Latest Articles Include:
- Teetering on the brink
- Nat Biotech 28(10):987 (2010)
Nature Biotechnology | Editorial Teetering on the brink Journal name:Nature BiotechnologyVolume: 28 ,Page:987Year published:(2010)DOI:doi:10.1038/nbt1010-987Published online13 October 2010 The US Congress must authorize federal funding of human embryonic stem cell research. View full text Additional data - Geron trial resumes, but standards for stem cell trials remain elusive
- Nat Biotech 28(10):989-990 (2010)
As stem cell research findings percolate into the clinic, there is a growing realization that a lack of procedural and regulatory standards are creating huge translational potholes. "I can tell you based on my experience," says Tara Clark, general manager of North American clinical operations for Bergisch Gladbach, Germany–based Miltenyi Biotec, "that two investigators from different institutions have submitted a similar stem cell research proposal to the FDA [US Food and Drug Administration]. - China's $2.4 billion splurge
- Nat Biotech 28(10):990 (2010)
The Chinese government is pouring an estimated 16 billion yuan ($2.4 billion) to shore up drug development while introducing policies to promote the biotech sector. - US courts throw ES cell research into disarray
- Nat Biotech 28(10):991 (2010)
Funds for human embryonic stem cell (hESC) research are flowing again following a temporary ban on federal support for such research. A lower court injunction imposed by US District Court Judge Royce Lamberth on August 23 was lifted mid-September by the US Court of Appeals for the District of Columbia. - Drug user fees top $1 million
- Nat Biotech 28(10):992 (2010)
For the ninth straight year, the US Food and Drug Administration (FDA) is raising the fees companies must pay to have their drugs reviewed. As of October 1, new applications will cost over a million dollars. - Sugar beets still in the game
- Nat Biotech 28(10):992 (2010)
Seed producers will be allowed to plant biotech sugar beets again following a September decision from the United States Department of Agriculture's crop approval arm to allow planting under interim guidelines. The Animal and Plant Health Inspection Service (APHIS) will issue limited permits to seed developers authorizing genetically modified (GM) beet planting this fall as long as the harvested beets are not allowed to flower. - Roche backs Aileron's stapled peptides
- Nat Biotech 28(10):992-993 (2010)
A company that staples peptides into drugs to target 'undruggable' proteins has landed a $1.1 billion deal with Swiss drug maker Roche. - Life swallows Ion Torrent
- Nat Biotech 28(10):994 (2010)
Instruments provider Life Technologies has acquired sequencing firm Ion Torrent of Guilford, Connecticut and S. San Francisco in a deal worth $725 million—a price tag that has left some industry observers reeling. - Anti-anemics price hike
- Nat Biotech 28(10):994 (2010)
New payment rules for dialysis services could further erode the use of erythropoietin-stimulating agents (ESAs), already under scrutiny for potential safety risks. The US Centers for Medicare & Medicaid Services are changing how Medicare pays for end-stage renal disease services. - Genzyme resumes shipping as Sanofi-aventis hovers
- Nat Biotech 28(10):994 (2010)
Genzyme is moving towards resolving the manufacturing issues that have curtailed supplies of its biologics to treat Gaucher's disease and Fabry's disease for over a year. In late August, in the midst of reacting to a hostile takeover bid from French drug maker Sanofi-aventis, the biotech sent patient communities separate letters detailing the company's near-term plans for supply of the drugs. - Cancer research fund launches biologics pilot plant
- Nat Biotech 28(10):995-996 (2010)
Cancer Research UK, the country's largest cancer research funding charity, has opened a £18 ($28) million facility at Clare Hall, Hertfordshire, to serve as a pilot plant for investigational biologics. The charity's small-scale Biotherapeutics Development Unit (BDU), launched on July 30, will produce small batches of clinical grade material ready for testing, in what could become an attractive new model for industry-academia collaborations. - Wellcome partners with India
- Nat Biotech 28(10):996 (2010)
A £45 ($70) million fifty-fifty partnership between the UK's Wellcome Trust and India's Department of Biotechnology (DBT) to support development of "affordable healthcare products" is just the kind of boost small Indian biotech companies hankered after. The initiative announced 29 July builds on the existing £80 ($124) million alliance launched in 2008 to strengthen the biomedical research base in India (Nat. Biotechnol. 26, 1202, 2008 - Hungary eyes biotech jobs
- Nat Biotech 28(10):996 (2010)
The Hungarian Ministry for National Economy has unveiled a $4.5 billion scheme aimed at creating one million jobs within ten years. - Monsanto relaxes restrictions on sharing seeds for research
- Nat Biotech 28(10):996 (2010)
Public sector scientists who complained last year that seed companies were curbing their rights to study commercial biotech crops are negotiating research agreements with industry. In August, the Agricultural Research Service (ARS), an agency within the US Department of Agriculture in Washington, DC, finalized an umbrella license with St. - Newsmaker: Constellation Pharmaceuticals
- Nat Biotech 28(10):997 (2010)
Nature Biotechnology | News Newsmaker: Constellation Pharmaceuticals * Randy Osborne1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:997Year published:(2010)DOI:doi:10.1038/nbt1010-997Published online13 October 2010 Replete with investor funds, the Cambridge, Massachusetts–based epigenetics firm is taking aim at methylases and demethylases linked to disease. View full text Additional data Affiliations * Atlanta, Georgia * Randy Osborne - Drug pipeline: Q310
- Nat Biotech 28(10):998 (2010)
The number of small-molecule approvals declined more sharply than that of biologics over the past decade. However, new targets, such as atrium-specific K+ channel, phosphodiesterase-4 and renal Na+-glucose co-transporter, continue to open up new opportunities. - Turning the tide in lung cancer
- Nat Biotech 28(10):999-1002 (2010)
Nature Biotechnology | News | News Feature Turning the tide in lung cancer * Malorye Allison1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:999–1002Year published:(2010)DOI:doi:10.1038/nbt1010-999Published online13 October 2010 Researchers are testing a slew of targeted therapeutic strategies in lung cancer. Signs are emerging that these therapies are gaining increasing traction in what has long been one of oncology's minefields. Malorye Allison investigates. View full text Additional data Affiliations * Acton, Massachusetts * Malorye Allison - At the heart of genetic testing
- Nat Biotech 28(10):1003-1005 (2010)
Nature Biotechnology | News | News Feature At the heart of genetic testing * Stephen Strauss1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:1003–1005Year published:(2010)DOI:doi:10.1038/nbt1010-1003Published online13 October 2010 Genetic testing for rare heart conditions might someday expand to more common cardiac ailments. Already there are signs testing is dramatically changing how some conditions are treated and doctors' definition of who a patient is. Stephen Strauss reports. View full text Additional data Affiliations * Toronto * Stephen Strauss - Why you need a lawyer
- Nat Biotech 28(10):1007-1009 (2010)
- Safe and effective synthetic biology
- Nat Biotech 28(10):1010-1012 (2010)
A letter in your January issue highlights the need for harmonizing biosecurity oversight for gene synthesis1. The US government is currently preparing to publish its final, formal 'guidelines' on the procedures at DNA synthesis companies for screening incoming orders for sequences of potential dual-use concern. - The regulatory bottleneck for biotech specialty crops
- Nat Biotech 28(10):1012-1014 (2010)
Nature Biotechnology | Opinion and Comment | Correspondence The regulatory bottleneck for biotech specialty crops * Jamie K Miller1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kent J Bradford1kjbradford@ucdavis.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1012–1014Year published:(2010)DOI:doi:10.1038/nbt1010-1012Published online13 October 2010 To the Editor: Specialty crops, which include fruits, vegetables, nuts, turf and ornamental crops, are important components of human diets and provide environmental amenities1. In 2007, such crops represented ~40% of the $140 billion in total agricultural receipts, despite being cultivated on just 4% of the total cropped area2. Although tomato was the first genetically modified (GM) food crop to be commercialized in 1994, the only GM specialty crop traits currently marketed are virus-resistant papaya and squash, insect-resistant sweet corn and violet carnations. All of these received initial regulatory approval over 10 years ago. As a group, GM specialty crops have garnered limited market share (the exception is GM papaya resistant to papaya ringspot virus1, which now produces 90% of Hawaii's crop). In contrast, GM field crops, such as soybean, maize, cotton and canola, have come to dominate the markets in countries where they have been released3. What is responsible for this disparity in ! the commercialization of GM field crops versus specialty crops? View full text Figures at a glance * Figure 1: International scientific journal publications on transgenic crops. () Number of published articles describing research on the top 20 GM specialty crops (of 46 total species). The percentage of reports on each crop is also shown (inset). () Number of published articles according to country of origin. The percentage of total articles by country is also shown (inset). A complete list of all publications is in Supplementary Table 1. * Figure 2: Field trials and regulatory approvals. () Using the UNU-MERIT database, field trials conducted in 24 developed countries between 2003 and 2008 were separated on the basis of commodity, forest tree or specialty crop. From this, the specialty crops were further subdivided based on the country in which the field trial was conducted. () The numbers of field trial permits acknowledged or issued in the United States are plotted by year for commodity crops and specialty crops. () The 84 unique transgenic events that have been granted regulatory approval by one or more countries are plotted by year of approval. If the year of approval varied among countries, the first year of regulatory approval granted by any agency for a given event was used. Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Seed Biotechnology Center, University of California, Davis, California, USA. * Jamie K Miller & * Kent J Bradford Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Kent J Bradford (kjbradford@ucdavis.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Data, Supplementary Tables 1 and 2 Additional data - ProHits: integrated software for mass spectrometry–based interaction proteomics
- Nat Biotech 28(10):1015-1017 (2010)
Nature Biotechnology | Opinion and Comment | Correspondence ProHits: integrated software for mass spectrometry–based interaction proteomics * Guomin Liu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianping Zhang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Brett Larsen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chris Stark1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ashton Breitkreutz1 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhen-Yuan Lin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Bobby-Joe Breitkreutz1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yongmei Ding1 Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Colwill1 Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian Pasculescu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tony Pawson1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey L Wrana1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexey I Nesvizhskii3 Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Raught4 Search for this author in: * NPG journals * PubMed * Google Scholar * Mike Tyers1, 2, 5m.tyers@ed.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Anne-Claude Gingras1, 2gingras@lunenfeld.ca Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 28 ,Pages:1015–1017Year published:(2010)DOI:doi:10.1038/nbt1010-1015Published online13 October 2010 To the Editor: Affinity purification coupled with mass spectrometric identification (AP-MS) is now a method of choice for charting novel protein-protein interactions and has been applied to a large number of both small-scale and high-throughput studies1. However, general and intuitive computational tools for sample tracking, AP-MS data analysis and annotation have not kept pace with rapid methodological and instrument improvements. To address this need, we have developed the ProHits laboratory information management system platform. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Centre for Systems Biology, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada. * Guomin Liu, * Jianping Zhang, * Brett Larsen, * Chris Stark, * Ashton Breitkreutz, * Zhen-Yuan Lin, * Bobby-Joe Breitkreutz, * Yongmei Ding, * Karen Colwill, * Adrian Pasculescu, * Tony Pawson, * Jeffrey L Wrana, * Mike Tyers & * Anne-Claude Gingras * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Tony Pawson, * Jeffrey L Wrana, * Mike Tyers & * Anne-Claude Gingras * Departments of Pathology and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. * Alexey I Nesvizhskii * Ontario Cancer Institute and McLaughlin Centre for Molecular Medicine, Toronto, Ontario, Canada. * Brian Raught * Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK. * Mike Tyers Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Mike Tyers (m.tyers@ed.ac.uk) or * Anne-Claude Gingras (gingras@lunenfeld.ca) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (11M) Supplementary Figures 1–21 Additional data - More sizzle than fizzle
