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A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder

Genome medicine 2025年第1期17卷 43页

作者： Jiawei Wang Yujing Liu Hongyu Li Tuan P Nguyen John Lee Soto-Vargas Rashaun Wilson Weiwei Wang TuKiet T Lam Chi Zhang Chen Lin David A Lewis Jill Glausier Paul E Holtzheimer Matthew J Friedman Kenneth R Williams Marina R Picciotto Angus C Nairn John H Krystal Ronald S Duman Keith A Young Hongyu Zhao Matthew J Girgenti Program of Computational Biology & Bioinformatics Yale University New Haven CT 06511 USA. Department of Psychiatry Yale School of Medicine New Haven CT 06511 USA. Department of Biostatistics Yale School of Public Health New Haven CT 06510 USA. NIDA Neuroproteomics Center Yale School of Medicine New Haven CT 06511 USA. Department of Molecular Biophysics and Biochemistry Yale School of Medicine New Haven CT 06511 USA. Keck MS & Proteomics Resource Yale School of Medicine New Haven CT 06510 USA. Department of Psychiatry University of Pittsburgh Pittsburgh PA 15213 USA. National Center for PTSD United States Department of Veterans Affairs White River Junction VT 05009 USA. Department of Psychiatry Geisel School of Medicine at Dartmouth Lebanon NH 03756 USA. Central Texas Veterans Health Care System Research Service Temple TX 76504 USA. Department of Psychiatry and Behavioral Sciences Texas A&M University School of Medicine Bryan TX 77807 USA. Department of Psychiatry Yale School of Medicine New Haven CT 06511 USA. matthew.girgenti@yale.edu. National Center for PTSD United States Department of Veterans Affairs White River Junction VT 05009 USA. matthew.girgenti@yale.edu.

BACKGROUND:Post-traumatic stress disorder (PTSD) is a common and disabling psychiatric disorder. PTSD involves multiple brain regions and is often comorbid with other psychiatric disorders, such as major depressive disorder (MDD). Recent genome-wide association studies (GWASs) have identified many PTSD risk loci and transcriptomics studies of postmortem brain have found differentially expressed genes associated with PTSD cases. In this study, we integrated genome-wide measures across modalities to identify convergent molecular effects in the PTSD brain. METHODS:We performed tandem mass spectrometry (MS/MS) on a large cohort of donors (N = 66) in two prefrontal cortical areas, dorsolateral prefrontal cortex (DLPFC), and subgenual prefrontal cortex (sgPFC). We also coupled the proteomics data with transcriptomics and microRNA (miRNA) profiling from RNA-seq and small-RNA sequencing, respectively for the same cohort. Additionally, we utilized published GWAS results of multiple psychiatric disorders for integrative analysis. RESULTS:We found differentially expressed proteins and co-expression protein modules disrupted by PTSD. Integrative analysis with transcriptomics and miRNA data from the same cohort pointed to hsa-mir-589 as a regulatory miRNA responsible for dysregulation of neuronal protein networks for PTSD, including the gamma-aminobutyric acid (GABA) vesicular transporter, SLC32A1. In addition, we identified significant enrichment of risk genes for other psychiatric disorders, such as autism spectrum disorder (ASD) and major depressive disorder (MDD) within PTSD protein co-expression modules, suggesting shared molecular pathology. CONCLUSIONS:We integrated genome-wide measures of mRNA and miRNA expression and proteomics profiling from PTSD, MDD, and control (CON) brains to identify convergent and divergent molecular processes across genomic modalities. We substantially expand the number of differentially expressed genes and proteins in PTSD and identify downregu

关键词： Major depressive disorder MicroRNAs Multi-omics PTSD Prefrontal cortex

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Hot and cold: Spatial fluctuation in HIV-1 recombination rates

Hot and cold: Spatial fluctuation in HIV-1 recombination rat...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Rajaram, M.L. Minin, V.N. Suchard, M.A. Dorman, K.S. Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Statistics University of Washington Seattle WA 98195 United States Departments of Biomathematics Human Genetics and Biostatistics University of California Los Angeles Los Angeles CA 90095 United States Departments of Statistics and Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States

ISBN: (纸本)1424415098

Coinfection of a single cell with two or more HIV strains may produce recombinant viruses upon template switching by the replication machinery. We applied a hierarchical multiple change point model to simultaneously infer inter-subtype recombination breakpoints and spatial variation in the recombination rate along the HIV-1 genome. We examined thousands of publicly available HIV-1 sequences representing the worldwide epidemic and focused on 544 unique recombinants with 1, 701 recombination breakpoints. Estimates of per site recombination rate revealed the presence of a novel hotspot in the pol gene, surrounded by a cluster of mutations associated with resistance to reverse transcriptase inhibitors. We also confirm the presence of a known hotspot in the env gene and a previously hypothesized hotspot in the gag gene. ©2007 IEEE.

关键词： Genes

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Simultaneous identification for synergistic transcription factors and their transcription factor binding sites

Simultaneous identification for synergistic transcription fa...

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5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

作者： Tsai, Zing Tsung-Yeh Huang, Grace Tzu-Wei Tsai, Huai-Kuang Institute of Information Science Academia Sinica Taipei 115 Taiwan Bioinformatics Program Taiwan International Graduate Program Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh Carnegie Mellon University Pittsburgh PA United States Research Center for Information Technology Innovation Taipei 115 Taiwan Computer Science and Engineering National Taiwan Ocean University Keelung 202 Taiwan

ISBN: (纸本)9780769543734

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions. © 2011 IEEE.

