * Figure 2: Single-cell PCR gene-expression analysis of human colon tumor tissues. () Hierarchical clustering of single-cell PCR gene-expression data from the EpCAM+/CD44+ population of a large primary benign adenoma (sample: SU-COLON#76; see Supplementary Table 4). The analysis revealed the presence of multiple cell populations characterized by distinct gene signatures, closely mirroring lineages and differentiation stages observed in the EpCAM+/CD44+ population from the normal colon epithelium. (,) Principal component analysis (PCA) of single-cell PCR gene-expression analysis data confirmed hierarchical clustering results, visualizing cell types () and gene families () similar to those identified in normal tissues. (,) Gene-expression data were confirmed at the protein level by immunohistochemistry, testing for expression of KRT20 () and MUC2 () on corresponding tissue sections. (–) A similar study on a monoclonal colon cancer xenograft obtained from injection of a single (n = 1) cell in a NOD/SCID/IL2Rγ−/− mouse (UM-COLON#4 clone 8) produced simi! lar results in terms of hierarchical clustering (), cell types identified by PCA (), gene families identified by PCA (), immunohistochemistry results for KRT20 () and immunohistochemistry results for MUC2 (). Results from the monoclonal tumor xenograft indicated that the distinct cell populations visualized by single-cell PCR did not arise as the result of the coexistence within the tumor tissue of independent genetic subclones, but as the result of multilineage differentiation processes during tumor growth. Color coding of normalized threshold cycle (Ct) values in hierarchical clustering plots and of gene families in PC loading plots are identical to those of Figure 1. * Figure 3: Analysis of a monoclonal human colon cancer xenograft obtained from injection of a single (n = 1) cell in NOD/SCID/IL2Rγ−/− mice. () In human colon cancer, the frequency of EpCAMhigh/CD44+ cells capable to establish a tumor upon xenotransplantation in NOD/SCID/IL2Rγ−/− mice varies based on the xenograft line, as shown by comparative limiting-dilution experiments. () Single (n =1) lentivirus-infected EGFP+/EpCAMhigh/CD44+ cancer cells can be sorted by flow cytometry for injection in mice. (,) Analysis by flow cytometry of a monoclonal tumor derived from injection of a single (n = 1), lentivirus-tagged, EGFP+/EpCAMhigh/CD44+ cancer cell from the human colon cancer xenograft UM-COLON#4 (clone 8) confirmed that human cells expressed EGFP () and contained both EpCAMlow/CD44− and EpCAMhigh/CD44+ populations (). () The monoclonal origin of the UM-COLON#4 clone 8 tumor was confirmed by LM-PCR, showing the presence of a unique lentivirus integration site in both EGFP+/EpCAMlow/CD44− and EGFP+/EpCAMhigh/CD44+ populations, contrary to what was observed in its polyclonal parent tumor. A larger image of th! e LM-PCR gel is provided in Supplementary Figure 24. (,) Immunohistochemistry of monoclonal tumor tissues revealed heterogeneous and mutually exclusive expression patterns of KRT20 () and MKI67 (). () Similar to what is observed in parent tumors, EpCAMhigh/CD44+ and EpCAMlow/CD44− populations from UM-COLON#4 clone 8 were characterized by different tumorigenic capacity, as evaluated by tumorigenicity experiments in NOD/SCID/IL2Rγ−/− mice. * Figure 4: KRT20 and top-crypt genes can be used as prognostic markers in colorectal cancer patients. (–) We used the Hegemon software to graph individual arrays according to the expression levels of KRT20 and one of four genes characteristic of top-of-the-crypt CA1+/SLC26A3+ enterocyte-like cells: KRT20 versus CA1 (), KRT20 versus MS4A12 (), KRT20 versus CD177 (), KRT20 versus SLC26A3 (). We used the StepMiner algorithm to define gene-expression thresholds and identify three distinct gene-expression groups: Group 1 (green), defined as KRT20+/CA1high, KRT20+/MS4A12high, KRT20+/CD177+ or KRT20+/SLC26A3+, respectively; Group 2 (blue), defined as KRT20+/CA1−/low, KRT20+/MS4A12−/low, KRT20+/CD177− or KRT20+/SLC26A3−, respectively; Group 3 (red), defined as KRT20−/CA1−/low, KRT20−/MS4A12−/low, KRT20−/CD177− or KRT20−/SLC26A3−, respectively. (–) Survival analysis using Kaplan-Meier curves showed that, in all four cases, an increasingly immature gene-expression profile corresponded to a progressively worse prognosis. (–) Multivariate analysis of surviv! al data based on the Cox proportional hazards model indicated that the prognostic effect of these two-gene classifiers was not confounded by clinical stage, age or sex. The analysis was performed on a pooled database of 299 primary colon cancer gene-expression arrays annotated with disease-free survival (DFS) data41, 42 (Supplementary Table 1). *P < 0.05, **P < 0.001. Age modeled as a continuous variable. HR, hazard ratio; CI, confidence interval; M, male; F, female. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Piero Dalerba, * Tomer Kalisky, * Debashis Sahoo, * Michael F Clarke & * Stephen R Quake Affiliations * Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. * Piero Dalerba, * Debashis Sahoo, * Pradeep S Rajendran, * Michael E Rothenberg, * Sopheak Sim, * Darius M Johnston, * Dalong Qian, * Maider Zabala, * Shigeo Hisamori, * Yohei Shimono & * Michael F Clarke * Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA. * Piero Dalerba & * Michael F Clarke * Department of Bioengineering, Stanford University, Stanford, California, USA. * Tomer Kalisky, * Anne A Leyrat, * Jennifer Okamoto, * Darius M Johnston, * Norma F Neff, * Jianbin Wang & * Stephen R Quake * Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA. * Michael E Rothenberg * Howard Hughes Medical Institute, Chevy Chase, Maryland, USA. * Jennifer Okamoto, * Darius M Johnston & * Stephen R Quake * Tissue