- Nat Biotech 28(10):1017 (2010)
In an echo of Mark Twain's response when reading his own published obituary that "The report of my death has been exaggerated," I should like to correct an inaccuracy about GlaxoSmithKline's (Brentford, UK) EpiNova Discovery Performance Unit (DPU), which was mentioned in Catherine Shaffer's news article entitled "Pfizer explores rare disease path" from the September issue.The article suggested that EpiNova had 'fizzled out. - The path less costly
- Nat Biotech 28(10):1018 (2010)
Nature Biotechnology | Opinion and Comment | Commentary The path less costly * Brady Huggett1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1018Year published:(2010)DOI:doi:10.1038/nbt1010-1018Published online13 October 2010 When faced with a competitive threat, two companies took diametrically opposite approaches. Both were ultimately successful, but Genzyme's decision proved to be the cleaner and cheaper option. 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 * Brady Huggett is Business Editor at Nature Biotechnology. Additional data - Faculty and employee ownership of inventions in Australia
- Nat Biotech 28(10):1019-1022 (2010)
Nature Biotechnology | Feature | Patents Faculty and employee ownership of inventions in Australia * Amanda McBratney1, 2amanda.mcbratney@qut.edu.au Search for this author in: * NPG journals * PubMed * Google Scholar * Julie-Anne Tarr1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1019–1022Year published:(2010)DOI:doi:10.1038/nbt1010-1019Published online13 October 2010 A recent Australian legal decision means that, unless faculty members are bound by an assignment or intellectual property policy, they may own inventions resulting from their research. 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 * Amanda McBratney and Julie-Anne Tarr are in the Faculty of Business, Queensland University of Technology, Brisbane, Queensland, Australia. * Amanda McBratney is also a consultant with McCullough Robertson Lawyers, Brisbane, Queensland, Australia. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Amanda McBratney (amanda.mcbratney@qut.edu.au) Additional data - Recent patent applications in gene synthesis
- Nat Biotech 28(10):1023 (2010)
Table 1Table 2 - Timing is everything in the human embryo
- Nat Biotech 28(10):1025-1026 (2010)
A noninvasive imaging method for predicting how human embryos will develop may improve the success and safety of in vitro fertilization. - Taking the measure of the methylome
- Nat Biotech 28(10):1026-1028 (2010)
Nature Biotechnology | News and Views Taking the measure of the methylome * Stephan Beck1s.beck@ucl.ac.uk Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:1026–1028Year published:(2010)DOI:doi:10.1038/nbt1010-1026Published online13 October 2010 Two comparative studies from the International Human Epigenome Project find high concordance between different methods for measuring genomic methylation. Article tools * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg With the rapid development of new methods for epigenomic analysis, the need for a systematic assessment of available technologies has become acute. In this issue, Harris et al.1 and Bock et al.2 compare the performance of commonly used techniques for DNA methylation analysis in terms of cost, resolution, genome coverage and accuracy. The findings provide a first benchmark of which method works best for which part of the methylome. In humans, DNA methylation occurs predominantly at cytosine bases in the form of methyl cytosines (mCs), methyl cytosine guanine dinucleotides (mCGs), hydroxymethyl cytosines (hmCs) and, possibly, in other, yet unknown forms. Collectively, these modifications define the DNA methylome of a cell. Together with the study of other epigenetic marks, methylome analysis forms an integral part of ongoing efforts to elucidate the epigenomes of healthy and diseased cell types. Such methylome maps will allow the identification of genomic regions involved in cell differentiation and disease. Following years of planning by the Epigenome Taskforce3 and other initiatives, the studies of Harris et al.1 and Bock et al.2 mark another milestone for the International Human Epigenome Project4. Together with recent papers by Li et al.5 and Robinson et al.6 (not discussed here), they compare the performance of the main technologies for mCG methylome analysis. Until now, we did not know how well these methods work, what their particular strengths and weaknesses are, or the extent to which the resulting methylation maps overlap. Understanding these issues is especially important when choosing a method for generating so-called reference methylomes, which will be used as definitive resources in future research and must therefore be as accurate and comprehensive as possible. In all, Harris et al.1 and Bock et al.2 tested six methods, of which five are sequencing-based and one is array-based. Three of the methods—MethylC-seq (data from ref. 7), reduced representation bisulfite sequencing (RRBS) and the Infinium-27K bead-array—use sodium bisulfite treatment of DNA, which converts unmethylated but not methylated cytosine to uracil. The other three—methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq) and methylated DNA binding domain sequencing (MBD-seq)—rely on capture of methylated DNA by a monoclonal antibody or by the recombinant methyl-binding domains of MECP2 or MBD2, respectively. Each method was subjected to rigorous quality control, and all results were supported by comprehensive statistical analysis of at least two replicate samples. Table 1 summarizes some of the metrics examined. In addition to cost, the other important parameters when choosing a method for a particular methylome analysis are resolution, coverage and accuracy. With respect to resolution, the choice is straightforward between the high resolution (1 bp) achieved with the bisulfite-based methods and the low resolution (≥100 bp) of capture-based methods. Although the highest possible resolution is usually desirable, single-base-pair resolution is not always required because the methylation status of adjacent CpG sites is highly correlated for up to 1,000 bp. Table 1: Key metrics of the technology comparison Full table Coverage and accuracy are much more difficult to assess as the different methods have different dependencies—including CpG density, fragment length, capture affinity, read length, read depth and, for capture methods, absence of reads in unmethylated regions—making a direct comparison challenging. Based on the fraction of the genome that can potentially be analyzed by each method, the theoretical mCG coverage is ~100% for MethylC-seq, MeDIP-seq, MethylCap-seq and MBD-seq, ~10% for RRBS and ~0.1% for Infinium-27K. Determining whether this coverage is actually achievable in practice requires the generation and analysis of saturation data for each method. This was done only for MethylC-seq and RRBS and is not applicable to Infinium-27K. Applying thresholds of 1 to 10 reads per mCG, the determined actual coverage ranges from 96–76% for MethylC-seq and 12–9% for RRBS, which is close to the theoretical limits of coverage, particularly for RRBS. Because saturation sequencing was not carried out for MeDIP-seq, MethylCap-seq and MBD-seq, the actual coverage data presented for these methods are less representative, as evident from the large variation (67–9%) in coverage when applying the same thresholds of 1 to 10 reads per mCG. Both studies1, 2 assessed accuracy by comparing overlapping data sets between methods. For most comparisons, the Infinium rather than the more comprehensive MethylC data were used as a common standard. This is somewhat unfortunate as the resulting comparisons are therefore between sequencing- and array-derived data and limited to the small set of highly selected CpG sites on the Infinium-27K array. Nevertheless, the overall concordance is encouragingly high (84–100%), depending on the comparison (2- to 4-way comparisons were conducted) and the parameters. This is good news and lends confidence to the many existing data sets already generated by any of the methods. Both studies1, 2 conclude that all of the evaluated methods are capable of producing accurate data, and neither recommends a particular method for the generation of reference methylomes, although Harris et al.1 suggest the possibility of hybrid methods and show improved results for MeDIP-seq integrated with MRE-seq (based on methylation-sensitive restriction). Although the two studies1, 2 have successfully resolved many long-standing questions in the epigenomics community, several challenges remain. The most pressing concern is that a full methylome analysis should include mC and hmC in addition to mCG, although the biological functions of these modifications have yet to be determined. Another challenge is that bisulfite-based methods (the current gold standard of methylation analysis) cannot distinguish between methylation and hydroxymethylation8, which has implications for all bisulfite-based data already deposited in public databases. As the International Human Epigenome Consortium gears up to generate 1,000 reference epigenomes, the participating laboratories will undoubtedly use different methylome analysis methods. It will therefore be important to develop a procedure for assigning quality values to the methylation status of each cytosine. A similar metric proved to be very helpful in the assembly and use of the draft sequence of the human genome. For the future, there are great expectations that one day we will be able to read the different forms of DNA methylation directly using methods such as nanopore9 and single-molecule, real-time10 sequencing. For now, however, with careful management, our current technology is adequate to move 'AHEAD'. References * References * Author information * Harris, R.A.et al. Nat. Biotechnol.28, 1097–1105 (2010). * ChemPort * Article * Bock, C.et al. Nat. Biotechnol.28, 1079–1088 (2010). * Article * Jones, P.A. & Martienssen, R.Cancer Res.65, 11241–11246 (2005). * ChemPort * ISI * PubMed * Article * Satterlee, J.Nat. Biotechnol.28, 1039–1044 (2010). * Article * Li, N.et al. Methods published online, doi: doi:10.1016/j.ymeth.2010.04.009, 27 April 2010. * Article * Robinson, M.et al. Epigenomics2, 587–598 (2010). * ChemPort * Article * Lister, R.et al. Nature462, 315–322 (2009). * ChemPort * ADS * PubMed * Article * Huang, Y.et al. PLoS ONE5, e8888 (2010). * ChemPort * ADS * PubMed * Article * Clarke, J.et al. Nat. Nanotechnol.4, 265–270 (2009). * ChemPort * ADS * PubMed * Article * Flusberg, B.A.et al. Nat. Methods7, 461–465 (2010). * ChemPort * PubMed * Article Download references Author information * References * Author information Affiliations * Stephan Beck is at the UCL Cancer Institute, University College London, London, UK. Corresponding author Correspondence to: * Stephan Beck (s.beck@ucl.ac.uk) Additional data - Tracing cancer networks with phosphoproteomics
- Nat Biotech 28(10):1028-1029 (2010)
A mass-spectrometry approach for identifying downstream events in cancer signaling pathways may help to tailor therapies to individual patients. - Research highlights
- Nat Biotech 28(10):1030 (2010)
- Making a mark
- Nat Biotech 28(10):1031 (2010)
Nature Biotechnology | Editorial Making a mark Journal name:Nature BiotechnologyVolume: 28 ,Page:1031Year published:(2010)DOI:doi:10.1038/nbt1010-1031Published online13 October 2010 High-throughput technologies are enabling epigenetic modifications to be mapped on a genome-wide scale, but whether such knowledge can be rapidly translated into biomedical applications remains unclear. View full text Additional data - Linking cell signaling and the epigenetic machinery
- Nat Biotech 28(10):1033-1038 (2010)
