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Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures.
Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A
(2019) Structure 27: 1745-1759
MeSH Terms: Computational Biology, Crystallography, X-Ray, Databases, Protein, Magnetic Resonance Spectroscopy, Models, Molecular, Protein Conformation, Proteins
Show Abstract · Added March 21, 2020
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
Copyright © 2019.
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7 MeSH Terms
Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.
Lowe ME, Andersen DK, Caprioli RM, Choudhary J, Cruz-Monserrate Z, Dasyam AK, Forsmark CE, Gorelick FS, Gray JW, Haupt M, Kelly KA, Olive KP, Plevritis SK, Rappaport N, Roth HR, Steen H, Swamidass SJ, Tirkes T, Uc A, Veselkov K, Whitcomb DC, Habtezion A
(2019) Pancreas 48: 1250-1258
MeSH Terms: Biomarkers, Biomedical Research, Computational Biology, Datasets as Topic, Deep Learning, Humans, Metabolomics, Pancreatic Diseases, Precision Medicine, Research
Show Abstract · Added March 3, 2020
A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.
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Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program.
Hellwege JN, Velez Edwards DR, Giri A, Qiu C, Park J, Torstenson ES, Keaton JM, Wilson OD, Robinson-Cohen C, Chung CP, Roumie CL, Klarin D, Damrauer SM, DuVall SL, Siew E, Akwo EA, Wuttke M, Gorski M, Li M, Li Y, Gaziano JM, Wilson PWF, Tsao PS, O'Donnell CJ, Kovesdy CP, Pattaro C, Köttgen A, Susztak K, Edwards TL, Hung AM
(2019) Nat Commun 10: 3842
MeSH Terms: Adult, Aged, Animals, Cell Line, Chromosome Mapping, Cohort Studies, Computational Biology, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Kidney, Male, Mice, Middle Aged, Polymorphism, Single Nucleotide, RNA-Seq, Renal Insufficiency, Chronic, Transcriptome, United States, United States Department of Veterans Affairs, Veterans
Show Abstract · Added March 3, 2020
Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.
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Automated cell boundary and 3D nuclear segmentation of cells in suspension.
Kesler B, Li G, Thiemicke A, Venkat R, Neuert G
(2019) Sci Rep 9: 10237
MeSH Terms: Algorithms, Biological Phenomena, Cell Nucleus, Computational Biology, Image Processing, Computer-Assisted, Microscopy, Optical Imaging, Single-Cell Analysis, Staining and Labeling, Suspensions
Show Abstract · Added February 5, 2020
To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.
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Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13.
Kuenze G, Meiler J
(2019) Proteins 87: 1341-1350
MeSH Terms: Computational Biology, Magnetic Resonance Spectroscopy, Models, Molecular, Protein Conformation, Protein Folding, Proteins, Software
Show Abstract · Added March 21, 2020
Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.
© 2019 Wiley Periodicals, Inc.
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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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Response to Anti-PD-1 in Uveal Melanoma Without High-Volume Liver Metastasis.
Johnson DB, Bao R, Ancell KK, Daniels AB, Wallace D, Sosman JA, Luke JJ
(2019) J Natl Compr Canc Netw 17: 114-117
MeSH Terms: Antineoplastic Agents, Immunological, Computational Biology, Gene Expression Profiling, Humans, Liver Neoplasms, Melanoma, Molecular Targeted Therapy, Neoplasm Staging, Prognosis, Programmed Cell Death 1 Receptor, Treatment Outcome, Uveal Neoplasms
Show Abstract · Added March 30, 2020
Uveal melanoma (UM) is an uncommon melanoma subtype with poor prognosis. Agents that have transformed the management of cutaneous melanoma have made minimal inroads in UM. We conducted a single-arm phase II study of pembrolizumab in patients with metastatic UM and performed bioinformatics analyses of publicly available datasets to characterize the activity of anti-PD-1 in this setting and to understand the mutational and immunologic profile of this disease. A total of 5 patients received pembrolizumab in this study. Median overall survival was not reached, and median progression-free survival was 11.0 months. One patient experienced a complete response after one dose and 2 others experienced prolonged stable disease (20% response rate, 60% clinical benefit rate); 2 additional patients had rapidly progressing disease. Notably, the patients who benefited had either no liver metastases or small-volume disease, whereas patients with rapidly progressing disease had bulky liver involvement. We performed a bioinformatics analysis of The Cancer Genome Atlas for UM and confirmed a low mutation burden and low rates of T-cell inflammation. Note that the lack of T-cell inflammation strongly correlated with pathway overexpression. Anti-PD-1-based therapy may cause clinical benefit in metastatic UM, seemingly more often in patients without bulky liver metastases. Lack of mutation burden and T-cell infiltration and overexpression may be factors limiting therapeutic responses. NCT02359851.
Copyright © 2019 by the National Comprehensive Cancer Network.
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12 MeSH Terms
Genome-wide maps of distal gene regulatory enhancers active in the human placenta.
Zhang J, Simonti CN, Capra JA
(2018) PLoS One 13: e0209611
MeSH Terms: Chromosome Mapping, Computational Biology, Enhancer Elements, Genetic, Female, Genes, Regulator, Genome-Wide Association Study, Genomics, Humans, Machine Learning, Molecular Sequence Annotation, Placenta, Pregnancy, ROC Curve
Show Abstract · Added March 3, 2020
Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies.
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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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