Bank, Stanford University, Stanford, California, USA. * Janet Bueno * Department of Surgery, Stanford University, Stanford, California, USA. * Andrew A Shelton & * Brendan Visser * Hubrecht Institute for Developmental Biology and Stem Cell Research, Utrecht, The Netherlands. * Marc van de Wetering & * Hans Clevers Contributions P.D., T.K., D.S., M.F.C. and S.R.Q. conceived the study and designed the experiments. P.S.R., M.E.R., A.A.L., M.Z., N.F.N., M.v.d.W. and H.C. provided intellectual guidance in the design of selected experiments. P.D., T.K., D.S., P.S.R., A.A.L., S.S., J.O., D.M.J., D.Q., J.W., and S.H. performed the experiments. P.D., T.K., D.S., N.F.N., Y.S., M.F.C. and S.R.Q. analyzed the data and/or provided intellectual guidance in their interpretation. J.B., A.A.S. and B.V. provided samples and reagents. P.D., T.K., D.S., M.F.C. and S.R.Q. wrote the paper. Competing financial interests S.R.Q. is a founder, consultant and shareholder of Fluidigm. S.R.Q. and M.F.C. are founders, consultants and shareholders of QuantiCell. The authors have filed a patent application based on the results described in this study. Corresponding authors Correspondence to: * Stephen R Quake or * Michael F Clarke Author Details * Piero Dalerba Search for this author in: * NPG journals * PubMed * Google Scholar * Tomer Kalisky Search for this author in: * NPG journals * PubMed * Google Scholar * Debashis Sahoo Search for this author in: * NPG journals * PubMed * Google Scholar * Pradeep S Rajendran Search for this author in: * NPG journals * PubMed * Google Scholar * Michael E Rothenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Anne A Leyrat Search for this author in: * NPG journals * PubMed * Google Scholar * Sopheak Sim Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Okamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Darius M Johnston Search for this author in: * NPG journals * PubMed * Google Scholar * Dalong Qian Search for this author in: * NPG journals * PubMed * Google Scholar * Maider Zabala Search for this author in: * NPG journals * PubMed * Google Scholar * Janet Bueno Search for this author in: * NPG journals * PubMed * Google Scholar * Norma F Neff Search for this author in: * NPG journals * PubMed * Google Scholar * Jianbin Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew A Shelton Search for this author in: * NPG journals * PubMed * Google Scholar * Brendan Visser Search for this author in: * NPG journals * PubMed * Google Scholar * Shigeo Hisamori Search for this author in: * NPG journals * PubMed * Google Scholar * Yohei Shimono Search for this author in: * NPG journals * PubMed * Google Scholar * Marc van de Wetering Search for this author in: * NPG journals * PubMed * Google Scholar * Hans Clevers Search for this author in: * NPG journals * PubMed * Google Scholar * Michael F Clarke Contact Michael F Clarke Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen R Quake Contact Stephen R Quake Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (19M) Supplementary Tables 1–4, Supplementary Methods and Supplementary Figures 1–24 Additional data - Efficacy of genetically modified Bt toxins against insects with different genetic mechanisms of resistance
- Nat Biotechnol 29(12):1128-1131 (2011)
Nature Biotechnology | Research | Letter Efficacy of genetically modified Bt toxins against insects with different genetic mechanisms of resistance * Bruce E Tabashnik1 * Fangneng Huang2 * Mukti N Ghimire2 * B Rogers Leonard2 * Blair D Siegfried3 * Murugesan Rangasamy3 * Yajun Yang4 * Yidong Wu4 * Linda J Gahan5 * David G Heckel6 * Alejandra Bravo7 * Mario Soberón7 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1128–1131Year published:(2011)DOI:doi:10.1038/nbt.1988Received07 June 2011Accepted24 August 2011Published online09 October 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transgenic crops that produce Bacillus thuringiensis (Bt) toxins are grown widely for pest control1, but insect adaptation can reduce their efficacy2, 3, 4, 5, 6. The genetically modified Bt toxins Cry1AbMod and Cry1AcMod were designed to counter insect resistance to native Bt toxins Cry1Ab and Cry1Ac7. Previous results suggested that the modified toxins would be effective only if resistance was linked with mutations in genes encoding toxin-binding cadherin proteins7. Here we report evidence from five major crop pests refuting this hypothesis. Relative to native toxins, the potency of modified toxins was >350-fold higher against resistant strains of Plutella xylostella and Ostrinia nubilalis in which resistance was not linked with cadherin mutations. Conversely, the modified toxins provided little or no advantage against some resistant strains of three other pests with altered cadherin. Independent of the presence of cadherin mutations, the relative potency of the modified t! oxins was generally higher against the most resistant strains. View full text Figures at a glance * Figure 1: Responses of susceptible and resistant strains of P. xylostella to native and genetically modified Bt toxins. () Cry1Ab. () Cry1AbMod. () Cry1Ac. () Cry1AcMod. * Figure 2: Resistance to native Bt toxins Cry1Ab and Cry1Ac (light bars) and genetically modified Bt toxins Cry1AbMod and Cry1AcMod (dark bars) in six species of insect pests. Data are reported here for P. xylostella (Px), O. nubilalis (On), D. saccharalis (Ds), and H. armigera (Ha) (Supplementary Table 2) and were reported previously for P. gossypiella (Pg)7 and T. ni (Tn)10. Resistance ratios are the concentration of toxin killing 50% of larvae (LC50) for each resistant strain divided by the LC50 for the conspecific susceptible strain. The arrows pointing up indicate resistance ratios higher than the top of the bar that cannot be