Hot off the presses! May 01 Nature biotechnology

The May 01 issue of the Nature biotechnology is now up on Pubget (About Nature biotechnology): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution subscribes to Pubget.)

Latest Articles Include:

• Sitting up and taking notice
  - Nature Biotechnology 28(5):381 (2010)
  Nature Biotechnology | Editorial Sitting up and taking notice Journal name:Nature BiotechnologyVolume:28,Page:381Year published:(2010)DOI:doi:10.1038/nbt0510-381 The sheer pace of discovery in genetics is placing companies that pursue an aggressive infringement strategy for gene patents increasingly at odds with innovation. View full text Additional data
• Biomarker-led adaptive trial blazes a trail in breast cancer
  - Nature Biotechnology 28(5):383-384 (2010)
  A breast cancer screening study that pairs oncology therapies with biological markers (biomarkers) launched by a consortium of public health agencies, academics and companies is being heralded as a milestone in clinical trials. The I-SPY 2 TRIAL, which involves 20 US cancer centers, will follow an adaptive trial design that promises both time and cost savings.
• Biotechs adjust to new landscape as US healthcare reform takes off
  - Nature Biotechnology 28(5):385-386 (2010)
  Even before the political uproar surrounding the passage of the Patient Protection and Affordable Care Act (PPACA) in March had subsided, biotech-industry watchers were applauding the passage of the historic health care bill. Among the favorite measures in the legislation are generous exclusivity terms for innovative therapeutics within a newly drafted pathway for biogenerics, a lucrative tax credit for eligible smaller companies developing therapeutics, and a substantial boost—30 million or more—in the number of potential clients for biotech therapeutics due to the expansion of health insurance to so many more Americans.
• Genentech, UCSF discovery pact
  - Nature Biotechnology 28(5):386 (2010)
  Genentech and the University of California, San Francisco (UCSF) announced in February a drug discovery partnership, a union they proclaim is a new model for industry-academic relationships. The deal, which focuses on neurodegenerative diseases, goes beyond providing funds for several groups from the Small Molecule Discovery Center (SMDC) at UCSF.
• Abbott outbids Biogen for Facet's multiple sclerosis antibody
  - Nature Biotechnology 28(5):387-389 (2010)
  Abbott Laboratories has made a bid for a slice of the multiple sclerosis (MS) market, through its $450 million cash acquisition of Facet Biotech. The deal, announced in March, gives Abbott a stake in Zenapax (daclizumab), a potential MS treatment poised to move into phase 3 testing, as well as a portfolio of early and mid-stage cancer compounds.
• FDA crackdown on Genzyme
  - Nature Biotechnology 28(5):388 (2010)
  Genzyme's Allston Landing Facility in Massachusetts, one of the world's largest cell culture manufacturing plants, has become the focus of an enhanced enforcement action in what is perhaps a sign of an increasingly tough stance at the US Food and Drug Administration (FDA) on manufacturing standards. The action, announced in March, has led to a draft consent decree from FDA that requires Genzyme to pay a $175 million "up-front disgorgement of past profits," the company said.
• Ariad's NF-κB blow
  - Nature Biotechnology 28(5):389 (2010)
  The US Court of Appeals for the Federal Curcuit in March ruled for Eli Lilly in Indianapolis, Indiana, and against Ariad Pharmaceuticals, affirming an earlier decision by a three-judge panel and dealing a possible death blow to Ariad's broad claims on the nuclear factor κB (NF-κB) pathway (Nat. Biotechnol. 27, 494, 2009
• Orphan drug workshops
  - Nature Biotechnology 28(5):389 (2010)
  In an effort to increase the number of drugs available to treat rare diseases and to help make the US Food and Drug Administration (FDA) more approachable, the FDA is hosting a series of workshops to encourage regulatory submissions for orphan drug designation for drugs aimed at treating rare diseases. The agency's Office of Orphan Products Development (OOPD) is holding these events to help academics, biotech companies and those unfamiliar with the process complete the best application possible.
• Texas splurges on cancer
  - Nature Biotechnology 28(5):390 (2010)
  Texas doled out the first round of grants from a $3 billion publicly funded program to boost in-state cancer research. Almost all of the initial $61 million went to in-state academic institutions like University of Texas, Rice University and Baylor College of Medicine.
• Chinese green light for GM rice and maize prompts outcry
  - Nature Biotechnology 28(5):390-391 (2010)
  Biosafety certificates for genetically modified (GM) rice and maize issued by the Chinese Ministry of Agriculture late last year have prompted a protest from over a hundred intellectuals and prominent public officials. This represents one of the most high-profile challenges to China's aggressive policy for the adoption of transgenic crops.
• Above water in Q1
  - Nature Biotechnology 28(5):392 (2010)
  Biotech stocks remain buoyant, and although funding dipped compared with the preceding two quarters, 1Q09 remained above the dire levels seen last winter. Excluding US partnership monies, the industry pulled in $5.
• How green biotech turned white and blue
  - Nature Biotechnology 28(5):393-395 (2010)
  Nature Biotechnology | News | News Feature How green biotech turned white and blue * Lucas Laursen1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume:28,Pages:393–395Year published:(2010)DOI:doi:10.1038/nbt0510-393 Argentina has blazed a trail as one of the leading genetically modified (GM) crop producers. Can other developing countries import the seeds of its success? Lucas Laursen investigates. View full text Additional data Affiliations * Madrid * Lucas Laursen
• Drug marketing and the new media
  - Nature Biotechnology 28(5):396-398 (2010)
  Nature Biotechnology | News | News Feature Drug marketing and the new media * Sarah Webb1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume:28,Pages:396–398Year published:(2010)DOI:doi:10.1038/nbt0510-396 Social media represent a new way for drug companies to interact with consumers. But transitioning medical communication and marketing campaigns to the internet poses several thorny legal and regulatory issues. Sarah Webb investigates. View full text Additional data Affiliations * Brooklyn, New York * Sarah Webb
• Avoiding capital punishment
  - Nature Biotechnology 28(5):399-401 (2010)
  
• Natural variation in crop composition and the impact of transgenesis
  - Nature Biotechnology 28(5):402-404 (2010)
  Nature Biotechnology | Opinion and Comment | Correspondence Natural variation in crop composition and the impact of transgenesis * George G Harrigan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Denise Lundry1 Search for this author in: * NPG journals * PubMed * Google Scholar * Suzanne Drury1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kristina Berman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan G Riordan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret A Nemeth1 Search for this author in: * NPG journals * PubMed * Google Scholar * William P Ridley1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin C Glenn1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:402–404Year published:(2010)DOI:doi:10.1038/nbt0510-402 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To the Editor: Compositional equivalence of crops improved through biotech-derived transgenic, or genetically modified (GM), traits and their conventional (non-GM) comparators is an important criterion in breeding as well as a key aspect of risk assessments of commercial candidates. We present here an analysis evaluated from compositional data on GM corn and GM soybean varieties grown across a range of geographies and growing seasons with the aim of not only assessing the relative impact of transgene insertion on compositional variation in comparison with the effect of environmental factors but also reviewing the implications of these results on the safety assessment process. Specifically, our analysis includes evaluation of seven GM crop varieties from a total of nine countries and eleven growing seasons. On the basis of our data, we conclude that compositional differences between GM varieties and their conventional comparators were encompassed within the natural variability of the conven! tional crop and that the composition of GM and conventional crops cannot be disaggregated. View full text Figures at a glance * Figure 1: Summary of amino acid levels in conventional and GM corn from a total of eight growing seasons. Each vertical bar represents the range of values for the corresponding amino acids as measured in studies listed in Supplementary Table 1. See Supplementary Table 20 for further details and Supplementary Figures 1–11 for summarized data on other nutrient and antinutrient components in corn and soybean. * Figure 2: Hierarchical cluster analysis and principal component analysis of compositional data generated on the harvested seed of insect-protected MON 87701 and glyphosate-tolerant MON 89788 soybean grown in the northern and southern regions of Brazil during the 2007–2008 season. The sample codes are as follows. The first three digits indicate the sample: 77T, MON 87701; R2T, MON 89788; and 77C, conventional control for both MON 87701 and MON 89788. The remaining digits indicate the sites: Cachoeira Dourada; Minas Gerais (BrNCD); Sorriso; Mato Grosso (BrNSR); Nao-Me-Toque; Rio Grande do Sul (BrSNT); Rolandia; and Parana (BrSRO). BrN indicates the northern region, BrS represents the southern region. Author information * Author information * Supplementary information Affiliations * Product Safety Center, Monsanto Company, 800 North Lindbergh Blvd., St. Louis, Missouri, USA. * George G Harrigan, * Denise Lundry, * Suzanne Drury, * Kristina Berman, * Susan G Riordan, * Margaret A Nemeth, * William P Ridley & * Kevin C Glenn Competing financial interests The authors are employees of Monsanto. Monsanto develops and sells GM crops, including those discussed in the manuscript. Corresponding author Correspondence to: * George G Harrigan (george.g.harrigan@monsanto.com) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (716K) Supplementary Tables 1–21, Supplementary Figs. 1–24, Supplementary Methods, Supplementary Notes and Supplementary References Additional data
• GM crops and gender issues
  - Nature Biotechnology 28(5):404-406 (2010)
  Nature Biotechnology | Opinion and Comment | Correspondence GM crops and gender issues * Arjunan Subramanian1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kerry Kirwan1 Search for this author in: * NPG journals * PubMed * Google Scholar * David Pink2 Search for this author in: * NPG journals * PubMed * Google Scholar * Matin Qaim3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:404–406Year published:(2010)DOI:doi:10.1038/nbt0510-404 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To the Editor: Correspondence in the December issue by Jonathan Gressel1 not only states that gender issues in rural settings have not been adequately addressed with respect to weed control biotech but also asserts that such technology can increase the quality of life of rural women in developing countries. Improved weed control is a labor-saving technology that can result in less employment in a labor surplus rural economy. Often in rural areas, wage income is the main source of income and an important determinant of the quality of life, particularly where employment opportunities are generally limited2. Apart from soil preparation, planting and weeding, harvesting is also 'femanual' work that can generate more employment if yields are higher. Biotech can enhance the quality of life of women but only if the technology is associated with overall generation of rural employment. View full text Author information * Author information * Supplementary information Affiliations * University of Warwick, Warwick Manufacturing Group, Coventry, UK. * Arjunan Subramanian & * Kerry Kirwan * University of Warwick, WHRI, Warwickshire, UK. * Arjunan Subramanian & * David Pink * Georg-August University of Goettingen, Goettingen, Germany. * Matin Qaim Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Arjunan Subramanian (s.arjunan@warwick.ac.uk) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (716K) Supplementary Table 1, Supplementary Fig.1 and Supplementary Methods Additional data
• BIO's track record on emerging companies
  - Nature Biotechnology 28(5):406 (2010)
  As executives at emerging biotech companies and chairs of the Biotechnology Industry Organization's (BIO; Washington, DC) Board of Directors (S.S.
• South-South entrepreneurial collaboration in health biotech
  - Nature Biotechnology 28(5):407-416 (2010)
  Nature Biotechnology | Feature South-South entrepreneurial collaboration in health biotech * Halla Thorsteinsdóttir1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Christina C Melon1 Search for this author in: * NPG journals * PubMed * Google Scholar * Monali Ray1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon Chakkalackal1 Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle Li1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan E Cooper1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Chadder1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tirso W Saenz3 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Carlota de Souza Paula3 Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Ke4 Search for this author in: * NPG journals * PubMed * Google Scholar * Lexuan Li4 Search for this author in: * NPG journals * PubMed * Google Scholar * Magdy A Madkour5 Search for this author in: * NPG journals * PubMed * Google Scholar * Sahar Aly6 Search for this author in: * NPG journals * PubMed * Google Scholar * Nefertiti El-Nikhely6 Search for this author in: * NPG journals * PubMed * Google Scholar * Sachin Chaturvedi7 Search for this author in: * NPG journals * PubMed * Google Scholar * Victor Konde8 Search for this author in: * NPG journals * PubMed * Google Scholar * Abdallah S Daar1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter A Singer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:407–416Year published:(2010)DOI:doi:10.1038/nbt0510-407 A survey of entrepreneurial collaborations among health biotech firms in developing countries reveals a surprisingly high level of collaboration but a lack of emphasis on new or improved health biotech products and processes. View full text Figures at a glance * Figure 1: Extent of international collaboration of health biotech firms in developing countries and comparisons of their South-South versus South-North collaborations. * Figure 2: Percentages of firms in the countries we surveyed that engage in South-South health biotech collaboration. * Figure 3: Collaboration network of health biotech firms in South-South collaborations. The size of each node represents the total number of South-South collaborations for the country, while the width of each line represents the number of collaborations between the two linked countries. For clarity, only linkages of two or more collaborations were included on this map. * Figure 4: Distribution of the activities involved in the South-South entrepreneurial collaborations for all the countries we surveyed. * Figure 5: The network of collaborations involving end-stage commercialization versus R&D. () Collaborations involving end-stage commercialization. () Collaborations involving R&D. As in Figure 3, node size and line width denote numbers of collaborations. For clarity, only linkages of two or more distribution and marketing collaborations are included in ; all of the linkages are shown in . 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 * Halla Thorsteinsdóttir, Christina C. Melon, Monali Ray, Sharon Chakkalackal, Michelle Li, Jan E. Cooper, Jennifer Chadder, Abdallah S. Daar and Peter A. Singer are at the McLaughlin Rotman Centre for Global Health, University of Toronto and University Health Network, Toronto, Ontario, Canada. * Halla Thorsteinsdóttir and Abdallah S. Daar are also at the Dalla Lana School of Public Health, University of Toronto, Ontario, Canada. * Tirso W. Saenz and Maria Carlota de Souza Paula are at the Centre for Sustainable Development, University of Brasilia, Brazil. * Wen Ke and Lexuan Li are at the Institute of Policy and Management, Chinese Academy of Sciences, Beijing, China. * Magdy A. Madkour is at the Arid Lands Agricultural Research Institute, Ain Shams University, Cairo, Egypt. * Sahar Aly and Nefertiti El-Nikhely are at the Center for Special Studies and Programs, Bibliotheca Alexandrina, Alexandria, Egypt. * Sachin Chaturvedi is at the Research and Information System for Developing Countries, India. * Victor Konde is at the University of Zambia, Lusaka, Zambia. Competing financial interests P.A.S. has received consulting funds from Merck Frosst Canada and is on the scientific advisory board of the Bioveda II fund in China. Corresponding author Correspondence to: * Halla Thorsteinsdóttir (halla.thorsteinsdottir@mrcglobal.org) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (64K) Supplementary Methods Additional data
• Open biotechnology: licenses needed
  - Nature Biotechnology 28(5):417-419 (2010)
  Nature Biotechnology | Feature | Patents Open biotechnology: licenses needed * Yann Joly1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume:28,Pages:417–419Year published:(2010)DOI:doi:10.1038/nbt0510-417 Open biotechnology may be the ideal solution to ensure scientific progress and the realization of the common good, but it has yet to deliver on its promises. 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 * Yann Joly is at the Centre of Genomics and Policy, McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Yann Joly (yann.joly@mail.mcgill.ca) Additional data
• Recent patent applications in fluorescent imaging
  - Nature Biotechnology 28(5):420 (2010)
  Table 1
• Advancing RNA-Seq analysis
  - Nature Biotechnology 28(5):421-423 (2010)
  New methods for analyzing RNA-Seq data enable de novo reconstruction of the transcriptome.
• Haploidy with histones
  - Nature Biotechnology 28(5):423-424 (2010)
  An engineered centromere-specific histone could enable homozygous diploid lines to be generated at high frequency, simplifying crop breeding.
• High-content imaging
  - Nature Biotechnology 28(5):424-425 (2010)
  Multiparametric imaging of siRNA screening data sheds light on endocytosis.
• Third-generation sequencing fireworks at Marco Island
  - Nature Biotechnology 28(5):426-428 (2010)
  Advances in sequencing platforms promise to make this technology more accessible.
