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Single-cell (sc) transcriptomics has revolutionized our understanding of the biological characteristics and dynamics of cancer development. It can help us identify rare cell subpopulations and understand mechanisms associated with tumor genesis, progression, and response to therapy. The most important step in the analyses of any scRNA-seq dataset is subpopulation identification, usually performed via unsupervised clustering, followed by gene marker identification. We created a highly customizable workflow for sc data analysis, implemented in Common Workflow Language (CWL) on the Cancer Genomics Cloud (CGC) platform. The NCI-funded CGC platform, powered by Seven Bridges, provides a collaborative cloud base computation infrastructure that collocates computation, over 750 bioinformatics workflows, and 3+ PB data to researchers, making the analysis of large datasets accessible from any environment. The “Multi-Sample Clustering and Gene Marker Identification with Seurat 4.1.0” workflow comprises the following steps: Loading scRNA-seq Expression Datasets, Quality Control and Preprocessing, and Clustering and Identification of Gene Markers. Our solution supports gene-cell count matrices generated by several commonly used quantifiers (for example, Cell Ranger counts, Salmon Alevin, Kallisto BUStools, STAR) from single or multiple sc datasets from different batches, as well as single or multiple single-cell samples combined in a single SingleCellExperiment object. The versatility of the pipeline is obtained using several implemented options in each of the steps. Quality control can be performed manually or automatically using several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm) and for batch effect correction (Seurat and Harmony). For clustering, the pipeline uses Seurat's graph-based approach, with options for different clustering resolutions. After performing identification of gene markers for each cluster, a researcher can test differential expression using various packages including wilcox, bimod, roc, and DESeq2. Here, we demonstrate the application of this workflow to a typical sc analysis, by processing an open access dataset of 61k cells isolated from embryonal mouse pons and forebrain, two major brain tumor locations. We used different clustering resolutions to achieve different degrees of granularity and identified cluster-specific marker genes used to identify vulnerable cell populations. To enable researchers to use this analysis as a guideline, we made this analysis available as a public project. Further development of single-cell sequencing techniques will undoubtedly improve our understanding of tumor biology and highlight promising drug targets. CGC’s cloud base computation infrastructure, along with numerous available cancer datasets and easy-to-use single-cell data processing workflows, among others, will be instrumental in this process. Citation Format: Nevena Vukojicic, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, Brandi Davis-Dusenbery. Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2075.

Inorganic arsenic (iAs) is an environmental diabetogen, but mechanisms underlying its diabetogenic effects are poorly understood. Exposures to arsenite (iAs(III)) and its methylated metabolites, methylarsonite (MAs(III)) and dimethylarsinite (DMAs(III)), have been shown to inhibit glucose stimulated insulin secretion (GSIS) in pancreatic β-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 β-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 iAs(III), MAs(III) and DMAs(III). 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 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 β-cells exposed to trivalent arsenicals.

Arsenic is a pervasive environmental toxin that is listed as the top priority for investigation by the Agency for Toxic Substance and Disease Registry. While chronic exposure to arsenic is associated with type 2 diabetes (T2D), the underlying mechanisms are largely unknown. We have recently demonstrated that arsenic treatment of INS-1 832/13 pancreatic beta cells impairs glucose-stimulated insulin secretion (GSIS), a T2D hallmark. We have also shown that arsenic alters the microRNA profile of beta cells. MicroRNAs have a well-established post-transcriptional regulatory role in both normal beta cell function and T2D pathogenesis. We hypothesized that there are microRNA master regulators that shape beta cell gene expression in pathways pertinent to GSIS after exposure to arsenicals. To test this hypothesis, we first treated INS-1 832/13 beta cells with either inorganic arsenic (iAsIII) or monomethylarsenite (MAsIII) and confirmed GSIS impairment. We then performed multi-omic analysis using chromatin run-on sequencing, RNA-sequencing, and small RNA-sequencing to define profiles of transcription, gene expression, and microRNAs, respectively. Integrating across these data sets, we first showed that genes downregulated by iAsIII treatment are enriched in insulin secretion and T2D pathways, whereas genes downregulated by MAsIII treatment are enriched in cell cycle and critical beta cell maintenance factors. We also defined the genes that are subject primarily to post-transcriptional control in response to arsenicals and demonstrated that miR-29a is the top candidate master regulator of these genes. Our results highlight the importance of microRNAs in arsenical-induced beta cell dysfunction and reveal both shared and unique mechanisms between iAsIII and MAsIII.

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.

Exposure to inorganic arsenic (iAs) remains a global public health problem. Urinary arsenicals are the current gold-standard for estimating both iAs exposure and iAs metabolism. However, the distribution of these arsenicals may differ between the urine and target organs. Instead, plasma arsenicals may better represent internal dose and capture target organ exposure to arsenicals. Drinking water iAs, plasma and urinary arsenicals were quantified in individuals living in the Zimapan and Lagunera regions of Mexico. The relationship between drinking water iAs and plasma arsenicals was examined using both Spearman correlations and multivariable linear regression models. In addition, the distribution of arsenicals in plasma and urine was examined and the association between plasma and urinary arsenicals was assessed using both Spearman correlations and multivariable linear regression models. Levels of iAs in drinking water were significantly associated with plasma arsenicals in unadjusted and adjusted analyses and the strength of these associations was similar to that of drinking water iAs and urinary arsenicals. These results suggest that plasma arsenicals are reliable biomarkers of iAs exposure via drinking water. However, there were notable differences between the profiles of arsenicals in the plasma and the urine. Key differences between the proportions of arsenicals in plasma and urine may indicate that urine and plasma arsenicals reflect different aspects of iAs toxicokinetics, including metabolism and excretion.

