Respiratory viral infections are a significant burden to healthcare worldwide. Many whole genome expression profiles have identified different respiratory viral infection signatures, but these have not translated to clinical practice. Here, we performed two integrated, multi-cohort analyses of publicly available transcriptional data of viral infections. First, we identified a common host signature across different respiratory viral infections that could distinguish (a) individuals with viral infections from healthy controls and from those with bacterial infections, and (b) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (a) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (b) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (c) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.
Immunoreactive class 1 and class 2 major histocompatibility complex gene products (MHCP) and beta 2 microglobulin (beta 2 MG) were demonstrated by microscopic immunocytochemistry in cryostat sections of skeletal muscle biopsies of 67 patients with various neuromuscular diseases. Diagnoses included normal muscle, chronic partial denervation, Duchenne dystrophy, polymyositis, dermatomyositis, inclusion body myositis, and miscellaneous neuromuscular diseases. Normal mature muscle fibers did not express MHCP, but blood vessels showed both class 1 and 2 MHCP and beta 2 MG. Regenerating muscle fibers showed consistent sarcolemmal class 1 MHCP expression irrespective of the disease. In polymyositis, the majority of extrafusal muscle fibers of most patients showed strong sarcolemmal class 1 MHCP expression. In dermatomyositis, muscle fibers situated either in perifascicular or in randomly clustered distribution revealed strong class 1 MHCP reactivity. In inclusion body myositis, scattered small clusters of muscle fibers were positive for class 1 MHCP. In polymyositis and inclusion body myositis, particularly strong class 1 MHCP expression was invariably seen in nonnecrotic muscle fibers partially invaded by lymphocytes whose cytotoxic effects are believed to be class 1 MHCP restricted. Factors or agents that trigger class 1 MHCP expression are presumed also to sensitize lymphocytes to muscle fibers in these diseases, but their identity remains obscure at this time. In dermatomyositis, the expression of MHCP in perifascicular muscle fibers and in areas of capillary loss may represent the triggering of MHCP expression by a nonspecific cellular stress reaction, in this case probably low-grade ischemia.
Adverse drug reactions (ADRs) can have severe consequences, such that the ability to predict ADRs prior to market introduction is desirable. Computational approaches applied to pre-clinical data might be one way to inform drug labeling and marketing with respect to potential ADRs. Based on the premise that some of the molecular actors of ADRs involve interactions detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate post-marketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, the System Organ Classes (SOCs). Nine of the 19 SOCs under consideration were found to be significantly correlated with pre-clinical screening data. For 6 of the 8 established drugs for which we could retropredict SOC-specific adversities, prior knowledge was found that support these predictions. We conclude by predicting SOC-specific adversities for three unapproved or recently introduced drugs.
With the continued exponential expansion of publicly available genomic data and access to low-cost, high-throughput molecular technologies for profiling patient populations, computational technologies and informatics are becoming vital considerations in genomic medicine. Although cloud computing technology is being heralded as a key enabling technology for the future of genomic research, available case studies are limited to applications in the domain of high-throughput sequence data analysis. The goal of this study was to evaluate the computational and economic characteristics of cloud computing in performing a large-scale data integration and analysis representative of research problems in genomic medicine. We find that the cloud-based analysis compares favorably in both performance and cost in comparison to a local computational cluster, suggesting that cloud computing technologies might be a viable resource for facilitating large-scale translational research in genomic medicine.
The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments.
BackgroundUsing computational database searches, we have demonstrated previously that no gene sequences could be found for at least 36% of enzyme activities that have been assigned an Enzyme Commission number. Here we present a follow-up literature-based survey involving a statistically significant sample of such "orphan" activities. The survey was intended to determine whether sequences for these enzyme activities are truly unknown, or whether these sequences are absent from the public sequence databases but can be found in the literature.ResultsWe demonstrate that for ~80% of sampled orphans, the absence of sequence data is bona fide. Our analyses further substantiate the notion that many of these enzyme activities play biologically important roles.ConclusionThis survey points toward significant scientific cost of having such a large fraction of characterized enzyme activities disconnected from sequence data. It also suggests that a larger effort, beginning with a comprehensive survey of all putative orphan activities, would resolve nearly 300 artifactual orphans and reconnect a wealth of enzyme research with modern genomics. For these reasons, we propose that a systematic effort to identify the cognate genes of orphan enzymes be undertaken.
Background: This article addresses the problem of interoperation of heterogeneous bioinformatics databases.
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