• Research highlights
  - Nature Biotechnology 28(5):429 (2010)
  
• Biomarkers on a roll
  - Nature Biotechnology 28(5):431 (2010)
  Nature Biotechnology | Editorial Biomarkers on a roll Journal name:Nature BiotechnologyVolume:28,Page:431Year published:(2010)DOI:doi:10.1038/nbt0510-431 A consortium of industry, nonprofit institutions and regulators outlines a rolling biomarker qualification process, providing the first clear path for translation of such markers from discovery to preclinical and clinical practice. View full text Additional data
• Research at the interface of industry, academia and regulatory science
  - Nature Biotechnology 28(5):432-433 (2010)
  Nature Biotechnology | Foreword Research at the interface of industry, academia and regulatory science * William B Mattes1 Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth Gribble Walker1 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Abadie2 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank D Sistare3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher4 Search for this author in: * NPG journals * PubMed * Google Scholar * Janet Woodcock5 Search for this author in: * NPG journals * PubMed * Google Scholar * Raymond L Woosley1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:432–433Year published:(2010)DOI:doi:10.1038/nbt0510-432 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A medicine is defined as 'a substance or preparation used in treating disease'. Society expects that the benefits of medicines should substantially exceed their risks, and this expectation has been translated into governmental policy around the world. Part of the mission of the US Food and Drug Administration (FDA) is to protect the public health by assuring the safety and efficacy of medicines1. The FDA has carried out its mission by relying upon the best current scientific knowledge and practice2. By definition, gaps in current scientific knowledge and practice limit the ability of regulatory agencies, such as the FDA and the European Medicines Agency (EMEA; London), to carry out their mission. Current gaps include a limited ability to extrapolate animal data to humans3, 4, 5, the difficulty of evaluating genetic and carcinogenic risks6, 7, and our poor understanding of gender-specific responses8. It is hoped that new knowledge, technologies and tools can address these and! other gaps and improve the evaluation of new drugs and medicines9, 10, 11, 12. In this context, the FDA has advocated a 'Critical Path Initiative'13, 14 to intentionally address gaps in applied and regulatory science. The initial report and subsequent listing of specific opportunities15 called attention to research and tools needed to improve the process of drug development that extends from preclinical testing to ultimate regulatory registration. Although this area is vital for improving the development of new medicines and getting them to the public, it receives little academic, public or legislative attention and, thus, little funding. Rather, the focus of both academic research and news organizations is often on novel discoveries and/or the risks and benefits of drugs after they have reached the marketing phase. Nevertheless, a great deal of essential work must be accomplished between discovery and delivery (that is, in the critical path) to accomplish the delivery of safe and effective medicines to the public. With the goal of improving that proce! ss, the FDA has not only identified gaps in 'Critical Path Research' but also suggested that an effective approach to address these gaps would be to form consortia of industry, academic and regulatory scientists to share resources, expertise and experience toward accomplishing shared common specific objectives. Consortia have played key roles in addressing technological problems common to a competitive industry. For instance, the Sematech consortium, formed in 1987 and comprising 14 leading US semiconductor producers, addressed common issues in semiconductor manufacture and increased R&D efficiency by avoiding duplicative research16. Sematech demonstrates that consortia provide the opportunity for industry scientists to share their experiences in identifying and solving problems, to pool their expertise and to collectively consider mutual questions. To create similar models in drug, diagnostic and device development, the Critical Path Institute (C-Path) was incorporated as a "neutral, third party" to serve as a consortium organizer14 and interface between industry members and the FDA17. One of the first consortia formed by C-Path to address one of the Critical Path gaps was the Predictive Safety Testing Consortium (PSTC)18, 19. As noted in the Critical Path Opportunities list, there is a need for "preclinical biomarkers that predict human liver or kidney toxicity" and "collaborations among sponsors to share what is known about existing safety assays"15. Indeed, the preamble to the legal agreement that binds PSTC members notes that "the parties to this Agreement also recognize the importance of validated safety biomarkers to pharmaceutical and biotechnology research and development efforts and wish...to conduct research and development projects, under the coordination of C-Path, to identify and validate such biomarkers to increase drug safety." Thus, the PSTC is committed to cooperative research resulting in tools beneficial to both pharmaceutical development and regulatory science (termed Critical Path Research). Of course, these tools could be ! valuable to medical situations where improved monitoring for drug safety would improve outcomes. The PSTC legal agreement furnishes not only a clear set of goals and deliverables that provide guidance for actions and decisions of the consortium, but also a framework to address issues such as antitrust, intellectual property and confidentiality. This assures open data sharing and collaboration in a manner consistent with applicable legal requirements. In particular, the confidentiality provisions also assure that publications (which are encouraged) respect member contributions, again fostering openness and participation. As noted above, C-Path provides executive functions and contributes overall scientific leadership, whereas members lead strategic and technical execution of the scientific working groups pursuing biomarkers of several critical toxicities where understanding of new biomarkers is desired. Members also participate in an advisory committee that, among other functions, reviews new proposals and ongoing projects and guides their scope and growth. A key component of Critical Path Research is the participation and critical evaluations of the very regulatory scientists who will later rely on the results obtained with these new tools as they are applied to the development of new pharmaceuticals. Participation of FDA scientists in PSTC is made possible by a memorandum of understanding between C-Path and the FDA. In addition, the PSTC has representatives from the EMEA who, like FDA scientists, serve to advise the target-organ biomarker working groups (e.g., the Nephrotoxicity Working Group and the Hepatotoxicity Working Group); as experts in their respective fields, these advisors bring not only their expertise but also the experience of how problems of a given target-organ toxicity will need to be confronted in a regulatory setting. The biomarker data generated by a working group is ultimately reviewed by a different set of regulators, thus safeguarding an impartial scientific evaluation and recommendations for how the bi! omarkers may be used in regulatory decision making. Implicit in the formation and the goals of the PSTC is the realization that the current approach to the discovery, development, industry uptake and regulatory acceptance of new safety biomarkers is simply too slow and too inefficient to meet the growing needs of the worldwide healthcare system. For example, serum alanine aminotransferase was described as a marker for liver damage in the early 1960s and now is widely used for that purpose20. Even so, it has never been rigorously evaluated as a nonclinical or clinical marker for hepatocellular damage (e.g., by receiver operator characteristic curves analysis21), its specificity for detecting such damage remains in question, and defined cut-off values for patient monitoring in clinical trials are only now gaining consensus agreement22, 23. Newly discovered biomarkers suffer from a similar liability in not having a clear or expedient path for reaching a consensus as to their value and specific terms of use. Thus, one goal of the PSTC is to establish an intentional process for developing data sets that would support the use of a given biomarker for a specific purpose. This process, appropriately termed biomarker 'qualification'24, should be distinguished from technical validation of a biomarker assay25. Wagner26 describes this qualification process as the "fit-for-purpose evidentiary process of linking a biomarker with biological processes and clinical endpoints," and notes that a certain body of data may support one purpose, whereas a larger body of data may support a broader purpose26. Clearly, this process must entail interaction between those developing the data set and regulatory scientists, and a framework for beginning that dialog has now been created27. Importantly, the result of such an exchange would be a clear statement or guidance from regulatory authorities as to the acceptable uses of a given biomarker in support of medical product development and registration.! Furthermore, the process should allow the expansion of those qualified uses after the development of a larger, relevant body of biomarker data, aptly described as "progressive qualification." The papers in this issue describe critical recent accomplishments of the PSTC for the regulatory qualification of kidney safety biomarkers for preclinical applications. In particular, urinary biomarkers were considered, as this fluid passes unmodified through the ureter and bladder to the exterior, is easy to archive and its contents offers a monitor of kidney function. The standard biomarkers for kidney injury, serum creatinine (SCr) and blood urea nitrogen (BUN) are widely recognized as highly insensitive, and thus measures with improved sensitivity are desired28. Several PSTC companies had internal experience with other biomarkers for kidney toxicity, and, after sharing these data, determined that seven biomarkers in particular showed promise for higher sensitivity than SCr and BUN and had technically sound assays available for their measurement. Furthermore, histopathology in animal models of nephrotoxicity was tractable as a metric against which the urine biomarker perf! ormance could be compared. An open collaboration among 17 pharmaceutical/biotech companies, regulatory bodies and academia has generated a data set supporting the qualification of several new biomarkers of drug-induced kidney injury. In addition, this effort, with the involvement of the FDA and EMEA, explored pilot processes for optimization of content, structure of presentation and expectations for regulatory review of similar data sets. The collaboration extends beyond that between scientists in competing companies, academic scientists and regulatory scientists, to that between regulatory scientists in different jurisdictions. The power of these collaborations has as its proof the speed at which the data set was developed, the process of review put into place and the establishment of an initial model that future biomarker qualification efforts can follow. PSTC is also a testimony to the benefits that can be derived from productive open collaborations between academia, regulatory agencies and the pri! vate sector. For an area of research long neglected, these accomplishments are all the more noteworthy. References * References * Author information * Borchers, A.T., Hagie, F., Keen, C.L. & Gershwin, M.E.Clin. Ther.29, 1–16 (2007). * ChemPort * PubMed * Article * Miller, S.A.J. Nutr.123, 279–284 (1993). * ChemPort * PubMed * Collins, J.M.Chem. Biol. Interact.134, 237–242 (2001). * ChemPort * PubMed * Article * Peters, T.S.Toxicol. Pathol.33, 146–154 (2005). * ChemPort * PubMed * Article * Voisin, E.M., Ruthsatz, M., Collins, J.M. & Hoyle, P.C.Regul. Toxicol. Pharmacol.12, 107–116 (1990). * ChemPort * PubMed * Article * Jacobs, A. & Jacobson-Kram, D.Toxicol. Sci.81, 260–262 (2004). * ChemPort * PubMed * Article * Jacobson-Kram, D. & Contrera, J.F.Toxicol. Sci.96, 16–20 (2007). * ChemPort * PubMed * Article * Miller, M.A.Int. J. Toxicol.20, 149–152 (2001). * ChemPort * PubMed * Article * MacGregor, J.T.Toxicol. Sci.75, 236–248 (2003). * ChemPort * PubMed * Article * Tong, W.et al. Environ. Health Perspect.111, 1819–1826 (2003). * ChemPort * PubMed * Gutman, S. & Kessler, L.G.Nat. Rev. Cancer6, 565–571 (2006). * ChemPort * ISI * PubMed * Article * Lesko, L.J.Clin. Pharmacol. Ther.81, 807–816 (2007). * ChemPort * ISI * PubMed * Article * Anonymous. Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products (FDA, Washington, DC, 2004; accessed 23 April 2010). http://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ucm077262.htm * Woosley, R.L. & Cossman, J.Clin. Pharmacol. Ther.81, 129–133 (2007). * ChemPort * PubMed * Article * Anonymous. US Food and Drug Administration. Critical Path Opportunities List - March 2006 (FDA, Washington, DC, 2006; accessed 23 April 2010). http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/UCM077258.pdf * Irwin, D.A. & Klenow, P.J.Proc. Natl. Acad. Sci. USA93, 12739–12742 (1996). * ChemPort * PubMed * Article * The Food and Drug Adminstration. Fed. Regist.70, 74823–74826 (2005). * Marrer, E. & Dieterle, F.Chem. Biol. Drug Des.69, 381–394 (2007). * ChemPort * PubMed * Article * Mattes, W.B.Methods Mol. Biol.460, 221–238 (2008). * PubMed * Kim, W.R., Flamm, S.L., Di Bisceglie, A.M. & Bodenheimer, H.C.Hepatology47, 1363–1370 (2008). * ChemPort * PubMed * Article * Zweig, M.H. & Campbell, G.Clin. Chem.39, 561–577 (1993). * ChemPort * ISI * PubMed * Senior, J.R.Clin. Liver Dis.11, 507–524 (2007). * PubMed * Article * The Food and Drug Adminstration. Fed. Regist.72, 60681–60682 (2007). * Wagner, J.A., Williams, S.A. & Webster, C.J.Clin. Pharmacol. Ther.81, 104–107 (2007). * ChemPort * ISI * PubMed * Article * Lee, J.W.et al. Pharm. Res.23, 312–328 (2006). * ChemPort * ISI * PubMed * Article * Wagner, J.A.Annu. Rev. Pharmacol. Toxicol.48, 631–651 (2008). * ChemPort * PubMed * Article * Goodsaid, F.M., Frueh, F.W. & Mattes, W.Toxicology245, 219–223 (2008). * ChemPort * PubMed * Article * Schetz, M., Dasta, J., Goldstein, S. & Golper, T.Curr. Opin. Crit. Care11, 555–565 (2005). * PubMed * Article Download references Author information * References * Author information Affiliations * Critical Path Institute, Tucson, Arizona, USA. * William B Mattes, * Elizabeth Gribble Walker & * Raymond L Woosley * European Medicines Agency, Canary Wharf, London, UK. * Eric Abadie * Department of Laboratory Sciences and Investigative Toxicology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Frank D Sistare * Novartis, San Diego, California, USA. * Jacky Vonderscher * Food and Drug Administration, Silver Spring, Maryland, USA. * Janet Woodcock Competing financial interests F.D.S. is an employee of Merck and J.V. is an employee of Novartis. Corresponding author Correspondence to: * William B Mattes (wbmattes@gmail.com) Additional data
• Glossary
  - Nature Biotechnology 28(5):434-435 (2010)
  Nature Biotechnology | Focus Glossary Journal name:Nature BiotechnologyVolume:28,Pages:434–435Year published:(2010)DOI:doi:10.1038/nbt0510-434 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg . Rapid damage to cells of the kidney, resulting in loss of function. Acute kidney injury may be caused by nephrotoxic drugs, insufficient blood flow to the kidneys (resulting in ischemia or other insults. It is functionally defined by the Acute Kidney Injury Network (an international interdisciplinary group of nephrologists and critical care physicians) as being characterized by a rapid time course (<48 h) and a reduction of kidney function, detected by either an increase in absolute serum creatinine (SCr) of ≥0.3 mg/dl (≥26.4 μmol/l) or a >50% increase in SCr, as well as a reduction in urine output to <0.5 ml/kg/h for >6 hours. The initial histomorphological changes in acute kidney injury may include changes in cell morphology or architecture (degeneration), including dilation and cell death (necrosis). Several days after the initial insult, tubular epithelial cells respond to epithelial cell loss and damage by regeneration or proliferation. Severe acute kidney injury! or prolonged insults, termed chronic kidney injury, can result in progressive toxicity or typically a cascade of inflammation and fibrosis that irreversibly damages kidney integrity and function. for a ROC curve (see 'Receiver operating characteristic curve') is a metric to summarize the ability of a classifier to discriminate between two outcomes. As the name suggests, it can be calculated by integrating the receiver operating characteristic curve. It can be loosely interpreted as the sensitivity averaged across the levels of specificity. . A biological marker (DNA, RNA, protein, protein modification or metabolite) that reflects a biological state (see also 'Safety biomarker,' 'Diagnostic biomarker', 'Prognostic biomarker', 'Prodromal biomarker' and 'Predictive biomarker'. It is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention (Clin. Pharmacol. Therapeut.69, 89–95, 2001). Typically, the development process of a biomarker can be described as a pipeline similar to drug development (see 'Biomarker discovery', 'Biomarker qualification' and 'Biomarker verification'. . The phase of research in which candidate biomarkers are identified, often with the help of '-omics' technologies, such as genomics, proteomics or metabonomics, or genetics. These approaches allow a nontargeted discovery of biomarkers correlated to certain biological processes or states. . The process of accumulating evidence about the utility and limitations of a biomarker for use in a specific context. The term biomarker validation refers to the same concept as biomarker qualification but is more and more outdated, as it does not imply the fit-for-purpose concept of qualification (intended use) but rather means 'all or nothing'. Biomarker validation also is often mistakenly confused with the concept of analytical assay validation, which is the validation of the analytical performance of an assay. . The phase of research in which the correlation of the biomarker candidate with biological processes or states is reproduced with additional investigations, often with a more targeted technology (e.g., reverse transcriptase (RT)-PCR assays or protein assays). . A progressive loss of renal function over a period of months to years. It is often diagnosed by increases in levels of serum creatinine. There are many causes and types of renal diseases, such as diabetic nephropathy, inflammatory glomerular injury, also called glomerulonephritis, or hypertensive nephropathy. Renal injury may also result from hereditary diseases such as autosomal dominant polycystic kidney disease or focal segmental glomerulosclerosis. . Part of kidney that makes up the outer layer and the bulk of the organ, encloses a smaller inner layer of the kidney (see 'Medulla'). The cortex contains glomeruli (see 'Glomeruli'). . Reports the concurrent presence or absence of injury. or . An abnormal distension of the tubule lumen (see 'Tubules'). . The increase in urine flow, resulting in abundant urine or polyuria, as the kidney responds to ongoing toxicity. . Statistical analysis method that excludes samples from animals treated with a toxicant that did not exhibit the anticipated histomorphological changes and samples from control animals that were unexpectedly positive for these histomorphological changes. This is in contrast to inclusion analysis, in which samples from these animals were included.The primary motivation for using exclusion analysis is to avoid penalizing a marker that might be prodromal (see 'Prodromal biomarker') or more sensitive than the histomorphological assessment. . The flow rate of fluid filtered through the kidney. The glomerular filtration rate is a common measure of the functional state of the kidney. It is often approximated by the creatinine clearance rate, which is the volume of blood plasma that is cleared of the waste product creatinine per unit time. Creatinine clearance rate is measured by timed urine and plasma determinations of creatinine or estimated by serum creatinine levels. Similarly, the level of blood urea nitrogen is a common parameter to estimate the glomerular filtration rate. . Located in the cortex, glomeruli filter blood through capillary tufts surrounded by specialized epithelial cells called podocytes that cover the glomerular basement membrane and function as a blood filter. This filtrate then enters Bowman's space, which is continuous with a series of tubules (see 'Tubules') that collectively comprise the remainder of the nephron (see 'Nephron'). . Routinely used (hematoxylin & eosin Y) staining approach to help visualize tissue features. Hematoxylin is a blue dye that stains basophilic structures such as nuclei, and eosin Y is a red dye that stains eosinophilic structures such as cytoplasm and other protein-rich materials. . A process for visual examination of animal tissues to determine whether there are microscopic changes. Routine histopathology in pharmaceutical safety studies is conducted by fixing tissues in formalin, sectioning the tissues using a microtome, fixing these to microscope slides and staining the tissues before microscopic evaluation (see 'H&E staining'). . A biochemical assay that enables the concentration of a substance to be measured by exploiting the specific binding between an analyte and the corresponding detection antibody. The analyte can be a relatively simple chemical substance, such as a drug, or a complex entity such as a protein or a virus in biological fluids. Different variants of immunoassays exist, which can by characterized by the measurement steps (e.g., sandwich or competitive assays) and the use of nonlabeled or labeled reagents (e.g., enzyme-linked immunosorbent assay). . A method to localize a biomarker protein or other antigen using an antibody that recognizes that antigen. The use of labeled (e.g., chromagen, fluorochrome, enzyme) antibodies allows localization of biomarker proteins at the organ, cellular and subcellular level. In situ. A method for staining the mRNA encoding a protein of interest, to determine which cells express that mRNA using a labeled complementary RNA (riboprobe) sequence to hybridize to the target sequence of interest. . See 'Tubules'. . Part of kidney that is enclosed by the cortex (see 'Cortex') and which contains the renal pelvis (see 'Renal pelvis'). The inner medulla is thus enriched for the Loops of Henle (see 'Tubules') and the larger collecting ducts that coalesce to form papillae. . Functional unit of the kidney. The kidney comprises many nephrons, which filter small waste products from the blood for excretion in urine, recover excess water and useful solutes, and regulate kidney and vascular function through the production of hormones. . See 'Medulla'. . Appears in the absence of any injury with an ability to foretell future injury with some certainty. . Represents a symptom of the initial stage of onset of an injury before any observation of certain injury. . Predicts the course or outcome (e.g., end, stabilization or progression) of an injury. . A central space into which the large collecting ducts of the papillae (see 'Medulla') empty urine. This in turn empties into the urinary bladder through the ureter. The renal pelvis, ureter and urinary bladder are lined with transitional epithelium. . A graphical plot to assess the ability of a classifier to discriminate between two outcomes. For a given classifier, sensitivity is plotted against (1 – specificity) or, equivalently, the true-positive rate versus the false-positive rate. This allows the assessment of classifier performance across the entire range of decision rules. As classifiers with higher sensitivity for a given specificity are preferred over those with lower sensitivity, a higher ROC curve value is considered to denote better performance (see also 'Area under the curve (AUC)' and 'Exclusion analysis'). . Biomarkers typically used to monitor organ safety and diagnose or predict onset or reversibility of injury. . Areas of regeneration in tubular epithelial cells that appear blue-purple in H&E-stained sections (see 'Histopathology') due to regenerating cells and/or increased density of nuclei in these tubules. . Kidney tubular structures, surrounded by the tubulointerstitium, that recover proteins as well as organic and inorganic solutes from glomerular filtrate. Connected to a glomerulus (see 'Glomeruli'), a proximal tubule begins in the cortex (see 'Cortex') as a straight tubule that then changes into the Loop of Henle in the kidney medulla (see 'Medulla'). The thick ascending Loop of Henle rises back into the cortex, past the glomerulus and transitions into the distal convoluted tubule, which in turn transitions into the collecting duct. Additional data
