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GIMLET: Identifying biological modulators in context-specific gene regulation using local energy statistics 1

14th International Meeting on Computational Intelligence Methods for bioinformatics and Biostatistics, CIBB 2017

作者： Shimamura, Teppei Matsui, Yusuke Kajino, Taisuke Ito, Satoshi Takahashi, Takashi Miyano, Satoru Division of Systems Biology Nagoya University Graduate School of Medicine 65 Tsurumai-cho Showa-ku Nagoya466-8550 Japan Laboratory of Intelligence Healthcare Nagoya University Graduate School of Medicine 1-1-20 Daiko-Minami Higashi-ku Nagoya461-8673 Japan Division of Molecular Carcinogenesis Nagoya University Graduate School of Medicine 65 Tsurumai-cho Showa-ku Nagoya466-8550 Japan Human Genome Center Institute of Medical Science The University of Tokyo 4-6-1 Shirokane-dai Minato-ku Tokyo108-8639 Japan

ISBN: (数字)9783030141608

ISBN: (纸本)9783030141592

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator;these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R (≥ 3.2.2) and is available from github (https://***/tshimam/GIMLET ). © 2019, Springer Nature Switzerland AG.

关键词： Statistical tests

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QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data

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bioinformatics 2018年第1期34卷 163-170页

作者： Skums, Pavel Zelikovsky, Alex Singh, Rahul Gussler, Walker Dimitrova, Zoya Knyazev, Sergey Mandric, Igor Ramachandran, Sumathi Campo, David Jha, Deeptanshu Bunimovich, Leonid Costenbader, Elizabeth Sexton, Connie O'Connor, Siobhan Xia, Guo-Liang Khudyakov, Yury Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Ctr Dis Control & Prevent Div Viral Hepatitis Atlanta GA 30303 USA San Francisco State Univ Dept Comp Sci San Francisco CA 94132 USA Georgia Inst Technol Sch Math Atlanta GA 30313 USA FHI 360 Durham NC 27701 USA Ctr Dis Control & Prevent Div Global HIV & TB Atlanta GA 30333 USA Ctr Dis Control & Prevent Div HIV AIDS Prevent Atlanta GA 30333 USA

Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://***/skumsp/QUENTIN. Contact: pskums@*** or alexz@*** or rahul@***0@*** Supplementary information: Supplementary data are available at bioinformatics online.

关键词： INFECTIOUS disease transmission GENOMICS VIRAL genomes EPIDEMICS HIV (Viruses) HEPATITIS C virus

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Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda (vol , pg , 2020)

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LANCET HIV 2020年第12期7卷 E803-E803页

作者： Ratmann, O. Kagaayi, J. Hall, M. Department of Mathematics Imperial College London London UK. Electronic address: oliver.ratmann@imperial.ac.uk. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda. Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford Oxford UK. Department of Mathematics Imperial College London London UK. European Molecular Biology Laboratory-European Bioinformatics Institute Wellcome Genome Campus Hinxton UK. Division of Infection and Immunity University College London London UK. Department of Medicine Imperial College London London UK. Department of Medicine Johns Hopkins School of Medicine Baltimore MD USA Division of Intramural Research National Institute of Allergy and Infectious Diseases NIH Bethesda MD USA. Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda Department of Medicine Johns Hopkins School of Medicine Baltimore MD USA Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. KwaZulu-Natal Research Innovation and Sequencing Platform College of Health Sciences University of KwaZulu-Natal Durban South Africa. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda Department of Medicine Johns Hopkins School of Medicine Baltimore MD USA Division of Intramural Research National Institute of Allergy and Infectious Diseases NIH Bethesda MD USA. Department of Statistics University of Warwick Coventry UK. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda Makerere University School of Public Health Kampala Uganda. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Rakai Health Sciences Program Kalisizo Old-Bukoba Road Uganda Department of Pathology Johns Hopkins School of Medicine Baltimore MD