estimated precisely because mortality of the resistant strains of Px and Pg against native toxins was so low that we could not accurately estimate LC50 values. The arrow pointing down indicates a resistance ratio <1 (0.41) for Cry1AcMod versus Pg7. * Figure 3: Potency of modified Bt toxins relative to native Bt toxins. Data are reported here for P. xylostella (Px), O. nubilalis (On), D. saccharalis (Ds), and H. armigera (Ha) (Supplementary Table 3) and were reported previously for P. gossypiella (Pg)7 and T. ni (Tn)10. Potency ratio is the LC50 of a native toxin divided by the LC50 of the corresponding modified toxin for a resistant strain (dark bars) or a susceptible strain (light bars). Values >1 indicate the modified toxin was more potent than the native toxin. Values <1 indicate the native toxin was more potent than the modified toxin. The arrows pointing up indicate potency ratios higher than the top of the bar that cannot be estimated precisely because mortality of the resistant strains of Px and Pg against native toxins was so low that we could not accurately estimate LC50 values. Author information * Author information * Supplementary information Affiliations * Department of Entomology, University of Arizona, Tucson, Arizona, USA. * Bruce E Tabashnik * Department of Entomology, Louisiana State University Agricultural Center, Baton Rouge, Louisiana, USA. * Fangneng Huang, * Mukti N Ghimire & * B Rogers Leonard * Department of Entomology, University of Nebraska, Lincoln, Nebraska, USA. * Blair D Siegfried & * Murugesan Rangasamy * Department of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing, China. * Yajun Yang & * Yidong Wu * Department of Biological Sciences, Clemson University, Clemson, South Carolina, USA. * Linda J Gahan * Department of Entomology, Max Planck Institute for Chemical Ecology, Jena, Germany. * David G Heckel * Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico. * Alejandra Bravo & * Mario Soberón Contributions B.E.T., L.J.G., D.G.H., B.D.S., F.H., B.R.L., Y.W., M.S. and A.B. contributed to research design; M.S. and A.B. provided the toxins; L.J.G., M.R., M.N.G. and Y.Y. conducted bioassays; B.E.T., B.D.S., F.H. and Y.W. analyzed data. B.E.T. wrote the paper. All authors discussed the results and commented on the manuscript. Competing financial interests Pioneer Hi-Bred International, which may be affected financially by publication of this article, provided partial funding for the research. Dow AgroSciences, Monsanto and Syngenta did not provide funding to support this work, but may be affected financially by publication of this paper and have supported other research by some of the authors. While conducting work for this paper, M.R. was a postdoctoral research associate at the University of Nebraska. He is now employed by Dow AgroSciences. A.B., M.S. and B.E.T. are coauthors of a patent application on engineering modified Bt toxins to counter pest resistance, which is related to research described by M.S. et al. (Science ,1640–1642, 2007). Corresponding author Correspondence to: * Bruce E Tabashnik Author Details * Bruce E Tabashnik Contact Bruce E Tabashnik Search for this author in: * NPG journals * PubMed * Google Scholar * Fangneng Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Mukti N Ghimire Search for this author in: * NPG journals * PubMed * Google Scholar * B Rogers Leonard Search for this author in: * NPG journals * PubMed * Google Scholar * Blair D Siegfried Search for this author in: * NPG journals * PubMed * Google Scholar * Murugesan Rangasamy Search for this author in: * NPG journals * PubMed * Google Scholar * Yajun Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Yidong Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Linda J Gahan Search for this author in: * NPG journals * PubMed * Google Scholar * David G Heckel Search for this author in: * NPG journals * PubMed * Google Scholar * Alejandra Bravo Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Soberón Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (512K) Supplementary Tables 1–7 and Supplementary Figures 1,2 Additional data - Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage
- Nat Biotechnol 29(12):1132-1144 (2011)
Nature Biotechnology | Research | Resources Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage * The International Stem Cell Initiative * Affiliations * ContributionsJournal name:Nature BiotechnologyVolume: 29,Pages:1132–1144Year published:(2011)DOI:doi:10.1038/nbt.2051Received06 September 2011Accepted26 October 2011Published online27 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The International Stem Cell Initiative analyzed 125 human embryonic stem (ES) cell lines and 11 induced pluripotent stem (iPS) cell lines, from 38 laboratories worldwide, for genetic changes occurring during culture. Most lines were analyzed at an early and late passage. Single-nucleotide polymorphism (SNP) analysis revealed that they included representatives of most major ethnic groups. Most lines remained karyotypically normal, but there was a progressive tendency to acquire changes on prolonged culture, commonly affecting chromosomes 1, 12, 17 and 20. DNA methylation patterns changed haphazardly with no link to time in culture. Structural variants, determined from the SNP arrays, also appeared sporadically. No common variants related to culture were observed on chromosomes 1, 12 and 17, but a minimal amplicon in chromosome 20q11.21, including three genes expressed in human ES cells, ID1, BCL2L1 and HM13, occurred in >20% of the lines. Of these genes, BCL2L1 is a strong ca! ndidate for driving culture adaptation of ES cells. View full text Figures at a glance * Figure 1: Population structure of the human ES cell lines analyzed. Principal