L1000CDS2: LINCS L1000 characteristic direction signatures search engine.

Abstract The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000...

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Open chromatin profiling of human postmortem brain infers functional roles for non-coding schizophrenia loci.

Abstract Open chromatin provides access to DNA binding proteins for the correct spatiotemporal regulation of gene expression. Mapping chromatin accessibility has been widely used to identify the location of cis regulatory elements (CREs) including promoters and enhancers. CREs show tissue- and cell-type specificity and disease-associated variants are often enriched for CREs in the tissues and cells that pertain to a given disease. To better understand the role of CREs in neuropsychiatric disorders we applied the Assay for Transposase Accessible Chromatin followed by sequencing (ATAC-seq) to neuronal and non-neuronal nuclei isolated from frozen postmortem human brain by fluorescence-activated nuclear sorting (FANS). Most of the identified open chromatin regions (OCRs) are differentially accessible between neurons and non-neurons, and show enrichment with known cell type markers, promoters and enhancers. Relative to those of non-neurons, neuronal OCRs are more evolutionarily conserved and are enriched in distal regulatory elements. Transcription factor (TF) footprinting analysis identifies differences in the regulome between neuronal and non-neuronal cells and ascribes putative functional roles to a number of non-coding schizophrenia (SCZ) risk variants. Among the identified variants is a Single Nucleotide Polymorphism (SNP) proximal to the gene encoding SNX19. In vitro experiments reveal that this SNP leads to an increase in transcriptional activity. As elevated expression of SNX19 has been associated with SCZ, our data provides evidence that the identified SNP contributes to disease. These results represent the first analysis of OCRs and TF binding sites in distinct populations of postmortem human brain cells and further our understanding of the regulome and the impact of neuropsychiatric disease-associated genetic risk variants. Full PDF...

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The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit.

Abstract The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient...

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Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams.

Abstract Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care. Full PDF...

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Transcriptome-based repurposing of apigenin as a potential anti-fibrotic agent targeting hepatic stellate cells.

Abstract We have used a computational approach to identify anti-fibrotic therapies by querying a transcriptome. A transcriptome signature of activated hepatic stellate cells (HSCs), the primary collagen-secreting cell in liver, and queried against a transcriptomic database that quantifies changes in gene expression in response to 1,309 FDA-approved drugs and bioactives (CMap). The flavonoid apigenin was among 9 top-ranked compounds predicted to have anti-fibrotic activity; indeed, apigenin dose-dependently reduced collagen I in the human HSC line, TWNT-4. To identify proteins mediating apigenin’s effect, we next overlapped a 122-gene signature unique to HSCs with a list of 160 genes encoding proteins that are known to interact with apigenin, which identified C1QTNF2, encoding for Complement C1q tumor necrosis factor-related protein 2, a secreted adipocytokine with metabolic effects in liver. To validate its disease relevance, C1QTNF2 expression is reduced during hepatic stellate cell activation in culture and in a mouse model of alcoholic liver injury in vivo, and its expression correlates with better clinical outcomes in patients with hepatitis C cirrhosis (nā€‰=ā€‰216), suggesting it may have a protective role in cirrhosis progression.These findings reinforce the value of computational approaches to drug discovery for hepatic fibrosis, and identify C1QTNF2 as a potential mediator of apigenin’s anti-fibrotic activity. Full PDF...

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