• Next-generation biomarkers for detecting kidney toxicity
  - Nature Biotechnology 28(5):436-440 (2010)
  Nature Biotechnology | Opinion and Comment | Commentary Next-generation biomarkers for detecting kidney toxicity * Joseph V Bonventre1 Search for this author in: * NPG journals * PubMed * Google Scholar * Vishal S Vaidya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Schmouder2 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Feig3 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Dieterle4 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:436–440Year published:(2010)DOI:doi:10.1038/nbt0510-436 There is a paucity of biomarkers that reliably detect nephrotoxicity. The Predictive Safety Testing Consortium (PSTC) faced several challenges in identifying novel safety biomarkers in the renal setting. 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 * Joseph V. Bonventre and Vishal S. Vaidya are in the Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; * Robert Schmouder is in Translational Sciences, Novartis Institutes for BioMedical Research, East Hanover, New Jersey, USA; * Peter Feig is in Cardiovascular Clinical Research, Merck Research Laboratories, Rahway, New Jersey, USA; * Frank Dieterle is in Translational Sciences, Novartis Institutes for BioMedical Research, Basel, Switzerland. Competing financial interests R.S. and F.D. are employees of Novartis and P.F. is an employee of Merck. Corresponding author Correspondence to: * Joseph V Bonventre (joseph_bonventre@hms.harvard.edu) Additional data
• Evolution of biomarker qualification at the health authorities
  - Nature Biotechnology 28(5):441-443 (2010)
  Nature Biotechnology | Opinion and Comment | Commentary Evolution of biomarker qualification at the health authorities * Federico Goodsaid1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marisa Papaluca1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume:28,Pages:441–443Year published:(2010)DOI:doi:10.1038/nbt0510-441 By streamlining the qualification process for biomarkers, coordinated protocols recently implemented at the different regulatory agencies can facilitate progress and provide impetus to novel biomarker discovery and validation. 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 * Federico Goodsaid is at the Food and Drug Administration, Silver Spring, Maryland, USA, and * Marisa Papaluca * Marisa Papaluca is at The European Medicines Agency, London, UK. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Federico Goodsaid (federico.goodsaid@fda.hhs.gov) or * Marisa Papaluca (Marisa.Papaluca@ema.europa.eu) Additional data
• A roadmap for biomarker qualification
  - Nature Biotechnology 28(5):444-445 (2010)
  Nature Biotechnology | News and Views A roadmap for biomarker qualification * David G Warnock1 Search for this author in: * NPG journals * PubMed * Google Scholar * Carl C Peck2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume:28,Pages:444–445Year published:(2010)DOI:doi:10.1038/nbt0510-444 A collaborative effort between pharmaceutical companies, regulatory agencies and academia to qualify biomarkers for kidney toxicity provides a model for investigating and identifying reliable safety markers for preclinical applications. Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The dependence of preclinical screens on histopathology and weakly informative biomarkers causes considerable delays and inefficiency in transitioning new drugs into human testing. This delays confirmation of the safety and effectiveness of new therapies. Four papers1, 2, 3, 4 in this issue describe the utility of previously described markers of kidney damage to specifically assess renal damage in rats exposed to a range of nephrotoxic agents. Two additional manuscripts5, 6 further describe the protocols used to qualify these biomarkers and explain the broader implications of the assessments issued by two major regulatory bodies, the Food and Drug Administration (FDA) and European Medicines Agency (EMEA; London). Together, the papers document progress toward establishing a formal process that will hopefully emerge as a model for developing better biomarkers for predicting a range of toxicities frequently encountered during drug development. The work described in this collection of papers was done by the Nephrotoxicity Working Group of the Predictive Safety Testing Consortium (PSTC)7, which was created as part of the FDA's Critical Path Initiative8. Other PSTC groups are currently involved in qualifying biomarkers to detect hepatotoxicity, vascular injury, nongenotoxic carcinogenicity and myopathy. The PSTC aims to pioneer a process framework to critically vet a range of previously reported candidate safety biomarkers for various organ and tissue types, qualify them for preclinical applications and eventually assess their feasibility for use in humans. The need for reliable in vitro systems and preclinical models to predict nephrotoxicity in humans poses a major impediment to developing and using new drugs. The limitations of using detectable changes in serum creatinine (SCr) or blood urea nitrogen (BUN) are well recognized, and even histopathology, which is widely regarded as the "gold standard" for animal studies, has inadequate sensitivity and specificity for certain applications9. In the context of this challenge, the Nephrotoxicity Working Group of the PSTC selected 23 urinary biomarkers and systematically evaluated the utility of the most promising biomarkers in several rat models of kidney injury. The immediate intent of the collaborative effort was to apply the patterns of renal injury discerned using these biomarkers to developing a knowledge base that eventually permits preclinical results to predict potential renal injury in a clinical setting before frank nephrotoxicity becomes apparent. This culminated in the detailed presentation of data for seven safety biomarkers (kidney injury molecule 1 (Kim-1), albumin, total protein, β2-microglobulin, cystatin C, clusterin and trefoil factor-3 (TFF3)) for consideration by the FDA and EMEA. Until now, none of these markers could be used to support drug applications. A notable aspect of the analyses1, 2, 3, 4 is formal evaluation of the sensitivity and specificity for each of the biomarkers. This was accomplished by using histologic scoring as a benchmark for renal injury and rigorous analyses employing the area under the receiver operator characteristics (ROC) curve method (Fig. 1). In this figure, the dashed diagonal line, which represents identity between the true-positive rate and the false-positive rate, signifies when the test is not informative. The area under the curve (0 < AUC < 1.0) represents the overall probability that the disease state being investigated (e.g., the presence or absence of drug-induced ren! al injury) of a randomly chosen subject is correctly identified by the test10. These analyses are especially valuable for comparing the costs and benefits of single test measures with panels of tests that include more than one diagnostic measure or test. Thus, ROC curves can be used to interpret the interplay of the sensitivity and specificity of each candidate biomarker in isolation—and even more informatively—together with others. Members of the consortium were able to take advantage of the fact that issues such as timing, extent and specific location(s) of the injury (e.g., whether it occurs in the glomerulus or kidney tubule), together with progress of the recovery phase can be assessed with multiple biomarkers, each of which may provide unique temporal information about each of these injury phases. Figure 1: Receiver operating characteristics (ROC) curve analysis. ROC curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. Each point on the curve represents the true-positive rate and false-positive rate associated with a particular test value. The AUC provides a useful metric to compare different tests (indicator variables). Whereas an AUC value close to 1 indicates an excellent diagnostic test, a curve that lies close to the diagonal (AUC = 0.5) has no information content and therefore no diagnostic utility. More than one ROC curve can be presented in the same plot, and the absolute areas under each curve compared to determine which test, or combination of tests, has the better diagnostic performance. The ability to superimpose curves, as shown here, permits tests to be chosen based on considerations such as cost and availability. Modified from http://en.wikipedia.org/wiki/Receiver_operating_characteristic. * Full size image (62 KB) Dieterle et al.1 use this approach to show that urinary clusterin outperforms SCr and BUN in detecting proximal tubular injury and that total protein, cystatin C and urinary β2-microglobulin each outperform either SCr or BUN in detecting glomerular injury. Their findings suggest that some biomarkers may perform better with glomerular rather than tubular injury. Yu et al.2 show that urinary albumin is superior to either SCr or BUN in detecting tubule damage and that urinary TFF3 abundance complements the capacity of combined SCr and BUN levels to detect renal injury. Vaidya et al.3 show that changes in levels of Kim-1 clearly outperform changes in the abundances of SCr, BUN or N-acetyl-β-D-glucosaminidase for detecting kidney damage induced in rats by a range of nephrotoxic agents. These efforts culminated in the recommendation by biomarker qualification review teams of the FDA11 and EMEA12 that voluntary measurement of these seven kidney biomarkers be regarded as acceptable evidence of nephrotoxicity in rat studies. Moreover, they are deemed to be of value in complementing information obtained from measuring levels of SCr and BUN. Both agencies recommended that studies in different species and models be undertaken to enhance understanding of the generality of the rat findings for preclinical toxicity testing. At this time, there is no intent to replace histological assessments in the preclinical models. Nonetheless, the limitations of bridging from traditional animal findings to the clinical setting, where histological assessments are rarely available, cannot be overemphasized. Data from a fourth study, by Ozer et al.4, was not part of the initial submission but address key issues related to evaluating recovery from injury as well as the severity of the initial nephrotoxic injury. They find that a panel of urinary biomarkers enables the progression of renal injury and subsequent repair and recovery to be monitored after exposure of rats to either of two nephrotoxic agents. The authors complement this study by demonstrating that serum cystatin C is more sensitive than SCr and BUN in monitoring general renal failure caused by drug exposure. Overall, the clinical relevance of these findings must be viewed as suggestive because they are based on preclinical models that were chosen to emphasize different injury patterns that may not pertain to clinical settings where 'injury' is often multifactorial and frequently progresses from one compartment of the kidney to another. Furthermore, not all markers were evaluated in all of the injury models, and combinations of markers would also be worth further consideration. The regulatory agencies have encouraged the community to provide additional collaborative clinical studies to provide additional information about the utility of these and other biomarkers in humans. Because histopathology is not usually an option for most clinical applications, physicians and clinical investigators currently rely on safety biomarkers that are insensitive both to the initiation of an injury phase, as well as its extent and recovery9. Measuring levels of SCr and BUN, along with other tradit! ional urinary measurements (volume flow, epithelial cell loss, changes in concentrating ability and sodium absorption), have not fulfilled the needs in the clinical setting of early predictors of renal damage and compromised function. Another benefit of the application of the well-defined preclinical findings to the clinical setting is the possibility that the currently available traditional markers of kidney injury could be better defined in their timing and application to specific clinical settings, which could in turn optimize the timing and application of the biomarker measurements. The FDA has concluded that although none of the seven biomarkers are broadly qualified to be used as primary renal monitoring tests or dose-stopping criteria, their use may be appropriate on a case-by-case basis. In each case, risks and benefits must be carefully evaluated for monitoring and providing assurances of kidney safety in patients and therefore enabling early clinical investigations of promising therapeutic agents. We look forward to seeing how many of the validated biomarkers from the preclinical initiatives are eventually brought forward to the clinic. At this point, a fairly wide net has been cast because the ideal set of biomarkers in the preclinical studies may not be the same set that will be validated in the clinical setting, accounting for the obvious difficulties of defining the true gold standard for kidney injury in the clinical studies. The most telling progress along these lines will be made by exploiting the model developed for the Critical Path Initiative, which involves close collaboration between the pharmaceutical companies, the regulatory agencies and nephrologists involved in both the basic and clinical research arenas. A well-defined, drug-induced nephrotoxic event where the dosing schedule is prospectively defined would be a logical model for predictive biomarker testing. Examples of such models could be nephrotoxicity due to intravenous radio-contrast agents or n! ephrotoxicity resulting from cisplatin chemotherapy. Monitoring of clinically relevant measures of kidney function along with the candidate biomarkers seems the most obvious first step along this path. But beyond these accomplishments and the remaining challenges to improve early detection of nephrotoxicity in humans, these studies introduce a model collaborative process and set new standards for scientific and regulatory qualification of safety biomarkers in general. Until now, both the FDA and EMEA required pharmaceutical companies to submit the results of renal toxicity biomarker qualification tests separately. However, the new framework established as a result of this initiative will simplify submission of such data to both the FDA and EMEA, as both agencies have found the qualification procedure to be acceptable. The successful collaboration of fiercely competitive pharmaceutical companies (overcoming substantial intellectual property barriers) with scientists from academia and regulatory bodies is particularly notable. If the momentum generated by this pilot biomarker qualification process can be sustained to translate this rigorous safety biomarker qualification pro! cess to human testing, and the predictive value of novel biomarkers are clinically confirmed, we will have realized the ultimate goal of ensuring safer new therapeutic agents. References * References * Author information * Dieterle, F.et al. Nat Biotechnol.28, 463–469 (2010). * Article * Yu, Y.et al. Nat. Biotechnol.28, 470–477 (2010). * Article * Vaidya, V.S.et al. Nat. Biotechnol.28, 478–485 (2010). * Article * Ozer, J.S.et al. Nat. Biotechnol.28, 486–494 (2010). * Article * Sistare, F.D.et al. Nat. Biotechnol.28, 446–454 (2010). * Article * Dieterle, F.et al. Nat. Biotechnol.28, 455–462 (2010). * Article * http://www.fda.gov/oc/initiatives/criticalpath/projectsummary/consortium.html * http://www.fda.gov/oc/initiatives/criticalpath/ * Bonventre, J.V.et al. Nat. Biotechnol.28, 436–440 (2010). * Article * Hanley, J.A. & NcNeil, B.J.Radiology143, 29–36 (1982). * ChemPort * ISI * PubMed * The Food and Drug Administration Biomarker Qualification Review Team. Review of Qualification Data for Biomarkers of Nephrotoxicity Submitted by the Predictive Safety Testing Consortium (FDA CDER, 21 February 2008). * EMEA. Biomarkers Qualification: Guidance to Applicants (doc. ref. EMEA/CHMP/SAWP/72894/2008-CONSULTATION, 24 April 2008). Download references Author information * References * Author information Affiliations * David G. Warnock is in the Division of Nephrology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Carl C. Peck is at the Center for Drug Development Science, Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, UC Washington Center, Washington DC, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * David G Warnock (dwarnock@uab.edu) or * Carl C Peck (carl@carlpeck.com) Additional data
• Towards consensus practices to qualify safety biomarkers for use in early drug development
  - Nature Biotechnology 28(5):446-454 (2010)