component (PC) analyses were conducted on the entire final merged data set. PC1 and PC2 are plotted on the y and x axes, respectively. () The overall distribution of the human ES cell lines studied compared to the major ethnic groups identified in the HapMap study41, the human genome diversity panel (HGDP)42 and the Pan-Asian SNP Initiative43. (–) The cell lines were further subdivided to show their relationships to European (), East Asian and Indian () and Middle East-European–Central South Asian populations (). * Figure 2: Cytogenetic changes occurring during prolonged passage of human ES cells. () Percentage of human ES cell line pairs that exhibited a karyotypic abnormality in either early or late passages, or both. Cell lines were excluded if they were known to be derived from karyotypically abnormal embryos. The ES cell pairs are grouped according to whether the chromosome change was observed at late passage only (normal early, abnormal late), both at early and late passages (abnormal early, abnormal late) or early passage only (abnormal early, normal late) and no chromosomal change (normal early, normal late). The percentage of cell lines that have individual gains of chromosomes 1, 12, 17 and 20, gain of chromosomes 1 and 17, or gain of chromosomes 1, 12, 17 and 20 are highlighted. Chromosome changes not involving 1, 12, 17 and 20 are indicated as 'Other'. The numbers above each bar indicate the total number of lines that fall into the four categories out of the total number of pairs of lines analyzed. Two cell lines (C02 and CC05) in the 'abnormal early, abno! rmal late' category were known to be derived from karyotypically abnormal embryos (a trisomy 13 and ring chromosome 18). One abnormal cell line (AA06) has been excluded from this figure as only one passage was available for analysis. () Proportion of pairs of lines that acquired karyotypic abnormalities over different periods in culture. The pairs of lines are grouped according to 'Delta', the difference in estimated population doublings between the early and late passages. Only those lines that had a normal karyotype at the early-passage level were included in the analysis, and of those only 115 pairs could reliably be assigned an estimated population doubling time estimate. * Figure 3: Ideogram demonstrating the chromosome changes found in this study. Each colored bar represents one chromosome change occurrence in one cell line. Chromosome losses and gains are shown to the left and right of the ideogram, respectively, except that those instances where a single chromosome rearrangement results in a gain and a loss the colored bars are shown together for clarity. The cytogenetic changes are color coded: Maroon, loss of a whole chromosome (monopsony); red, loss via a structural chromosome rearrangement (unbalanced translocation or interstitial deletion); dark green, gain of a whole chromosome (trisomy); light green is gain via a structural chromosome rearrangement (unbalanced translocation or interstitial duplication); blue represents the occurrence of an apparently balanced rearrangement the nature of which is labeled. Instances in which a change affected only a single chromosome are denoted by •, whereas changes associated with complex karyotypes (>5 unrelated chromosome aberrations) are denoted by ⋆. Two cell lines (C! 02 and CC05) were known to be derived from karyotypically abnormal embryos and contain a trisomy 13 and ring chromosome 18 respectively. iPS cell lines are excluded from this figure. Based upon these studies the minimal critical chromosomal regions subject to gain in culture adapted human ES cell lines were 1q21-qter, 12p11-pter, 17q21.3-qter and 20q11.2. The minimal regions subject to loss were 10p13-pter, 18q21-qter and 22q13-qter. * Figure 4: Copy number variation occurrence in human ES cell lines during prolonged passage. () 20q11.21 gain. The region on chromosome 20 frequently found to experience gain over extended human ES cell culture is indicated by the red boxed region in the chromosome ideogram. Also shown are the B allele frequency and logR ratio plots representing instances of one of the longest and one of the shortest 20q11.21 structural variants. () Length representation of all individual occurrences of gains in the 20q11.21 region. Samples from which the structural variant was derived are indicated on the left-hand column. The invariant 5′ region and the variable 3′ positions are indicated. Position of genes outside of the minimal amplicon that show greater than 20 RPKM level of expression in human ES cells are shown (RPKM = number of reads that map per kilobase of exon model per million mapped reads for each gene). () Expression, RefSeq gene, and regulation tracks in the minimal amplicon. Positive and negative strand mRNA-Seq data from H1 human ES cells indicating polyA RNA tr! anscripts expressed within the minimal amplicon region (chr20:29,267,954-29,853,264) are shown together with H1 human ES cell ChIP-Seq data of histone modifications considered universal predictors of enhancer and promoter activity. () Comparison of expression levels of three genes (HM13, ID1, BCL2L1) contained within the identified minimal 20q11.2 amplicon between early- (normal) and late-passage (20q11.2 CNV carrying) samples. MM01 and FF02 are genetically identical sub-lines from two separate laboratories, MM01 has no amplification at 20q11.2, whereas FF02 possesses a copy number change at 20q11.2 that includes the identified minimal amplicon (). * Figure 5: Cumulative distribution function of methylation changes in human ES cells in this study. The change in DNA methylation is represented by empirical CDF curves of the absolute difference in DNA methylation between early- and late-passage cell-line pairs for all 1,536 analyzed probes. The black curves denote genetically stable lines; the red curves denote genetically unstable lines. All analyzed lines were divided into quartiles based on the passage-number difference between the early and late member of each pair. The first quartile contains the lines with the lowest difference in passage number between the early and late sample (range 4 to 47), whereas the fourth quartile contains the lines with the highest difference in estimated population doublings (range 210 to 1,482). * Figure 6: Recent pericentric inversion associated with 20q11. 