Nature Biotechnology | Opinion and Comment | Commentary Linking cell signaling and the epigenetic machinery * Helai P Mohammad1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen B Baylin1sbaylin@jhmi.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1033–1038Year published:(2010)DOI:doi:10.1038/nbt1010-1033Published online13 October 2010 One of the biggest gaps in our knowledge about epigenomes is how their interplay with cellular signaling influences development, adult cellular differentiation and disease. View full text Figures at a glance * Figure 1: Depiction of potential cell signaling in Waddington's model of epigenetic determination of development, as interpreted by Hochedlinger and Plath7. Colored marbles correspond to differentiation states. Arrows represent the directionality of factor influence for development with '+' indicating addition and '–' indicating removal of a given factor or signal. The downward blue arrow at the top left of the 'hill' reflects direction of normal development, whereas the upward blue arrow at the bottom right of the hill depicts the direction of cellular reprogramming during generation of iPSCs. Coloring of text for names of factors and signaling pathways correspond to their function within the given developmental stage. * Figure 2: Potential mechanisms by which the chromatin of key developmental genes may be regulated by cellular signaling. The left panel represents the ESC state whereby extrinsic signaling may impinge upon regulation of the DNA methylation status of pluripotency genes. The transcription start sites (arrows) of the three genes are depicted at the bottom left with circles representing CpG sites as DNA unmethylated (white) or methylated (black). The DNA methylated gene is transcriptionally repressed (red circle over transcription start) and nucleosomes (blue) are in a more compact structure in contrast to the more open structure of the expressed genes on the bottom left. Right panel represents the committed progenitor state that ensues when the pluripotency factors are silenced in ESCs. Subsequent resolution of bivalency to active or inactive target gene transcriptional states is depicted as discussed in the text. * Figure 3: Modeling signaling that may promote cancer-specific DNA hypermethylation imposed on a normally non-DNA methylated, PcG-marked gene. Black solid arrows, direct regulation; black dashed line arrows, potential regulation and intersection of signal transduction with chromatin regulating machinery; red arrows, feedback in that the signaling pathway itself becomes activated in association with genes that are abnormally DNA methylated and silenced; yellow star, active 2/3meH3K4; red star, the PcG-associated 3meH3K27; green polygon, AcH3K9; black circles, methylated CpG sites. HMT, histone methyltransferase; KDM, lysine demethylase; HDAC, histone deacetylase; DNMT, DNA methyltransferase. 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 * Helai P. Mohammad and Stephen B. Baylin are at the Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins Medical Institutions, Baltimore, Maryland, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen B Baylin (sbaylin@jhmi.edu) Additional data - Tackling the epigenome: challenges and opportunities for collaboration
- Nat Biotech 28(10):1039-1044 (2010)
Nature Biotechnology | Opinion and Comment | Commentary Tackling the epigenome: challenges and opportunities for collaboration * John S Satterlee1satterleej@nida.nih.gov Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Schübeler2 Search for this author in: * NPG journals * PubMed * Google Scholar * Huck-Hui Ng3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1039–1044Year published:(2010)DOI:doi:10.1038/nbt1010-1039Published online13 October 2010 What are the key considerations to take into account when large-scale epigenomics projects are being implemented? 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 * John S. Satterlee is at the US National Institute on Drug Abuse, Bethesda, Maryland, USA. * Dirk Schübeler is at the Friedrich Miescher Institute, Basel, Switzerland. * Huck-Hui Ng is at the Genome Institute of Singapore, Singapore. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John S Satterlee (satterleej@nida.nih.gov) Additional data - The NIH Roadmap Epigenomics Mapping Consortium
- Nat Biotech 28(10):1045-1048 (2010)
Nature Biotechnology | Opinion and Comment | Commentary The NIH Roadmap Epigenomics Mapping Consortium * Bradley E Bernstein1, 2Bernstein.Bradley@mgh.harvard.edu Search for this author in: * NPG journals * PubMed * Google Scholar * John A Stamatoyannopoulos4 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Costello5 Search for this author in: * NPG journals * PubMed * Google Scholar * Bing Ren6 Search for this author in: * NPG journals * PubMed * Google Scholar * Aleksandar Milosavljevic7 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Manolis Kellis1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marco A Marra8 Search for this author in: * NPG journals * PubMed * Google Scholar * Arthur L Beaudet7 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph R Ecker9 Search for this author in: * NPG journals * PubMed * Google Scholar * Peggy J Farnham10 Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Hirst8 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric S Lander1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tarjei S Mikkelsen1 Search for this author in: * NPG journals * PubMed * Google Scholar * James A Thomson11 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1045–1048Year published:(2010)DOI:doi:10.1038/nbt1010-1045Published online13 October 2010 The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease. View full text Figures at a glance * Figure 1: Layers of genome organization. Genome function and cellular phenotypes are influenced by DNA methylation and the protein-DNA complex known as chromatin. In mammals, DNA methylation occurs on cytosine bases, primarily in the context of CpG dinucleotides. Accessible chromatin that is hypersensitive to DNase I digestion marks promoters and functional elements bound by transcription factors or other regulatory proteins. Histone modifications, associated proteins such as Polycomb repressors and noncoding RNAs constitute an additional layer of chromatin structure that affects genome function in a context-dependent manner. * Figure 2: Portal for the NIH Roadmap Epigenomics Mapping Consortium. A public portal (http://www.roadmapepigenomics.org/) provides general information about the consortium and its participants, along with links to experimental protocols, consortium data and interfaces for visualizing epigenomic maps. 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 * Bradley E. Bernstein, Alexander Meissner, Manolis Kellis, Eric S. Lander and Tarjei S. Mikkelsen are at the Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Bradley E. Bernstein is also at the Howard Hughes Medical Institute, Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Alexander Meissner is in the Department of Stem Cell and Regenerative Biology at Harvard University, Cambridge, Massachusetts, USA. * John A. Stamatoyannopoulos is in the Departments of Genome Sciences and Medicine, University of Washington School of Medicine, Seattle, Washington, USA. * Joseph F. Costello is in the Department of Neurosurgery, University of California at San Francisco, San Francisco, California, USA. * Bing Ren is at the Ludwig Institute for Cancer Research, University of California San Diego School of Medicine, La Jolla, California, USA. * Aleksandar Milosavljevic and Arthur L. Beaudet are in the Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Marco A. Marra and Martin Hirst are at the Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada. * Joseph R. Ecker is in the Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA. * Peggy J. Farnham is at the Genome Center, University of California at Davis, Davis, California, USA. * James A. Thomson is at the University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bradley E Bernstein (Bernstein.Bradley@mgh.harvard.edu) Additional data - Epigenomics reveals a functional genome anatomy and a new approach to common disease
- Nat Biotech 28(10):1049-1052 (2010)
Nature Biotechnology | Opinion and Comment | Commentary Epigenomics reveals a functional genome anatomy and a new approach to common disease * Andrew P Feinberg1afeinberg@jhu.edu Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:1049–1052Year published:(2010)DOI:doi:10.1038/nbt1010-1049Published online13 October 2010 Epigenomics provides the context for understanding the function of genome sequence, analogous to the functional anatomy of the human body provided by Vesalius a half-millennium ago. Much of the seemingly inconclusive genetic data related to common diseases could therefore become meaningful in an epigenomic context. 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 * Andrew P. Feinberg is at the Center for Epigenetics and Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Andrew P Feinberg (afeinberg@jhu.edu) Additional data - Putting epigenome comparison into practice
- Nat Biotech 28(10):1053-1056 (2010)
Nature Biotechnology | Computational Biology | Commentary Putting epigenome comparison into practice * Aleksandar Milosavljevic1amilosav@bcm.edu Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:1053–1056Year published:(2010)DOI:doi:10.1038/nbt1010-1053Published online13 October 2010 Comparative analysis of epigenomes offers new opportunities to understand cellular differentiation, mutation effects and disease processes. But the scale and heterogeneity of epigenetic data present numerous computational challenges. 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 * Aleksandar Milosavljevic is at The NIH Epigenomics Roadmap Data Analysis and Coordination Center, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, Texas, USA. Competing financial interests The author is the founder of and owns shares in IP Genesis, Inc., a company that commercializes academically developed software for genomic and epigenomic research. Corresponding author Correspondence to: * Aleksandar Milosavljevic (amilosav@bcm.edu) Additional data - Epigenetic modifications and human disease
- Nat Biotech 28(10):1057-1068 (2010)
Nature Biotechnology | Research | Review Epigenetic modifications and human disease * Anna Portela1 Search for this author in: * NPG journals * PubMed * Google Scholar * Manel Esteller1, 2mesteller@iconcologia.net Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1057–1068Year published:(2010)DOI:doi:10.1038/nbt.1685Published online13 October 2010 Abstract * Abstract * Author information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Epigenetics is one of the most rapidly expanding fields in biology. The recent characterization of a human DNA methylome at single nucleotide resolution, the discovery of the CpG island shores, the finding of new histone variants and modifications, and the unveiling of genome-wide nucleosome positioning maps highlight the accelerating speed of discovery over the past two years. Increasing interest in epigenetics has been accompanied by technological breakthroughs that now make it possible to undertake large-scale epigenomic studies. These allow the mapping of epigenetic marks, such as DNA methylation, histone modifications and nucleosome positioning, which are critical for regulating gene and noncoding RNA expression. In turn, we are learning how aberrant placement of these epigenetic marks and mutations in the epigenetic machinery is involved in disease. Thus, a comprehensive understanding of epigenetic mechanisms, their interactions and alterations in health and disease, h! as become a priority in biomedical research. View full text Figures at a glance * Figure 1: DNA methylation patterns. DNA methylation can occur in different regions of the genome. The alteration of these patterns leads to disease in the cells. The normal scenario is depicted in the left column and alterations of this pattern are shown on the right. () CpG islands at promoters of genes are normally unmethylated, allowing transcription. Aberrant hypermethylation leads to transcriptional inactivation. () The same pattern is observed when studying island shores, which are located up to 2 kb upstream of the CpG island. () However, when methylation occurs at the gene body, it facilitates transcription, preventing spurious transcription initiations. In disease, the gene body tends to demethylate, allowing transcription to be initiated at several incorrect sites. () Finally, repetitive sequences appear to be hypermethylated, preventing chromosomal instability, translocations and gene disruption through the reactivation of endoparasitic sequences. This pattern is also altered in disease. * Figure 2: Epigenetic machinery and interplay among epigenetic factors. Epigenetic marks are catalyzed by different epigenetic complexes, whose principal families are illustrated here. () Epigenetic regulation depends on the interplay among the different players: DNA methylation (), histone marks () and nucleosome positioning (). The interaction among the different factors brings about the final outcome. This figure illustrates selected examples of the possible interrelations among the various epigenetic players. * Figure 3: Histone modifications. All histones are subject to post-transcriptional modifications, which mainly occur in histone tails. The main post-transcriptional modifications are depicted in this figure: acetylation (blue), methylation (red), phosphorylation (yellow) and ubiquitination (green). The number in gray under each amino acid represents its position in the sequence. * Figure 4: Nucleosome positioning patterns. Nucleosome positioning plays an important role in transcriptional regulation. Transcriptionally active gene promoters possess a nucleosome-free region at the 5′ and 3′ untranslated region, providing space for the assembly and disassembly of the transcription machinery. The loss of a nucleosome directly upstream of the TSS is also necessary for gene activation, whereas the occlusion of this position leads to transcription repression. DNA methylation regulates transcription, and thus interferes with nucleosome positioning. Methylated DNA seems to be associated with 'closed' chromatin domains, where DNA is condensed into strictly positioned nucleosomes, thereby impeding transcription. Conversely, unmethylated DNA is associated with 'opened' chromatin domains, which allow transcription. Author information * Abstract * Author information Affiliations * Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Catalonia, Spain. * Anna Portela & * Manel Esteller * Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain. * Manel Esteller Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Manel Esteller (mesteller@iconcologia.net.) Additional data - Epigenetic modifications as therapeutic targets
- Nat Biotech 28(10):1069-1078 (2010)