  Nature Biotechnology | Research | Perspective Towards consensus practices to qualify safety biomarkers for use in early drug development * Frank D Sistare1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Dieterle2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Troth1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Holder1 Search for this author in: * NPG journals * PubMed * Google Scholar * David Gerhold1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dina Andrews-Cleavenger3 Search for this author in: * NPG journals * PubMed * Google Scholar * William Baer4 Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Betton5 Search for this author in: * NPG journals * PubMed * Google Scholar * Denise Bounous6 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Carl2 Search for this author in: * NPG journals * PubMed * Google Scholar * Nathaniel Collins7 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Goering8 Search for this author in: * NPG journals * PubMed * Google Scholar * Federico Goodsaid8 Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Zhong Gu7 Search for this author in: * NPG journals * PubMed * Google Scholar * Valerie Guilpin9 Search for this author in: * NPG journals * PubMed * Google Scholar * Ernie Harpur9 Search for this author in: * NPG journals * PubMed * Google Scholar * Alita Hassan4 Search for this author in: * NPG journals * PubMed * Google Scholar * David Jacobson-Kram8 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kasper10 Search for this author in: * NPG journals * PubMed * Google Scholar * David Laurie2 Search for this author in: * NPG journals * PubMed * Google Scholar * Beatriz Silva Lima11 Search for this author in: * NPG journals * PubMed * Google Scholar * Romaldas Maciulaitis10 Search for this author in: * NPG journals * PubMed * Google Scholar * William Mattes12 Search for this author in: * NPG journals * PubMed * Google Scholar * Gérard Maurer2 Search for this author in: * NPG journals * PubMed * Google Scholar * Leslie Ann Obert13 Search for this author in: * NPG journals * PubMed * Google Scholar * Josef Ozer13 Search for this author in: * NPG journals * PubMed * Google Scholar * Marisa Papaluca-Amati10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan A Phillips14 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Pinches5 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Schipper4 Search for this author in: * NPG journals * PubMed * Google Scholar * Karol L Thompson8 Search for this author in: * NPG journals * PubMed * Google Scholar * Spiros Vamvakas10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Marc Vidal10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher15 Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth Walker12 Search for this author in: * NPG journals * PubMed * Google Scholar * Craig Webb4 Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Yu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:446–454Year published:(2010)DOI:doi:10.1038/nbt.1634Published online10 May 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Application of any new biomarker to support safety-related decisions during regulated phases of drug development requires provision of a substantial data set that critically assesses analytical and biological performance of that biomarker. Such an approach enables stakeholders from industry and regulatory bodies to objectively evaluate whether superior standards of performance have been met and whether specific claims of fit-for-purpose use are supported. It is therefore important during the biomarker evaluation process that stakeholders seek agreement on which critical experiments are needed to test that a biomarker meets specific performance claims, how new biomarker and traditional comparators will be measured and how the resulting data will be merged, analyzed and interpreted. View full text Author information * Abstract * Author information * Supplementary information Affiliations * Merck Research Laboratories, Safety Assessment, West Point, Pennsylvania, USA. * Frank D Sistare, * Sean Troth, * Daniel J Holder, * David Gerhold & * Yan Yu * Novartis Pharma AG, Basel, Switzerland. * Frank Dieterle, * Kevin Carl, * David Laurie & * Gérard Maurer * Amgen, Inc., Thousand Oaks, California, USA. * Dina Andrews-Cleavenger * ClinXus, and Van Andel Research Institute, Grand Rapids, Michigan, USA. * William Baer, * Alita Hassan, * Matthew J Schipper & * Craig Webb * AstraZeneca Pharmaceuticals, Cheshire, England. * Graham Betton & * Mark Pinches * Bristol-Myers Squibb, Princeton, New Jersey, USA. * Denise Bounous * Schering-Plough Research Institute, Summit, New Jersey, USA. * Nathaniel Collins & * Yi-Zhong Gu * US Food and Drug Administration, Silver Spring, Maryland, USA. * Peter Goering, * Federico Goodsaid, * David Jacobson-Kram & * Karol L Thompson * Sanofi-aventis, Malvern, Pennsylvania, USA. * Valerie Guilpin & * Ernie Harpur * European Medicines Agency, London, UK. * Peter Kasper, * Romaldas Maciulaitis, * Marisa Papaluca-Amati, * Spiros Vamvakas & * Jean-Marc Vidal * iMED.UL, Lisbon University, Portugal. * Beatriz Silva Lima * Critical Path Institute, Tucson, Arizona, USA. * William Mattes & * Elizabeth Walker * Pfizer Inc., Groton, Connecticut, USA. * Leslie Ann Obert & * Josef Ozer * Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut, USA. * Jonathan A Phillips * Hoffman La Roche, Basel, Switzerland. * Jacky Vonderscher Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Frank D Sistare (frank_sistare@merck.com) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (116K) Supplementary Table 1 Additional data
• Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium
  - Nature Biotechnology 28(5):455-462 (2010)
  Nature Biotechnology | Research | Perspective Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium * Frank Dieterle1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Sistare2 Search for this author in: * NPG journals * PubMed * Google Scholar * Federico Goodsaid3 Search for this author in: * NPG journals * PubMed * Google Scholar * Marisa Papaluca4 Search for this author in: * NPG journals * PubMed * Google Scholar * Josef S Ozer2, 28 Search for this author in: * NPG journals * PubMed * Google Scholar * Craig P Webb5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * William Baer5, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Senagore5, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Schipper5, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher10 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Sultana5 Search for this author in: * NPG journals * PubMed * Google Scholar * David L Gerhold2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan A Phillips11 Search for this author in: * NPG journals * PubMed * Google Scholar * Gérard Maurer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Carl1 Search for this author in: * NPG journals * PubMed * Google Scholar * David Laurie1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ernie Harpur12 Search for this author in: * NPG journals * PubMed * Google Scholar * Manisha Sonee13 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Ennulat14 Search for this author in: * NPG journals * PubMed * Google Scholar * Dan Holder15 Search for this author in: * NPG journals * PubMed * Google Scholar * Dina Andrews-Cleavenger16 Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Zhong Gu17, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Karol L Thompson3 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter L Goering3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Marc Vidal4 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Abadie4 Search for this author in: * NPG journals * PubMed * Google Scholar * Romaldas Maciulaitis4, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * David Jacobson-Kram3 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert F Defelice3 Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth A Hausner3 Search for this author in: * NPG journals * PubMed * Google Scholar * Melanie Blank3 Search for this author in: * NPG journals * PubMed * Google Scholar * Aliza Thompson3 Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia Harlow3 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas Throckmorton3 Search for this author in: * NPG journals * PubMed * Google Scholar * Shen Xiao3 Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy Xu3 Search for this author in: * NPG journals * PubMed * Google Scholar * William Taylor3 Search for this author in: * NPG journals * PubMed * Google Scholar * Spiros Vamvakas4 Search for this author in: * NPG journals * PubMed * Google Scholar * Bruno Flamion4 Search for this author in: * NPG journals * PubMed * Google Scholar * Beatriz Silva Lima4 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kasper4 Search for this author in: * NPG journals * PubMed * Google Scholar * Markku Pasanen4, 19 Search for this author in: * NPG journals * PubMed * Google Scholar * Krishna Prasad4 Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Troth20 Search for this author in: * NPG journals * PubMed * Google Scholar * Denise Bounous21 Search for this author in: * NPG journals * PubMed * Google Scholar * Denise Robinson-Gravatt22 Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Betton23 Search for this author in: * NPG journals * PubMed * Google Scholar * Myrtle A Davis24 Search for this author in: * NPG journals * PubMed * Google Scholar * Jackie Akunda25 Search for this author in: * NPG journals * PubMed * Google Scholar * James Eric McDuffie13 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Suter10 Search for this author in: * NPG journals * PubMed * Google Scholar * Leslie Obert22 Search for this author in: * NPG journals * PubMed * Google Scholar * Magalie Guffroy12 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Pinches23 Search for this author in: * NPG journals * PubMed * Google Scholar * Supriya Jayadev11 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Blomme26 Search for this author in: * NPG journals * PubMed * Google Scholar * Sven A Beushausen22 Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie G Barlow12 Search for this author in: * NPG journals * PubMed * Google Scholar * Nathaniel Collins17, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeff Waring26 Search for this author in: * NPG journals * PubMed * Google Scholar * David Honor26 Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra Snook13 Search for this author in: * NPG journals * PubMed * Google Scholar * Jinhe Lee26 Search for this author in: * NPG journals * PubMed * Google Scholar * Phil Rossi27 Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth Walker27 Search for this author in: * NPG journals * PubMed * Google Scholar * William Mattes27 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:455–462Year published:(2010)DOI:doi:10.1038/nbt.1625Published online10 May 2010 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The first formal qualification of safety biomarkers for regulatory decision making marks a milestone in the application of biomarkers to drug development. Following submission of drug toxicity studies and analyses of biomarker performance to the Food and Drug Administration (FDA) and European Medicines Agency (EMEA) by the Predictive Safety Testing Consortium's (PSTC) Nephrotoxicity Working Group, seven renal safety biomarkers have been qualified for limited use in nonclinical and clinical drug development to help guide safety assessments. This was a pilot process, and the experience gained will both facilitate better understanding of how the qualification process will probably evolve and clarify the minimal requirements necessary to evaluate the performance of biomarkers of organ injury within specific contexts. View full text Author information * Abstract * Author information Affiliations * Novartis Pharma AG, Basel, Switzerland. * Frank Dieterle, * Gérard Maurer, * Kevin Carl & * David Laurie * Department of Investigative Laboratory Sciences, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Frank Sistare, * Josef S Ozer & * David L Gerhold * US Food and Drug Administration, Silver Spring, Maryland, USA. * Federico Goodsaid, * Karol L Thompson, * Peter L Goering, * David Jacobson-Kram, * Albert F Defelice, * Elizabeth A Hausner, * Melanie Blank, * Aliza Thompson, * Patricia Harlow, * Douglas Throckmorton, * Shen Xiao, * Nancy Xu & * William Taylor * European Medicines Agency, London, UK. * Marisa Papaluca, * Jean-Marc Vidal, * Eric Abadie, * Romaldas Maciulaitis, * Spiros Vamvakas, * Bruno Flamion, * Beatriz Silva Lima, * Peter Kasper, * Markku Pasanen & * Krishna Prasad * ClinXus, Grand Rapids, Michigan, USA. * Craig P Webb, * William Baer, * Anthony Senagore, * Matthew J Schipper & * Stefan Sultana * Van Andel Research Institute, Grand Rapids, Michigan, USA. * Craig P Webb * Grand Valley Medical Specialists, Grand Rapids, Michigan, USA. * William Baer * Spectrum Health, Grand Rapids, Michigan, USA. * Anthony Senagore * Innovative Analytics, Inc., Kalamazoo, Michigan, USA. * Matthew J Schipper * Hoffman-La Roche, Basel, Switzerland. * Jacky Vonderscher & * Laura Suter * Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut, USA. * Jonathan A Phillips & * Supriya Jayadev * Sanofi-Aventis, Malvern, Pennsylvania, USA. * Ernie Harpur, * Magalie Guffroy & * Valérie G Barlow * Johnson & Johnson, San Diego, California, USA. * Manisha Sonee, * James Eric McDuffie & * Sandra Snook * GlaxoSmithKline, King of Prussia, Pennsylvania, USA. * Daniela Ennulat * Department of Biometrics, Merck Research Laboratories, West Point, Pennsylvania, USA. * Dan Holder * Amgen, Inc., Thousand Oaks, California, USA. * Dina Andrews-Cleavenger * Schering-Plough Research Institute, Summit, New Jersey, USA. * Yi-Zhong Gu & * Nathaniel Collins * Nephrology Clinic of Kaunas Medical University Clinics, Department of Basic and Clinical Pharmacology of Kaunas Medical University and State Medicines Control Agency, Kaunas, Lithuania. * Romaldas Maciulaitis * University of Kuopio, Department of Pharmacology and Toxicology, Kuopio, Finland. * Markku Pasanen * Department of Pathology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Sean Troth * Bristol-Myers Squibb, Princeton, New Jersey, USA. * Denise Bounous * Pfizer, Inc., Groton, Connecticut, USA. * Denise Robinson-Gravatt, * Leslie Obert & * Sven A Beushausen * AstraZeneca Pharmaceuticals, Alderley Park, Macclesfield, UK. * Graham Betton & * Mark Pinches * National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. * Myrtle A Davis * Eli Lilly and Co., Indianapolis, Indiana, USA. * Jackie Akunda * Abbott Laboratories, Abbott Park, Illinois, USA. * Eric A Blomme, * Jeff Waring, * David Honor & * Jinhe Lee * Critical Path Institute, Tucson, Arizona, USA. * Phil Rossi, * Elizabeth Walker & * William Mattes * Present address: Dynamics and Metabolism, PGRD, Pfizer, Andover Laboratories, Andover, Massachusetts, USA. * Josef S Ozer * Present affiliation: Merck Research Laboratories, Summit, New Jersey, USA. * Yi-Zhong Gu & * Nathaniel Collins Contributions Members of the PSTC Nephrotoxicity Working Group compiling the submission for biomarker qualification: F.D., F.S., J.S.O., C.P.W., W.B., A.S., M.J.S., J.V., S.S., D.L.G., J.A.P., G.M., K.C., D.L., E.H., M.S., D.E., D.H., D.A.-C., Y.-Z.G., K.L.T., P.L.G., J.-M.V., S.T., D.B., D.R.-G., G.B., M.A.D., J.A., J.E.MD., L.S.-D., L.O., M.G., M. Papaluca, S.J., E.A.B., S.A.B., V.G.B., N.C., J.W., D.H., S.S., J.L., P.R., E.W. and W.M.; members of the FDA Biomarker Qualification Review Team, reviewing the submission for biomarker qualification: F.G., D.J.-K., A.F.D., E.A.H., M.B., A.T., P.H., D.T., S.X., W.T. and N.X.; members of the EMEA Biomarker Qualification Review Team, reviewing the submission for biomarker qualification: M. Papaluca, J.-M.V., E.A., R.M., S.V., B.F., B.S.L., P.K., M. Pasanen and K.P. Competing financial interests F.D., G.M., K.C. and D.L. are employees of Novartis; F.S., D.H., S.T., Y.-Z.G., N.C. and D.L.G. are employees of Merck; W.B., A.S., M.J.S. and S.S. are employees of ClinXus; C.P.W. is an employee of Van Andel Research Institute; W.B. is an employee of Grand Valley Medical Specialists; A.S. is an employee of Spectrum Health; M.J.S. is an employee of Innovative Analytics; J.V. and L.S. are employees of Hoffman-La Roche; J.A.P. and S.J. are employees of Boehringer Ingelheim; E.H., M.G. and V.G.B. are employees of Sanofi-Aventis; M.S., J.E.M. and S.S. are employees of Johnson & Johnson; D.E. is an employee of GlaxoSmithKline; D.A.-C. is an employee of Amgen; D.B. is an employee of Bristol-Myers Squibb; D.R.-G., L.O. and S.A.B. are employees of Pfizer; G.B. and M.P. are employees of AstraZeneca; J.A. is an employee of Eli Lilly; E.A.B., J.W., D.H. and J.L. are employees of Abbott; J.S.O. was an employee of Merck. Corresponding author Correspondence to: * Frank Dieterle (frank.dieterle@novartis.com) Additional data
• Urinary clusterin, cystatin C, β2-microglobulin and total protein as markers to detect drug-induced kidney injury
  - Nature Biotechnology 28(5):463-469 (2010)
  Nature Biotechnology | Research | Article Urinary clusterin, cystatin C, β2-microglobulin and total protein as markers to detect drug-induced kidney injury * Frank Dieterle1 Search for this author in: * NPG journals * PubMed * Google Scholar * Elias Perentes1 Search for this author in: * NPG journals * PubMed * Google Scholar * André Cordier1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel R Roth1 Search for this author in: * NPG journals * PubMed * Google Scholar * Pablo Verdes1 Search for this author in: * NPG journals * PubMed * Google Scholar * Olivier Grenet1 Search for this author in: * NPG journals * PubMed * Google Scholar * Serafino Pantano1 Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre Moulin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Wahl1 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Mahl1 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter End1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Staedtler1 Search for this author in: * NPG journals * PubMed * Google Scholar * François Legay1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Carl1 Search for this author in: * NPG journals * PubMed * Google Scholar * David Laurie1 Search for this author in: * NPG journals * PubMed * Google Scholar * Salah-Dine Chibout1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gérard Maurer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:463–469Year published:(2010)DOI:doi:10.1038/nbt.1622Received13 October 2009Accepted22 March 2010Published online10 May 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Earlier and more reliable detection of drug-induced kidney injury would improve clinical care and help to streamline drug-development. As the current standards to monitor renal function, such as blood urea nitrogen (BUN) or serum creatinine (SCr), are late indicators of kidney injury, we conducted ten nonclinical studies to rigorously assess the potential of four previously described nephrotoxicity markers to detect drug-induced kidney and liver injury. Whereas urinary clusterin outperformed BUN and SCr for detecting proximal tubular injury, urinary total protein, cystatin C and β2-microglobulin showed a better diagnostic performance than BUN and SCr for detecting glomerular injury. Gene and protein expression analysis, in-situ hybridization and immunohistochemistry provide mechanistic evidence to support the use of these four markers for detecting kidney injury to guide regulatory decision making in drug development. The recognition of the qualification of these biomarkers! by the EMEA and FDA will significantly enhance renal safety monitoring. View full text Figures at a glance * Figure 1: Receiver operator characteristic (ROC) analyses for animals from all ten rat studies demonstrating sensitivity and specificity of BUN, SCr and urinary clusterin with respect to a composite histopathology score for tubular injury. (–) Data included were from all histopathology grades (), histopathology grades 0–2 () and histopathology grades 0 and 1 (). () AUC of BUN, SCr and urinary clusterin compared to the 'gold standard' histopathology, for the different histopathology grade subsets and corresponding standard errors represented by error bars. Animal numbers, n. Negative: n = 289. Positive: all, n = 132; 0 to 2, n = 129; 0 to 1, n = 94. * Figure 2: Marker levels for proximal tubular injury. (–) Correlation of clusterin mRNA in kidney (), of clusterin protein in kidney (), of clusterin protein in urine (), of BUN () and of SCr () with severity grades of histopathology for 739 animals in all ten studies. All values are represented as fold-changes versus the average values of study-matched and time-matched control animals on a logarithmic scale (–) and on a linear scale (,). The animals are ordered by study, within each study by dose group (with increasing doses) and within each dose group by termination time point (with increasing time). The symbols and the colors represent the histopathology readout for proximal tubular damage for the corresponding animal, from grades 0–3, on a 5-grade scale, with 0 denoting no histopathology finding observed. The magenta lines represent the thresholds determined for 95% specificity in the ROC exclusion analysis of peripheral biomarkers for all histopathology grades (1.854 for urinary cystatin C, 1.203 for BUN and 1.148 fo! r SCr). * Figure 3: ROC exclusion analysis for animals from all ten studies demonstrating sensitivity and specificity of BUN, SCr, urinary cystatin C, urinary β2-microglobulin and urinary total protein with respect to a composite histopathology score for glomerular alterations and/or damage. (,) Data included were from all histopathology grades () and histopathology grade 0 to 1 (). () AUC of BUN, SCr, urinary cystatin C, urinary β2-microglobulin and urinary total protein compared to the 'gold standard', histopathology for the different histopathology grade subsets and corresponding standard errors represented by error bars. Animal numbers, n. Negative: n = 291. Positive: all, n = 40; 0 to 1, n = 33. * Figure 4: Marker levels for glomerular injury. (–) Correlation of cystatin C protein in kidney (), cystatin C protein in urine (), β2-microglobulin protein in kidney () and β2-microglobulin in urine () with severity grades of histopathology for 739 animals in ten studies. All values are represented as in Figure 2, except that all are on a logarithmic scale. The animals are ordered as in Figure 2. The symbols and the colors represent the histopathology readout for glomerular alterations, similarly to Figure 2 for tubular damage. The magenta lines represent the thresholds determined for 99% specificity in the ROC analyses of peripheral biomarkers for all histopathology grades (3.108 for urinary cystatin C and 3.594 for urinary β2-microgolobulin) and the yellow lines represent the thresholds determined for 85% sensitivity in the ROC exclusion analyses of peripheral biomarkers for all histopathology grades (1.601 for urinary cystatin C and 1.966 for urinary β2-microglobulin). * Figure 5: Marker levels for glomerular injury. (–) Correlation of total protein in urine (), BUN () and SCr () with severity grades of histopathology for 739 animals in ten studies. All values are represented as fold-changes versus the average values of study-matched and time-matched control animals on a logarithmic scale () and on a linear scale (,). The animals are ordered by study, within each study by dose-group (with increasing doses) and within each dose-group by termination time point (with increasing time). The symbols and the colors represent the histopathology readout for glomerular alterations/damage for each animal (red = no histopathology finding observed, green = grade 1, black = grade 2 on a 5-grade scale). The magenta lines represent the thresholds determined for 99% specificity in the ROC analyses for all histopathology grades (1.904 for urinary total protein, 1.293 for BUN and 0.914 for SCr) and the yellow lines represent the thresholds determined for 85% sensitivity in the ROC exclusion analyses of p! eripheral biomarkers for all histopathology grades (0.872 for urinary total protein, 0.919 for BUN and 1.401 for SCr). The unbiased ROC analysis treats SCr as a marker negatively correlated with glomerular injury histopathology scores to obtain an AUC > 0.5 (0.5 = random). * Figure 6: Localization of clusterin, β2-microglobulin and cystatin C in kidneys. (–) Localization of clusterin by immunohistochemistry (,) and in situ hybridization (,) in control (,) and vancomycin-treated (,) animals. (–) Localization of β2-microglobulin in control animals (,) and animals treated with puromycin (), gentamicin (), doxorubicin () and vancomycin (). (–) Localization of cystatin C in control animals (,) and animals treated with puromycin (), gentamicin (), doxorubicin () and vancomycin (). Bars, 500 μm (–); 200 μm (,); 50 μm (–,–). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * NM_053021 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Novartis Institutes for BioMedical Research, Novartis, Basel, Switzerland. * Frank Dieterle, * Elias Perentes, * André Cordier, * Daniel R Roth, * Pablo Verdes, * Olivier Grenet, * Serafino Pantano, * Pierre Moulin, * Daniel Wahl, * Andreas Mahl, * Peter End, * Frank Staedtler, * François Legay, * Kevin Carl, * David Laurie, * Salah-Dine Chibout, * Jacky Vonderscher & * Gérard Maurer Contributions F.D. supervised the project, performed the data analysis and prepared the manuscript, E.P. designed the studies, supervised the histopathology and edited the manuscript, A.C. designed the studies and edited the manuscript, D.R.R. designed the studies, supervised the histopathology and edited the manuscript, P.V. performed the data analyses and edited the manuscript, O.G. performed the genomic analyses, S.P. performed the in situ hybridization and immunohistochemistry analyses and edited the manuscript, P.M. performed the in situ hybridization and immunohistochemistry and edited the manuscript, D.W. designed the database, A.M. designed the studies and edited the manuscript, P.E. performed the protein extraction and edited the manuscript, F.S. designed the studies and edited the manuscript, F.L. supervised the protein analyses, K.C. performed the regulatory submission and edited the manuscript, D.L. performed the regulatory submission and edited the manuscript, S.-D.C. supervi! sed the project, J.V. supervised the project and G.M. supervised the project, designed the studies and edited the manuscript. Competing financial interests All authors are employees of Novartis. Corresponding author Correspondence to: * Frank Dieterle (frank.dieterle@novartis.com) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 9 (444K) Data PDF files * Supplementary Text and Figures (7M) Supplementary Tables 1–8 and Supplementary Data Additional data
• Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury
  - Nature Biotechnology 28(5):470-477 (2010)
  Nature Biotechnology | Research | Article Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury * Yan Yu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Hong Jin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Holder2 Search for this author in: * NPG journals * PubMed * Google Scholar * Josef S Ozer1, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephanie Villarreal3 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Shughrue3 Search for this author in: * NPG journals * PubMed * Google Scholar * Shu Shi1 Search for this author in: * NPG journals * PubMed * Google Scholar * David J Figueroa4 Search for this author in: * NPG journals * PubMed * Google Scholar * Holly Clouse5 Search for this author in: * NPG journals * PubMed * Google Scholar * Ming Su1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nagaraja Muniappa6 Search for this author in: * NPG journals * PubMed * Google Scholar * Sean P Troth6 Search for this author in: * NPG journals * PubMed * Google Scholar * Wendy Bailey1 Search for this author in: * NPG journals * PubMed * Google Scholar * John Seng7 Search for this author in: * NPG journals * PubMed * Google Scholar * Amy G Aslamkhan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas Thudium1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank D Sistare1 Search for this author in: * NPG journals * PubMed * Google Scholar * David L Gerhold1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:470–477Year published:(2010)DOI:doi:10.1038/nbt.1624Received09 October 2009Accepted22 March 2010Published online10 May 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The capacities of urinary trefoil factor 3 (TFF3) and urinary albumin to detect acute renal tubular injury have never been evaluated with sufficient statistical rigor to permit their use in regulated drug development instead of the current preclinical biomarkers serum creatinine (SCr) and blood urea nitrogen (BUN). Working with rats, we found that urinary TFF3 protein levels were markedly reduced, and urinary albumin were markedly increased in response to renal tubular injury. Urinary TFF3 levels did not respond to nonrenal toxicants, and urinary albumin faithfully reflected alterations in renal function. In situ hybridization localized TFF3 expression in tubules of the outer stripe of the outer medulla. Albumin outperformed either SCr or BUN for detecting kidney tubule injury and TFF3 augmented the potential of BUN and SCr to detect kidney damage. Use of urinary TFF3 and albumin will enable more sensitive and robust diagnosis of acute renal tubular injury than traditional b! iomarkers. View full text Figures at a glance * Figure 1: Cisplatin kidney toxicity study. (–) Cisplatin was administered in a single intraperitoneal dose at 0, 0.5, 3.5 and 7 mg/kg. Levels of SCr (), urinary TFF3 () and urinary albumin/urinary creatinine (alb/uCr) () were measured in rats on days 3 and 8. Day and dose are indicated at the bottom. Circles indicate biomarker values from individual animals. Kidney histopathological assessment was performed on days 3 and 8. Grades 0–5 were indicated by white, yellow, orange, red, blue and black, respectively. Line indicates average for each group. * Figure 2: Gentamicin kidney toxicity study. Gentamicin sulfate was administered intraperitoneally with a dosing volume of 5 ml/kg at 0, 20, 80 or 240 mg/kg/d to groups of five rats for up to 15 d. (–) Levels of SCr (), urinary TFF3 () and urinary albumin/urinary creatinine (alb/uCr) () were measured. Day and dose are indicated at the bottom. Circles indicate biomarker values from individual animals. Kidney histopathological assessment was performed on days 3, 9, 12 and 15. Grades 0–5 were indicated by white, yellow, orange, red, blue and black, respectively. Line indicates average for each group. The 240 mg/kg/d gentamicin sulfate day-15 group was euthanized early at drug day 12 owing to signs of physical distress. * Figure 3: Carbapenem A kidney toxicity study. Carbapenem A was administered intravenously at doses of 0, 75, 150 or 225 mg/kg/d to groups of five rats. (–) Levels of SCr (), urinary TFF3 () and urinary albumin/urinary creatinine (alb/uCr) () were measured in rats. Day and dose are indicated at the bottom. Circles indicate biomarker values from individual animals. Kidney histopathological assessment was performed on days 3, 8 and 14. Grades 0–5 were indicated by white, yellow, orange, red, blue and black, respectively. Line indicates average for each group. The 225 mg/kg/d day-14 group was euthanized on day 9 owing to signs of physical distress and toxicity. * Figure 4: Genipin liver toxicity study and isoproterenol muscle and heart toxicity study. (–) Levels of urinary TFF3 ng/ml or urinary albumin/urinary creatinine (alb/uCr) were measured on day 3 after intraperitoneal administration of genipin at 75 mg/kg/d (,) and day 8 after intravenous doses of isoproterenol at 0, 0.064, 0.25 or 1 mg/kg/d (,). Day and dose are indicated at the bottom. Circles indicate biomarker values from individual animals. Liver or heart and muscle histology grades 0–5 are indicated by white, yellow, orange, red, blue and black, respectively. Line indicates average for each group. No kidney histopathology was observed. mkd, mg/kg/d. * Figure 5: ROC curves for TFF3 and for albumin compared to those for BUN, and creatinine. () ROC curves for TFF3, BUN and creatinine from ten rat studies (gentamicin, cisplatin, cyclosporin, and thioacetamide plus carbapenem A-DRS and –TS renal toxicant studies and for isoproterenol, genipin, cerivastatin, and diuresis). () ROC curves for albumin, BUN, and creatinine from 20 rat studies. The biomarkers are rank ordered for performance from top to bottom. The broken arrow marks 95% specificity. SEN, sensitivity at 95% specificity; and threshold (fold-cutoff) relative to concurrent controls to achieve 95% specificity are shown. Note that all animals with grade 0 histopathology despite treatment with a kidney toxicant were excluded for this analysis. * Figure 6: Determination of the renal source of Tff3 mRNA by in situ hybridization. (–) 35S-labeled antisense cRNA for rat Tff3 was hybridized to cross-sections of rat kidneys from vehicle-treated control rats (,,) or rats treated with carbapenem A for 11 d (,,). and represent entire sections exposed to film. and represent dark-field images expanded from outer medullary regions from and such that Tff3-hybridization shows white against a dark background of kidney tissue. and show brightfield images of the regions in rectangles from and . Scale bars, 2.4 mm (,); 240 μm (,); 40 μ (,). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Investigative Laboratory Sciences, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Yan Yu, * Hong Jin, * Josef S Ozer, * Shu Shi, * Ming Su, * Wendy Bailey, * Amy G Aslamkhan, * Douglas Thudium, * Frank D Sistare & * David L Gerhold * Department of Biometrics, Merck Research Laboratories, West Point, Pennsylvania, USA. * Daniel Holder * Department of Integrative Systems Neuroscience, Merck Research Laboratories, West Point, Pennsylvania, USA. * Stephanie Villarreal & * Paul Shughrue * Department of Clinical Oncology, GlaxoSmithKline, Collegeville, Pennsylvania, USA. * David J Figueroa * Department of Exploratory Toxicology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Holly Clouse * Department of Pathology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Nagaraja Muniappa & * Sean P Troth * Preclinical Services, Charles River Laboratories–Redfield, Redfield, Arkansas, USA. * John Seng * Present address: Pharmacokinetics, Dynamics, and Metabolism, PGRD, Pfizer, Andover Laboratories, Andover, Massachusetts, USA. * Josef S Ozer Contributions Y.Y., D.H., J.S.O., P.S., S.P.T., W.B., A.G.A., F.D.S. and D.L.G. designed and analyzed experiments. Y.Y., H.J., S.V., D.J.F., H.C., M.S., J.S., N.M., S.P.T. and S.S. performed experiments. Y.Y., D.H., J.S.O., P.S., A.G.A., D.T., F.D.S. and D.L.G. wrote and edited the manuscript. Competing financial interests All authors are employees of Merck, with the exception of J.S., who works for Charles River Laboaratories. Corresponding author Correspondence to: * David L Gerhold (david_gerhold@merck.com) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (33M) Supplementary Tables 1–3, Supplementary Fig. 1, Supplementary Data, Supplementary Assay Validation and Supplementary Results Additional data
• Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies
  - Nature Biotechnology 28(5):478-485 (2010)
  Nature Biotechnology | Research | Article Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies * Vishal S Vaidya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Josef S Ozer2, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Dieterle3 Search for this author in: * NPG journals * PubMed * Google Scholar * Fitz B Collings1 Search for this author in: * NPG journals * PubMed * Google Scholar * Victoria Ramirez1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Troth4 Search for this author in: * NPG journals * PubMed * Google Scholar * Nagaraja Muniappa4 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas Thudium2 Search for this author in: * NPG journals * PubMed * Google Scholar * David Gerhold2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Holder5 Search for this author in: * NPG journals * PubMed * Google Scholar * Norma A Bobadilla6 Search for this author in: * NPG journals * PubMed * Google Scholar * Estelle Marrer3 Search for this author in: * NPG journals * PubMed * Google Scholar * Elias Perentes3 Search for this author in: * NPG journals * PubMed * Google Scholar * André Cordier3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher3 Search for this author in: * NPG journals * PubMed * Google Scholar * Gérard Maurer3 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter L Goering7 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank D Sistare2 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph V Bonventre1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume:28,Pages:478–485Year published:(2010)DOI:doi:10.1038/nbt.1623Received08 October 2009Accepted22 March 2010Published online10 May 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Kidney toxicity accounts both for the failure of many drug candidates as well as considerable patient morbidity. Whereas histopathology remains the gold standard for nephrotoxicity in animal systems, serum creatinine (SCr) and blood urea nitrogen (BUN) are the primary options for monitoring kidney dysfunction in humans. The transmembrane tubular protein kidney injury molecule-1 (Kim-1) was previously reported to be markedly induced in response to renal injury. Owing to the poor sensitivity and specificity of SCr and BUN, we used rat toxicology studies to compare the diagnostic performance of urinary Kim-1 to BUN, SCr and urinary N-acetyl-β-D-glucosaminidase as predictors of kidney tubular damage scored by histopathology. Kim-1 outperforms SCr, BUN and urinary NAG in multiple rat models of kidney injury. Urinary Kim-1 measurements may facilitate sensitive, specific and accurate prediction of human nephrotoxicity in preclinical drug screens. This should enable early identific! ation and elimination of compounds that are potentially nephrotoxic. View full text Figures at a glance * Figure 1: Correlation of Kim-1 mRNA and protein levels in the kidney and urine, and comparison of urinary Kim-1 levels with SCr, BUN and urinary NAG with severity grades of histopathology following a dose response and time course in ten Novartis rat toxicology studies. (–) Male Han Wistar rats (n = 739) were dosed with a low, medium or high dose of eight mechanistically distinct nephrotoxicants and two hepatotoxicants, and renal Kim-1 mRNA (), renal Kim-1 protein () and urinary Kim-1 protein levels () were measured. (–) Conventional markers for kidney toxicity including SCr (), BUN () and urinary NAG () were also measured and compared to different grades of kidney tubular histopathology. All values are represented as fold-changes versus the average values of study-matched and time-matched control animals on a logarithmic scale. The animals are ordered by study, within each study by dose group (with increasing doses) and within each dose group by termination time point (with increasing time). The symbols and the colors represent the histopathology readout for proximal tubular damage (red = no histopathology finding observed, green = grade 1, blue = grade 2, black = grade 3 on a 5-grade scale). For each toxicant the animals are ordered l! eft to right by dose group (low to high). For each dose the animals are ordered from left to right by termination time point. The magenta lines represent the thresholds determined for 95% specificity in the ROC analysis for all histopathology grades. ANIT, α-naphthyl isothiocyanate. * Figure 2: ROC analysis for Novartis studies. (–) ROC curves from eight different nephrotoxicant studies and two different hepatotoxicant studies from Novartis, demonstrating sensitivity and specificity of BUN, SCr, urinary Kim-1 and NAG with respect to a composite histopathology score that included all histopathology grades (), histopathology grade 0 to 2 () and histopathology grade 0 to 1 (). (,) Area under the curve () and sensitivity () (at 95% specificity) compared to the 'gold standard', histopathology. Urinary Kim-1 and NAG were normalized to urinary creatinine. Animal numbers, n. Negative: n = 283. Positive: all, n = 132; 0 to 2, n = 129; 0 to 1, n = 94. * Figure 3: Correlation of BUN, SCr, urinary Kim-1 and urinary NAG with severity grades of histopathologic change after gentamicin treatment in the Merck study. Male Sprague Dawley rats were administered gentamicin sulfate intraperitoneally at 0, 20, 80 or 240 mg/kg/d to groups of five rats/dose/time point and the animals were euthanized on days 3, 9 or 15 for toxicity evaluation, which included serum clinical chemistry (BUN, SCr), urinary Kim-1 and NAG levels and renal histopathology (H&E staining). Open squares indicate grade 0 pathology and the composite tubular severity score is color coded from yellow (1), orange (2), purple (4) to blue (5). Black circles indicate average values of dose groups. * Figure 4: Correlation of BUN, SCr, urinary Kim-1 and urinary NAG with severity grades of histopathologic change after cisplatin nephrotoxicity treatment in the Merck study. Male Sprague Dawley rats were administered cisplatin intraperitoneally (n = 5/dose/time point) at doses of 0, 0.5, 3.5 or 7 mg/kg and rats were killed on days 3 and 8 for toxicity evaluation, which included serum clinical chemistry (BUN, SCr), urinary Kim-1 and NAG levels and renal histopathology (H&E staining). Open squares indicate grade 0 pathology and the composite tubular severity score is color coded from yellow (1), orange (2), purple (4) and blue (5). Black circles indicate average values of dose groups. uCr, urinary creatinine. * Figure 5: ROC analysis for Merck studies. (–) ROC curves from four different nephrotoxicant studies showing sensitivity and specificity of BUN, SCr, urinary Kim-1 and NAG with respect to a composite histopathology score that included all histopathology grades (), histopathology grade 0 to 3 (), histopathology grade 0 to 2 () and histopathology grade 0 and 1 (). (,) Area under the curve () and sensitivity () (at 95% specificity) of BUN, SCr, urinary Kim-1 and NAG compared to the gold standard, histopathology. Urinary Kim-1 and NAG were normalized to urinary creatinine. Animal numbers, n. Negative: n = 45. Positive: all, n = 75; 0 to 3, n = 54; 0 to 2, n = 49; 0 to 1, n = 20. * Figure 6: Comparison of Kim-1 with routinely used biomarkers as an early diagnostic indicator of kidney injury after 20-min bilateral I/R. Male Wistar rats were subjected to 0 (sham) or 20 min of bilateral ischemia by clamping the renal pedicles for 20 min and then removing the clamps and confirming reperfusion. Two hours after reperfusion the rats were placed in metabolic cages and urine, blood and tissue collected at 3, 6, 9, 12, 18, 24, 48, 72, 96 and 120 h after reperfusion. Urinary Kim-1, BUN, SCr and urinary NAG were measured and these levels were correlated to histopathology (H&E staining). Open squares indicate grade 0 pathology and the composite tubular severity score is color coded yellow (1), orange (2), red (3), purple (4) and blue (5). Author information * Abstract * Author information * Supplementary information Affiliations * Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Vishal S Vaidya, * Fitz B Collings, * Victoria Ramirez & * Joseph V Bonventre * Department of Investigative Laboratory Sciences, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Josef S Ozer, * Douglas Thudium, * David Gerhold & * Frank D Sistare * Translational Sciences, Novartis Institutes for BioMedical Research, Basel, Switzerland. * Frank Dieterle, * Estelle Marrer, * Elias Perentes, * André Cordier, * Jacky Vonderscher & * Gérard Maurer * Department of Pathology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Sean Troth & * Nagaraja Muniappa * Department of Biometrics, Merck Research Laboratories, West Point, Pennsylvania, USA. * Daniel J Holder * Molecular Physiology Unit, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México and Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico. * Norma A Bobadilla * Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, USA. * Peter L Goering * Present address: Pharmacokinetics, Dynamics, and Metabolism, PGRD, Pfizer, Andover Laboratories, Andover, Massachusetts, USA. * Josef S Ozer Contributions V.S.V., J.S.O., N.A.B., F.D.S., F.D., J.V., G.M. and J.V.B. designed research; V.S.V., J.S.O., F.B.C., V.R., S.T., N.M., D.T., D.G., D.J.H., E.P. and A.C. performed research; V.S.V., J.S.O., S.T., D.J.H., N.A.B., F.D.S. and J.V.B. contributed new reagents/analytic tools; V.S.V., J.S.O., S.T., N.M., D.T., D.G., D.J.H., N.A.B., F.D.S., E.M., F.D. and J.V.B. analyzed data; and V.S.V., J.S.O., N.A.B., F.D.S., E.M., F.D., P.L.G. and J.V.B. wrote the paper. Competing financial interests J.V.B. is an inventor on KIM-1 patents, which have been licensed by Partners Healthcare to Johnson & Johnson, Genzyme and BiogenIdec. J.S.O., S.T., N.M., D.T., D.G., D.J.H. and F.D.S. are employed by Merck. F.D., E.M. E.P. A.C. J.V. and G.M.are employed by Novartis. Corresponding authors Correspondence to: * Vishal S Vaidya (vvaidya@partners.org) or * Josef S Ozer (josef_ozer@merck.com) or * Frank D Sistare (frank.dieterle@novartis.com) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (15M) Supplementary Tables 1–7, Supplementary Figs. 1–3, Supplementary Methods and Supplementary Data Additional data
• A panel of urinary biomarkers to monitor reversibility of renal injury and a serum marker with improved potential to assess renal function
  - Nature Biotechnology 28(5):486-494 (2010)