International and global organisations advocate targeting interventions to areas of high HIV prevalence (ie, hotspots). To better understand the potential benefits of geo-targeted control, we assessed the extent to which HIV hotspots along Lake Victoria sustain transmission in neighbouring populations in south-central Uganda.;We did a population-based survey in Rakai, Uganda, using data from the Rakai Community Cohort Study. The study surveyed all individuals aged 15-49 years in four high-prevalence Lake Victoria fishing communities and 36 neighbouring inland communities. Viral RNA was deep sequenced from participants infected with HIV who were antiretroviral therapy-naive during the observation period. Phylogenetic analysis was used to infer partial HIV transmission networks, including direction of transmission. Reconstructed networks were interpreted through data for current residence and migration history. HIV transmission flows within and between high-prevalence and low-prevalence areas were quantified adjusting for incomplete sampling of the population.;Between Aug 10, 2011, and Jan 30, 2015, data were collected for the Rakai Community Cohort Study. 25 882 individuals participated, including an estimated 75·7% of the lakeside population and 16·2% of the inland population in the Rakai region of Uganda. 5142 participants were HIV-positive (2703 [13·7%] in inland and 2439 [40·1%] in fishing communities). 3878 (75·4%) people who were HIV-positive did not report antiretroviral therapy use, of whom 2652 (68·4%) had virus deep-sequenced at sufficient quality for phylogenetic analysis. 446 transmission networks were reconstructed, including 293 linked pairs with inferred direction of transmission. Adjusting for incomplete sampling, an estimated 5·7% (95% credibility interval 4·4-7·3) of transmissions occurred within lakeside areas, 89·2% (86·0-91·8) within inland areas, 1·3% (0·6-2·6) from lakeside to inland areas, and 3·7% (2·3-5·8) from inland to lakeside areas.;Cros

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A Hybrid Stochastic Model of the Budding Yeast Cell Cycle control Mechanism 16

A Hybrid Stochastic Model of the Budding Yeast Cell Cycle Co...

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7th ACM International Conference on bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Wang, Shuo Ahmadian, Mansooreh Chen, Minghan Tyson, John Cao, Young Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Dept Biol Sci Blacksburg VA 24061 USA

ISBN: (纸本)9781450342254

The budding yeast cell cycle is regulated by a complex chemical reaction network. Several deterministic models have been proposed to model this control mechanism. However, experimental data exhibit considerable variability from cell to cell during cell growth and division. It is also observed that certain mutant cells are more vulnerable to noise than wild type cells. The observed variability comes from two sources: intrinsic noise coming from fluctuations of molecules and extrinsic noise introduced by variations in the division process. As a result, molecular fluctuations cannot be neglected, and they may significantly affect the behavior of a cell. To accurately model the cell cycle control mechanism, stochastic models are needed. A rigorous solution to this problem is to convert a deterministic model to its stochastic equivalent and apply Gillespie's stochastic simulation algorithm (SSA). But the conversion process is not straightforward and often results in a much larger system. Moreover, the high computational cost of Gillespie's algorithm make it difficult to simulate a practical budding yeast cell cycle model. In this manuscript, we present a hybrid (ODE/SSA) stochastic model for the budding yeast cell cycle. Based on error analysis for multiscale systems and the observation that fluctuations of mRNAs are the primary sources of noise, in this hybrid model the dynamics of mRNAs are simulated by Gillespie's algorithm, while the cell cycle mechanism at the protein level is modeled by ordinary differential equations (ODEs). Numerical experiments, implemented by Haseltine and Rawlings' hybrid simulation algorithm, demonstrate that the hybrid model matches very well with biological experimental data for both wild-type cells and mutant cells in different nutrient media.

关键词： budding yeast cell cycle model stochastic simulation algorithm (SSA) hybrid ODE/SSA method

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Proceedings of the Frontiers of Retrovirology Conference 2016

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Retrovirology 2016年第1期13卷 1-40页