关键词： Yeast

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PRTAD: A database for protein residue torsion angle distributions

PRTAD: A database for protein residue torsion angle distribu...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaoyong Sun Di Wu Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Western Kentucky University Bowling Green KY USA Department of Biochemistry Biophysics and Molecular Biology Department of Mathematics Iowa State University Ames IA USA

PRTAD is a dedicated database and structural bioinformatics system for protein analysis and modeling. The database is developed to host and analyze the statistical data for protein residue level "virtual" bond and torsion angles obtained from their distributions in databases of known protein structures such as in the PDB Data Bank. PRTAD is capable of generating, caching, and displaying the statistical distributions of the angles of various types. The collected information can be used to extract geometric restraints or define statistical potentials for protein structure determination. PRTAD is supported with a friendly designed web interface so that users can easily specify the angle types, and retrieve, visualize, or download the distributions of the angles as they desire. The database PRTAD is freely accessible at http://***/prtad.

关键词： Bonding Spatial databases Protein engineering Amino acids bioinformatics computational biology Mathematics Data mining Niobium compounds Nuclear magnetic resonance

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Geometry based data generation 18

Geometry based data generation

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Ofir Lindenbaum Jay S. Stanley, III Guy Wolf Smita Krishnaswamy Applied Mathematics Program Yale University New Haven CT Computational Biology & Bioinformatics Program Yale University New Haven CT Departments of Genetics & Computer Science Yale University New Haven CT

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis.

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Finite Size Effects on Thermal Denaturation of Globular Proteins

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Physical Review Letters 2004年第26期93卷 268107-268107页

作者： Mai Suan Li Dmitri K. Klimov D. Thirumalai Institute of Physics Polish Academy of Sciences Al. Lotnikow 32/46 02-668 Warsaw Poland Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA Bioinformatics and Computational Biology Program School of Computational Sciences George Mason University Manassas Virginia 20110 USA

Finite size effects on the cooperative thermal denaturation of proteins are considered. A dimensionless measure of cooperativity, Ωc, scales as Nζ, where N is the number of amino acids. Surprisingly, we find that ζ is universal with ζ=1+γ, where the exponent γ characterizes the divergence of the susceptibility for a self-avoiding walk. Our lattice model simulations and experimental data are consistent with the theory. Our finding rationalizes the marginal stability of proteins and substantiates the earlier predictions that the efficient folding of two-state proteins requires TF≈Tθ, where Tθ and TF are the collapse and folding transition temperatures, respectively.

关键词： .stability Transitions PRL ending susceptibility experimental data Thermal denaturation folding transition exponent unfolded native state Globular Proteins cooperativity two-state NBA

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Flipping Watson and CrickAssociated Content

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Nature 2011年第7335期470卷 472-473页

作者： Barry Honig Remo Rohs Department of Biochemistry and Molecular Biophysics Barry Honig is at the Howard Hughes Medical Institute Center for Computational Biology and Bioinformatics Columbia University New York New York 10032 USA. Department of Biological Sciences Remo Rohs is in the Molecular and Computational Biology Program University of Southern California Los Angeles California 90089 USA.

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Adjustment of familial relatedness in association test for rare variants

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BMC proceedings 2014年第Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo期8卷 S39页

作者： Cong Li Can Yang Mengjie Chen Xiaowei Chen Lin Hou Hongyu Zhao Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Biostatistics Yale School of Public Health 60 College Street New Haven CT 06520 USA.

High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.

关键词： Linear Mixed Model Rare Variant Genetic Analysis Workshop Familial Relatedness Genetic Similarity Matrix

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Exploring transposable element-based markers to identify allelic variations underlying agronomic traits in rice

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Plant Communications 2022年第3期3卷 59-71页

作者： Haidong Yan David C.Haak Song Li Linkai Huang Aureliano Bombarely School of Plant and Environmental Sciences Virginia TechBlacksburgVA 24061USA Graduate Program in Genetics Bioinformatics and Computational Biology(GBCB)Virginia TechBlacksburgVA 24061USA Department of Grassland Science Animal Science and Technology CollegeSichuan Agricultural UniversityChengdu 611130China Department of Bioscience Universita degli Studi di Milano(UNIMI)20133 MilanoItaly Instituto de Biologıa Molecular y Celular de Plantas(IBMCP) UPV-CSIC46022 ValenciaSpain

Transposable elements(TEs)are a major force in the production of new alleles during domestication;nevertheless,their use in association studies has been limited because of their *** have developed a TE genotyping pipeline(TEmarker)and applied it to whole-genome genome-wide association study(GWAS)data from 176 Oryza sativa *** accessions to identify genetic elements associated with specific agronomic *** markers recovered a large proportion(69%)of single-nucleotide polymorphism(SNP)-based GWAS peaks,and these TE peaks retained ca.25%of the *** use of TEs in GWASs may reduce false positives associated with linkage disequilibrium(LD)among SNP markers.A genome scan revealed positive selection on TEs associated with agronomic *** found several cases of insertion and deletion variants that potentially resulted from the direct action of TEs,including an allele of LOC_Os11g08410 associated with plant height and panicle length ***,these findings reveal the utility of TE markers for connecting genotype to phenotype and suggest a potential role for TEs in influencing phenotypic variations in rice that impact agronomic traits.

关键词： transposable element marker agronomic trait rice GWAS

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