  Nature Biotechnology | Research | Article A panel of urinary biomarkers to monitor reversibility of renal injury and a serum marker with improved potential to assess renal function * Josef S Ozer1, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Dieterle2, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Troth3 Search for this author in: * NPG journals * PubMed * Google Scholar * Elias Perentes2 Search for this author in: * NPG journals * PubMed * Google Scholar * André Cordier2 Search for this author in: * NPG journals * PubMed * Google Scholar * Pablo Verdes2 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Staedtler2 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Mahl2 Search for this author in: * NPG journals * PubMed * Google Scholar * Olivier Grenet2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel R Roth2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Wahl2 Search for this author in: * NPG journals * PubMed * Google Scholar * François Legay2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Holder4 Search for this author in: * NPG journals * PubMed * Google Scholar * Zoltan Erdos1 Search for this author in: * NPG journals * PubMed * Google Scholar * Katerina Vlasakova1 Search for this author in: * NPG journals * PubMed * Google Scholar * Hong Jin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Yu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nagaraja Muniappa3 Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Forest1 Search for this author in: * NPG journals * PubMed * Google Scholar * Holly K Clouse5 Search for this author in: * NPG journals * PubMed * Google Scholar * Spencer Reynolds1 Search for this author in: * NPG journals * PubMed * Google Scholar * Wendy J Bailey1 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas T Thudium1 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Topper1 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas R Skopek1 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Sina1 Search for this author in: * NPG journals * PubMed * Google Scholar * Warren E Glaab1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacky Vonderscher2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Gérard Maurer2 Search for this author in: * NPG journals * PubMed * Google Scholar * Salah-Dine Chibout2 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank D Sistare1 Search for this author in: * NPG journals * PubMed * Google Scholar * David L Gerhold1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:486–494Year published:(2010)DOI:doi:10.1038/nbt.1627Received09 October 2009Accepted22 March 2010Published online10 May 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Predictive Safety Testing Consortium's first regulatory submission to qualify kidney safety biomarkers revealed two deficiencies. To address the need for biomarkers that monitor recovery from agent-induced renal damage, we scored changes in the levels of urinary biomarkers in rats during recovery from renal injury induced by exposure to carbapenem A or gentamicin. All biomarkers responded to histologic tubular toxicities to varied degrees and with different kinetics. After a recovery period, all biomarkers returned to levels approaching those observed in uninjured animals. We next addressed the need for a serum biomarker that reflects general kidney function regardless of the exact site of renal injury. Our assay for serum cystatin C is more sensitive and specific than serum creatinine (SCr) or blood urea nitrogen (BUN) in monitoring generalized renal function after exposure of rats to eight nephrotoxicants and two hepatotoxicants. This sensitive serum biomarker will ena! ble testing of renal function in animal studies that do not involve urine collection. View full text Figures at a glance * Figure 1: For carbapenem A–treated rats, correlation of urinary ELISA and MesoScale Discovery biomarker levels with histomorphologic change. Male or female Sprague Dawley rats were administered carbapenem A at 150 mg/kg/d (groups of five rats/dose/time point for up to 3 d). The animals were euthanized on days 2, 4, 8 or 18 for toxicity evaluation and urinary ELISA and immunoturbometric biomarker levels (TFF3 and albumin, respectively) and MesoScale Discovery GSTα levels were measured (ng/ml) and normalized to urinary creatinine. Treated male (T:M, square), treated female (T:F, circle), vehicle male (V:M, star), and vehicle female (V:F, triangle) are indicated. TFF3 (top left), albumin (top right), and GSTα (bottom left) abundances are shown as fold-change relative to the average of concurrent controls. SCr change (bottom right) is indicated as fold-change relative to the average of concurrent controls. Red dotted line indicates threshold from ROC analysis5. The severity grades of histopathologic change after carbapenem A treatment are indicated on a scale of 0 (no observed pathology) to 5 with the indicated gra! des displayed as the following color: grade 0 (white), grade 1 (yellow), grade 2 (orange), grade 3 (red), grade 4 (blue), grade 5 (black). The histomorphologic change is shown at each necropsy day and vehicle-treated animals (control) are shown in white. Renal tubular necrosis and degeneration is shown in all biomarker panels except SCr, which is correlated to the renal composite, an overall score of tubular damage29. TFF3 control levels are high and are reduced with toxicity. TFF3 levels are displayed as fold-change in the negative direction (minus FΔ). * Figure 2: Correlation of urinary MesoScale Discovery biomarker levels with histomorphologic change for carbapenem A-treated rats. Male or female Sprague Dawley rats were administered carbapenem A at 150 mg/kg/d (groups of five rats/dose/time point for up to 3 d) and the animals were euthanized on days 2, 4, 8 or 18 for toxicity evaluation and measurement and normalization of urinary biomarker levels (Kim-1, LCN2, OPN and CLU) (ng/ml) relative to urinary creatinine. Treated male (T:M, square), treated female (T:F, circle), vehicle male (V:M, star) and vehicle female (V:F, triangle) are indicated. Abundances of Kim-1 (top left), LCN2 (top right), OPN (bottom left) and CLU (bottom right) are shown as fold-change relative to the average of concurrent controls. Red dotted line indicates threshold from ROC analysis5, 6, 7 (data not shown). The severity grades of histopathologic change after carbapenem A treatment are indicated on a scale of 0 (no observed pathology) to 5 with the indicated grades displayed as the following color: grade 0 (white), grade 1 (yellow), grade 2 (orange), grade 3 (red), grade 4 (bl! ue), grade 5 (black). The histomorphologic change is shown at each necropsy day and vehicle-treated animals (control) are shown in white. Renal tubular necrosis and degeneration are shown in all panels. * Figure 3: Correlation of urinary ELISA- and MesoScale Discovery-derived biomarker levels with histomorphologic change in gentamicin-treated rats. Male Sprague Dawley rats were administered gentamicin at 120 mg/kg/d to groups of five rats/dose/time point for 9 d and the animals were euthanized either on day 10 (upper panel) or 39 (lower panel) for toxicity evaluation. Urinary biomarker levels of albumin (ALB), CLU, GSTα, Kim-1, LCN2, OPN were measured (ng/ml) and serum chemistry parameters BUN and SCr were determined. Treated male (T:M, square), vehicle male (V:M, circle) and treated average (T:A, black triangle) are indicated. Urinary biomarker and serum chemistry values are shown as fold-change relative to the average of concurrent controls. The severity grades of histopathologic change after gentamicin treatment are indicated on a scale of 0 (no observed pathology) to 5 with the indicated grades displayed as the following color: grade 0 (white), grade 1 (yellow), grade 2 (orange), grade 3 (red), grade 4 (blue), grade 5 (black). Renal tubular necrosis and degeneration at day 10 (top panel) and regeneration at day 39! (bottom panel) is shown. Fold-change is relative to day 10 control group average. * Figure 4: ROC curves for the inclusion and exclusion analysis with eight different nephrotoxicant studies and two different hepatotoxicant studies from Novartis. (–) The sensitivity and specificity of BUN, SCr, and S-cystatin C with respect to a composite histopathology score includes data involving all histopathology grades (), histopathology grade 0 to 3 (), histopathology grade 0 to 2 () and histopathology grade 0 and 1 (). (,) Area under the curve () and sensitivity () (at 95% specificity) of BUN, SCr, and S-cystatin C compared to the gold standard, histopathology. Animal numbers, n. Negative: n = 322. Positive: all, n = 253; 0 to 3, n = 251; 0 to 2, n = 204; 0 to 1, n = 127. * Figure 5: Levels of S-cystatin C, BUN and SCr observed in individual animals. (–) Correlation of S-cystatin C (), BUN () and SCr () levels with severity grades of histopathology for 470 animals in five Novartis studies (cisplatin, gentamicin, vancomycin, tacrolimus and puromycin) involving Han Wistar rats. All values are represented as fold-changes versus the average values of study-matched and time-matched control animals on a logarithmic scale. The animals are ordered by study, within each study by dose group (with increasing doses) and within each dose group by termination time point (with increasing time). The symbols and the colors represent the histopathology readout (no histopathology finding observed (red), grade 1 (green), grade 2 (blue), grade 3 (orange) and grade 4 (black) on a 5 grade severity scale). The magenta lines represent the thresholds determined for 95% specificity in the ROC analysis for all histopathology grades (1.209 for S-cystatin C, 1.208 for BUN and 1.129 for SCr). * Figure 6: Levels of S-cystatin C, BUN and SCr observed in individual animals. (–) Correlation of S-cystatin C (), BUN () and SCr () levels with severity grades of histopathology for 474 animals in five Novartis studies (doxorubicin, lithium, furosemide, methapyrilene and ANIT) involving Han Wistar rats. All values are represented as fold-changes versus the average values of study-matched and time-matched control animals on a logarithmic scale. The animals are ordered by study, within each study by dose-group (with increasing doses) and within each dose-group by termination time point (with increasing time). The symbols and the colors represent the histopathology readout [no histopathology finding observed (red), grade 1 (green), grade 2 (blue), grade 3 (orange) on a 5 grade severity scale]. The magenta lines represent the thresholds determined for 95% specificity in the ROC analysis for all histopathology grades (1.209 for S-cystatin C, 1.208 for BUN and 1.129 for SCr). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Josef S Ozer & * Frank Dieterle Affiliations * Department of Investigative Laboratory Sciences, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Josef S Ozer, * Zoltan Erdos, * Katerina Vlasakova, * Hong Jin, * Yan Yu, * Tom Forest, * Spencer Reynolds, * Wendy J Bailey, * Douglas T Thudium, * Michael J Topper, * Thomas R Skopek, * Joseph F Sina, * Warren E Glaab, * Frank D Sistare & * David L Gerhold * Translational Sciences, Novartis Institutes for BioMedical Research, Novartis, Basel, Switzerland. * Frank Dieterle, * Elias Perentes, * André Cordier, * Pablo Verdes, * Frank Staedtler, * Andreas Mahl, * Olivier Grenet, * Daniel R Roth, * Daniel Wahl, * François Legay, * Jacky Vonderscher, * Gérard Maurer & * Salah-Dine Chibout * Department of Pathology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Sean Troth & * Nagaraja Muniappa * Department of Biometrics, Merck Research Laboratories, West Point, Pennsylvania, USA. * Daniel Holder * Department of Exploratory Toxicology, Safety Assessment, Merck Research Laboratories, West Point, Pennsylvania, USA. * Holly K Clouse * Present addresses: Pharmacokinetics, Dynamics, and Metabolism, PGRD, Pfizer, Andover Laboratories, Andover, Massachusetts, USA (J.S.O.) and Molecular Medicine Labs, Group Research, Hoffmann-La Roche, Basel, Switzerland (J.V.). * Josef S Ozer & * Jacky Vonderscher Contributions J.S.O., F.D., W.J.B., M.J.T., T.R.S., J.F.S., W.E.G., E.P., A.C., F.S., A.M., O.G., D.R.R., F.L., S.-D.C., G.M., J.V., D.L.G., F.D.S. and D.W. designed research; Z.E., T.F., N.M., E.P., D.R.R., S.T., H.K.C., S.R., D.T.T., K.V. and H.J. performed research; Z.E. and K.V. contributed new reagents/analytic tools; J.S.O., D.H., N.M., W.E.G., F.D., Y.Y., G.M., P.V., A.C., D.L.G. and F.D.S. analyzed data; and J.S.O., S.T., Z.E., K.V., F.D., D.L.G. and F.D.S. wrote the paper. Competing financial interests All authors are present or past employees of Merck or Novartis. Corresponding author Correspondence to: * David L Gerhold (david_gerhold@merck.com) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Data Set (315K) PDF files * Supplementary Text and Figures (656K) Supplementary Tables 1,2 and Supplementary Figs. 1–4 Additional data
• GREAT improves functional interpretation of cis-regulatory regions
  McLean CY Bristor D Hiller M Clarke SL Schaar BT Lowe CB Wenger AM Bejerano G - Nature Biotechnology 28(5):495-501 (2010)
  Nature Biotechnology | Research | Analysis GREAT improves functional interpretation of cis-regulatory regions * Cory Y McLean1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dave Bristor1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Hiller2 Search for this author in: * NPG journals * PubMed * Google Scholar * Shoa L Clarke3 Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce T Schaar2 Search for this author in: * NPG journals * PubMed * Google Scholar * Craig B Lowe4 Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron M Wenger1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gill Bejerano1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:495–501Year published:(2010)DOI:doi:10.1038/nbt.1630Published online02 May 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chroma! tin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets. View full text Figures at a glance * Figure 1: Enrichment analysis of a set of cis-regulatory regions. () The current prevailing methodology associates only proximal binding events with genes and performs a gene-list test of functional enrichments using tools originally designed for microarray analysis. () GREAT's binomial approach over genomic regions uses the total fraction of the genome associated with a given ontology term (green bar) as the expected fraction of input regions associated with the term by chance. * Figure 2: Binding profiles and their effects on statistical tests. () ChIP-seq data sets of several regulatory proteins show that the majority of binding events lie well outside the proximal promoter, both for sequence-specific transcription factors (SRF and NRSF, ref. 8; Stat3, ref. 43) and a general enhancer-associated protein (p300, refs. 33,43). Cell type is given in parentheses: H, human; M, mouse. () When not restricted to proximal promoters, the gene-based hypergeometric test (red) generates false positive enriched terms, especially at the size range of 1,000–50,000 input regions typical of a ChIP-seq set. Negligible false positive enrichment was observed for the region-based binomial test (blue). For each set size, we generated 1,000 random input sets in which each base pair in the human genome was equally likely to be included in each set, avoiding assembly gaps. We calculated all GO term enrichments for both hypergeometric and binomial tests using GREAT's 5+1 kb basal promoter and up to 1 Mb extension association rule (see Resul! ts). Plotted is the average number of terms artificially significant at a threshold of 0.05 after application of the conservative Bonferroni correction. () GO enrichment P values using the genomic region-based binomial (x axis) and gene-based hypergeometric (y axis) tests on the SRF data8 with GREAT's 5+1 kb basal promoter and up to 1 Mb extension association rule (see Results). b1 through b10 denote the top ten most enriched terms when we used the binomial test. h1 through h10 denote the top ten most enriched terms when we used the hypergeometric test. Terms significant by both tests (B ∩ H) provide specific and accurate annotations supported by multiple genes and binding events (Table 3). Terms significant by only the hypergeometric test (H\B) are general and often associated with genes of large regulatory domains, whereas terms significant by only the binomial test (B\H) cluster four to six genomic regions near only one or two genes annotated with the term (Supplementa! ry Table 46). * Figure 3: Distal binding events contribute substantially to accurate functional enrichments of p300 limb peaks. We examined properties of the 2,105 p300 mouse embryonic limb peaks33 in the context of three known limb-related terms and a negative control term (GO cortical cytoskeleton). Three different association rules were used (see Results): a gene-based GREAT analysis using only peaks within 2 kb of the nearest transcription start site (labeled 2 kb), an analysis with 5+1 kb basal and up to 50 kb extension (50 kb), and an analysis with 5+1 kb basal and up to 1 Mb extension (1 Mb). For each term, we examined the relevance of distal binding peaks by comparing the experimental results (black bars) to the average values of 1,000 simulated data sets (gray bars) in which the 192 proximal ChIP-seq peaks within 2 kb of the nearest transcription start site were fixed and the 1,913 distal peaks were shuffled uniformly within the mouse genome, avoiding assembly gaps and proximal promoters. By design, simulation results for proximal, 2-kb GREAT are identical to the actual data and are thus omi! tted. () Lengthening a 2-kb proximal promoter to a 50-kb extension, expected to increase genome coverage per term (pπ in Fig. 1b) by 25-fold, causes an actual increase of 19- to 24-fold; in contrast, lengthening a 50-kb extension rule to a 1-Mb extension rule, expected to raise genome coverage 20-fold, leads to an actual increase of only 2.5- to 6-fold because regulatory domains are not extended through neighboring genes. () As regulatory domains increase in length from only the proximal 2 kb up to 50 kb and 1 Mb, the number of relevant genes with a p300 limb peak in their regulatory domain increases. The added genes selected only by distal associations are typically enriched for limb functionality compared to simulated data. () As regulatory domains increase in length, the number of p300 limb peaks associated with a relevant gene in excess of the number expected by chance increases for all limb-related terms. () As in , the inclusion of distal peaks markedly increases the! statistical significance of the correct terms alone. *Statist! ical significance is measured using the hypergeometric test over genes for 2 kb to mimic current gene-based approaches, and using the binomial test over genomic regions for 50 kb and 1 Mb. Error bars indicate s.d.; NS, not significant at a threshold of 0.05 after false discovery rate multiple test correction; obs, observed; exp, expected. Note scale changes on x axes. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Computer Science, Stanford University, Stanford, California, USA. * Cory Y McLean, * Dave Bristor, * Aaron M Wenger & * Gill Bejerano * Department of Developmental Biology, Stanford University, Stanford, California, USA. * Dave Bristor, * Michael Hiller, * Bruce T Schaar & * Gill Bejerano * Department of Genetics, Stanford University, Stanford, California, USA. * Shoa L Clarke * Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California, USA. * Craig B Lowe Contributions C.Y.M. developed the core calculation engine, processed ontologies, analyzed data sets and co-wrote the manuscript. D.B. designed and developed the web application. M.H. added key ontologies and calculated ontology statistics. S.L.C. performed and wrote the SRF analysis. B.T.S. contributed to data set analysis and manuscript writing. A.M.W. guided website design and wrote user documentation. G.B. and C.B.L. devised the different enrichment tests and developed early core calculation engines. G.B. supervised the project and co-wrote the manuscript. All authors edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gill Bejerano (bejerano@stanford.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Note, Supplementary Figures 1–4 and Supplementary Tables 1–46 Additional data
• Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs
  Guttman M Garber M Levin JZ Donaghey J Robinson J Adiconis X Fan L Koziol MJ Gnirke A Nusbaum C Rinn JL Lander ES Regev A - Nature Biotechnology 28(5):503-510 (2010)