Nature Biotechnology | Research | Review Epigenetic modifications as therapeutic targets * Theresa K Kelly1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel D De Carvalho1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter A Jones1pjones@med.usc.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1069–1078Year published:(2010)DOI:doi:10.1038/nbt.1678Published online13 October 2010 Abstract * Abstract * Author information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Epigenetic modifications work in concert with genetic mechanisms to regulate transcriptional activity in normal tissues and are often dysregulated in disease. Although they are somatically heritable, modifications of DNA and histones are also reversible, making them good targets for therapeutic intervention. Epigenetic changes often precede disease pathology, making them valuable diagnostic indicators for disease risk or prognostic indicators for disease progression. Several inhibitors of histone deacetylation or DNA methylation are approved for hematological malignancies by the US Food and Drug Administration and have been in clinical use for several years. More recently, histone methylation and microRNA expression have gained attention as potential therapeutic targets. The presence of multiple epigenetic aberrations within malignant tissue and the abilities of cells to develop resistance suggest that epigenetic therapies are most beneficial when combined with other antican! cer strategies, such as signal transduction inhibitors or cytotoxic treatments. A key challenge for future epigenetic therapies will be to develop inhibitors with specificity to particular regions of chromosomes, thereby potentially reducing side effects. View full text Figures at a glance * Figure 1: Epigenetic aberrations of CpG island promoters in cancer cells and the epigenetic therapies that target them. Tumor suppressor genes (such as FBXO32, MLH1 and RUNX3) are expressed in normal cells and become silenced in cancer cells. This can occur either by PRC reprogramming (as for FBXO32), where the polycomb group protein EZH2 catalyzes the methylation of H3K27, or by 5-methylcytosine (5mC) reprogramming (as for MLH1 and RUNX3) owing to de novo DNA methylation by DNMT3A and DNMT3B. Polycomb-mediated repression can be targeted by inhibitors of PRC2, such as DZNep, and re-expression of these genes can be enhanced by HDAC and LSD1 inhibitors allowing acetylation of H3 and H4 and methylation of H3K4, respectively. Polycomb-mediated repression can also be reversed by inducing miR-101 expression, which inhibits the expression and function of EZH2. 5mC reprogramming can be reversed, mainly by DNMT inhibitors, but also by re-expression of miR-143 and miR-29, two miRNAs that target de novo DNMTs. LSD1 inhibitors may also reactivate tumor suppressor genes by inhibiting DNMT1 stabilization, ! leading to loss of DNA methylation maintenance. Genes that are polycomb-repressed in normal cells (such as PAX7) can undergo epigenetic switching by DNA methylation, thus losing their plasticity during transformation. It is not known whether treatment of cancer cells with DNMT inhibitors alone can reverse epigenetic switching to restore the polycomb-repressed state or whether it will reactivate this set of genes. Cancer-testis antigens (CTAs, such as NY-ESO-1) can become silenced by DNA methylation in cancer. Treatment with DNMT inhibitors can induce CTA expression, allowing the immune system to recognize and kill the cancer cells. Red arrows represent epigenetic alterations during transformation; green arrows represent reversion of these alterations by epigenetic therapy. * Figure 2: Chemical structures of selected compounds that target epigenetic modifications. Several molecules that target epigenetic alterations in pathological states are currently at different stages of drug development. The nucleoside analogs 5-azacytidine and 5-aza-2′-deoxycytidine are approved by the US Food and Drug Administration (FDA) to treat high-risk MDS, and successful clinical results have been reported. The drug hydralazine is currently being investigated in clinical trials as a putative demethylating agent against solid tumors. S110, a dinucleotide containing 5-aza-CdR, has been shown in vitro to demethylate DNA and is more stable than 5-aza-CdR because it is less sensitive to deamination by cytidine deaminase. Targeting of histone acetylation has also been a successful example of epigenetic therapy. Several HDAC inhibitors are FDA approved, including the hydroxamic acid–based compound SAHA and the depsipeptide romidepsin, whereas others are currently in clinical trials for cancer (phenylbutyrate and entinostat) and neurologic diseases (entinosta! t). New molecules targeting specific HDACs are under preclinical investigation (such as PCI-34051, which targets HDAC8). More recently, significant effort is under way to find new molecules able to target histone methylation. To our knowledge, no drugs targeting histone methylation are FDA approved or in clinical trials. Even so, preclinical trials suggest antitumor activity of the oligoamine analog SL11144, which inhibits LSD1, and the S-adenosylhomocysteine hydrolase inhibitor DZNep, which depletes cellular levels of PRC2 components. Author information * Abstract * Author information Primary authors * These authors contributed equally to this work. * Theresa K Kelly & * Daniel D De Carvalho Affiliations * Departments of Urology and Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Theresa K Kelly, * Daniel D De Carvalho & * Peter A Jones Competing financial interests P.A.J. is a consultant to Lilly and Millipore. Corresponding author Correspondence to: * Peter A Jones (pjones@med.usc.edu) Additional data - Epigenetic modifications in pluripotent and differentiated cells
- Nat Biotech 28(10):1079-1088 (2010)
Nature Biotechnology | Research | Review Epigenetic modifications in pluripotent and differentiated cells * Alexander Meissner1, 2, 3alexander_meissner@harvard.edu Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Pages:1079–1088Year published:(2010)DOI:doi:10.1038/nbt.1684Published online13 October 2010 Abstract * Abstract * Author information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Epigenetic modifications constitute a complex regulatory layer on top of the genome sequence. Pluripotent and differentiated cells provide a powerful system for investigating how the epigenetic code influences cellular fate. High-throughput sequencing of these cell types has yielded DNA methylation maps at single-nucleotide resolution and many genome-wide chromatin maps. In parallel to epigenome mapping efforts, remarkable progress has been made in our ability to manipulate cell states; ectopic expression of transcription factors has been shown to override developmentally established epigenetic marks and to enable routine generation of induced pluripotent stem (iPS) cells. Despite these advances, many fundamental questions remain. The roles of epigenetic marks and, in particular, of epigenetic modifiers in development and in disease states are not well understood. Although iPS cells appear molecularly and functionally similar to embryonic stem cells, more genome-wide studies! are needed to define the extent and functions of epigenetic remodeling during reprogramming. View full text Figures at a glance * Figure 1: Epigenetic dynamics during in vitro and in vivo differentiation. Left (in vivo): Sperm and oocyte, come together at fertilization to form the totipotent zygote. After extrusion of the second polar body the maternal and paternal pronuclei (PN) migrate and fuse after several hours. Both genomes, paternal and maternal, subsequently undergo substantial epigenetic changes although at different rates. These changes are indicated for two epigenetic marks as examples to the right. Many of the central enzyme genes have been knocked out and result in a lethal phenotype. The respective phenotypes and approximate time observed are shown in the middle. Far right (in vitro): ES cells are derived from the hypomethylated ICM and regain genome-wide DNA methylation and other epigenetic marks by the time ES cell lines are established. For most of the investigated cell types these marks appear not to change globally although locus specific changes are observed upon differentiation. As indicated by the simplified schematic of two epigenetic marks (DNA methyla! tion and H3K27me3), many details about their presence during normal development are still lacking. The drawings are simplified and indicate global levels that remain stable. Both marks will differ between cell types in their distribution. #, lethal. ##, maintenance fine, but has differentiation defects. *Dnmt3a knockout mice die around 3 weeks postnatally and are smaller/runted. **No observed phenotype, no observed effect on DNA methylation, effect on RNA methylation not well studied but possible. ***Mice are viable, but have hematopoietic and neural abnormalities. ****Homozygous mice are sterile, offspring of homozygous female mice and heterozygous crosses show imprinting defects and die. *****Wild-type ES cells cannot differentiate into trophectodermal cells. Loss of Dnmt1 and global loss of DNA methylation restores this developmental potential. d.p.c., days post coitum; E, embryonic; P, paternal; M, maternal; S, sperm; O, oocyte; PN, pronuclei; EN, endoderm; ME, mesoderm! ; EC, ectoderm; TE, trophectoderm. * Figure 2: Epigenetic reprogramming during iPS cell derivation. Shown are selected genes with their chromatin and expression state across distinct cell types color-coded as shown on the bottom (data taken from ref. 81). Data are for uninduced mouse embryonic fibroblasts (MEFs), a hypothetical primary iPS cell colony (nascently reprogrammed) as well as an established iPS (MCV8.1) and ES (V6.5) cell line. Upon induction of Oct4, Sox2, Klf4 and c-Myc (OSKM), the MEF epigenome begins remodeling. The initial events and required factors have not been described yet. After ~10–14 days, iPS cell colonies appear that express markers such as Oct4-GFP. The expression of housekeeping genes is not affected, and they remain active throughout the reprogramming process (Gapdh and Dnmt1 are representative examples). With the exception of a few marker genes, the global extent of remodeling is unknown for this stage. Primary colonies are then picked and expanded as clonal iPS cell lines. Usually several more passages are needed before extensive marker sta! ins can be performed. Typically, at least 5 -8 passages are required to obtain sufficient material for genome-wide studies. The chromatin and expression states for the selected marker genes are identical in the iPS cell line MCV8.1 and in a wild-type ES cell line (V6.5) used to construct this schematic81. Ink4a (Cdkn2a) remains bivalent and sensitive to rapid induction in normal (not transformed or immortalized) cells. Overexpression of OSKM or extended cell culture can induce expression of Ink4a, but ES and iPS cells show bivalent marks and lack of DNA methylation. Snai1 is an expressed somatic gene that becomes repressed and regains bivalency upon reprogramming. MyoD is silent in both MEF and iPS cells, but switches from H3K27 only to a bivalent state upon reprogramming. Lin28 and Fgf4 are repressed by H3K27 methylation, whereas Oct4 and Nanog are repressed by DNA methylation, and all become transcriptionally reactivated only upon reprogramming. Author information * Abstract * Author information Affiliations * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Alexander Meissner * Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. * Alexander Meissner * Broad Institute, Cambridge, Massachusetts, USA. * Alexander Meissner Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Alexander Meissner (alexander_meissner@harvard.edu) Additional data - Genomics tools for unraveling chromosome architecture
- Nat Biotech 28(10):1089-1095 (2010)