21 susceptibility to gain. () The ancestral condition of chromosome 20 before a pericentric inversion in the last common ancestor of the gorilla, chimp and human. () Structure of human chromosome 20 with the location of the gap indicated in which the proximal end of all 20q11.21 amplicons lie. Author information * Abstract * Author information * Supplementary information Affiliations * Centre for Stem Cell Biology, Department of Biomedical Science, The University of Sheffield, Sheffield, UK. * Katherine Amps, * Peter W Andrews, * Angela Ford, * Paul J Gokhale & * Harry Moore * North East England Stem Cell Institute at Life, International Centre for Life, Newcastle upon Tyne, UK. * George Anyfantis, * Lyle Armstrong & * Majlinda Lako * Institute of Medical Biology, A-STAR, Immunos, Singapore. * Stuart Avery, * Alan Colman, * Jeremy M Crook & * Barbara B Knowles * Royan Institute for Reproductive Biomedicine, Department of Genetics, Tehran, Islamic Republic of Iran. * Hossein Baharvand & * Hamid Gourabi * Stanford University, Stanford, California, USA. * Julie Baker & * Eric Chiao * Sheffield Diagnostic Genetic Services, Sheffield Children's NHS Trust, Sheffield, UK. * Duncan Baker, * Anne Dalton & * Edna Maltby * Wolfson Centre for Stem Cells, Tissue Engineering & Modelling (STEM), Centre for Biomolecular Sciences, University of Nottingham, UK. * Maria B Munoz, * Chris Denning & * Lorraine Young * USC Stem Cell Core Facility, The Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Stephen Beil & * Victoria Fox * Stem Cell Unit, Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel. * Nissim Benvenisty, * Tamar Golan-Lev, * Oded Kopper & * Yoav Mayshar * Racine IVF Unit, Lis Maternity Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. * Dalit Ben-Yosef & * Tzvia Frumkin * Department of Cell Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. * Dalit Ben-Yosef * Regenerative Medicine Institute, Cedars-Sinai Medical Institute, Los Angeles, California, USA. * Juan-Carlos Biancotti, * Neta Lavon & * Kavita Narwani * Department of Pathology and Immunology, Faculty of Medicine, Geneva University, Geneva, Switzerland. * Alexis Bosman & * Marisa Jaconi * USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Romulo Martin Brena, * Peter W Laird, * Fei Pan, * Hui Shen & * Daniel J Weisenberger * Department of Reproductive Medicine, St. Marys's Hospital, Central Manchester NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK. * Daniel Brison * Cellartis AB, Goteborg, Sweden. * Gunilla Caisander, * Johan Hyllner & * Raimund Strehl * Faculty of Life Sciences, University of Manchester, Manchester, UK. * María V Camarasa & * Susan Kimber * Genome Institute of Singapore, Singapore. * Jieming Chen, * Wishva Herath, * Astrid Kresentia Irwanto, * Linda S Lim, * Paul Robson & * Jameelah Sheik Mohamed * Hoffmann-LaRoche, Nutley, New Jersey, USA. * Eric Chiao * Department of Obstetrics & Gynaecology, Seoul National University College of Medicine, Seoul, Republic of Korea. * Young Min Choi & * Shin Yong Moon * Bioprocessing Technology Institute, Singapore. * Andre B H Choo & * Steve K W Oh * Roslin Cells Ltd., Roslin Biocentre, Roslin, Midlothian, UK. * Daniel Collins, * Paul A De Sousa & * Janet Downie * Singapore Stem Cell Consortium, A-STAR, Singapore. * Alan Colman, * Jeremy M Crook, * Grace Selva Raj & * Shirani Sivarajah * Centre for Neural Engineering, The University of Melbourne, Parkville, Australia. * Jeremy M Crook & * Shirani Sivarajah * Optics and Nanoelectronics Research Group, NICTA Victorian Research Laboratory, The University of Melbourne, Parkville, Australia. * Jeremy M Crook & * Shirani Sivarajah * Department of Surgery, St. Vincent's Hospital, The University of Melbourne, Fitzroy, Australia. * Jeremy M Crook * Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Children's Hospital Boston and Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * George Q Daley * Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * George Q Daley * Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * George Q Daley * Harvard Stem Cell Institute, Boston, Massachusetts, USA. * George Q Daley * Medical Research Council Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK. * Paul A De Sousa & * Steve Pells * Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. * Petr Dvorak * WiCell Research Institute, Madison, Wisconsin, USA. * Karen D Montgomery & * Tenneille E Ludwig * Department of Obstetrics and Gynecology, Hopital Cantonal Fribourgois, Freibourg, Switzerland. * Anis Feki * National Laboratory for Embryonic Stem Cell Research (LaNCE), Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil. * Ana M Fraga & * Lygia V Pereira * Institute of Reproductive & Stem Cell Engineering, Central South University, Reproductive & Genetic Hospital CITIC-XIANGYA, Changsha, Hunan, People's Republic of China. * Lin Ge, * Lu Guangxiu & * Ouyang Qi * The Hadassah Human Embryonic Stem Cell Research Center, The Goldyne Savad Institute of Gene Therapy, Hadassah University Medical Center, Jerusalem, Israel. * Michal Gropp & * Benjamin Reubinoff * Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. * Ales