Oral Metformin Inhibits Choroidal Neovascularization by Modulating the Gut-Retina Axis.

Purpose: Emerging data indicate that metformin may prevent the development of age-related macular degeneration (AMD). Whereas the underlying mechanisms of metformin's anti-aging properties remain undetermined, one proposed avenue is the gut microbiome. Using the laser-induced choroidal neovascularization (CNV) model, we investigate the effects of oral metformin on CNV, retinal pigment epithelium (RPE)/choroid transcriptome, and gut microbiota. Methods: Specific pathogen free (SPF) male mice were treated via daily oral gavage of metformin 300 mg/kg or vehicle. Male mice were selected to minimize sex-specific differences to laser induction and response to metformin. Laser-induced CNV size and macrophage/microglial infiltration were assessed by isolectin and Iba1 immunostaining. High-throughput RNA-seq of the RPE/choroid was performed using Illumina. Fecal pellets were analyzed for gut microbiota composition/pathways with 16S rRNA sequencing/shotgun metagenomics, as well as microbial-derived metabolites, including small-chain fatty acids and bile acids. Investigation was repeated in metformin-treated germ-free (GF) mice and antibiotic-treated/GF mice receiving fecal microbiota transplantation (FMT) from metformin-treated SPF mice. Results: Metformin treatment reduced CNV size (P < 0.01) and decreased Iba1+ macrophage/microglial infiltration (P < 0.005). One hundred forty-five differentially expressed genes were identified in the metformin-treated group (P < 0.05) with a downregulation in pro-angiogenic genes Tie1, Pgf, and Gata2. Furthermore, metformin altered the gut microbiome in favor of Bifidobacterium and Akkermansia, with a significant increase in fecal levels of butyrate, succinate, and cholic acid. Metformin did not suppress CNV in GF mice but colonization of microbiome-depleted mice with metformin-derived FMT suppressed CNV. Conclusions: These data suggest that oral metformin suppresses CNV, the hallmark lesion of advanced neovascular AMD, via gut microbiome modulation.

The Host-specific Microbiota is Required for Diet-Specific Metabolic Homeostasis.

In complex mammals, the importance and host-specificity of microbial communities have been demonstrated through their positive effects on host immune fitness or performance. However, whether host metabolic physiology homeostasis depends on a specific bacterial community exclusive to the host remains unclear. Here, we show that the coevolved host-specific microbiota is required to maintain diet-specific flexible and sufficient metabolic homeostasis through a high colonization rate, modulating gut metabolites, and related targets. Using germ-free (GF) mice, we tested whether the fitness benefiting the host metabolic phenotype of microbiota was host-specific. We demonstrated that GF mice associated with exogenous microbiota (human microbiota (HM)), which exhibited different and reduced gut microbial species diversity, significantly elevated metabolic rate, and exhibited metabolic insufficiency, all characteristics of GF mice. Strikingly, the absence of the host-specific microbiome attenuated high-fat diet-specific metabolism features. Different diets caused different metabolic changes in only host-specific microbiota-associated mice, not the host-microbiota mismatched mice. While RNA sequencing revealed subtle changes in the expression of genes in the liver, GF mice and HM mice showed considerably altered expression of genes associated with metabolic physiology compared to GF mice associated with host-specific microbiota. The effect of diet outweighed microbiota in the liver transcriptome. These changes occurred in the setting of decreased luminal short-chain fatty acids (SCFAs) and the secondary bile acid (BAs) pool and downstream gut signaling targets in HM and GF mice, which affects whole-body metabolism. These data indicate that a foreign microbial community provides little metabolic benefit to the host when compared to a host-specific microbiome, due to the colonization selection pressure and microbiota-derived metabolites dysfunction. Overall, microbiome fitness effects on the host metabolic phenotype were host-specific. Understanding the impact of the host-specificity of the microbiome on metabolic homeostasis may provide important insights for building a better probiotic. Highlights: Microbiome fitness effects on the host metabolic phenotype were host-specific in mammals.Human microbiota-associated mice exhibited lower host metabolic fitness or performance, and similar functional costs in GF mice.Different diets cause different metabolic changes only in host-specific microbiota-associated mice, not the host-microbiota mismatched mice.The defective gut microbiota in host-specific microbiota, microbial metabolites and related targets likely drive the metabolic homeostasis.

Correction: Reproducible big data science: A case study in continuous FAIRness.

