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Results: 1 to 10 of 381

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The human body at cellular resolution: the NIH Human Biomolecular Atlas Program.
HuBMAP Consortium
(2019) Nature 574: 187-192
MeSH Terms: Aging, Atlases as Topic, Biomedical Research, Female, Health, Humans, International Cooperation, Male, Models, Anatomic, Molecular Biology, National Institutes of Health (U.S.), Organ Specificity, Single-Cell Analysis, United States
Show Abstract · Added January 22, 2020
Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.
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MeSH Terms
Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM
(2019) Nat Genet 51: 933-940
MeSH Terms: Algorithms, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mental Disorders, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Transcriptome
Show Abstract · Added July 17, 2019
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
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An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.
Gutierrez DB, Gant-Branum RL, Romer CE, Farrow MA, Allen JL, Dahal N, Nei YW, Codreanu SG, Jordan AT, Palmer LD, Sherrod SD, McLean JA, Skaar EP, Norris JL, Caprioli RM
(2018) J Proteome Res 17: 3396-3408
MeSH Terms: Gene Expression Profiling, Genomics, HL-60 Cells, Humans, Metabolomics, NF-E2-Related Factor 2, NF-kappa B, Proteomics, Signal Transduction, Systems Biology, Zinc
Show Abstract · Added August 27, 2018
Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.
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11 MeSH Terms
In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers.
Cai C, Fang J, Guo P, Wang Q, Hong H, Moslehi J, Cheng F
(2018) J Chem Inf Model 58: 943-956
MeSH Terms: Antineoplastic Agents, Cardiovascular System, Computational Biology, Computer Simulation, Drug Discovery, Drug-Related Side Effects and Adverse Reactions, Humans, Molecular Targeted Therapy, Myocytes, Cardiac, Pluripotent Stem Cells, Product Surveillance, Postmarketing, Safety
Show Abstract · Added October 1, 2018
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.
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Comprehensive Characterization of Cancer Driver Genes and Mutations.
Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortés-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, MC3 Working Group, Cancer Genome Atlas Research Network, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L
(2018) Cell 173: 371-385.e18
MeSH Terms: Algorithms, B7-H1 Antigen, Computational Biology, Databases, Genetic, Entropy, Humans, Microsatellite Instability, Mutation, Neoplasms, Principal Component Analysis, Programmed Cell Death 1 Receptor
Show Abstract · Added October 30, 2019
Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.
Published by Elsevier Inc.
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Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas.
Schaub FX, Dhankani V, Berger AC, Trivedi M, Richardson AB, Shaw R, Zhao W, Zhang X, Ventura A, Liu Y, Ayer DE, Hurlin PJ, Cherniack AD, Eisenman RN, Bernard B, Grandori C, Cancer Genome Atlas Network
(2018) Cell Syst 6: 282-300.e2
MeSH Terms: Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Basic Helix-Loop-Helix Transcription Factors, Biomarkers, Tumor, Carcinogenesis, Chromatin, Computational Biology, Genes, myc, Genomics, Humans, Neoplasms, Oncogenes, Proteomics, Proto-Oncogene Proteins c-myc, Repressor Proteins, Signal Transduction, Transcription Factors
Show Abstract · Added October 30, 2019
Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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Integrating linear optimization with structural modeling to increase HIV neutralization breadth.
Sevy AM, Panda S, Crowe JE, Meiler J, Vorobeychik Y
(2018) PLoS Comput Biol 14: e1005999
MeSH Terms: Algorithms, Amino Acid Motifs, Antibodies, Neutralizing, Computational Biology, Epitopes, HIV Antibodies, HIV Infections, HIV-1, Humans, Linear Models, Machine Learning, Regression Analysis, Software, Support Vector Machine
Show Abstract · Added March 14, 2018
Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.
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14 MeSH Terms
Survey of checkpoints along the pathway to diverse biomedical research faculty.
Meyers LC, Brown AM, Moneta-Koehler L, Chalkley R
(2018) PLoS One 13: e0190606
MeSH Terms: Academies and Institutes, Biology, Biomedical Research, Cultural Diversity, Faculty, Humans, Minority Groups
Show Abstract · Added March 9, 2018
There is a persistent shortage of underrepresented minority (URM) faculty who are involved in basic biomedical research at medical schools. We examined the entire training pathway of potential candidates to identify the points of greatest loss. Using a range of recent national data sources, including the National Science Foundation's Survey of Earned Doctorates and Survey of Doctoral Recipients, we analyzed the demographics of the population of interest, specifically those from URM backgrounds with an interest in biomedical sciences. We examined the URM population from high school graduates through undergraduate, graduate, and postdoctoral training as well as the URM population in basic science tenure track faculty positions at medical schools. We find that URM and non-URM trainees are equally likely to transition into doctoral programs, to receive their doctoral degree, and to secure a postdoctoral position. However, the analysis reveals that the diversions from developing a faculty career are found primarily at two clearly identifiable places, specifically during undergraduate education and in transition from postdoctoral fellowship to tenure track faculty in the basic sciences at medical schools. We suggest focusing additional interventions on these two stages along the educational pathway.
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7 MeSH Terms
Local ancestry transitions modify snp-trait associations.
Fish AE, Crawford DC, Capra JA, Bush WS
(2018) Pac Symp Biocomput 23: 424-435
MeSH Terms: Adult, African Continental Ancestry Group, Chromosomes, Human, Computational Biology, Epistasis, Genetic, European Continental Ancestry Group, Evolution, Molecular, Gene Frequency, Genetics, Population, Genome-Wide Association Study, Haplotypes, Humans, Linear Models, Models, Genetic, Polymorphism, Single Nucleotide, Recombination, Genetic
Show Abstract · Added March 14, 2018
Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.
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16 MeSH Terms
Amyloid Accumulation Drives Proteome-wide Alterations in Mouse Models of Alzheimer's Disease-like Pathology.
Savas JN, Wang YZ, DeNardo LA, Martinez-Bartolome S, McClatchy DB, Hark TJ, Shanks NF, Cozzolino KA, Lavallée-Adam M, Smukowski SN, Park SK, Kelly JW, Koo EH, Nakagawa T, Masliah E, Ghosh A, Yates JR
(2017) Cell Rep 21: 2614-2627
MeSH Terms: Alzheimer Disease, Amyloid beta-Peptides, Animals, Apolipoproteins E, Brain, Calcium Channels, Computational Biology, Female, Mass Spectrometry, Mice, Mice, Inbred C57BL, Proteome
Show Abstract · Added March 21, 2018
Amyloid beta (Aβ) peptides impair multiple cellular pathways and play a causative role in Alzheimer's disease (AD) pathology, but how the brain proteome is remodeled by this process is unknown. To identify protein networks associated with AD-like pathology, we performed global quantitative proteomic analysis in three mouse models at young and old ages. Our analysis revealed a robust increase in Apolipoprotein E (ApoE) levels in nearly all brain regions with increased Aβ levels. Taken together with prior findings on ApoE driving Aβ accumulation, this analysis points to a pathological dysregulation of the ApoE-Aβ axis. We also found dysregulation of protein networks involved in excitatory synaptic transmission. Analysis of the AMPA receptor (AMPAR) complex revealed specific loss of TARPγ-2, a key AMPAR-trafficking protein. Expression of TARPγ-2 in hAPP transgenic mice restored AMPA currents. This proteomic database represents a resource for the identification of protein alterations responsible for AD.
Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
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12 MeSH Terms