Hampl * Institute of Experimental Medicine ASCR, Prague, Czech Republic. * Ales Hampl * MRC Centre of Epidemiology for Child Health, Institute of Child Health, University College London, London, UK. * Katie Harron * UK Stem Cell Bank, Division of Cell Biology and Imaging, National Institute for Biological Standards and Control, South Mimms, Herts, UK. * Lyn Healy & * Glyn N Stacey * Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden. * Frida Holm & * Outi Hovatta * Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India. * Maneesha S Inamdar & * Parvathy Venu * Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan. * Tetsuya Ishii, * Kazutoshi Takahashi & * Shinya Yamanaka * Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes of Biological Sciences, CAS/Shanghai JiaoTong University School of Medicine, Shanghai, People's Republic of China. * Ying Jin * Stem Cell Department, NRC Kurchatov Institute, Moscow, Russia. * Sergey Kiselev * Vavilov Institute of General Genetics, Moscow, Russia. * Sergey Kiselev & * Maria A Lagarkova * Reproductive Genetics Institute, Chicago, Illinois, USA. * Valeri Kukharenko, * Anver Kuliev, * Nick Strelchenko & * Yuri Verlinsky * CSIRO Material Science and Engineering, Clayton, Australia. * Andrew L Laslett & * Qi Zhou * Department of Anatomy and Developmental Biology, Monash University, Clayton, Australia. * Andrew L Laslett * Department of Biomedical Science, CHA Stem Cell Institute, CHA University, Gangnam-gu, Seoul, Republic of Korea. * Dong Ryul Lee * CHA Stem Cell Institute, CHA University, Gangnam-gu, Seoul, Republic of Korea. * Jeoung Eun Lee * Shanghai Stem Cell Institute, Shanghai JiaoTong University School of Medicine, Shanghai, People's Republic of China. * Chunliang Li, * Yu Ma & * Bowen Sun * Department of Embryology and Genetics, Vrije Universiteit Brussel, Brussels, Belgium. * Ileana Mateizel, * Karen Sermon & * Claudia Spits * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. * Maria Mileikovsky & * Andras Nagy * Wolfson Centre for Age-Related Diseases, King's College London, London, UK. * Stephen L Minger & * Yue Wu * GE Healthcare, Cardiff, UK. * Stephen L Minger & * Yue Wu * Laboratory of Embryonic Stem Cell Research, Stem Cell Research Center, Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan. * Takamichi Miyazaki & * Hirofumi Suemori * Department of Anatomy & Embryology, Leiden University Medical Center, Leiden, The Netherlands. * Christine Mummery & * Dorien Ward-van Oostwaard * Institute for Integrated Cell-Material Sciences, Kyoto University, Ushinomiya-cho, Yoshida, Sakyo-ku, Kyoto, Japan. * Norio Nakatsuji & * Shinya Yamanaka * Institute of Reproductive Medicine & Population, Medical Research Center, Seoul National University, Seoul, Republic of Korea. * Sun Kyung Oh * Research Programs Unit, Molecular Neurology, Biomedicum Stem Cell Centre, University of Helsinki, Finland. * Cia Olson, * Timo Otonkoski & * Timo Tuuri * Children's Hospital, University of Helsinki and Helsinki University Central Hospital, Finland. * Timo Otonkoski * Department of Genetics, Yale Stem Cell Center, Yale School of Medicine, New Haven, Connecticut, USA. * In-Hyun Park * The Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Martin F Pera * Viacyte, Athens, Georgia, USA. * Alan Robins, * Thomas C Schulz & * Eric Sherrer * Program for Developmental Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. * Janet Rossant * Department of Molecular Systems Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Islamic Republic of Iran. * Ghasem H Salekdeh * Stem Cell Laboratory, Faculty of Medicine, University of New South Wales, Australia. * Kuldip Sidhu * Institute for Regenerative Medicine, University of Tampere, Tampere, Finland. * Heli Skottman & * Riitta Suuronen * Yamanaka iPS Cell Special Project, Japan Science and Technology Agency, Kawaguchi, Japan. * Shinya Yamanaka * Gladstone Institute of Cardiovascular Disease, San Francisco, California, USA. * Shinya Yamanaka * Present addresses: Japan Science and Technology Agency, Tokyo, Japan (T. Ishii) and Department of Obstetrics and Gynecology, New York University Langone Medical Center, New York, New York, USA (N. Strelchenko). * Tetsuya Ishii & * Nick Strelchenko * Deceased. * Yuri Verlinsky Consortia * The International Stem Cell Initiative * Katherine Amps, * Peter W Andrews, * George Anyfantis, * Lyle Armstrong, * Stuart Avery, * Hossein Baharvand, * Julie Baker, * Duncan Baker, * Maria B Munoz, * Stephen Beil, * Nissim Benvenisty, * Dalit Ben-Yosef, * Juan-Carlos Biancotti, * Alexis Bosman, * Romulo Martin Brena, * Daniel Brison, * Gunilla Caisander, * María V Camarasa, * Jieming Chen, * Eric Chiao, * Young Min Choi, * Andre B H Choo, * Daniel Collins, * Alan Colman, * Jeremy M Crook, * George Q Daley, * Anne Dalton, * Paul A De Sousa, * Chris Denning, * Janet Downie, * Petr Dvorak, * Karen D Montgomery, * Anis Feki, * Angela Ford, * Victoria Fox, * Ana M Fraga, * Tzvia Frumkin, * Lin Ge, * Paul J Gokhale, * Tamar Golan-Lev, * Hamid Gourabi, * Michal Gropp, * Lu Guangxiu, * Ales Hampl, * Katie Harron, * Lyn Healy, * Wishva Herath, * Frida Holm, * Outi Hovatta, * Johan Hyllner, * Maneesha S Inamdar, * Astrid Kresentia Irwanto, * Tetsuya Ishii, * Marisa Jaconi, * Ying Jin, * Susan Kimber, * Sergey Kiselev, * Barbara B Knowles, * Oded Kopper, * Valeri Kukharenko, * Anver Kuliev, * Maria A Lagarkova, * Peter W Laird, * Majlinda Lako, * Andrew L Laslett, * Neta Lavon, * Dong Ryul Lee, * Jeoung Eun Lee, * Chunliang Li, * Linda S Lim, * Tenneille E Ludwig, * Yu Ma, * Edna Maltby, * Ileana Mateizel, * Yoav Mayshar, * Maria Mileikovsky, * Stephen L Minger, * Takamichi Miyazaki, * Shin Yong Moon, * Harry Moore, * Christine Mummery, * Andras Nagy, * Norio Nakatsuji, * Kavita Narwani, * Steve K W Oh, * Sun Kyung Oh, * Cia Olson, * Timo Otonkoski, * Fei Pan, * In-Hyun Park, * Steve Pells, * Martin F Pera, * Lygia V Pereira, * Ouyang Qi, * Grace Selva Raj, * Benjamin Reubinoff, * Alan Robins, * Paul Robson, * Janet Rossant, * Ghasem H Salekdeh, * Thomas C Schulz, * Karen Sermon, * Jameelah Sheik Mohamed, * Hui Shen, * Eric Sherrer, * Kuldip Sidhu, * Shirani Sivarajah, * Heli Skottman, * Claudia Spits, * Glyn N Stacey, * Raimund Strehl, * Nick Strelchenko, * Hirofumi Suemori, * Bowen Sun, * Riitta Suuronen, * Kazutoshi Takahashi, * Timo Tuuri, * Parvathy Venu, * Yuri Verlinsky, * Dorien Ward-van Oostwaard, * Daniel J Weisenberger, * Yue Wu, * Shinya Yamanaka, * Lorraine Young & * Qi Zhou Contributions : P.W.A. : D.B., A.D., E.M., K.D.M. and T.G.