[This corrects the article DOI: 10.1371/journal.pone.0213013.].

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.

Purpose: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. Methods: We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. Results: The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. Conclusion: This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Proliferative Vitreoretinopathy.

Purpose: Proliferative vitreoretinopathy (PVR) is the dreaded cause of failure following retinal detachment repair; however, no cures or preventative therapies exist to date. The purpose of this study was to use bioinformatics tools to identify drugs or compounds that interact with biomarkers and pathways involved in PVR pathogenesis that could be eligible for further testing for the prevention and treatment of PVR. Methods: We queried PubMed to compile a comprehensive list of genes described in PVR to date from human studies, animal models, and genomic studies found in the National Center for Biotechnology Information database. Gene enrichment analysis was performed using ToppGene on PVR-related genes against drug-gene interaction databases to construct a pharmacome and estimate the statistical significance of overrepresented compounds. Compounds with no clinical indications were filtered out from the resulting drug lists. Results: Our query identified 34 unique genes associated with PVR. Out of 77,146 candidate drugs or compounds in the drug databases, our analysis revealed multiple drugs and compounds that have significant interactions with genes involved in PVR, including antiproliferatives, corticosteroids, cardiovascular agents, antioxidants, statins, and micronutrients. Top compounds, including curcumin, statins, and cardiovascular agents such as carvedilol and enalapril, have well-established safety profiles and potentially could be readily repurposed for PVR. Other significant compounds such as prednisone and methotrexate have shown promising results in ongoing clinical trials for PVR. Conclusions: This bioinformatics approach of studying drug-gene interactions can identify drugs that may affect genes and pathways implicated in PVR. Predicted bioinformatics studies require further validation by preclinical or clinical studies; however, this unbiased approach could identify potential candidates among existing drugs and compounds that could be repurposed for PVR and guide future investigations. Translational Relevance: Novel repurposable drug therapies for PVR can be found using advanced bioinformatics models.

Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma.

BACKGROUND: Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage. METHODS: The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage. RESULTS: Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity). CONCLUSIONS: Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Cavernous angiomas (CAs) are clusters of abnormal blood vessels found in the brain or spinal cord. A blood test that could identify people with CAs that have recently bled would help determine who need surgery or closer medical monitoring. We looked at the blood of people with CAs to compare the levels of metabolites, a type of small molecule produced within the body, in those who had recently bled and those who had not. We found that some metabolites may contribute to CA and have an impact on CA symptoms. Monitoring the levels of these metabolites can determine whether there had been a recent bleed. In the future, drugs or other therapies could be developed that would block or change the levels of these molecules and possibly be used to treat CA disease.

Imbalanced gut microbiota predicts and drives the progression of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis in a fast-food diet mouse model.

Nonalcoholic fatty liver disease (NAFLD) is multifactorial in nature, affecting over a billion people worldwide. The gut microbiome has emerged as an associative factor in NAFLD, yet mechanistic contributions are unclear. Here, we show fast food (FF) diets containing high fat, added cholesterol, and fructose/glucose drinking water differentially impact short- vs. long-term NAFLD severity and progression in conventionally-raised, but not germ-free mice. Correlation and machine learning analyses independently demonstrate FF diets induce early and specific gut microbiota changes that are predictive of NAFLD indicators, with corresponding microbial community instability relative to control-fed mice. Shotgun metagenomics showed FF diets containing high cholesterol elevate fecal pro-inflammatory effectors over time, relating to a reshaping of host hepatic metabolic and inflammatory transcriptomes. FF diet-induced gut dysbiosis precedes onset and is highly predictive of NAFLD outcomes, providing potential insights into microbially-based pathogenesis and therapeutics.

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease.

Patients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients. Herein, homologous DE plasma miRNAs with mechanistic putative gene targets within the transcriptome of preclinical and human CCM lesions were identified. Several of these gene targets were additionally found to be associated with CCM-enriched pathways identified using the Kyoto Encyclopedia of Genes and Genomes. DE miRNAs were also identified in familial-CCM patients who developed new brain lesions within the year following blood sample collection. The miRNome results were then validated in an independent cohort of human subjects with real-time-qPCR quantification, a technique facilitating plasma assays. Finally, a Bayesian-informed machine learning approach showed that a combination of plasma levels of miRNAs and circulating proteins improves the association with familial-CCM disease in human subjects to 95% accuracy. These findings act as an important proof of concept for the future development of translatable circulating biomarkers to be tested in preclinical studies and human trials aimed at monitoring and restoring gene function in CCM and other diseases.