作者： Irena Zurnic Sylvia Hütter Nicole Stanke Tobias Kern Fabian Lindel Gesche Gerresheim Martin Hamann Erik Müllers Dirk Lindemann Ute Lehmann Juliane Reh Paul Lesbats Peter Cherepanov Melanie Mann Erik Serrao Alan Engelman Claire Da Silva Santos Kevin Tartour Andrea Cimarelli Rya Burdick Jianbo Chen Jaya Sastri Wei-Shau Hu Vinay Pathak Oliver T. Keppler Patrícia de Sousa-Pereira Hanna-Mari Baldauf Marcel Stern Karine Pradeau Sylvia Eiler Nicolas Levy Marc Ruff Sarah Lennon Sarah Cianferani Stéphane Emiliani Vincent Parissi Sylvie Rato Miguel Munoz Angela Ciuffi Antonio Rausell Amalio Telenti Alexander Zhyvoloup Ian Anderson Chen- Hsuin Lee Ariberto Fassati Anat Melamed Delphine Planas Petronela Ancuta Yuwei Zhang Huicheng Chen Annie Bernier Annie Gosselin Janos Kriston-Vizi Robin Ketteler Andy Merritt Charles Bangham Jocelyn Turpin Anthony Rodari Benoit Van Driessche Mathilde Galais Nadége Delacourt Sylvain Fauquenoy Caroline Vanhulle Anna Kula Carine Van Lint Ernst J. Wolvetang Arsène Burny Olivier Rohr Thijs van Montfort Renee van der Sluis Ben Berkhout Dave Speijer Gang Wang Na Zhao Atze T. Das Mirte Scherpenisse Bo Meng Andrzej Rutkowski Lars Dölken Andrew Lever Neil Berry Claire Ham Neil Almond Hoi Ping Mok Nicholas Norton Axel Fun Jack Hirst Mark Wills Chris Hellmund Harriet Groom Thomas Schuster Benedikt Asbach Ralf Wagner Christine Gross Martina Kalmer Andrea K. Thoma-Kress Norbert Donhauser Sebastian Millen Sandra M. Müller-Schmucker Veit Wiesmann Thomas Wittenberg Jan Gettemans Minghua Li Shan-Lu Liu Eric O. Freed Janis Müller Jan Münch Simone Joas Clement Wesley Gnanadurai Edina Lump Christina M. Stürzel Daniel Sauter Frank Kirchhoff Katharina Mack Kathrin Starz Christina Stürzel Xaver Sewald Nasim Motamedi Pradeep Uchil Ruoxi Pi Christin Herrmann Walther Mothes Mark Ladinsky Pamela Bjorkman Jagadish Beloor Priti Kumar Thomas Murooka Thorsten Mempel Michael Brehm Dale Greiner Erica Parrish Nicholas F. Parrish Beatrice H. Hahn Frederic Bibollet-Ruche Ulrike Sauermann Katharina Töpfer Tina Schultheiss Chris Molecular Virology and Gene Therapy Molecular Medicine Leuven Belgium Molecular Virology Technical University Dresden Dresden Germany Center for Regenerative Therapies Dresden Dresden Germany Potsdam University Potsdam Germany The Francis Crick Institute London Great Britain Institute of Clinical and Molecular Virology Friedrich-Alexander-Universität Erlangen-Nuremberg Erlangen Germany Dana Farber Cancer Institute Boston MA United States CIRI Lyon France National Cancer Institute Frederick MD United States Virology Max von Pettenkofer-Institut LMU Munich Munich Germany Institute of Medical Virology University of Frankfurt Frankfurt a. M. Germany German Center for Infection Research Munich Germany Institute of Medical Virology University Hospital Frankfurt Frankfurt a. M. Germany CIBIO/InBIO University of Porto Vairão Portugal Faculty of Sciences University of Porto Porto Portugal Virology Max von Pettenkofer Institute LMU Munich Germany Institute for Medical Virology Frankfurt a. M. Germany Integrative structural Biology IGBMC Illkirch France Institut Pluridisciplinaire Hubert Curien Strasbourg France Institut Cochin Paris France CNRS UMR5234 MFP Lab Bordeaux France Associated International Laboratory (LIA) Microbiology and Immunology CNRS/Université de Bordeaux/Heinrich Pette Institute-Leibniz Institute for Experimental Virology Bordeaux/Hamburg France Institute of Microbiology University Hospital Center and University of Lausanne Lausanne Switzerland Imagine Institute Paris Descartes University Paris France J. Craig Venter Institute La Jolla CA United States Infection University College London London Great Britain Medicine Imperial College London Great Britain Microbiology & Infection University of Montreal Montreal Great Britain CRCHUM Montreal Canada University College London LMCB London Great Britain MRC Technology Centre for Therapeutic Discovery London Great Britain Section of Virology Department of Medicine Imperial Co

Oral presentationsSession 1: Entry & uncoatingO1 Host cell polo-like kinases (PLKs) promote early prototype foamy virus (PFV) replicationIrena Zurnic, Sylvia Hütter, Ute Lehmann, Nicole Stanke, Juliane Re... 详细信息

Oral presentations

Session 1: Entry & uncoating

O1 Host cell polo-like kinases (PLKs) promote early prototype foamy virus (PFV) replication

Irena Zurnic, Sylvia Hütter, Ute Lehmann, Nicole Stanke, Juliane Reh, Tobias Kern, Fabian Lindel, Gesche Gerresheim, Martin Hamann, Erik Müllers, Paul Lesbats, Peter Cherepanov, Erik Serrao, Alan Engelman, Dirk Lindemann

O2 A novel entry/uncoating assay reveals the presence of at least two species of viral capsids during synchronized HIV-1 infection

Claire Da Silva Santos, Kevin Tartour, Andrea Cimarelli

O3 Dynamics of nuclear envelope association and nuclear import of HIV-1 complexes