Nature Biotechnology | Research | Review Genomics tools for unraveling chromosome architecture * Bas van Steensel1b.v.steensel@nki.nl Search for this author in: * NPG journals * PubMed * Google Scholar * Job Dekker2job.dekker@umassmed.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature BiotechnologyVolume: 28 ,Pages:1089–1095Year published:(2010)DOI:doi:10.1038/nbt.1680Published online13 October 2010 Abstract * Abstract * Author information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The spatial organization of chromosomes inside the cell nucleus is still poorly understood. This organization is guided by intra- and interchromosomal contacts and by interactions of specific chromosomal loci with relatively fixed nuclear 'landmarks' such as the nuclear envelope and the nucleolus. Researchers have begun to use new molecular genome-wide mapping techniques to uncover both types of molecular interactions, providing insights into the fundamental principles of interphase chromosome folding. View full text Figures at a glance * Figure 1: Cartoon of nucleus depicting the spatial interactions that contribute to the overall architecture of interphase chromosomes. Labels A–D refer to corresponding entries in Table 1. * Figure 2: Mapping of interactions of the genome with nuclear landmarks, here shown for the nuclear lamina. See text for explanation. Adenine-methylated DNA is specifically amplified using a PCR-based protocol using restriction endonucleases that selectively digest DNA depending on the adenine-methylation state, as described elsewhere12, 13. NL, nuclear lamina. * Figure 3: Principles of the major 3C-based technologies. All protocols start with treatment of cells with formaldehyde (not shown), leading to cross-linking of DNA segments in close proximity to one another. After digestion with one or more restriction enzymes, linked restriction fragments are intramolecularly ligated. In the case of Hi-C, the ends of the restriction fragments are first filled in with biotinylated dNTPs before ligation to facilitate purification of ligation junctions using streptavidin-coated beads. Single or multiple ligation events are detected directly (using 3C, 4C, 5C and Hi-C), or immunoprecipitation is first used to enrich for DNA associated with a protein of interest (using ChIP-loop and ChIA-PET). See Table 2 for overview of different detection strategies and their scope. * Figure 4: Speculative cartoon model of chromatin organization. LADs may consist of relatively condensed chromatin (thick lines) and aggregate at the nuclear lamina. Other repressed regions may interact with each other in the nuclear interior, as do active regions. Complexes formed by components of the transcription machinery (transcription factories) and CTCF may tether active regions together. Parts of only two chromosomes are depicted, each in a different color for clarity. Most interactions occur within chromosomes, and relatively few occur between chromosomes. Author information * Abstract * Author information Affiliations * Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands. * Bas van Steensel * Program in Gene Function and Expression, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Job Dekker Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Bas van Steensel (b.v.steensel@nki.nl) or * Job Dekker (job.dekker@umassmed.edu) Additional data - Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications
- Nat Biotech 28(10):1097-1105 (2010)
Nature Biotechnology | Research | Analysis Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications * R Alan Harris1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ting Wang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Cristian Coarfa1 Search for this author in: * NPG journals * PubMed * Google Scholar * Raman P Nagarajan3 Search for this author in: * NPG journals * PubMed * Google Scholar * Chibo Hong3 Search for this author in: * NPG journals * PubMed * Google Scholar * Sara L Downey3 Search for this author in: * NPG journals * PubMed * Google Scholar * Brett E Johnson3 Search for this author in: * NPG journals * PubMed * Google Scholar * Shaun D Fouse3 Search for this author in: * NPG journals * PubMed * Google Scholar * Allen Delaney4 Search for this author in: * NPG journals * PubMed * Google Scholar * Yongjun Zhao4 Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Olshen3 Search for this author in: * NPG journals * PubMed * Google Scholar * Tracy Ballinger5 Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Zhou2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin J Forsberg2 Search for this author in: * NPG journals * PubMed * Google Scholar * Junchen Gu2 Search for this author in: * NPG journals * PubMed * Google Scholar * Lorigail Echipare6 Search for this author in: * NPG journals * PubMed * Google Scholar * Henriette O'Geen6 Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan Lister7 Search for this author in: * NPG journals * PubMed * Google Scholar * Mattia Pelizzola7 Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanxin Xi8 Search for this author in: * NPG journals * PubMed * Google Scholar * Charles B Epstein9 Search for this author in: * NPG journals * PubMed * Google Scholar * Bradley E Bernstein9, 10, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * R David Hawkins12 Search for this author in: * NPG journals * PubMed * Google Scholar * Bing Ren12, 13 Search for this author in: * NPG journals * PubMed * Google Scholar * Wen-Yu Chung14, 15 Search for this author in: * NPG journals * PubMed * Google Scholar * Hongcang Gu9 Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph Bock9, 16, 17, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Gnirke9 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Q Zhang14, 15 Search for this author in: * NPG journals * PubMed * Google Scholar * David Haussler5 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph R Ecker7 Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Li8 Search for this author in: * NPG journals * PubMed * Google Scholar * Peggy J Farnham6 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert A Waterland1, 19 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner9, 16, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Marco A Marra4 Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Hirst4 Search for this author in: * NPG journals * PubMed * Google Scholar * Aleksandar Milosavljevic1 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Costello3jcostello@cc.ucsf.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1097–1105Year published:(2010)DOI:doi:10.1038/nbt.1682Published online19 September 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive rest! riction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression. View full text Figures at a glance * Figure 1: CpG coverage by each method. (,) The percentage of CpGs covered genome-wide () or in CpG islands () are plotted as a function of read-coverage threshold. () The percentage of genome-wide CpGs (28,163,863) covered by multiple, single or no methods are shown. * Figure 2: Comparison of bisulfite-based methods. () Calls of highly/partially/weakly methylated (0.80–0.20 or 0.75–0.25 cutoff) or highly/weakly methylated (0.20 cutoff) were made for CpGs covered at several minimum read depths by MethylC-seq and by RRBS (both on replicate no. 3). The number and percent of genome-wide CpGs covered and the percent of concordant calls are shown for each minimum read depth and methylation call cutoff. () Differences (MethylC-seq - RRBS) in methylated proportions (methylated reads/(methylated reads + unmethylated reads)) for CpGs with a minimum coverage of five reads by both methods. Percentages of concordant and discordant methylation were determined at cutoffs of ±0.1 (green dashed lines) and ±0.25 (red dashed lines). (,) CpG density in a 400-bp window () and genomic context of concordant and discordant CpGs at the 0.25 cutoff (). * Figure 3: Comparison of methylated DNA enrichment methods. () Calls of highly/weakly methylated were made by averaging methylation scores for CpGs covered at varying minimum read depths by MeDIP-seq or MBD-seq in 1,000- and 200-bp windows. The number of windows, percent of genome-wide CpGs covered and the percent of concordant calls are shown for each minimum read depth and window size. (,) For the 1,000-bp windows with a minimum read depth of 5, the CpG density () and genomic context () of the concordant and discordant windows are shown. The inset in shows a close-up of the concordance/discordance of CpG densities consistent with CpG islands. () For the 1,000-bp windows with a minimum read depth of 5, MethylC-seq methylation proportions for CpGs and non-CpG cytosines covered at a minimum read depth of 5, 444,590 windows, were summed and the windows were binned by the sum. For each of these bins, the number of windows called highly methylated by MeDIP-seq or MBD-seq is shown on the left y axis and the percent of total windows with c! alls of highly methylated is shown on the right y axis. Windows with a MethylC-seq methylation proportion sum >15, representing 83% of all windows, were called highly methylated by MeDIP-seq and MBD-seq in 99.9% of cases. The windows with a methylation proportion sum of 1–15, representing 17% of all windows, were called highly methylated by MeDIP-seq and MBD-seq in at least 99.1% of cases. * Figure 4: Comparison of all methods. () The table shows the percentage of 1,000-bp windows with concordant and discordant MethylC-seq (replicate no. 3), RRBS (replicate no. 3), MeDIP-seq (replicate no. 2) and MBD-seq (replicate no. 2) calls at minimum read depths of 5 and 10. Methods making the same call are grouped together in parentheses. Calls were made for MethylC-seq and RRBS by averaging the methylation proportion of CpGs within the window that were covered at the minimum read depth and applying a highly/weakly methylated cutoff of 0.2. Calls were made for MeDIP-seq and MBD-seq by averaging the methylation score of CpGs within the window that were covered at the minimum read depth. () Genome browser view of the 100-kb CpG rich Protocadherin alpha cluster (PCDHA), exemplifying the significant concordance in methylation status seen on a genome-wide level. For MethylC-seq and RRBS, the y axis displays methylation scores of individual CpGs. Scores range between −500 (unmethylated) and 500 (methylated) and t! he zero line is equivalent to 50% methylated. Negative scores are displayed as green bars and positive scores are displayed as orange bars. For MeDIP-seq (1), MeDIP-seq (2) and MBD-seq, the y axis indicates extended read density. Browsable genome-wide views of these data sets are available at http://www.genboree.org/ and http://genome.ucsc.edu/. * Figure 5: Integrative method increases methylome coverage and enables identification of a DMR. () MRE-seq involves parallel digests with methylation-sensitive restriction enzymes (HpaII, AciI and Hin6I), selection of cut fragments of ~50–300 bp, pooling the digests, library construction and sequencing. For every 600-bp window along chromosome 21, MeDIP-seq scores were plotted against MRE-seq scores. The plot depicts the inverse relationship between MRE-seq and MeDIP-seq signals. () Coverage of CpGs in the human genome by MeDIP-seq alone (red), MRE-seq alone (green), both (yellow) or neither method (no fill). Sequence from replicate nos. 1 and 2 were used in these calculations. () UCSC Genome Browser view of ZNF331 in H1 ESC, showing overlap of MeDIP-seq, MRE-seq and H3K4me3 (from ChIP-seq) signals at bisulfite region 1 and only MeDIP-seq signal at bisulfite region 2. () Clonal bisulfite sequencing results for specified regions in ESC from replicate no. 1. A filled circle represents a methylated CpG and an open circle indicates an unmethylated CpG. * Figure 6: Allelic DNA methylation, histone methylation and gene expression in ESCs. () Venn diagram summarizing the number of loci exhibiting monoallelic DNA methylation, histone methylation or monoallelic expression and their overlap. The top 1,000 loci (average size of 2.9 kb and encompassing a CpG island) with potential allelic DNA methylation were further evaluated, using the following assays: MRE-Seq and MeDIP-Seq for allelic DNA methylation within the loci, MethylC-seq and expression data for monoallelic expression of genes associated (±50 kb) with the loci, MethylC-seq and histone modifications H3K4me3 and H3K9me3 for monoallelic histone methylation within 1 kb from the loci. (,) Validation of known and novel DMRs identified from MeDIP-seq and MRE-seq. DMRs are presented in a UCSC Genome Browser window with MeDIP-seq and MRE-seq signals in human H1 ESC, along with bisulfite sequencing results. The results from the biological replicates (nos. 1 and 2) were very similar. () Imprinted gene GRB10 including a known DMR (Bisulfite region 1) and an upstrea! m unmethylated CpG island (Bisulfite region 2). () Novel DMR upstream of POTEB, which exhibits allele-specific DNA methylation. Open circle indicates an unmethylated CpG site. Filled circle represents a methylated CpG site. 'x' indicates absence of a CpG site due to a heterozygous SNP, which destroyed the 28th CpG. All clones without the CpG were unmethylated, whereas all the clones containing the CpG were methylated. Furthermore, the alleles could be distinguished in the sequence reads from MeDIP-seq (G allele, 9 of 9 reads) and MRE-seq (A allele, 30 of 30 reads). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * R Alan Harris, * Cristian Coarfa, * Robert A Waterland & * Aleksandar Milosavljevic * Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. * Ting Wang, * Xin Zhou, * Kevin J Forsberg & * Junchen Gu * Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA. * Raman P Nagarajan, * Chibo Hong, * Sara L Downey, * Brett E Johnson, * Shaun D Fouse, * Adam Olshen & * Joseph F Costello * Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada. * Allen Delaney, * Yongjun Zhao, * Marco A Marra & * Martin Hirst * Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California, USA. * Tracy Ballinger & * David Haussler * Department of Pharmacology and the Genome Center, University of California-Davis, Davis, California, USA. * Lorigail Echipare, * Henriette O'Geen & * Peggy J Farnham * Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA. * Ryan Lister, * Mattia Pelizzola & * Joseph R Ecker * Division of Biostatistics, Dan L. Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA. * Yuanxin Xi & * Wei Li * Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Charles B Epstein, * Bradley E Bernstein, * Hongcang Gu, * Christoph Bock, * Andreas Gnirke & * Alexander Meissner * Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Bradley E Bernstein * Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts, USA. * Bradley E Bernstein * Ludwig Institute for Cancer Research, University of California San Diego, La Jolla, California, USA. * R David Hawkins & * Bing Ren * Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA. * Bing Ren * Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. * Wen-Yu Chung & * Michael Q Zhang * Department of Molecular and Cell Biology, Center for Systems Biology, University of Texas at Dallas, Dallas, Texas, USA. * Wen-Yu Chung & * Michael Q Zhang * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Max Planck Institute for Informatics, Saarbrücken, Germany. * Christoph Bock * USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA. * Robert A Waterland Contributions J.F.C., R.A.H., T.W., M.H., M.A.M. and A. Milosavljevic conceived and designed the experiments. R.P.N., C.H., S.L.D., B.E.J., S.D.F., Y.Z. and M.H. performed the MeDIP, MRE and bisulfite sequencing experiments. R.A.W. and X.Z. designed and performed pyrosequencing and data analyses. H.G., C.B., A.G. and A. Meissner9 performed and analyzed RRBS. L.E., H.O., P.J.F., B.E.B., C.B.E., R.D.H. and B.R. performed and analyzed Chip-seq experiments. R.L., M.P. and J.R.E. analyzed MethylC-seq data and performed Bowtie aligner testing. R.A.H., T.W., K.J.F., J.G., C.C., M.H., X.Z., A.D. and A.O. performed data analysis. T.W., T.B. and D.H. developed MeDIP and methyl-sensitive restriction enzyme scoring algorithms and performed coverage analyses including repetitive sequence analyses. Y.X., W.-Y.C., R.L., M.Q.Z. and W.L. compared bisulfite sequence aligners. J.F.C., R.A.H., M.H., T.W., R.P.N. and R.A.W. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Joseph F Costello (jcostello@cc.ucsf.edu) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 1 (36K) Primer designs for bisulfite pyrosequencing. See Excel spreadsheet Supplementary_Table_1.xls. * Supplementary Table 3 (120K) Bisulfite data for Supplementary Figure 12. * Supplementary Table 6 (224K) Genome-wide catalogue of CpG island regions exhibiting overlapping MeDIP-seq (methylated) signals and MRE-seq (unmethylated) signals. * Supplementary Table 7 (252K) Validation of known and putative DMRs by bisulfite, PCR, cloning and sequencing. * Supplementary Table 9 (412K) Details of the comparison of genomic variation between pairs of assays to determine allele-specific epigenetic states. PDF files * Supplementary Text and Figures (4M) Supplementary Tables 2, 4, 5 and 8 and Supplementary Figs. 1–18 Additional data - Quantitative comparison of genome-wide DNA methylation mapping technologies