-L. : P.R. : R.M.B. and P.W.L. : A. Ford and P.J.G. : P.W.A., S.A., D.B., N.B., R.M.B., P.J.G., K.H., L.H., B.B.K., Y. Mayshar, S.K.W.O., M.F.P. and P.R. P.W.A., N.B., B.B.K., S.K.W.O., M.F.P., J.R. and G.N.S. : A. Colman, A. Robins, A. Hampl, A. Bosman, A.M. Fraga, A. Nagy, A.B.H. Choo, A.L. Laslett, A. Feki, A. Kuliev, A. Kresentia Irwanto, B. Reubinoff, B. Sun, C. Denning, C. Mummery, C. Li, C. Olson, C. Spits, D. Ben-Yosef, D. Collins, D.J. Weisenberger, D. Ryul Lee, D. Ward-van Oostwaard, E. Chiao, E. Sherrer, Fei Pan, F. Holm, G. Anyfantis, G.Q. Daley, G.H. Salekdeh, G. Selva Raj, G. Caisander, H. Gourabi, H. Moore, H. Skottman, H. Suemori, H. Baharvand, H. Shen, I. Mateizel, In-Hyun Park, J. Sheik Mohamed, J. Downie, J. Eun Lee, J.M. Crook, J. Chen, J. Hyllner, J.-C. Biancotti, J. Baker, K. Sermon, K. Amps, K. Narwani, K. Takahashi, K. Sidhu, L. Ge, L.S. Lim, L. Young, Q. Zhou, L. Guangxiu, L.V. Pereira, L. Armstrong, M. La! ko, M.S. Inamdar, M.A. Lagarkova, M.B. Munoz, M. Mileikovsky, M.V. Camarasa, M. Jaconi, M. Gropp, N. Lavon, N. Strelchenko, N. Nakatsuji, O. Kopper, O. Hovatta, O. Qi, P. Venu, P.A. De Sousa, P. Dvorak, R. Strehl, R. Suuronen, S. Kiselev, S. Yong Moon, S. Yamanaka, S. Sivarajah, S. Beil, S.L. Minger, S.K.W. Oh, S. Pells, S. Kyung Oh, S. Kimber, T. Miyazaki, T.E. Ludwig, T. Ishii, T.C. Schulz, T. Otonkoski, T. Tuuri, T. Frumkin, V. Kukharenko, V. Fox, W. Herath, Y. Jin, Y. Min Choi, Y. Ma, Y. Wu and Y. Verlinsky. Competing financial interests G. Caisander, J. Hyllner and R. Strehl are employees of Cellartis AB. E. Chiao is an employee of Hoffmann-LaRoche. D. Collins and J. Downie are employees of Roslin Cells Ltd. P.A. De Sousa is CSO of Roslin Cells Ltd. S.L. Minger is an employee of GE Healthcare. B. Reubinoff holds shares and is the CSO of CellCure Neurosciences Ltd. A. Robins, T.C. Schulz and E. Sherrer are employees of Viacyte. S. Yamanaka is a member of the scientific advisory board of iPierian Inc and iPS Academia Japan, Inc. without salary Author Details Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (668K) Supplementary Figures 1–5 and Supplementary Notes 1 and 2. * Supplementary Table 3 (45K) The extent of DNA methylation changes in ES cell lines in relation to the difference between early and late passage levels. Excel files * Supplementary Table 1 (274K) Full details of cell line samples provided. * Supplementary Table 2 (233K) ES-associated structural variants. * Supplementary Table 4 (442K) Polycomb array manifest. * Supplementary Table 5 (29K) Cell line availability. * Supplementary Data Set 1 (7M) .bed file of all LOH calls for all samples in the ISCI-2 sample set Zip files * Supplementary Data Set 2 (15M) LOH calls for all samples in the ISCI-2 sample set * Supplementary Data Set 4 (2M) Complete β-scores (methylation level) of all ISCI-2 samples Text files * Supplementary Data Set 3 (7M) .bed file of CNV calls from karyotypically normal samples in the ISCI-2 sample set. Additional data - The challenges of modern interdisciplinary medical research
- Nat Biotechnol 29(12):1145-1148 (2011)
Nature Biotechnology | Careers and Recruitment The challenges of modern interdisciplinary medical research * Philipp von Roth1 * Benedict J Canny2 * Hans-Dieter Volk3 * J Alison Noble4 * Charles G Prober5 * Carsten Perka6 * Georg N Duda6 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 29,Pages:1145–1148Year published:(2011)DOI:doi:10.1038/nbt.2062Published online08 December 2011 The increasing complexity of medical science poses significant challenges to medical education, leading to a growing gap between medical researchers and treating practitioners. 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 * Philipp von Roth is at the Berlin-Brandenburg School for Regenerative Therapies and Julius Wolff Institute and Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Germany * Benedict J. Canny is in the Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia * Hans-Dieter Volk is at the Berlin-Brandenburg School for Regenerative Therapies and Institute of Medical Immunology, Charité–Universitätsmedizin Berlin, Germany * J. Alison Noble is at the Institute of Biomedical Engineering and RCUK Centre for Doctoral Training in Healthcare Innovation, Department of Engineering Science, University of Oxford, Oxford, UK * Charles G. Prober is at Stanford University School of Medicine, Stanford, California, USA * Carsten Perka and Georg N. Duda are at the Berlin-Brandenburg School for Regenerative Therapies, Berlin-Brandenburg Center for Regenerative Therapies and Julius Wolff Institute and Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Germany. Corresponding author Correspondence to: * Georg N Duda Author Details * Philipp von Roth Search for this author in: * NPG journals * PubMed * Google Scholar * Benedict J Canny Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Dieter Volk Search for this author in: * NPG journals * PubMed * Google Scholar * J Alison Noble Search for this author in: * NPG journals * PubMed * Google Scholar * Charles G Prober Search for this author in: * NPG journals * PubMed * Google Scholar * Carsten Perka Search for this author in: * NPG journals * PubMed * Google Scholar * Georg N Duda Contact Georg N Duda Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - People
