NCI Cancer Research Data Commons: Cloud-Based Analytic Resources.

The NCI's Cloud Resources (CR) are the analytical components of the Cancer Research Data Commons (CRDC) ecosystem. This review describes how the three CRs (Broad Institute FireCloud, Institute for Systems Biology Cancer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability to large, cloud-hosted, multimodal cancer datasets, as well as offer tools and workspaces for performing data analysis where the data resides, without download or storage. In addition, users can upload their own data and tools into their workspaces, allowing researchers to create custom analysis workflows and integrate CRDC-hosted data with their own. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Kim et al., p. 1404.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Exposure to inorganic arsenic and its methylated metabolites alters metabolomics profiles in INS-1 832/13 insulinoma cells and isolated pancreatic islets.

Inorganic arsenic (iAs) is an environmental diabetogen, but mechanisms underlying its diabetogenic effects are poorly understood. Exposures to arsenite (iAsIII) and its methylated metabolites, methylarsonite (MAsIII) and dimethylarsinite (DMAsIII), have been shown to inhibit glucose-stimulated insulin secretion (GSIS) in pancreatic ß-cells and isolated pancreatic islets. GSIS is regulated by complex mechanisms. Increase in ATP production through metabolism of glucose and other substrates is the ultimate trigger for GSIS in ß-cells. In the present study, we used metabolomics to identify metabolites and pathways perturbed in cultured INS-1 832/13 rat insulinoma cells and isolated murine pancreatic islets by exposures to iAsIII, MAsIII and DMAsIII. We found that the exposures perturbed multiple metabolites, which were enriched primarily in the pathways of amino acid, carbohydrate, phospholipid and carnitine metabolism. However, the effects of arsenicals in INS-1 832/13 cells differed from those in the islets and were exposure specific with very few overlaps between the three arsenicals. In INS-1 832/13 cells, all three arsenicals decreased succinate, a metabolite of Krebs cycle, which provides substrates for ATP synthesis in mitochondria. Acetylcarnitine was decreased consistently by exposures to arsenicals in both the cells and the islets. Acetylcarnitine is usually found in equilibrium with acetyl-CoA, which is the central metabolite in the catabolism of macronutrients and the key substrate for Krebs cycle. It is also thought to play an antioxidant function in mitochondria. Thus, while each of the three trivalent arsenicals perturbed specific metabolic pathways, which may or may not be associated with GSIS, all three arsenicals appeared to impair mechanisms that support ATP production or antioxidant defense in mitochondria. These results suggest that impaired ATP production and/or mitochondrial dysfunction caused by oxidative stress may be the mechanisms underlying the inhibition of GSIS in ß-cells exposed to trivalent arsenicals.

Arsenic is more potent than cadmium or manganese in disrupting the INS-1 beta cell microRNA landscape.

Diabetes is a metabolic disorder characterized by fasting hyperglycemia and impaired glucose tolerance. Laboratory and population studies have shown that inorganic arsenic (iAs) can impair these pathways. Other metals including cadmium (Cd) and manganese (Mn) have also been linked to diabetes phenotypes. MicroRNAs, short non-coding RNAs that regulate gene expression, have emerged as potential drivers of metabolic dysfunction. MicroRNAs responsive to metal exposures in vitro have also been reported in independent studies to regulate insulin secretion in vivo. We hypothesize that microRNA dysregulation may associate with and possibly contribute to insulin secretion impairment upon exposure to iAs, Cd, or Mn. We exposed insulin secreting rat insulinoma cells to non-cytotoxic concentrations of iAs (1 µM), Cd (5 µM), and Mn (25 µM) for 24 h followed by small RNA sequencing to identify dysregulated microRNAs. RNA sequencing was then performed to further investigate changes in gene expression caused by iAs exposure. While all three metals significantly inhibited glucose-stimulated insulin secretion, high-throughput sequencing revealed distinct microRNA profiles specific to each exposure. One of the most significantly upregulated microRNAs post-iAs treatment is miR-146a (~ + 2-fold), which is known to be activated by nuclear factor κB (NF-κB) signaling. Accordingly, we found by RNA-seq analysis that genes upregulated by iAs exposure are enriched in the NF-κB signaling pathway and genes down-regulated by iAs exposure are enriched in miR-146a binding sites and are involved in regulating beta cell function. Notably, iAs exposure caused a significant decrease in the expression of Camk2a, a calcium-dependent protein kinase that regulates insulin secretion, has been implicated in type 2 diabetes, and is a likely target of miR-146a. Further studies are needed to elucidate potential interactions among NF-kB, miR-146a, and Camk2a in the context of iAs exposure.

Circulating miRNAs Associated with Arsenic Exposure.

Arsenic (As) is a toxic metalloid. Inorganic arsenic (iAs) is a form of As commonly found in drinking water and in some foods. Overwhelming evidence suggests that people chronically exposed to iAs are at risk of developing cancer or cardiovascular, neurological, and metabolic diseases. Although the mechanisms underlying iAs-associated illness remain poorly characterized, a growing body of literature raises the possibility that microRNAs (miRNAs), post-transcriptional gene suppressors, may serve as mediators and/or early indicators of the pathologies associated with iAs exposure. To characterize the circulating miRNA profiles of individuals chronically exposed to iAs, samples of plasma were collected from 109 healthy residents of the city of Zimapán and the Lagunera area in Mexico, the regions with historically high exposures to iAs in drinking water. These plasma samples were analyzed for small RNAs using high-throughput sequencing and for iAs and its methylated metabolites. Associations between plasma levels of arsenic species and miRNAs were evaluated. Six circulating miRNAs (miRs-423-5p, -142-5p -2, -423-5p +1, -320c-1, -320c-2, and -454-5p), two of which have been previously linked to cardiovascular disease and diabetes (miRs-423-5p, -454-5p), were found to be significantly correlated with plasma MAs. No miRNAs were associated with plasma iAs or DMAs after correction for multiple testing. These miRNAs may represent mechanistic links between iAs exposure and disease or serve as markers of disease risks associated with this exposure.