Rya Burdick, Jianbo Chen, Jaya Sastri, Wei-Shau Hu, Vinay Pathak

O4 Human papillomavirus protein E4 potently enhances the susceptibility to HIV infection

Oliver T. Keppler

Session 2: Reverse transcription & integration

O5 Structure and function of HIV-1 integrase post translational modifications

Karine Pradeau, Sylvia Eiler, Nicolas Levy, Sarah Lennon, Sarah Cianferani, Stéphane Emiliani, Marc Ruff

O6 Regulation of retroviral integration by RNA polymerase II associated factors and chromatin structure

Vincent Parissi

Session 3: Transcription and latency

O7 A novel single-cell analysis pipeline to identify specific biomarkers of HIV permissiveness

Sylvie Rato, Antonio Rausell, Miguel Munoz, Amalio Telenti, Angela Ciuffi

O8 A capsid-dependent integration program linking T cell activation to HIV-1 gene expression

Alexander Zhyvoloup, Anat Melamed, Ian Anderson, Delphine Planas, Janos Kriston-Vizi, Robin Ketteler, Chen-Hsuin Lee, Andy Merritt, Petronela Ancuta, Charles Bangham, Ariberto Fassati

O9 Characterisation of new RNA polymerase III and RNA polymerase II transcriptional promoters in the Bovine Leukemia Virus genome

Anthony Rodari, Benoit Van Driessche, Mathilde Galais, Nadége Delacourt, Sylv

关键词： Human Immunodeficiency Virus Infection Simian Immunodeficiency Virus Feline Immunodeficiency Virus Bovine Leukemia Virus Prototype Foamy Virus

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Efficient detection of viral transmission with threshold-based methods

Efficient detection of viral transmission with threshold-bas...

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IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

作者： Inna Rytsareva David S. Campo Yueli Zheng Seth Sims Cansu Tetik Jain Chirag Sriram P. Chockalingam Sharma V. Thankachan Amanda Sue Srinivas Aluru Yury Khudyakov Division of Viral hepatitis Centers for Disease Control and Prevention Atlanta USA Eagle Medical Services Centers for Disease Control and Prevention Atlanta USA School of Computational Science and Engineering Georgia Institute of Technology Atlanta USA Dept. of Computer Science and Engineering IIT Bombay Mumbai India

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed. The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples. For instance, our relatively small dataset of 401 samples, a total of 80200 pairwise sample comparisons must be performed, which account for 4.56 x 1010 pairwise sequence comparisons. We present a fast and efficient three-step filtering strategy that removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

关键词： bioinformatics Diseases Filtering Genetics Scientific computing Drugs Sequential analysis

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31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): part two Abstracts

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JOURNAL FOR IMMUNOTHERAPY OF CANCER 2016年第SUPPL 1期4卷 107-221页

作者： [Anonymous] Department of Immunology University of Texas MD Anderson Cancer Center Houston TX USA Department of Cancer Medicine University of Texas MD Anderson Cancer Center Houston TX USA Immune Design Seattle WA USA Innate Pharma Marseille Provence-Alpes-Cote d’Azur France MedImmune Cambridge England UK Georgetown-Lombardi Comprehensive Cancer Center Washington DC USA Westmead and Blacktown Hospitals Melanoma Institute Australia and the University of Sydney Westmead New South Wales Australia University of California Los Angeles CA USA University of Washington Seattle WA USA Dana-Farber Cancer Institute/Brigham and Women’s Hospital Harvard University Boston MA USA University of Texas MD Anderson Cancer Center Houston TX USA Beth Israel Deaconess Medical Center Boston MA USA Gallipoli Medical Research Foundation Greenslopes Private Hospital and the University of Queensland Greenslopes Queensland Australia Olivia Newton-John Cancer Research Institute Heidelberg Victoria Australia Canterbury District Health Board Christchurch Hospital Christchurch New Zealand Waikato Hospital Regional Cancer Centre Hamilton New Zealand Royal Prince Alfred Hospital Melanoma Institute Australia the University of Sydney and Chris O’Brien Lifehouse Camperdown New South Wales Australia Tasman Oncology Research Southport Gold Coast Queensland Australia Merck & Co. Inc. Kenilworth NJ USA Quantitative Solutions A Certara Company Oss Netherlands Melanoma Institute Australia the University of Sydney Mater Hospital and Royal North Shore Hospital Wollstonecraft New South Wales Australia Columbia University Medical Center New York NY USA Institut d’Investigacions Biomediques August Pi i Sunyer Barcelona Catalonia Spain Icahn School of Medicine at Mount Sinai New York NY USA New York Presbyterian/Columbia University Medical Center New York NY USA Memorial Sloan Kettering Cancer Center New York NY USA Peregrine Pharmaceuticals Inc. Tustin CA USA Regeneron Tarrytown NY USA University of