  Nature Biotechnology | Research | Article Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs * Mitchell Guttman1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Garber1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Z Levin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Julie Donaghey1 Search for this author in: * NPG journals * PubMed * Google Scholar * James Robinson1 Search for this author in: * NPG journals * PubMed * Google Scholar * Xian Adiconis1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Fan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Magdalena J Koziol1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Gnirke1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chad Nusbaum1 Search for this author in: * NPG journals * PubMed * Google Scholar * John L Rinn1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric S Lander1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Aviv Regev1, 2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume:28,Pages:503–510Year published:(2010)DOI:doi:10.1038/nbt.1633Received10 March 2010Accepted06 April 2010Published online02 May 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Massively parallel cDNA sequencing (RNA-Seq) provides an unbiased way to study a transcriptome, including both coding and noncoding genes. Until now, most RNA-Seq studies have depended crucially on existing annotations and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We applied it to mouse embryonic stem cells, neuronal precursor cells and lung fibroblasts to accurately reconstruct the full-length gene structures for most known expressed genes. We identified substantial variation in protein coding genes, including thousands of novel 5′ start sites, 3′ ends and internal coding exons. We then determined the gene structures of more than a thousand large intergenic noncoding RNA (lincRNA) and antisense loci. Our results open the way to direct experimental manipulation of thousands of noncoding RNAs and demonstrat! e the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes. View full text Figures at a glance * Figure 1: Scripture: a method for ab initio transcriptome reconstruction from RNA-Seq data. () Spliced and unspliced reads. A typical expressed four-exon gene (1500032D16Rik, top; exons, gray boxes) with coverage from different type of reads. Unspliced reads (black bars) fall within a single exon, whereas spliced reads (bars broken into 'dumbbells') span exon–exon junctions (thin horizontal lines connect the alignment of a read to the exons it spans). The coverage track (bottom) shows the aggregate coverage of both spliced and unspliced reads. (–) A schematic description of Scripture. () A cartoon example. Reads (black bars) originate from sequencing a contiguous RNA molecule. Shown are transcripts from two different genes (blue and red boxes), one with seven exons (blue boxes) and one with three exons (red boxes), which are adjacent in the genome (black line). The grayscale vertical shading in subsequent panels is shown for visual tracking. () Spliced reads. Scripture is initiated with a reference genome sequence and spliced aligned reads (dumbbells) with gaps! in their alignment (thin horizontal lines). Scripture uses splice site information to orient spliced reads (arrowheads). () Connectivity graph construction. Scripture builds a connectivity graph by drawing an edge (curved arrow) between any two bases that are connected by a spliced read gap. Edges are color coded to relate to the original RNA and eventual transcript. () Path scoring. Scripture scans the graph with fixed-sized windows and uses coverage from all reads (spliced and unspliced; bottom track) to score each path for significance (P-values shown as edge labels). () Transcript graph construction. Scripture merges all significant windows and uses the connectivity graph to give significant segments a graph structure (three graphs, in this example). () Refinement with paired-end data. Scripture uses paired-end (dashed curved lines) to join previously disconnected graphs (gene 1, bold dashed line), find breakpoint regions within contiguous segments (detectable in this ! example by the lack of dashed lines between genes 1 and 2) and! eliminate isoforms that result in paired-end reads mapping at a distance with low likelihood. * Figure 2: Scripture correctly reconstructs full-length transcripts for most annotated protein coding genes. () A typical Scripture reconstruction on mouse chromosome 9. Top, RNA-Seq read coverage (from both unspliced and spliced reads); middle, three transcripts reconstructed by Scripture, including exons (black boxes) and orientation (arrow heads); bottom, RefSeq annotations for this region. All three transcripts are fully reconstructed from 5′ to 3′ ends, capturing all internal exons; Scripture correctly reconstructed the overlapping transcripts Pus3 and Hyls1. () Fraction of genes fully reconstructed in different expression quantiles (in 5% increments) in ESC. Each bar represents a 5% quantile of read coverage for genes expressed; mean read coverage is noted in blue. The height of each bar is the fraction of genes in that quantile that were fully reconstructed. For example, ~20% of the transcripts at the bottom 5% of expression levels were fully reconstructed; ~94% of the genes at the top 95% of expression are fully reconstructed. () Portion of gene length reconstructed in ! different expression quantiles in ESC. Shown is a box plot of the portion of each transcript's length that was covered by a Scripture reconstruction in each 5% coverage quantile. Black line in each box, median; rectangle, 25%–75% coverage quantiles; whiskers, extreme coverage values within expression quantile. For example, at the bottom 5% of expression, Scripture reconstructed a median length of 60% of the full length transcript. * Figure 3: Alternative 5′ ends, 3′ ends and novel coding exons in transcripts reconstructed by Scripture. Representative examples (tracks, left) and summary counts (Venn diagrams, right numbers represent those unique to each cell type compared to other two) of five categories of variation discovered in Scripture transcripts compared to the known annotations. In each representative example, shown is the coverage by RNA-Seq reads (top track), the reconstructed annotation (middle track) and the known annotation (bottom track). The novel regions in the reconstruction are marked by gray shading. In each proportional Venn diagram we show the number of transcripts in this class in each cell type (ESC, green; NPC, blue; MLF, red) and their overlap. () Internal alternative 5′ start sites. () External alternative 5′ start sites. () Alternative downstream 3′ end (extended termination). () Alternative upstream 3′ end (early termination). () Novel coding exons. * Figure 4: Noncoding transcripts reconstructed by Scripture. () A representative example of a lincRNA expressed in ESC. Top: mouse genomic locus containing the lincRNA and its neighboring protein coding genes. Bottom: magnified view of the lincRNA locus showing the coverage of H3K4me3 (green track), H3K36me3 (blue track) and RNA-Seq reads (red track) overlapping the transcribed lincRNA locus, as well as its Scripture reconstructed transcript isoforms (black). () A representative example of a multi-exonic antisense ncRNA expressed in ESC. Top: mouse genomic locus containing the antisense transcript. Bottom: magnified view of the antisense locus showing the coverage of H3K4me3 (green track), H3K36me3 (blue track) and RNA-Seq reads (red track) overlapping the transcribed antisense locus, as well as its Scripture reconstructed gene structure (black) and the annotated overlapping transcript (blue). * Figure 5: Protein coding capacity, conservation levels and expression of lincRNAs and multi-exonic antisense transcripts. (,) Coding capacity of protein coding, lincRNAs and multi-exonic antisense transcripts. Shown is the cumulative distribution of CSF scores () and maximal ORF length () for protein coding transcripts, lincRNAs and multi-exonic antisense transcripts. () Conservation levels for exons from protein coding transcripts, lincRNAs, multi-exonic antisense transcripts and introns. Shown is the cumulative distribution of sequence conservation across 29 mammals for exons from protein coding exons, introns, exons from previously annotated lincRNA loci, exons from newly annotated lincRNA transcripts and exons from multi-exonic antisense transcripts. () Expression levels of protein coding, lincRNAs and multi-exonic antisense transcripts. Shown is the cumulative distribution of expression levels, in reads per kilobase of exonic sequence per million aligned reads (RPKM) in ESC for protein coding transcripts, transcripts from previously annotated lincRNA loci, transcripts from newly annotated ! lincRNA loci and multi-exonic antisense transcripts. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE20851 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Mitchell Guttman & * Manuel Garber Affiliations * Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Mitchell Guttman, * Manuel Garber, * Joshua Z Levin, * Julie Donaghey, * James Robinson, * Xian Adiconis, * Lin Fan, * Magdalena J Koziol, * Andreas Gnirke, * Chad Nusbaum, * John L Rinn, * Eric S Lander & * Aviv Regev * Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Mitchell Guttman, * Eric S Lander & * Aviv Regev * Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. * Magdalena J Koziol & * John L Rinn * Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. * Eric S Lander * Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Aviv Regev Contributions M. Guttman and M. Garber conceived the project, designed research, implemented Scripture, performed computational analysis and wrote the paper. A.G., C.N. and J.Z.L. oversaw cDNA sequencing, provided molecular biology advice and helped to edit the manuscript. J.D. constructed cDNA libraries, performed validation experiments and helped to edit the manuscript. J.R. implemented components of Scripture and provided computational support and technical advice. X.A., L.F. and M.J.K. constructed cDNA libraries. J.L.R. provided reagents and helped edit the manuscript. E.S.L. designed research direction and wrote the paper. A.R. provided cDNA sequencing guidance, conceived the project, designed research direction and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Mitchell Guttman (mguttman@mit.edu) or * Manuel Garber (mgarber@broadinstitute.org) or * Aviv Regev (aregev@broad.mit.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (12K) Number of novel transcriptional events in ES, MLF and NPC * Supplementary Table 2 (16K) Primer sequences used for validation of novel events Zip files * Supplementary Software (15M) scripture.jar scripture.src.tgz * Supplementary Data (38M) ES.gff.gz ESTranscriptGraphs.tar.gz * Supplementary Data (15M) MLF.gff.gz MLFTranscriptGraphs.tar.gz * Supplementary Data (42M) NPC.gff.gz NPCTranscriptGraphs.tar.gz PDF files * Supplementary Text and Figures (3M) Supplementary Notes 1 and 2, Supplementary Figures 1–7 Additional data
• Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
  Trapnell C Williams BA Pertea G Mortazavi A Kwan G van Baren MJ Salzberg SL Wold BJ Pachter L - Nature Biotechnology 28(5):511-515 (2010)
  Nature Biotechnology | Research | Letter Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation * Cole Trapnell1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Brian A Williams4 Search for this author in: * NPG journals * PubMed * Google Scholar * Geo Pertea2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ali Mortazavi4 Search for this author in: * NPG journals * PubMed * Google Scholar * Gordon Kwan4 Search for this author in: * NPG journals * PubMed * Google Scholar * Marijke J van Baren5 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven L Salzberg1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara J Wold4 Search for this author in: * NPG journals * PubMed * Google Scholar * Lior Pachter3, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:511–515Year published:(2010)DOI:doi:10.1038/nbt.1621Received02 February 2010Accepted22 March 2010Published online02 May 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation1, 2, 3. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-st! udied model of muscle development and that it can improve transcriptome-based genome annotation. View full text Figures at a glance * Figure 1: Overview of Cufflinks. () The algorithm takes as input cDNA fragment sequences that have been aligned to the genome by software capable of producing spliced alignments, such as TopHat. (–) With paired-end RNA-Seq, Cufflinks treats each pair of fragment reads as a single alignment. The algorithm assembles overlapping 'bundles' of fragment alignments (,) separately, which reduces running time and memory use, because each bundle typically contains the fragments from no more than a few genes. Cufflinks then estimates the abundances of the assembled transcripts (,). The first step in fragment assembly is to identify pairs of 'incompatible' fragments that must have originated from distinct spliced mRNA isoforms (). Fragments are connected in an 'overlap graph' when they are compatible and their alignments overlap in the genome. Each fragment has one node in the graph, and an edge, directed from left to right along the genome, is placed between each pair of compatible fragments. In this example, the ye! llow, blue and red fragments must have originated from separate isoforms, but any other fragment could have come from the same transcript as one of these three. Isoforms are then assembled from the overlap graph (). Paths through the graph correspond to sets of mutually compatible fragments that could be merged into complete isoforms. The overlap graph here can be minimally 'covered' by three paths, each representing a different isoform. Dilworth's Theorem states that the number of mutually incompatible reads is the same as the minimum number of transcripts needed to 'explain' all the fragments. Cufflinks implements a proof of Dilworth's Theorem that produces a minimal set of paths that cover all the fragments in the overlap graph by finding the largest set of reads with the property that no two could have originated from the same isoform. Next, transcript abundance is estimated (). Fragments are matched (denoted here using color) to the transcripts from which they could ha! ve originated. The violet fragment could have originated from ! the blue or red isoform. Gray fragments could have come from any of the three shown. Cufflinks estimates transcript abundances using a statistical model in which the probability of observing each fragment is a linear function of the abundances of the transcripts from which it could have originated. Because only the ends of each fragment are sequenced, the length of each may be unknown. Assigning a fragment to different isoforms often implies a different length for it. Cufflinks can incorporate the distribution of fragment lengths to help assign fragments to isoforms. For example, the violet fragment would be much longer, and very improbable according to the Cufflinks model, if it were to come from the red isoform instead of the blue isoform. Last, the program numerically maximizes a function that assigns a likelihood to all possible sets of relative abundances of the yellow, red and blue isoforms (γ1,γ2,γ3) (), producing the abundances that best explain the observed frag! ments, shown as a pie chart. * Figure 2: Distinction of transcriptional and post-transcriptional regulatory effects on overall transcript output. () When abundances of isoforms A, B and C of Myc are grouped by TSS, changes in the relative abundances of the TSS groups indicate transcriptional regulation. Post-transcriptional effects are seen in changes in levels of isoforms of a single TSS group. () Isoforms of Myc have distinct expression dynamics. () Myc isoforms are downregulated as the time course proceeds. The width of the colored band is the measure of change in relative transcript abundance and the color is the log ratio of transcriptional and post-transcriptional contributions to change in relative abundances (plot construction detailed in Supplementary Methods, section 5.3). Changes in relative abundances of Myc isoforms suggest that transcriptional effects immediately following differentiation at 0 h give way to post-transcriptional effects later in the time course, as isoform A is eliminated. * Figure 3: Excluding isoforms discovered by Cufflinks from the transcript abundance estimation affects the abundance estimates of known isoforms, in some cases by orders of magnitude. FHL3 inhibits myogenesis by binding MyoD and attenuating its transcriptional activity. () The C2C12 transcriptome contains a novel isoform that is dominant during proliferation. The new TSS for FHL3 is supported by proximal TAF1 and RNA polymerase II ChIP-Seq peaks. () The known isoform (solid line) is preferred at time points following differentiation. * Figure 4: Robustness of assembly and abundance estimation as a function of expression level and depth of sequencing. Subsets of the full 60-h read set were mapped and assembled with TopHat and Cufflinks, and the resulting assemblies were compared for structural and abundance agreement with the full 60-h assembly. Colored lines show the results obtained at different depths of sequencing in the full assembly; for example, the light blue line tracks the performance for transcripts with FPKM >60. () The fraction of transcript fragments fully recovered increases with additional sequencing data, although nearly 75% of moderately expressed transcripts (≥15 FPKM) are recovered with fewer than 40 million 75-bp paired-end reads (20 million fragments), a fraction of the data generated by a single run of the sequencer used in this experiment. () Abundance estimates are similarly robust. At 40 million reads, transcripts determined to be moderately expressed using all 60-h reads were estimated at within 15% of their final FPKM values. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE20846 Author information * Accession codes * Author information * Supplementary information Affiliations * Department of Computer Science, University of Maryland, College Park, Maryland, USA. * Cole Trapnell & * Steven L Salzberg * Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA. * Cole Trapnell, * Geo Pertea & * Steven L Salzberg * Department of Mathematics, University of California, Berkeley, California, USA. * Cole Trapnell & * Lior Pachter * Division of Biology and Beckman Institute, California Institute of Technology, Pasadena, California, USA. * Brian A Williams, * Ali Mortazavi, * Gordon Kwan & * Barbara J Wold * Genome Sciences Center, Washington University in St. Louis, St. Louis, Missouri, USA. * Marijke J van Baren * Department of Molecular and Cell Biology, University of California, Berkeley, California, USA. * Lior Pachter * Department of Computer Science, University of California, Berkeley, California, USA. * Lior Pachter Contributions C.T. and L.P. developed the mathematics and statistics and designed the algorithms; B.A.W. and G.K. performed the RNA-Seq and B.A.W. designed and executed experimental validations; C.T. implemented Cufflinks and Cuffdiff; G.P. implemented Cuffcompare; M.J.v.B. and A.M. tested the software; C.T., G.P. and A.M. performed the analysis; L.P., A.M. and B.J.W. conceived the project; C.T., L.P., A.M., B. J.W. and S.L.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Lior Pachter (lpachter@math.berkeley.edu) Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 4 (84K) Genes with complex isoform expression dynamics in C2C12 myogenesis Zip files * Supplementary Data (5M) PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1–3, Supplementary Figs. 1–11 and Supplementary Methods Additional data
• Single base–resolution methylome of the silkworm reveals a sparse epigenomic map
  Xiang H Zhu J Chen Q Dai F Li X Li M Zhang H Zhang G Li D Dong Y Zhao L Lin Y Cheng D Yu J Sun J Zhou X Ma K He Y Zhao Y Guo S Ye M Guo G Li Y Li R Zhang X Ma L Kristiansen K Guo Q Jiang J Beck S Xia Q Wang W Wang J - Nature Biotechnology 28(5):516-520 (2010)
  Nature Biotechnology | Research | Letter Single base–resolution methylome of the silkworm reveals a sparse epigenomic map * Hui Xiang1, 2, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jingde Zhu3, 4, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Quan Chen2, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Fangyin Dai5, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Li1, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Muwang Li6 Search for this author in: * NPG journals * PubMed * Google Scholar * Hongyu Zhang3 Search for this author in: * NPG journals * PubMed * Google Scholar * Guojie Zhang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Dong Li5 Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Dong1 Search for this author in: * NPG journals * PubMed * Google Scholar * Li Zhao1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ying Lin5 Search for this author in: * NPG journals * PubMed * Google Scholar * Daojun Cheng5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Yu3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jinfeng Sun3 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoyu Zhou3 Search for this author in: * NPG journals * PubMed * Google Scholar * Kelong Ma3 Search for this author in: * NPG journals * PubMed * Google Scholar * Yinghua He3 Search for this author in: * NPG journals * PubMed * Google Scholar * Yangxing Zhao3 Search for this author in: * NPG journals * PubMed * Google Scholar * Shicheng Guo3 Search for this author in: * NPG journals * PubMed * Google Scholar * Mingzhi Ye2 Search for this author in: * NPG journals * PubMed * Google Scholar * Guangwu Guo2 Search for this author in: * NPG journals * PubMed * Google Scholar * Yingrui Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ruiqiang Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiuqing Zhang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Lijia Ma2 Search for this author in: * NPG journals * PubMed * Google Scholar * Karsten Kristiansen7 Search for this author in: * NPG journals * PubMed * Google Scholar * Qiuhong Guo8 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianhao Jiang8 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan Beck9 Search for this author in: * NPG journals * PubMed * Google Scholar * Qingyou Xia5 Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Wang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang2, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume:28,Pages:516–520Year published:(2010)DOI:doi:10.1038/nbt.1626Received08 February 2010Accepted23 March 2010Published online02 May 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Epigenetic regulation in insects may have effects on diverse biological processes. Here we survey the methylome of a model insect, the silkworm Bombyx mori, at single-base resolution using Illumina high-throughput bisulfite sequencing (MethylC-Seq). We conservatively estimate that 0.11% of genomic cytosines are methylcytosines, all of which probably occur in CG dinucleotides. CG methylation is substantially enriched in gene bodies and is positively correlated with gene expression levels, suggesting it has a positive role in gene transcription. We find that transposable elements, promoters and ribosomal DNAs are hypomethylated, but in contrast, genomic loci matching small RNAs in gene bodies are densely methylated. This work contributes to our understanding of epigenetics in insects, and in contrast to previous studies of the highly methylated genomes of Arabidopsis1 and human2, demonstrates a strategy for sequencing the epigenomes of organisms such as insects that have low l! evels of methylation. View full text Figures at a glance * Figure 1: DNA methylation patterns and chromosomal distribution in Bombyx mori. () Fraction of mCs identified in each sequence context for the strain Dazao, indicating rather low and non-CG methylation, which are likely to be false positives. () Distribution of mCs (y axis) across methylation levels (x axis). Methylation level was determined by dividing the number of reads covering each mC by the total reads covering that cytosine. () Density of mCs identified on the two DNA strands (Watson and Crick) throughout chromosome 1 (out of 28). Density was calculated in 25-kb bins. The value refers to the number of mCs per base pair, as shown on the y axis. * Figure 2: Methylation of different functional regions of Bombyx mori (Dazao). Absolute methylation level was calculated as total methylation level of mCs divided by length of the corresponding region. Relative methylation level was calculated as total methylation level of mCs divided by total number of CGs in the corresponding regions. () Methylation level at different functional regions. (,) Analysis of coding genes. Two-kilobase regions upstream and downstream of each gene were divided into 100–base pair (bp) intervals. Each gene was divided into 20 intervals (5% per interval). Plots show methylation level () or percentage of TEs and smRNAs () in each interval. A schematic representation of a gene is shown as a thick horizontal bar (scaled to 10 kb, the average length of Bombyx mori gene coding regions). () Abundance of genomic loci of smRNAs was plotted for TEs, as in , except that the upstream and downstream lengths are 0.5 kb, considering that the average length of TEs is 200 bp. () Relationship between methylation level and GC content, CpG din! ucleotide density and CpG(O/E)22. Genes were ranked based on absolute methylation level or relative methylation level . 