- Nat Biotech 28(10):1106-1114 (2010)
Nature Biotechnology | Research | Analysis Quantitative comparison of genome-wide DNA methylation mapping technologies * Christoph Bock1, 2, 3, 4, 6cbock@broadinstitute.org Search for this author in: * NPG journals * PubMed * Google Scholar * Eleni M Tomazou1, 2, 3, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Arie B Brinkman5 Search for this author in: * NPG journals * PubMed * Google Scholar * Fabian Müller1, 2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Femke Simmer5 Search for this author in: * NPG journals * PubMed * Google Scholar * Hongcang Gu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Natalie Jäger1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Gnirke1 Search for this author in: * NPG journals * PubMed * Google Scholar * Hendrik G Stunnenberg5 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner1, 2, 3alexander_meissner@harvard.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 28 ,Pages:1106–1114Year published:(2010)DOI:doi:10.1038/nbt.1681Published online19 September 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths an! d weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies. View full text Figures at a glance * Figure 1: Outline of the DNA methylation technology comparison. Four methods for DNA methylation mapping were compared on two pairs of samples. The resulting 16 DNA methylation maps were bioinformatically analyzed and benchmarked against each other. In addition, clonal bisulfite sequencing was performed on selected genomic regions to validate DNA methylation differences that were detected exclusively by one method. * Figure 2: Comparison of DNA methylation maps obtained with four different methods. The screenshot shows genome browser tracks for MeDIP-seq (first two tracks, in green), MethylCap-seq (three tracks in blue, gray and red), RRBS (stacked light blue tracks) and Infinium (single black track with percentage values) across the HOXA cluster in a human ES cell line (HUES6). Each track represents data from a single sequencing lane (MeDIP-seq, MethylCap-seq, RRBS) or microarray hybridization (Infinium). MeDIP-seq and MethylCap-seq data are visually similar to ChIP-seq data, with peaks in regions that show high density of the target molecule (5-methylcytosine) and troughs in regions with low density of methylated cytosines. The heights of the peaks represents the number of reads in each genomic interval, for each track normalized to the same genome-wide read count. RRBS gives rise to clusters of CpGs with absolute DNA methylation measurements, separated by regions that are not covered due to the reduced-representation property of the RRBS protocol. Each data point co! rresponds to the methylation level at a single CpG, and dark blue points indicate higher methylation levels than light blue points. Infinium data is represented in a similar way to the RRBS data, and the methylation levels at single CpGs are shown as percentage values. For reference, the CpG density is indicated by stacked points (black) at the bottom of the diagram, and CpG islands (red) as well as known genes (blue) are listed as described previously55, 56. * Figure 3: Quantification of DNA methylation with MeDIP-seq, MethylCap-seq and RRBS. (–) Absolute DNA methylation levels were calculated from the data obtained by MeDIP-seq (), MethylCap-seq () and RRBS (), respectively, and compared to DNA methylation levels determined by the Infinium assay. For MeDIP-seq and MethylCap-seq, sequencing reads were counted in 1-kb regions surrounding each CpG that is interrogated by the Infinium assay, and a regression model was used to infer absolute DNA methylation levels. Scatter plots and correlation coefficients were calculated on a test set that was not used for model fitting or feature selection. For RRBS, the DNA methylation level was determined as the percentage of methylated CpGs within 200 bp surrounding each CpG that is interrogated by the Infinium assay. Data shown are for the HUES6 human ES cell line, and regions that did not have sufficient sequencing coverage were excluded. * Figure 4: Genomic coverage of MeDIP-seq, MethylCap-seq, RRBS and Infinium. Genomic coverage was quantified by the number of DNA methylation measurements that overlap with CpG islands (top row), gene promoters (center row) and a 1-kb tiling of the genome (bottom row). For MeDIP-seq and MethylCap-seq, the number of measurements is equal to the number of unique sequencing reads that fall inside each region. For RRBS, it refers to the number of valid DNA methylation measurements at CpGs within each region (one RRBS sequencing read typically yields one measurement, but can also give rise to more than one measurement if it contains several CpGs). For Infinium, the number of measurements is equal to the number of CpGs within each region that are present on the HumanMethylation27 microarray. CpG islands were calculated using CgiHunter (http://cgihunter.bioinf.mpi-inf.mpg.de/), requiring a minimum CpG observed versus expected ratio of 0.6, a minimum GC content of 0.5 and a minimum length of 700 bp55. Promoter regions were calculated based on Ensembl gene an! notations, such that the region starts 1 kb upstream of the annotated transcription start site (TSS) and extends to 1 kb downstream of the TSS. The genomic tiling was obtained by sliding a 1-kb window through the genome such that each tile starts at the position where the previous tile ends. No repeat-masking was performed for any of the three types of genomic regions. Data are shown for the HUES6 human ES cell line. * Figure 5: Detection of DMRs with MeDIP-seq, MethylCap-seq and RRBS. Average DNA methylation measurements were calculated for each CpG island and compared between two human ES cell lines (HUES6 and HUES8). (–) Total read frequencies are shown for MeDIP-seq () and MethylCap-seq (), and mean DNA methylation levels are shown for RRBS (). Regions with insufficient sequencing coverage were excluded. () The Venn diagram displays the total number and mutual overlap of differentially methylated CpG islands that could be identified by each method. CpG islands were classified as hypermethylated or hypomethylated (depending on the directionality of the difference) if the absolute DNA methylation difference exceeded 20 percentage points (for RRBS) or if there was at least a twofold difference in read number between the two samples (for MeDIP-seq and MethylCap-seq)—but only if Fisher's exact test with multiple-testing correction gave rise to an estimated false-discovery rate of differential DNA methylation that was <0.1. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Christoph Bock & * Eleni M Tomazou Affiliations * Broad Institute, Cambridge, Massachusetts, USA. * Christoph Bock, * Eleni M Tomazou, * Fabian Müller, * Hongcang Gu, * Natalie Jäger, * Andreas Gnirke & * Alexander Meissner * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock, * Eleni M Tomazou, * Fabian Müller, * Natalie Jäger & * Alexander Meissner * Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. * Christoph Bock, * Eleni M Tomazou, * Fabian Müller, * Natalie Jäger & * Alexander Meissner * Max Planck Institute for Informatics, Saarbrücken, Germany. * Christoph Bock & * Fabian Müller * Radboud University Department of Molecular Biology, Nijmegen Center for Molecular Life Sciences, Nijmegen, The Netherlands. * Arie B Brinkman, * Femke Simmer & * Hendrik G Stunnenberg Contributions C.B., E.M.T. and A.M. conceived and designed the study; E.M.T., A.B.B., F.S. and H.G. performed the experiments; C.B., F.M. and N.J. analyzed the data; C.B., A.G., H.G.S. and A.M. interpreted the results; and C.B. and A.M. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Christoph Bock (cbock@broadinstitute.org) or * Alexander Meissner (alexander_meissner@harvard.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figs. 1–12 * Supplementary Data 1 (809K) Validation of method-specific DMRs by clonal bisulfite sequencing * Supplementary Data 2 (5M) DNA methylation map of prototypic repeat sequences * Supplementary Data 3 (3M) Differential DNA methylation of prototypic repeat sequences Additional data - Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage
- Nat Biotech 28(10):1115-1121 (2010)
Nature Biotechnology | Research | Article Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage * Connie C Wong1, 2, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin E Loewke1, 2, 3, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy L Bossert4 Search for this author in: * NPG journals * PubMed * Google Scholar * Barry Behr2 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher J De Jonge4 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas M Baer5 Search for this author in: * NPG journals * PubMed * Google Scholar * Renee A Reijo Pera1, 2reneer@stanford.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1115–1121Year published:(2010)DOI:doi:10.1038/nbt.1686Received05 April 2010Accepted03 September 2010Published online03 October 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report studies of preimplantation human embryo development that correlate time-lapse image analysis and gene expression profiling. By examining a large set of zygotes from in vitro fertilization (IVF), we find that success in progression to the blastocyst stage can be predicted with >93% sensitivity and specificity by measuring three dynamic, noninvasive imaging parameters by day 2 after fertilization, before embryonic genome activation (EGA). These parameters can be reliably monitored by automated image analysis, confirming that successful development follows a set of carefully orchestrated and predictable events. Moreover, we show that imaging phenotypes reflect molecular programs of the embryo and of individual blastomeres. Single-cell gene expression analysis reveals that blastomeres develop cell autonomously, with some cells advancing to EGA and others arresting. These studies indicate that success and failure in human embryo development is largely determined before ! EGA. Our methods and algorithms may provide an approach for early diagnosis of embryo potential in assisted reproduction. View full text Figures at a glance * Figure 1: Experimental plan. We tracked the development of 242 two-pronucleate stage embryos in four experimental sets (containing 61, 80, 64 and 37 embryos, respectively). In each set of experiments, human zygotes were thawed on day 1 and cultured in small groups on multiple plates. Each plate was observed independently with time-lapse microscopy under dark-field illumination on separate imaging stations. At ~24 h intervals, one plate of embryos was removed from the imaging system and collected as either single embryos or single cells (blastomeres) for high-throughput qRT-PCR gene expression analysis. Each plate typically contained a mixture of embryos that reached the expected developmental stage at the time of harvest (termed 'normal') and those that were arrested or delayed at earlier development stages, or fragmented extensively (termed 'abnormal'). Gene expression analysis was carried out on single intact embryos or on single blastomeres of dissociated embryos. One hundred of the 242 embryos were ! imaged until day 5 or 6 to monitor blastocyst formation. * Figure 2: Abnormal embryos exhibit abnormal cytokinesis and mitosis timing during the first divisions. () The developmental time line of a healthy human preimplantation embryo. Scale bar, 50 μm. () The distribution of normal and arrested embryos among samples that were cultured to day 5 or 6. () Cytokinesis