Using Computational Drug-Gene Analysis to Identify Novel Therapeutic Candidates for Retinal Neuroprotection.

Retinal cell death is responsible for irreversible vision loss in many retinal disorders. No commercially approved treatments are currently available to attenuate retinal cell loss and preserve vision. We seek to identify chemicals/drugs with thoroughly-studied biological functions that possess neuroprotective effects in the retina using a computational bioinformatics approach. We queried the National Center for Biotechnology Information (NCBI) to identify genes associated with retinal neuroprotection. Enrichment analysis was performed using ToppGene to identify compounds related to the identified genes. This analysis constructs a Pharmacome from multiple drug-gene interaction databases to predict compounds with statistically significant associations to genes involved in retinal neuroprotection. Compounds with known deleterious effects (e.g., asbestos, ethanol) or with no clinical indications (e.g., paraquat, ozone) were manually filtered. We identified numerous drug/chemical classes associated to multiple genes implicated in retinal neuroprotection using a systematic computational approach. Anti-diabetics, lipid-lowering medicines, and antioxidants are among the treatments anticipated by this analysis, and many of these drugs could be readily repurposed for retinal neuroprotection. Our technique serves as an unbiased tool that can be utilized in the future to lead focused preclinical and clinical investigations for complex processes such as neuroprotection, as well as a wide range of other ocular pathologies.

Absence of Gut Microbiota Is Associated with RPE/Choroid Transcriptomic Changes Related to Age-Related Macular Degeneration Pathobiology and Decreased Choroidal Neovascularization.

Studies have begun to reveal significant connections between the gut microbiome and various retinal diseases, including age-related macular degeneration (AMD). As critical supporting tissues of the retina, the retinal pigment epithelium (RPE) and underlying choroid play a critical role in retinal homeostasis and degeneration. However, the relationship between the microbiome and RPE/choroid remains poorly understood, particularly in animal models of AMD. In order to better elucidate this role, we performed high-throughput RNA sequencing of RPE/choroid tissue in germ-free (GF) and specific pathogen-free (SPF) mice. Furthermore, utilizing a specialized laser-induced choroidal neovascularization (CNV) model that we developed, we compared CNV size and inflammatory response between GF and SPF mice. After correction of raw data, 660 differentially expressed genes (DEGs) were identified, including those involved in angiogenesis regulation, scavenger and cytokine receptor activity, and inflammatory response-all of which have been implicated in AMD pathogenesis. Among lasered mice, the GF group showed significantly decreased CNV lesion size and microglial infiltration around CNV compared to the SPF group. Together, these findings provide evidence for a potential gut-RPE/choroidal axis as well as a correlation with neovascular features of AMD.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration.

Purpose: Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD. Methods: We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered. Results: We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes. Conclusions: We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases. Translational Relevance: Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

High-Fat Diet Alters the Retinal Pigment Epithelium and Choroidal Transcriptome in the Absence of Gut Microbiota.

Relationships between retinal disease, diet, and the gut microbiome have started to emerge. In particular, high-fat diets (HFDs) are associated with the prevalence and progression of several retinal diseases, including age-related macular degeneration (AMD) and diabetic retinopathy (DR). These effects are thought to be partly mediated by the gut microbiome, which modulates interactions between diet and host homeostasis. Nevertheless, the effects of HFDs on the retina and adjacent retinal pigment epithelium (RPE) and choroid at the transcriptional level, independent of gut microbiota, are not well-understood. In this study, we performed the high-throughput RNA-sequencing of germ-free (GF) mice to explore the transcriptional changes induced by HFD in the RPE/choroid. After filtering and cleaning the data, 649 differentially expressed genes (DEGs) were identified, with 616 genes transcriptionally upregulated and 33 genes downregulated by HFD compared to a normal diet (ND). Enrichment analysis for gene ontology (GO) using the DEGs was performed to analyze over-represented biological processes in the RPE/choroid of GF-HFD mice relative to GF-ND mice. GO analysis revealed the upregulation of processes related to angiogenesis, immune response, and the inflammatory response. Additionally, molecular functions that were altered involved extracellular matrix (ECM) binding, ECM structural constituents, and heparin binding. This study demonstrates novel data showing that HFDs can alter RPE/choroid tissue transcription in the absence of the gut microbiome.