0, unmethylated genes. 1–10, the lowest to the highest methylated genes. Genes were divided into deciles based on methylation levels, from the bottom 10% to the top 10%. 0, unmethylated genes. 1–10, the lowest to the highest deciles of methylated genes. * Figure 3: Relationship between DNA methylation and expression levels of genes in Bombyx mori (Dazao). () Methylation level within gene bodies divided by expression level. Genes were classified into quintiles based on expression: 1st quintile is lowest and 5th is highest. Two-kilobase regions upstream and downstream of each gene were divided into 100-bp intervals. Each gene was divided into 20 intervals (5% per interval). Plots show the methylation level of each interval. () Expression of methylated compared with unmethylated genes. Genes were rank-ordered based on gene body methylation level and divided into quintiles. For the methylated genes, 1st quintile is the lowest and 5th is the highest. () Spearman correlation index between methylation level and gene expression level. Two-kilobase regions upstream and downstream of each gene were divided into 100-bp intervals. Each gene was divided into 20 intervals (5% each interval). Plots show the Spearman correlation index of each interval. () The same as , except for promoter methylation. Absolute and relative methylation levels! were calculated as described for Figure 2. * Figure 4: Annotation and microarray analysis of methylated and unmethylated genes. (,) Annotation of methylated () and unmethylated genes () with WEGO26. Of the 5,971 genes that have GO annotations, 2,333 methylated and 3,314 unmethylated genes showed significant enrichment difference (P < 0.05, χ2 test) compared with total analyzed genes. Annotations are grouped by molecular function or biological process based on the silkworm Bombyx mori GO annotation information (ftp://silkdb.org/pub/current/otherdata/Gene_ontology/silkworm_glean_gene.go). Gene numbers and percentages (on log scale) are listed for each category. (,) Expression in the anterior-mid silk gland () and posterior silk gland () of methylated and unmethylated genes examined by microarray analysis. () Tissue expression specificity of methylated and unmethylated genes measured by τ value27. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE18315 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hui Xiang, * Jingde Zhu, * Quan Chen, * Fangyin Dai & * Xin Li Affiliations * CAS-Max Planck Junior Research Group, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, The Chinese Academy of Sciences, Kunming, China. * Hui Xiang, * Xin Li, * Yang Dong, * Li Zhao & * Wen Wang * BGI-Shenzhen, Shenzhen, China. * Hui Xiang, * Quan Chen, * Guojie Zhang, * Mingzhi Ye, * Guangwu Guo, * Yingrui Li, * Ruiqiang Li, * Xiuqing Zhang, * Lijia Ma & * Jun Wang * Cancer Epigenetics and Gene Therapy Program, The State-key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiaotong University, Shanghai, China. * Jingde Zhu, * Hongyu Zhang, * Jian Yu, * Jinfeng Sun, * Xiaoyu Zhou, * Kelong Ma, * Yinghua He, * Yangxing Zhao & * Shicheng Guo * Cancer Epigenetics Laboratory, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China. * Jingde Zhu * The Key Sericultural Laboratory of Agricultural Ministry, College of Biotechnology, Institute of Sericulture and Systems Biology, Southwest University, Chongqing, China. * Fangyin Dai, * Dong Li, * Ying Lin, * Daojun Cheng & * Qingyou Xia * Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, China. * Muwang Li * Department of Biology, University of Copenhagen, Denmark. * Karsten Kristiansen & * Jun Wang * Shanghai Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, Shanghai, China. * Qiuhong Guo & * Jianhao Jiang * UCL Cancer Institute, University College London, London, UK. * Stephan Beck Contributions J.W., W.W., J.Z. and Q.X. designed the study. H.X., W.W. and X.L. wrote the manuscript. X.L., G.Z., Q.C., Y.L. and R.L. developed the method for mapping and processing BS reads. D.L. and D.C., performed microarray analysis. F.D. and M.L. provided the domestic silkworm samples and detailed background information on silkworm domestication and breeding. H.X. and X.L. analyzed the 454 data. H.X. did RT-PCR. Y.D. performed the methyltransferase assay. H.X., Y.L., Q.G. and J.J. extracted DNAs and RNAs. J.Z., H.Z., J.Y., J.S., X.Z., K.M., L.Z., Y.H., S.G. and Y.Z. constructed the BS-seq libraries and conducted the BS validation. G.G., X.Z., L.M., M.Y. and K.K. performed the Solexa sequencing. S.B. contributed to the interpretation of the results. All authors have read and contributed to the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jun Wang (wangj@genomics.org.cn) or * Wen Wang (wwang@mail.kiz.ac.cn) or * Qingyou Xia (xiaqy@swu.edu.cn) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (868K) Supplementary Tables 1–4 and Figures 1–5 Additional data
• Dynamic single-cell imaging of direct reprogramming reveals an early specifying event
  Smith ZD Nachman I Regev A Meissner A - Nature Biotechnology 28(5):521-526 (2010)
  Nature Biotechnology | Research | Letter Dynamic single-cell imaging of direct reprogramming reveals an early specifying event * Zachary D Smith1, 2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Iftach Nachman1, 3, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Aviv Regev1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume:28,Pages:521–526Year published:(2010)DOI:doi:10.1038/nbt.1632Received16 March 2010Accepted05 April 2010Published online02 May 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The study of induced pluripotency often relies on experimental approaches that average measurements across a large population of cells, the majority of which do not become pluripotent. Here we used high-resolution, time-lapse imaging to trace the reprogramming process over 2 weeks from single mouse embryonic fibroblasts (MEFs) to pluripotency factor–positive colonies. This enabled us to calculate a normalized cell-of-origin reprogramming efficiency that takes into account only the initial MEFs that respond to form reprogrammed colonies rather than the larger number of final colonies. Furthermore, this retrospective analysis revealed that successfully reprogramming cells undergo a rapid shift in their proliferative rate that coincides with a reduction in cellular area. This event occurs as early as the first cell division and with similar kinetics in all cells that form induced pluripotent stem (iPS) cell colonies. These data contribute to the theoretical modeling of reprog! ramming and suggest that certain parts of the reprogramming process follow defined rather than stochastic steps. View full text Figures at a glance * Figure 1: Continuous single-cell imaging allows tracking of reprogramming cells. () Tracking of uniquely labeled inducible fibroblast populations over a reprogramming time series. Selected images are displayed as a global 4 × 4 field in phase contrast (upper panel) and with respective wavelengths highlighted (lower panel). All images are at 10× magnification. () 4 × 4 multi-wavelength overlay at t = 0 d. These images were used to accurately count the number of seeded (and attached) starting MEFs for direct assessment of reprogramming efficiency in equivalently induced populations. Cells of a given wavelength (here yellow fluorescent protein (YFP), n = 78) within the tracked field were enumerated for downstream analysis. () Terminal (day 12.5) Cdh1 immunostaining demarcates successfully reprogrammed colonies and demonstrates the equitable distribution of colony-forming events across analyzed wavelengths and for the population as a whole. Yellow arrowheads mark colonies that originated from unique YFP-labeled MEFs. Red arrowheads mark colonies that orig! inated from red fluorescent protein (RFP)-labeled MEFs. Magenta numbers indicate colonies (circled with dashed line) that were counted. Efficiencies provided are based on the number of marker-positive colonies divided by the number of MEFs counted in (YFP and RFP) or the total number (including unlabeled) seeded. () Progression of a single fibroblast to an iPS cell colony over 12.5 d in phase contrast (upper panel) and with respective wavelengths highlighted (lower panel). Colonies were identified at the terminal time point and retrospectively traced to their founding fibroblast. Tracking of a single cell through the complete time series allows for comparative morphological characterization of cells that do reprogram against those that do not. Here, a reprogramming lineage beginning with a single YFP-labeled fibroblast (no. 16 shown in Fig. 1b, magenta square) is traced to the resulting iPS colony (Supplementary Movie 1). * Figure 2: Progressive accumulation of secondary, non-unique "satellite" colonies skew interpretation of reprogramming data. () GFP-labeled satellite colonies without unique origins over a global 5 × 5 field in 10× magnification. Satellite colonies (a subset highlighted with red arrowheads; see Supplementary Figure 4 for more images) typically without a traceable origin become macroscopically visible after day 6 and after the formation of primary colonies (yellow arrowheads) (Supplementary Movie 2). A grid (light gray) and squares (red) were added to the image to help orientation and facilitate comparison (as apparent in ). () Zoom-in view of two satellite colonies (nos. 4 and 5). In colony no. 4, it is clearly visible that between days 9 and 10 all cells are accounted for, but that a new cluster of cells (arrowhead) has appeared within 24 h. Note the small green dot that has not moved. Similarly, below it is apparent that neither of the two colonies present in the day-14 image originated from any cell in this field. The entire imaged area and additional colonies can be inspected in Supplementar! y Figure 4. () Corrected efficiencies accounting for colonies in which a unique cell of origin status can be assigned, and removing all apparent secondary events (means of all analyzed, n = 40, corrected and uncorrected counts are shown and significant to P = 0.00034, paired t-test). () A single YFP-labeled inducible MEF (yellow arrow) exhibits the potential to contribute multiple (at least six) colony-forming events (highlighted and enumerated by asterisks) before cells demonstrate an iPS cell morphology, suggesting that the ability to reprogram is specified in early precursors and can be distributed to multiple progeny (Supplementary Movie 3). () Cumulative primary and satellite colonies per well analyzed (n = 16). Primary colonies arise during the first 4–8 d, after which the number stabilizes. Satellites were scored at day 14 and traced to the earliest time (typically between days 6 and 12) in which a founding cell could be identified. Thin lines represent individual ! experiments. Bold line indicates the mean over all experiments. * Figure 3: Unique fates of induced fibroblasts reveal a conserved trajectory for reprogramming cells. () Representation of distinct cell fates in response to factor induction. From top to bottom: apoptotic/arrested (A), slow-dividing (SD), fast-dividing fibroblast (FD) and (iPS) cell morphologies at t = 0 d and across representative time points during the reprogramming process (Supplementary Movies 4a–d). The left and right images are transmitted, multi- or single-wavelength overlays. Center images show only the different wavelength images. Time is indicated in days. Images are 10×. () Cell number over the first 4 d of the reprogramming timeline (time point = 0.25 d); lines represent data for lineages of nonreprogramming cell types (FD, magenta, n = 5; SD, red, n = 5) and cells that will form iPS cell colonies (iPS, blue, n = 19). () Cellular area (in arbitrary units/pixels) as mapped over division number within iPS cell–forming lineages (n = 19, median values per timepoint). A stable ES/iPS-like cell size is reached within two to four divisions. * Figure 4: Effects of p53 knockdown on single cells during the reprogramming process. () A revised imaging experiment in which control cells were tagged as before with YFP, cyan fluorescent protein (CFP) or RFP. The control GFP vector was replaced with a p53-shRNA containing GFP vector27. Induction and acquisition were done as before over an 11 × 11 image field. Left: multi-wavelength overlay shows the notable increase in GFP colonies. Right: p53-depleted cells (tagged with GFP) exhibit an increased number of colony-like morphologies that display only minimal or incomplete activation of endogenous pluripotency markers. Most of the GFP colonies cannot be matched to an alkaline phosphatase (AP)-, Cdh1- or Nanog-positive colony. Note: the transmitted light and the marker stains show all colonies (including unlabeled controls, which represent the majority; white arrows: factor-negative colonies; colored arrows: factor-positive colonies). Colonies are circled with dashed lines to facilitate mapping across images. () Selected images of the progression for a single! p53-depleted cell (upper panel) and a control cell (tagged with RFP, bottom panel). Both exhibit similar enhanced proliferation and morphological characteristics at early time points but result in disparate fates (Supplementary Movie 5). Last panels on the right show alkaline phosphatase and Cdh1 staining. () Formation of primary colonies, alkaline phosphatase positivity and Nanog/cadherin signal for p53-depleted cells compared to alternatively labeled controls; P = 0.00004, 0.4 and 0.01, respectively, paired Kolmogorov-Smirnof test, as calculated by events over starting population. Means over eight wells are shown. () The proliferative characteristics of reprogramming p53 knockdown cells are comparable to reprogramming controls over the first 4 d. () p53 knockdown cells exhibit size reduction dynamics that are also similar to those for normally reprogramming cells within the first four divisions. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21361 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Zachary D Smith & * Iftach Nachman Affiliations * Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Zachary D Smith, * Iftach Nachman, * Aviv Regev & * Alexander Meissner * Harvard Stem Cell Institute and Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Zachary D Smith & * Alexander Meissner * Department of Biochemistry and Molecular Biology, Tel Aviv University, Tel Aviv, Israel. * Iftach Nachman * Howard Hughes Medical Institute and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Aviv Regev Contributions Z.D.S., I.N., A.R. and A.M. conceived the experiments and wrote the manuscript. Z.D.S. generated all reagents and performed the experiments. Z.D.S. and I.N. performed the analysis. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alexander Meissner (alexander_meissner@harvard.edu) Supplementary information * Accession codes * Author information * Supplementary information Movies * Supplementary Movie 1 (2.4M) Time-lapse movie of a single (1/16 of the 4×4) monitored site (upper right corner of and same as ). The movie follows the YFP labeled fibroblasts (#16 in ) as it divides and forms a colony over the course of 12 days. This MEF exhibits an immediate change in its proliferative behavior and size maintenance, rapidly forming many small cells that eventually give rise to multiple distinct iPS colonies within the formed cluster (shown in ). Non responding induced MEFs are apparent in the upper right and lower left hand corner for immediate comparison to the reprogramming lineage; these cells divide more slowly and maintain large, asymmetrical mesenchymal characteristics. Time in days is shown and the arrow in the first frame points to the source cell. * Supplementary Movie 2 (4.5M) Time-lapse movie of a global 5×5 field at 10× magnification in phase contrast and with GFP-labeled MEFs highlighted over a 14 day experiment. A single primary colony emerges at Day 4 while subsequent satellites continue to accumulate from Day 6 to the termination of the experiment at Day 14. * Supplementary Movie 3 (2.7M) Time-lapse movie following a YFP-labeled MEF progressing to a lineage in which multiple iPS cell colonies (~6) are formed ( shows selected images of this movie in grayscale). These subpopulations within the responding lineage can be clearly demarcated before unique iPS cell colonies are observed. Within the earliest time points, an additional YFP labeled responding population can be observed at the left, but these cells do not progress through the preliminary size reducing response, are not accountable for an iPS cell colony, and are lost or scored as non-proliferative by Day 6. Time in days is shown and the arrow in the first frame points to the source cell. * Supplementary Movies 4a (2.4M) Distinct morphologies of responding MEFs characterized from time-lapse imaging. responses in GFP, RFP, and YFP labeled MEFs. Several of these cells initially exhibit attributes of a positive response to factor induction as outlined in text but either arrest/apoptose or transition to a slower proliferative rate. * Supplementary Movies 4b (2.4M) Distinct morphologies of responding MEFs characterized from time-lapse imaging. (SD) YFP-labeled MEFs that do not reprogram, but slowly divide and maintain many of the morphological attributes of the original MEF population. * Supplementary Movies 4c (3.3M) Distinct morphologies of responding MEFs characterized from time-lapse imaging. (FD) YFP labeled MEFs that demonstrate a transition to a rapid cell cycle but become more striated and grow as a mesenchymal monolayer. Note, that within this fast dividing fibroblast population, three ectopic, unlabeled satellite colonies emerge within the final 4 days of the 14 daytime series. * Supplementary Movies 4d (2.3M) Distinct morphologies of responding MEFs characterized from time-lapse imaging. A RFP labeled MEF exhibiting an immediate change in proliferative rate and cell size, forming small clusters of dividing cells that lead to the formation of compacted subpopulations which give rise to pluripotency marker positive iPS cells. * Supplementary Movie 5 (2.4M) Time-lapse movie of a MEF infected with an EGFP labeled p53 targeting shRNA (Upper Panels) compared to an internal RFP labeled control MEF (Lower Panels) within the same experiment ( shows selected images from these movies). Size and proliferative rate are nearly identical within the first four days, but only the RFP labeled MEF progeny continue along an expected trajectory to a compact iPS colony whereas the p53 depleted MEF slows its proliferative rate and forms a colony that is less compact compared to the wild type control. Note that this p53 KD derived colony remains negative for classical pluripotency markers at the final time point (14 days). Time in days is shown and the arrow in the first frame points to the source cell. * Supplementary Movie 6 (3.3M) iPS cell colonies exhibit conserved formation dynamics. Shown are 12 independent iPS cell forming lineages imaged from the time of factor induction to termination after 14 days. As shown in , each population is governed by near identical kinetics and concludes in a tightly distributed time of primary colony emergence that is unlikely to be dictated by a single low probability stochastic event. * Supplementary Movie 7 (2.3M) Representative population wide overlays of RFP and YFP labeled inducible MEFs over a 12 day experiment and from which the YFP labeled fibroblast labeled #16 in was identified as a cell of origin for a primary iPS cell colony. Similar overlays were generated for every experiment and used to both identify primary colonies for downstream analysis and to gauge global reprogramming trends. PDF files * Supplementary Text and Figures (2.4M) Supplementary Figs. 1–10 Additional data
• First quarter resurgence in biotech job postings
  - Nature Biotechnology 28(5):527 (2010)
  The first quarter of 2010 saw a resurgence of biotech and pharma postings on the three representative job databases tracked by Nature Biotechnology (Tables 1 and 2). Most of the 10 largest biotech and 25 largest pharma companies saw an increase in listings compared with those in the fourth quarter of 2009 (Nat. Biotechnol. 28, 179, 2010
• People
  - Nature Biotechnology 28(5):528 (2010)
  The board of directors of Karo Bio (Stockholm) has appointed Fredrik Lindgren (right) as president and CEO. He succeeds Per Olof Wallström, who announced his resignation in February.