duration was measured from the appearance of a cleavage furrow to complete daughter-cell separation during the first division. Time between the first and second mitoses was measured from the completion of the first mitosis to the appearance of cleavage furrow of the second mitosis. Synchronicity of the second and third mitoses was defined as the time between the appearance of the cleavage furrows of the second and third mitoses. () Normal embryos followed strict timing in cytokinesis and mitosis during early divisions, before EGA begins. Out of the 100 embryos imaged to day 5 or 6, six were excluded from subsequent image analysis due to technical issues (e.g., inability to track identity after media change, or loss of image focus). Raw data for this plot are included as S! upplementary Data Set 1, and additional views can be seen in Supplementary Figure 2. () Normal cytokinesis (first row) was typically completed in 14.3 ± 6.0 min in a smooth, controlled manner. In the mild phenotype (second row), the cytokinesis mechanism appears normal although it is slightly prolonged. In the severe phenotype (third row), a one-sided cytokinesis furrow is formed, accompanied by unusual ruffling of cell membranes for a prolonged period of time. Cytokinesis was defined by the first appearance of the cytokinesis furrow (arrows) to the complete separation of daughter cells. Imaging was also performed on a subset of triploid embryos (fourth row), which exhibited a distinct phenotype of dividing into three cells in a single event. Scale bar, 50 μm. () Embryos that underwent abnormal development and behavior (right) would occasionally appear morphologically similar to normal embryos (left) at the time of sample collection. In this particular case, time-lapse vi! deo data showed that what appeared to be a six to eight-cell e! mbryo (right) was in fact the product of a highly aberrant cell division (Supplementary Video 10). Thus, the correlated imaging data served to ensure the accuracy of sample selection and identification for the gene expression analysis. * Figure 3: Automated image analysis confirms the utility of the imaging parameters to predict blastocyst formation. () Results of tracking algorithm for a single embryo. Images were captured every 5 min, and only a select group is displayed. The top row shows frames from the original time-lapse image sequence, and the bottom row shows the overlaid tracking results. () Set of 14 embryos that were analyzed (Supplementary Video 6). One embryo was excluded as it was floating and out of focus. () Comparison of image analysis by a human observer and automated analysis of the duration of cytokinesis (top) and of the time between first and second mitoses (bottom). There is excellent agreement between the two methods for embryos that reached the blastocyst stage with good morphology. The few cases of disagreement occurred mostly for abnormal embryos and were caused by unusual behavior that is difficult to characterize by both methods. The gray shade region shows the window for blastocyst prediction. The two methods agreed on blastocyst prediction except in the case of embryo 10, which was predicte! d as abnormal by the automated method and normal by the manual method. () Comparison of blastocysts with good (top) and bad (bottom) morphology. * Figure 4: Distinct gene expression profiles of developmentally delayed or arrested embryos. () An arrested 2-cell embryo that showed abnormal membrane ruffling during the first cytokinesis had significantly (P < 0.05) reduced expression level of all cytokinesis genes tested. Scale bar, 50 μm. () An arrested 4-cell embryo that underwent aberrant cytokinesis with a one-sided cytokinesis furrow and extremely prolonged cytokinesis during the first division showed lower expression of ANLN and ECT2. Scale bar, 50 μm. () The average expression level of 52 genes from six abnormal 1- to 2-cell embryos and five normal 1- to 2-cell embryos were plotted in a radar graph on a logarithmic scale. Arrested embryos in general expressed less mRNA than normal embryos, with genes related to cytokinesis, RNA processing and miRNA biogenesis most severely affected. Genes highlighted in orange with an asterisk indicate a statistically significant difference (P < 0.05) between normal and abnormal embryos as determined by the Mann-Whitney test. * Figure 5: Gene expression analysis of single human embryos and blastomeres. () Genes analyzed in human embryos are defined by four distinct ESSPs. Relative expression level of an ESSP was calculated by averaging the expression levels of genes with similar expression patterns. () The ratio of maternal to embryonic genes in embryos changes during preimplantation development (left). Some embryos contained blastomeres of different developmental ages (right). The expression levels of embryonic and maternal programs were calculated by averaging the relative expression of ten ESSP1 and ten ESSP2 markers, respectively. * Figure 6: Proposed model for human embryo development. Human embryos begin life with a set of oocyte RNAs inherited from the mother. After fertilization, a subset of maternal RNAs specific to the egg (ESSP1) must be degraded as the transition from oocyte to embryo begins. As development continues, other RNAs are partitioned equally to each blastomere (ESSP4). At EGA, ESSP2 genes are transcribed in a cell-autonomous manner. During the cleavage divisions, embryonic blastomeres may arrest or progress independently. 'Feature extraction' indicates the three imaging parameters for predicting successful development to the blastocyst stage: cytokinesis, the time between 1st and 2nd mitoses, and the time between 2nd and 3rd mitoses. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Connie C Wong & * Kevin E Loewke Affiliations * Institute for Stem Cell Biology and Regenerative Medicine, School of Medicine, Stanford University, Stanford, California, USA. * Connie C Wong, * Kevin E Loewke & * Renee A Reijo Pera * Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, California, USA. * Connie C Wong, * Kevin E Loewke, * Barry Behr & * Renee A Reijo Pera * Department of Mechanical Engineering, Stanford University, Stanford, California, USA. * Kevin E Loewke * Reproductive Medicine Center, University of Minnesota, Minneapolis, Minnesota, USA. * Nancy L Bossert & * Christopher J De Jonge * Stanford Photonics Research Center, Department of Applied Physics, Stanford University, Stanford, California, USA. * Thomas M Baer * Present address: Auxogyn, Inc., Menlo Park, California, USA. * Kevin E Loewke Contributions C.C.W. and K.E.L. performed and designed experiments, analyzed data and assisted in writing and editing of the manuscript. K.E.L. designed cell tracking algorithms. N.L.B. assisted in performing the experiments. B.B., N.L.B. and C.J.D.J. assisted in analyzing data and editing the manuscript. T.M.B. and K.E.L. designed and built the imaging instrumentation. T.M.B. and R.A.R.P. designed experiments, interpreted results and assisted in writing and editing the manuscript. Competing financial interests This research project was conducted at Stanford University, and at the time of original submission there were no competing financial interests. K.L. is now an employee of Auxogyn, Inc., which has licensed intellectual property resulting from this research. C.W., K.L., N.B., B.B., C.J.D., T.B. and R.R.P. own stock in Auxogyn. Corresponding author Correspondence to: * Renee A Reijo Pera (reneer@stanford.edu) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Data Set 1 (52K) Raw data used to generate Figure 1d. * Supplementary Data Set 2 (60K) Complete probe list used for each experiment, as well as the corresponding Unigene ID and RefSeq Accession ID of each ABI assay-on-demand probe, as provided on Applied Biosystems' website. * Supplementary Data Set 3 (52K) Comparison of our qRT-PCR gene expression data in 1-cell and 2-cell embryos to the microarray data in human oocytes as described in Kocabas et al. We note that due to the differences in experimental design and data handling, we would only expect qualitative agreement between these 2 data sets. Expression of two genes, AURKA and CCNA1, was also analyzed in a separate report by Keissling et al. (J Assist Reprod Genet (2009) 26:187–195)11; expression of these genes was consistent with our data and that of Kocabas et al. These genes are indicated by an asterisk; overlap between gene sets was minimal due to differences in experimental design. * Supplementary Data Set 4 (44K) Taqman probes used for qRT-PCR analysis. * Supplementary Data Set 5 (156K) High throughput qRT-PCR data set 1. This excel file contains the relative expression values of all samples and genes assayed in the first high throughput qRT-PCR experiment. Samples were named using a 3-part nomenclature: part 1 depicted the developmental stage of the embryo, part 2 indicated the order of the sample collected within its category, and part 3 reflected whether the embryo was collected as a whole embryo or single blastomere. For example, the name '2c-7-1' referred to the 1st blastomere of the 7th 2-cell embryo collected, whereas 'B-10-W' was the 10th blastocyst collected as a whole embryo. * Supplementary Data Set 6 (300K) High throughput qRT-PCR data set 2. This excel file contains the relative expression values of all samples and genes assayed in the second high throughput qRT-PCR experiment. The sample nomenclature scheme was the same as Supplementary Dataset 5. * Supplementary Data Set 7 (160K) High throughput qRT-PCR data set 3. This excel file contains the relative expression values of all samples and genes assayed in the second high throughput qRT-PCR experiment. The sample nomenclature scheme was the same as Supplementary Dataset 5. Movies * Supplementary Video 1 (8M) Video accompaniment to Figure 1a. The development of 15 human zygotes was documented with darkfield time-lapse microscopy. Images were taken at 1 second exposure time every 5 minutes for 6 days. Media was changed on Day 3, resulting in the rearrangement of individual embryo's location. The identity of each embryo was tracked by videotaping the process of sample transfer during media change and sample collection. Among the 15 embryos, 10 developed into a blastocyst and 5 became arrested at different stages of development. Embryo H in this video corresponds to the embryo depicted in Figure 2a. * Supplementary Video 2 (24K) Video accompaniment to Figure 1e (first panel). A normal embryo typically completed cytokinesis in 13.0 +/- 4.2 min in a smooth and controlled manner. * Supplementary Video 3 (28K) Video accompaniment to Figure 1e (second panel). Some embryos underwent a slightly delayed but otherwise morphologically normal cytokinesis. * Supplementary Video 4 (416K) Video accompaniment to Figure 1e (third panel). In the more severe phenotype, the abnormal embryos often formed a one-sided cytokinesis furrow accompanied by extensive membrane ruffling before finally completing the division, possibly resulting in embryo fragmentation. * Supplementary Video 5 (296K) Video accompaniment to Figure 1e (fourth panel). Imaging was also performed on a subset of triploid embryos which exhibited a distinct phenotype of dividing into 3-cells in a single event. * Supplementary Video 6 (6M) Video accompaniment to Figure 2a. Results of 2D tracking algorithm for a single embryo. Images are acquired every 5 minutes. The movie shows the most probable model, the original image, the Hessian (principle curvature image), the thresholded Hessian, and the simulated image (which corresponds to the most probable model). The plots on the bottom show the particles, with dots placed at the centers of the cells, before and after re-sampling. * Supplementary Video 7 (11M) Video accompaniment to Figure 2b. 2D tracking for a set of 14 embryos. One embryo was excluded from image analysis since it was floating and out of focus. Once the algorithm is capable of making a prediction of blastocyst, the embryo is labeled with 'viable' for blastocyst or 'non-viable' for non-blastocyst. On day 3 there is a media change that allows the embryos to be culturedto the blastocyst stage. This process was videotaped to assist in maintaining embryo identity. * Supplementary Video 8 (64K) Video accompaniment to Figure 3a. Abnormal membrane ruffling was observed during the first cytokinesis of this arrested 2-cell embryo. * Supplementary Video 9 (600K) Video accompaniment to Figure 3b. This arrested 4-cell embryo underwent a severely abnormal cytokinesis during its first division. * Supplementary Video 10 (7M) Video accompaniment to Supplementary Figure 1f. Video microscopy data aided in the identification of abnormal embryos (bottom) from normal embryos (top). PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1,2 and Supplementary Figs. 1–10 Additional data - Substrate elasticity provides mechanical signals for the expansion of hemopoietic stem and progenitor cells
- Nat Biotech 28(10):1123-1128 (2010)
