Solid organ transplantation (SOT) outcomes have continued to improve, although long-term use of immunosuppressants can lead to complications such as diabetes, compromising post-transplant outcomes. In this study, we have characterized the intestinal microbiome (IM) composition at the metagenomic level in the context of hyperglycemia induced by immunosuppressants. Sprague-Dawley rats were subjected to doses of tacrolimus and sirolimus that reliably induce hyperglycemia and an insulin-resistant state. Subsequent exposure to probiotics resulted in reversal of hyperglycemia. 16S rRNA and metagenomic sequencing of stool were done to identify the bacterial genes and pathways enriched in immunosuppression. Bacterial diversity was significantly decreased in sirolimus-treated rats, with 9 taxa significantly less present in both immunosuppression groups: Roseburia, Oscillospira, Mollicutes, Rothia, Micrococcaceae, Actinomycetales and Staphylococcus. Following probiotics, these changes were reversed to baseline. At the metagenomic level, the balance of metabolism was shifted towards the catabolic side with an increase of genes involved in sucrose degradation, similar to diabetes. Conversely, the control rats had greater abundance of anabolic processes and genes involved in starch degradation. Immunosuppression leads to a more catabolic microbial profile, which may influence development of diabetes after SOT. Modulation of the microbiome with probiotics may help in minimizing adverse long-term effects of immunosuppression.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the Western world, and encompasses a spectrum from simple steatosis to steatohepatitis (NASH). There is currently no approved pharmacologic therapy against NASH, partly due to an incomplete understanding of its molecular basis. The goal of this study was to determine the key differentially expressed genes (DEGs), as well as those genes and pathways central to its pathogenesis. We performed an integrative computational analysis of publicly available gene expression data in NASH from GEO (GSE17470, GSE24807, GSE37031, GSE89632). The DEGs were identified using GEOquery, and only the genes present in at least three of the studies, to a total of 190 DEGs, were considered for further analyses. The pathways, networks, molecular interactions, functional analyses were generated through the use of Ingenuity Pathway Analysis (IPA). For selected networks, we computed the centrality using igraph package in R. Among the statistically significant predicted networks (p-val < 0.05), three were of most biological interest: the first is involved in antimicrobial response, inflammatory response and immunological disease, the second in cancer, organismal injury and development and the third in metabolic diseases. We discovered that HNF4A is the central gene in the network of NASH connected to metabolic diseases and that it regulates HNF1A, an additional transcription regulator also involved in lipid metabolism. Therefore, we show, for the first time to our knowledge, that HNF4A is central to the pathogenesis of NASH. This adds to previous literature demonstrating that HNF4A regulates the transcription of genes involved in the progression of NAFLD, and that HNF4A genetic variants play a potential role in NASH progression.

Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.

While Brassica oleracea vegetables have been linked to cancer prevention, the exact mechanism remains unknown. Regulation of gene expression by cross-species microRNAs has been previously reported; however, its link to cancer suppression remains unexplored. In this study we address both issues. We confirm plant microRNAs in human blood in a large nutrigenomics study cohort and in a randomized dose-controlled trial, finding a significant positive correlation between the daily amount of broccoli consumed and the amount of microRNA in the blood. We also demonstrate that Brassica microRNAs regulate expression of human genes and proteins in vitro, and that microRNAs cooperate with other Brassica-specific compounds in a possible cancer-preventive mechanism. Combined, we provide strong evidence and a possible multimodal mechanism for broccoli in cancer prevention.