Bacteroidota and Lachnospiraceae integration into the gut microbiome at key time points in early life are linked to infant neurodevelopment.

The early life microbiome plays critical roles in host development, shaping long-term outcomes including brain functioning. It is not known which initial infant colonizers elicit optimal neurodevelopment; thus, this study investigated the association between gut microbiome succession from the first week of life and head circumference growth (HCG), the earliest validated marker for neurodevelopment. Fecal samples were collected weekly from a preterm infant cohort during their neonatal intensive care unit stay and subjected to 16S rRNA gene sequencing for evaluating gut microbiome composition, in conjunction with clinical data and head circumference measurements. Preterm infants with suboptimal HCG trajectories had a depletion in the abundance/prevalence of Bacteroidota and Lachnospiraceae, independent of morbidity and caloric restriction. The severity of gut microbiome depletion matched the timing of significant HCG pattern separation between study groups at 30-week postmenstrual age demonstrating a potential mediating relationship resultant from clinical practices. Consideration of the clinical variables indicated that optimal infant microbiome succession is primarily driven by dispersal limitation (i.e., delivery mode) and secondarily by habitat filtering (i.e., antibiotics and enteral feeding). Bacteroidota and Lachnospiraceae are known core taxa of the adult microbiome, with roles in dietary glycan foraging, beneficial metabolite production and immunity, and our work provides evidence that their integration into the gut microbiome needs to occur early for optimal neurodevelopment.

Unique somatic variants in DNA from urine exosomes of individuals with bladder cancer.

Bladder cancer (BC), a heterogeneous disease characterized by high recurrence rates, is diagnosed and monitored by cystoscopy. Accurate clinical staging based on biopsy remains a challenge, and additional, objective diagnostic tools are needed urgently. We used exosomal DNA (exoDNA) as an analyte to examine cancer-associated mutations and compared the diagnostic utility of exoDNA from urine and serum of individuals with BC. In contrast to urine exosomes from healthy individuals, urine exosomes from individuals with BC contained significant amounts of DNA. Whole-exome sequencing of DNA from matched urine and serum exosomes, bladder tumors, and normal tissue (peripheral blood mononuclear cells) identified exonic and 3' UTR variants in frequently mutated genes in BC, detectable in urine exoDNA and matched tumor samples. Further analyses identified somatic variants in driver genes, unique to urine exoDNA, possibly because of the inherent intra-tumoral heterogeneity of BC, which is not fully represented in random small biopsies. Multiple variants were also found in untranslated portions of the genome, such as microRNA (miRNA)-binding regions of the KRAS gene. Gene network analyses revealed that exoDNA is associated with cancer, inflammation, and immunity in BC exosomes. Our findings show utility of exoDNA as an objective, non-invasive strategy to identify novel biomarkers and targets for BC.

High-Fat Diet Alters the Retinal Transcriptome in the Absence of Gut Microbiota.

The relationship between retinal disease, diet, and the gut microbiome has shown increasing importance over recent years. In particular, high-fat diets (HFDs) are associated with development and progression of several retinal diseases, including age-related macular degeneration (AMD) and diabetic retinopathy. However, the complex, overlapping interactions between diet, gut microbiome, and retinal homeostasis are poorly understood. Using high-throughput RNA-sequencing (RNA-seq) of whole retinas, we compare the retinal transcriptome from germ-free (GF) mice on a regular diet (ND) and HFD to investigate transcriptomic changes without influence of gut microbiome. After correction of raw data, 53 differentially expressed genes (DEGs) were identified, of which 19 were upregulated and 34 were downregulated in GF-HFD mice. Key genes involved in retinal inflammation, angiogenesis, and RPE function were identified. Enrichment analysis revealed that the top 3 biological processes affected were regulation of blood vessel diameter, inflammatory response, and negative regulation of endopeptidase. Molecular functions altered include endopeptidase inhibitor activity, protease binding, and cysteine-type endopeptidase inhibitor activity. Human and mouse pathway analysis revealed that the complement and coagulation cascades are significantly affected by HFD. This study demonstrates novel data that diet can directly modulate the retinal transcriptome independently of the gut microbiome.

Reproducible big data science: A case study in continuous FAIRness.

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources.

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks.

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Lynx: a knowledge base and an analytical workbench for integrative medicine.

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

A case study for cloud based high throughput analysis of NGS data using the globus genomics system.

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the "Globus Genomics" system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.