Nature Biotechnology | Research | Letter Substrate elasticity provides mechanical signals for the expansion of hemopoietic stem and progenitor cells * Jeff Holst1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Watson1 Search for this author in: * NPG journals * PubMed * Google Scholar * Megan S Lord3 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven S Eamegdool4 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel V Bax4 Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa B Nivison-Smith4 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexey Kondyurin5 Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Ma6 Search for this author in: * NPG journals * PubMed * Google Scholar * Andres F Oberhauser6 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony S Weiss4 Search for this author in: * NPG journals * PubMed * Google Scholar * John E J Rasko1, 2, 7j.rasko@centenary.org.au Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 28 ,Pages:1123–1128Year published:(2010)DOI:doi:10.1038/nbt.1687Received16 June 2010Accepted07 September 2010Published online03 October 2010 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Surprisingly little is known about the effects of the physical microenvironment on hemopoietic stem and progenitor cells. To explore the physical effects of matrix elasticity on well-characterized primitive hemopoietic cells, we made use of a uniquely elastic biomaterial, tropoelastin. Culturing mouse or human hemopoietic cells on a tropoelastin substrate led to a two- to threefold expansion of undifferentiated cells, including progenitors and mouse stem cells. Treatment with cytokines in the presence of tropoelastin had an additive effect on this expansion. These biological effects required substrate elasticity, as neither truncated nor cross-linked tropoelastin reproduced the phenomenon, and inhibition of mechanotransduction abrogated the effects. Our data suggest that substrate elasticity and tensegrity are important mechanisms influencing hemopoietic stem and progenitor cell subsets and could be exploited to facilitate cell culture. View full text Figures at a glance * Figure 1: Tropoelastin increased mouse hemopoietic stem and progenitor cells. Mouse bone marrow cells were cultured on control or tropoelastin-coated plates for 7 d. (,) On day 3, cells were harvested, counted, and analyzed by flow cytometry (), and the numbers of LSK cells were compared to those in fresh uncultured bone marrow (baseline), expressed as mean ± s.e.m. (; n = 4–5). () On days 1, 3, 5 and 7, cells were analyzed by flow cytometry, expressed as mean ± s.e.m. (days 1, 5 and 7, n = 3; day 3, n = 10). () Cells were labeled with CFSE, cultured for 3 d and analyzed by flow cytometry; results are expressed as mean ± s.e.m., with left y axis denoting undivided cells and right axis denoting cell divisions 1–4 (n = 4). (,) Cells cultured for either 3 (n = 4) or 5 d (n = 3) were subsequently cultured in MethoCult medium and colonies enumerated (expressed as mean ± s.e.m.). () Cells cultured for 3 d were analyzed for SLAM markers by flow cytometry, expressed as mean ± s.e.m. (n = 4). CD45.1+ bone marrow cells cultured for 3 d were injected in! to irradiated CD45.2+ mice and analyzed after 8 weeks for engraftment. () The number of transplanted cells were plotted against the percentage of mice with unsuccessful engraftment to determine the frequency of repopulating cells (n = 25 recipients per group from five separate experiments). * Figure 2: Tropoelastin increased human hemopoietic progenitor cells. Human umbilical cord blood cells were cultured on control or tropoelastin-coated plates. (,) After 3 d cells were analyzed by flow cytometry with representative dot plots shown for lineage negative gated co-expression of CD34 and CD38. () The percentage of Lin−CD34+CD38+ cells is shown (mean ± s.e.m.; n = 5). () After 3 d, cells were cultured in MethoCult medium and colonies enumerated (expressed as mean ± s.e.m.) (n = 4). Statistical significance was determined using a two-tailed Wilcoxon signed rank t test. * Figure 3: Effect of tropoelastin truncations and cross-linking on the ability of mouse hemopoietic cells to respond to tropoelastin. () Schematic of full-length tropoelastin and truncations, including both domain structure and amino acid numbering of each construct end. (–) Mouse bone marrow cells were cultured on control, tropoelastin-coated or truncated tropoelastin–coated plates () or on glutaraldehyde–cross-linked tropoelastin-coated plates (). After 3 d cells were analyzed by flow cytometry for the absence of lineage-specific surface markers and for coexpression of Sca-1 and c-Kit. Shown is the fold increase in percentage of LSK cells relative to control plates. In ,, data are mean ± s.e.m. from three to six separate experiments. In , tropoelastin or truncations were deposited on glass slides (with or without cross-linking with glutaraldehyde) and their extensibilities (contour lengths) were determined by atomic force microscopy using the worm-like chain model of polymer elasticity. Data are the percentage of total events, including a Gaussian nonlinear fit curve. () The mean ± s.e.m. of the ! contour length was compared. A putative threshold of extensional elasticity to retain biological activity is shown (dotted line). * Figure 4: QCM-D analysis of collagen, fibronectin, tropoelastin and ELN27-540 binding to oxidized polystyrene, and the effect of myosin II heavy chain and myosin light chain kinase inhibitors on the ability of mouse hemopoietic cells to respond to tropoelastin. Intact collagen, fibronectin, tropoelastin and ELN27-540 adsorbed onto oxidized polystyrene were monitored by QCM-D for 1 h at 20 ± 0.1°C. () Changes in dissipation (Δ dissipation) versus changes in frequency (Δ frequency) are presented for the third overtone (Df plot). (–) These data were analyzed using the Voigt model to determine adsorbed layer thickness (), mass () and protein circular footprint (). Circular footprint diameter measurements were performed assuming globular proteins of tropoelastin (60 kDa), ELN27-540 (44.4 kDa), fibronectin (440 kDa) and collagen (300 kDa). Data are presented as mean ± s.d. from three separate experiments. (,) Mouse bone marrow cells were cultured on control (uncoated) or tropoelastin-coated plates in the presence or absence of blebbistatin () or DMSO ± ML-7 (). On day 3, cells were analyzed by flow cytometry to determine the percentage of LSK cells, relative to control (uncoated) plates. Data are mean ± s.e.m. from three separat! e experiments. Statistical significance was determined using a two-tailed Wilcoxon signed rank t test. Author information * Author information * Supplementary information Affiliations * Gene & Stem Cell Therapy Program, Centenary Institute, Camperdown, New South Wales, Australia. * Jeff Holst, * Sarah Watson & * John E J Rasko * Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia. * Jeff Holst & * John E J Rasko * Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales, Australia. * Megan S Lord * School of Molecular Bioscience, University of Sydney, Sydney, New South Wales, Australia. * Steven S Eamegdool, * Daniel V Bax, * Lisa B Nivison-Smith & * Anthony S Weiss * School of Physics, University of Sydney, Sydney, New South Wales, Australia. * Alexey Kondyurin * Department of Neuroscience and Cell Biology, University of Texas Medical Branch, Galveston, Texas, USA. * Liang Ma & * Andres F Oberhauser * Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia. * John E J Rasko Contributions J.H. and J.E.J.R. designed the experiments and wrote the paper, J.H. and M.S.L. analyzed the data, J.H., S.W., A.F.O., L.M., S.S.E., M.S.L. and A.K. generated the data, A.S.W. and J.E.J.R. provided conceptual input and D.V.B., L.B.N.-S. and S.S.E. provided tropoelastin reagents. Competing financial interests A.S.W. receives personal financial support from Elastagen Pty Ltd. A patent application has been filed for some of the technology disclosed in this publication. Corresponding author Correspondence to: * John E J Rasko (j.rasko@centenary.org.au) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figs. 1–10 Additional data - Pfizer explores rare disease path
- Nat Biotech 28(10):1129 (2010)
Nature Biotechnology | Research | Erratum Pfizer explores rare disease path * Catherine Shaffer Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1129Year published:(2010)DOI:doi:10.1038/nbt1010-1129aPublished online13 October 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.28, 881–882 (2010); published online 9 September 2010; corrected after print 22 September 2010 In the version of this article initially published, it was reported that GlaxoSmithKline's (GSK's) EpiNova was one of several "biotech-like ideas" that "have been known to fizzle in pharma hands"; in fact, EpiNova has not "fizzled" but is in its second year of operation as a discovery performance unit of GSK focusing on epigenetic approaches to autoimmune disease. The error has been corrected in the HTML and PDF versions of the article. Additional data - Public biotech 2009—the numbers
- Nat Biotech 28(10):1129 (2010)
Nature Biotechnology | Research | Erratum Public biotech 2009—the numbers * Brady Huggett Search for this author in: * NPG journals * PubMed * Google Scholar * John Hodgson Search for this author in: * NPG journals * PubMed * Google Scholar * Riku Lähteenmäki Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1129Year published:(2010)DOI:doi:10.1038/nbt1010-1129bPublished online13 October 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.28, 793–799 (2010); published online 9 August 2010; corrected after print 13 October 2010 In the version of this article initially published, in Table 6, Acorda was said to have entered into a licensing agreement with Bayer. In fact, Acorda entered into a licensing agreement with Biogen, not Bayer. The error has been corrected in the HTML and PDF versions of the article. Additional data - Food firms test fry Pioneer's trans fat-free soybean oil
- Nat Biotech 28(10):1129 (2010)
Nature Biotechnology | Research | Corrigendum Food firms test fry Pioneer's trans fat-free soybean oil * Emily Waltz Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1129Year published:(2010)DOI:doi:10.1038/nbt1010-1129cPublished online13 October 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.28, 769–770 (2010); published online 9 August 2010; corrected after print 13 October 2010 The version of the article originally published states that Monsanto petitioned the USDA for deregulation of two "soybean products with modified oil profiles, one with omega-3 fatty acids for nutrition and the other with enhanced texture and functionality, called high stearic acid soybeans." The article should have stated that "Monsanto has petitioned for deregulation of Vistive Gold soybeans, with mono-unsaturated fat levels similar to that of olive oil, and saturated fat levels similar to canola oil, which would produce an oil more stable than regular soybean oil at high frying temperatures." The high stearate soybeans are still in development. The error has been corrected in the HTML and PDF versions of the article. Additional data - Glyphosate resistance threatens Roundup hegemony
- Nat Biotech 28(10):1129 (2010)
Nature Biotechnology | Research | Corrigendum Glyphosate resistance threatens Roundup hegemony * Emily Waltz Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1129Year published:(2010)DOI:doi:10.1038/nbt1010-1129dPublished online13 October 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.28, 537–538 (2010); published online 7 June 2010; corrected after print 13 October 2010 The version of the article originally published erroneously states that "Unlike pesticide use, herbicide use is not regulated by the US federal government." The article should have stated "Unlike insect resistance, the US government does not have a mandated herbicide-resistance program." The error has been corrected in the HTML and PDF versions of the article. Additional data - Pluripotent patents make prime time: an analysis of the emerging landscape
- Nat Biotech 28(10):1129 (2010)
Nature Biotechnology | Research | Corrigendum Pluripotent patents make prime time: an analysis of the emerging landscape * Brenda M Simon Search for this author in: * NPG journals * PubMed * Google Scholar * Charles E Murdoch Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher T Scott Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1129Year published:(2010)DOI:doi:10.1038/nbt1010-1129ePublished online13 October 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.28, 557–559 (2010); published online 7 June 2010; corrected after print 13 October 2010 In the version of this article initially published, the authors state: "The patents have been cross-licensed, protecting against unlicensed use of either method. Both the Sakurada and Yamanaka patents are part of the portfolio held by iPierian, a company recently formed by the merger of iZumi Bio, a San Francisco Bay Area biotech and Boston-based Pierian." This statement is incorrect. The Yamanaka patent (owned by Kyoto University) is not licensed to iPierian. The Sakurada patent (owned by iPierian) is not licensed to Kyoto University. The error has been corrected in the HTML and PDF versions of the article. Additional data - Portfolio managing for scientists
- Nat Biotech 28(10):1131 (2010)
Nature Biotechnology | Careers and Recruitment Portfolio managing for scientists * David Sable1dsable@ssfund.com Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature BiotechnologyVolume: 28 ,Page:1131Year published:(2010)DOI:doi:10.1038/nbt1010-1131Published online13 October 2010 A doctor-turned-portfolio manager finds the ever-changing economic and business environment stimulating. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * David Sable is at Special Situations Life Sciences Fund, New York, New York, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * David Sable (dsable@ssfund.com) Additional data - People
- Nat Biotech 28(10):1132 (2010)
George F. Horner III (below, right) was named chairman of the board of directors of Luxembourg-based Creabilis.
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