Growing evidence suggests that exposure to environmental contaminants contributes to the current diabetes epidemic. Inorganic arsenic (iAs), a drinking water and food contaminant, is one of the most widespread environmental diabetogens according to epidemiological studies. Several schemes have been proposed to explain the diabetogenic effects of iAs exposure; however, the exact mechanism remains unknown. We have shown that in vitro exposure to low concentrations of arsenite (iAs(III)) or its trivalent methylated metabolites, methylarsonite (MAs(III)) and dimethylarsinite (DMAs(III)), inhibits glucose-stimulated insulin secretion (GSIS) from isolated pancreatic islets, with little effect on insulin transcription or total insulin content. The goal of this study was to determine if exposure to trivalent arsenicals impairs mitochondrial metabolism, which plays a key role in the regulation of GSIS in β cells. We used a Seahorse extracellular flux analyzer to measure oxygen consumption rate (OCR), a proxy for mitochondrial metabolism, in cultured INS-1 832/13 β cells exposed to iAs(III), MAs(III), or DMAs(III) and stimulated with either glucose or pyruvate, a final product of glycolysis and a substrate for the Krebs cycle. We found that 24-h exposure to 2 μM iAs(III) or 0.375–0.5 μM MAs(III) inhibited OCR in both glucose- and pyruvate-stimulated β cells in a manner that closely paralleled GSIS inhibition. In contrast, 24-h exposure to DMAs(III) (up to 2 μM) had no effects on either OCR or GSIS. These results suggest that iAs(III) and MAs(III) may impair GSIS in β cells by inhibiting mitochondrial metabolism, and that at least one target of these arsenicals is pyruvate decarboxylation or downstream reactions.

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.

Background: Inorganic arsenic (iAs) is a diabetogen. Interindividual differences in iAs metabolism have been linked to susceptibility to diabetes in iAs-exposed populations. Dietary folate intake has been shown to influence iAs metabolism, but to our knowledge its role in iAs-associated diabetes has not been studied. Objective: The goal of this study was to assess how folate intake, combined with low-fat (LFD) and high-fat diets (HFD), affects the metabolism and diabetogenic effects of iAs in wild-type (WT) mice and in As3mt-knockout (KO) mice that have limited capacity for iAs detoxification. Methods: Male and female WT and KO mice were exposed to 0 or 100 ppb iAs in drinking water. Mice were fed the LFD containing 0.2 mg/kg or 10 mg/kg folate for 24 weeks, followed by the HFD with the same folate levels for 13 weeks. Metabolic phenotype and iAs metabolism were examined before and after switching to the HFD. Results: iAs exposure had little effect on the phenotype of mice fed LFD regardless of folate intake. High folate intake stimulated iAs metabolism, but only in WT females. KO mice accumulated more fat than WT mice and were insulin resistant, with males more insulin resistant than females despite similar %fat mass. Feeding the HFD increased adiposity and insulin resistance in all mice. However, iAs-exposed male and female WT mice with low folate intake were more insulin resistant than unexposed controls. High folate intake alleviated insulin resistance in both sexes, but stimulated iAs metabolism only in female mice. Conclusions: Exposure to 100 ppb iAs in drinking water resulted in insulin resistance in WT mice only when combined with a HFD and low folate intake. The protective effect of high folate intake may be independent of iAs metabolism, at least in male mice. KO mice were more prone to developing insulin resistance, possibly due to the accumulation of iAs in tissues. https://doi.org/10.1289/EHP3951

Background: Exposure to inorganic arsenic (iAs) remains a public health issue. Typically, three major forms of arsenic are measured in urine as biomarkers of iAs exposure, namely, iAs, and its meta...

Purpose of Review The goal of this review is to delineate the following: (1) the primary means of inorganic arsenic (iAs) exposure for human populations, (2) the adverse public health outcomes associated with chronic iAs exposure, (3) the pathophysiological connection between arsenic and type 2 diabetes (T2D), and (4) the incipient evidence for microRNAs as candidate mechanistic links between iAs exposure and T2D. Recent Findings Exposure to iAs in animal models has been associated with the dysfunction of several different cell types and tissues, including liver and pancreatic islets. Many microRNAs that have been identified as responsive to iAs exposure under in vitro and/or in vivo conditions have also been shown in independent studies to regulate processes that underlie T2D etiology, such as glucose-stimulated insulin secretion from pancreatic beta cells. Summary Defects in insulin secretion could be, in part, associated with aberrant microRNA expression and activity. Additional in vivo studies need to be performed with standardized concentrations and durations of arsenic exposure in order to evaluate rigorously microRNAs as molecular drivers of iAs-associated diabetes.