- Nat Biotechnol 29(12):1150 (2011)
Article preview View full access options Nature Biotechnology | Careers and Recruitment | People People Journal name:Nature BiotechnologyVolume: 29,Page:1150Year published:(2011)DOI:doi:10.1038/nbt.2067Published online08 December 2011 Roche (Nutley, NJ, USA) has announced the appointment of (right) as Nutley oncology discovery site head and global lead, pathways biology within the company's Pharma Research and Early Development (pRED) division. She joins Roche from the Belfer Institute of Applied Cancer Sciences at Dana-Farber Cancer Institute, Harvard Medical School, where she was head of research. "With more than a decade's industry experience concentrated in cancer, Pam has built a reputation for scientific excellence and leadership in the field," says Mike Burgess, global head, oncology discovery and translational area and head, large-molecule research at Roche. "Her broad knowledge and successful history in leading oncology drug discovery efforts make her an ideal fit for this position." , founder and general partner of private hedge fund JALAA Equities, has been elected to the board of directors of Myrexis (Salt Lake City, UT, USA), bringing the total of board members to seven. Aryeh also serves on the boards of Ligand Pharmaceuticals, Nabi Biopharmaceuticals and CorMatrix Cardiovascular. Karo Bio (Huddinge, Sweden) has appointed CEO. He had been acting CEO and a member of the board since May. He previously served as medical director and therapeutic area head at Ferring, CEO of Probi and development manager at Bionor Immuno. Marinomed Biotechnologie (Vienna) has announced the appointment of 15-year industry veteran as the company's new head of development. She joins Marinomed from Onepharm Research & Development, where she held the position of chief medical officer for the last two years and head of development for one year prior to that. has been named finance director for Oxford Gene Technology (Oxford, UK). He has worked in the pharmaceutical and medical device industries for over 20 years, most recently serving in the same position at Lombard Medical Technologies. Prometheus Laboratories (San Diego) has named as senior vice president and chief medical officer. Heseltine joins the company with almost 35 years of experience in the healthcare field. Most recently, he served as vice president and medical director of global businesses at . has joined ContraFect (Yonkers, NY, USA) as chief medical officer. He most recently served as global medical director of medical affairs for Pfizer. Previously, he was associate director of clinical development for Boehringer Ingelheim. Genentech (S. San Francisco, CA, USA) chairman and former CEO has been named nonexecutive chairman of the board of Apple (Cupertino, CA, USA), filling the vacancy left when Apple's co-founder Steve Jobs died in October after battling pancreatic cancer. Levinson, who has served on Apple's board since 2000, most recently served as its co-lead director along with Andrea Jung, CEO of Avon. Ambit Biosciences (San Diego) has announced the appointment of as president, CEO and member of the board of directors, succeeding . Martino brings a nearly 30-year track record in leading life sciences companies, most recently serving as senior vice president and general manager of diagnostics and senior vice president of innovation, business development and strategy at CareFusion. He has also served as CEO of Arzeda and Sonus Pharmaceuticals. Sanofi (Paris) has announced the appointment of as CEO of Genzyme (Cambridge, MA, USA), a Sanofi company. Meeker joined Genzyme in 1994 as medical director to work on the cystic fibrosis gene therapy program and rose to become COO in 2009. Under his leadership, Genzyme will incorporate the rare disease business and the multiple sclerosis franchise. Previous Genzyme divisions—renal, biosurgery and oncology—have been integrated within the existing Sanofi portfolio giving them greater global scale and capabilities. To lead the new divisions, Genzyme has announced the appointment of as head of multiple sclerosis and as head of rare diseases. Sibold has more than 20 years of experience in the biopharma industry, most recently as chief commercial officer at Avanir Pharmaceuticals. Previously, he was senior vice president of Biogen Idec's US commercial operations. Vivaldi joined Genzyme in 1997 and was most recently president of the renal and endocrinology business. has been appointed executive vice president and chief medical officer of NeurogesX (San Mateo, CA, USA). He previously joined the company as a part-time consultant. Most recently, Peroutka was vice president, scientific affairs at PRA, a global clinical research organization. He was previously CMO of Zogenix. Catalyst Pharmaceutical Partners (Coral Gables, FL, USA) has named to the newly created position of vice president, commercial operations. He joins Catalyst from PhaseRx, where he served as vice president, business development and leader of the company's commercial initiatives. Previously, Rieger was vice president, business development at Dendreon. has been named chief medical officer and chief development officer at Curis (Lexington, MA, USA). Voi was most recently vice president of clinical development and medical affairs for the oncology business unit at Pfizer Global R&D. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
No comments:
Post a Comment