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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A systems biology analysis of long and short-term memories of osmotic stress adaptation in fungi

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BMC Research Notes 2012年第1期5卷 258-258页

作者： You, Tao Ingram, Piers Jacobsen, Mette D. Cook, Emily McDonagh, Andrew Thorne, Thomas Lenardon, Megan D. De Moura, Alessandro Ps Romano, M Carmen Thiel, Marco Stumpf, Michael Gow, Neil A.R. Haynes, Ken Grebogi, Celso Stark, Jaroslav Brown, Alistair J.P. Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences University of Aberdeen Old Aberdeen Aberdeen AB24 3UE United Kingdom Department of Mathematics Imperial College London London SW7 2AZ United Kingdom School of Medical Sciences University of Aberdeen Institute of Medical Sciences Aberdeen AB25 2ZD United Kingdom Department of Biosciences College of Life and Environmental Sciences University of Exeter Exeter EX4 4QD Stocker Road United Kingdom Department of Microbiology Imperial College London Flowers Building London SW7 2AZ United Kingdom Centre for Bioinformatics Division of Molecular Biosciences Imperial College London London SW7 2AY United Kingdom Computational Biology AstraZeneca Innovative Medicines Macclesfield Cheshire SK10 4TG Alderley Park United Kingdom Formally of the Department of Mathematics Imperial College London London SW7 2AZ United Kingdom

Background. Saccharomyces cerevisiae senses hyperosmotic conditions via the HOG signaling network that activates the stress-activated protein kinase, Hog1, and modulates metabolic fluxes and gene expression to generate appropriate adaptive responses. The integral control mechanism by which Hog1 modulates glycerol production remains uncharacterized. An additional Hog1-independent mechanism retains intracellular glycerol for adaptation. Candida albicans also adapts to hyperosmolarity via a HOG signaling network. However, it remains unknown whether Hog1 exerts integral or proportional control over glycerol production in C. albicans. Results. We combined modeling and experimental approaches to study osmotic stress responses in S. cerevisiae and C. albicans. We propose a simple ordinary differential equation (ODE) model that highlights the integral control that Hog1 exerts over glycerol biosynthesis in these species. If integral control arises from a separation of time scales (i.e. rapid HOG activation of glycerol production capacity which decays slowly under hyperosmotic conditions), then the model predicts that glycerol production rates elevate upon adaptation to a first stress and this makes the cell adapts faster to a second hyperosmotic stress. It appears as if the cell is able to remember the stress history that is longer than the timescale of signal transduction. This is termed the long-term stress memory. Our experimental data verify this. Like S. cerevisiae, C. albicans mimimizes glycerol efflux during adaptation to hyperosmolarity. Also, transient activation of intermediate kinases in the HOG pathway results in a short-term memory in the signaling pathway. This determines the amplitude of Hog1 phosphorylation under a periodic sequence of stress and non-stressed intervals. Our model suggests that the long-term memory also affects the way a cell responds to periodic stress conditions. Hence, during osmohomeostasis, short-term memory is dependent upon long-term me

关键词： Integral control Glycerol Production High Osmolarity Glycerol Hyperosmotic Condition Hyperosmotic Shock

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Analysis of Glyceraldehyde 3-phosphate Dehydrogenase Gene in Lactarius deliciosus Base on bioinformatics

Analysis of Glyceraldehyde 3-phosphate Dehydrogenase Gene in...

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2010 International colloquium on computing,Communication, control, and Management (CCCM2010)

作者： Li he Biotechnology Core Facilities,College of Forestry Central South University of Forestry and Technology Changsha,China

Glyceraldehyde 3-phosphate dehydrogenase is an enzyme that catalyzes the sixth step of glycolysis and thus serves to break down glucose for energy and carbon *** the present study,some characters of the amino acid sequence of GAPDH of *** were predicted and analyzed with the tools of *** results showed that the protein was composed of 20 kinds of amino acid;the theoretical pI of GAPDH was 7.08 and the theoretical molecular weight of GAPDH was 26165.9 Da;the total number of atoms was *** was a stable *** were 7 glycosylation sites and it was a tetrameric NAD-binding enzyme involved in glycolysis and glyconeogenesis.N-terminal domain is a Rossmann NAD(P) binding fold.C-terminal domain is a mixed alpha/antiparallel beta fold.

关键词： L.deliciosus GAPDH bioinformatics analysis

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