Bio-informatics based analysis of genes implicated in alcohol mediated liver injury

Monascuspiloin from Monascus-Fermented Red Mold Rice Alleviates Alcoholic Liver Injury and Modulates Intestinal Microbiota

Monascus-fermented red mold rice (RMR) has excellent physiological efficacy on lipid metabolism and liver function. This study investigated the ameliorative effects of monascuspiloin (MP) from RMR on alcoholic liver injury in mice, and further clarified its mechanism of action. Results showed that MP intervention obviously ameliorated lipid metabolism and liver function in mice with over-drinking. In addition, dietary MP intervention reduced liver MDA levels and increased liver CAT, SOD, and GSH levels, thus alleviating liver oxidative stress induced by excessive drinking. 16S rRNA amplicon sequencing showed that MP intervention was beneficial to ameliorate intestinal microbiota dysbiosis by elevating the proportion of norank_f_Lachnospiraceae, Lachnoclostridium, Alistipes, Roseburia, Vagococcus, etc., but decreasing the proportion of Staphylococcus, norank_f_Desulfovibrionaceae, Lachnospiraceae_UCG-001, Helicobacter, norank_f_Muribaculaceae, unclassified_f_Ruminococcaceae, etc. Additionally, correlation network analysis indicated that the key intestinal bacterial taxa intervened by MP were closely related to some biochemical parameters of lipid metabolism, liver function, and oxidative stress. Moreover, liver metabolomics analysis revealed that dietary MP supplementation significantly regulated the levels of 75 metabolites in the liver, which were involved in the synthesis and degradation of ketone bodies, taurine, and hypotaurine metabolism, and other metabolic pathways. Furthermore, dietary MP intervention regulated gene transcription and protein expression associated with hepatic lipid metabolism and oxidative stress. In short, these findings suggest that MP mitigates alcohol-induced liver injury by regulating the intestinal microbiome and liver metabolic pathway, and thus can serve as a functional component to prevent liver disease.

Construction of long non-coding RNA- and microRNA-mediated competing endogenous RNA networks in alcohol-related esophageal cancer

The current study aimed to explore the lncRNA–miRNA–mRNA networks associated with alcohol-related esophageal cancer (EC). RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas and the differentially expressed genes (DEGs), long non-coding RNAs (lncRNAs, DELs), and miRNAs (DEMs) in patients with alcohol-related and non-alcohol-related EC were identified. Prognostic RNAs were identified by performing Kaplan–Meier survival analyses. Weighted gene co-expression network analysis was employed to build the gene modules. The lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) networks were constructed based on our in silico analyses using data from miRcode, starBase, and miRTarBase databases. Functional enrichment analysis was performed for the genes in the identified ceRNA networks. A total of 906 DEGs, 40 DELs, and 52 DEMs were identified. There were eight lncRNAs and miRNAs each, including ST7-AS2 and miR-1269, which were significantly associated with the survival rate of patients with EC. Of the seven gene modules, the blue and turquoise modules were closely related to disease progression; the genes in this module were selected to construct the ceRNA networks. SNHG12–miR-1–ST6GAL1, SNHG3–miR-1–ST6GAL1, SPAG5-AS1–miR-133a–ST6GAL1, and SNHG12–hsa-miR-33a–ST6GA interactions, associated with the N-glycan biosynthesis pathway, may have key roles in alcohol-related EC. Thus, the identified biomarkers provide a novel insight into the molecular mechanism of alcohol-related EC.

Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response

AIM

To provide insight into potential regulatory mechanisms of gene expression underlying addiction, analgesia, psychotropic drug response and adverse drug events, genome-wide association studies searching for variants associated with these phenotypes has been undertaken with limited success. We undertook analysis of these results with the aim of applying epigenetic knowledge to aid variant discovery and interpretation.

METHODS

We applied conditional imputation to results from 26 genome-wide association studies and three candidate gene-association studies. The analysis workflow included data from chromatin conformation capture, chromatin state annotation, DNase I hypersensitivity, hypomethylation, anatomical localization and biochronicity. We also made use of chromatin state data from the epigenome roadmap, transcription factor-binding data, spatial maps from published Hi-C datasets and 'guilt by association' methods.

RESULTS

We identified 31 pharmacoepigenomic SNPs from a total of 2024 variants in linkage disequilibrium with lead SNPs, of which only 6% were coding variants. Interrogation of chromatin state using our workflow and the epigenome roadmap showed agreement on 34 of 35 tissue assignments to regulatory elements including enhancers and promoters. Loop boundary domains were inferred by association with CTCF (CCCTC-binding factor) and cohesin, suggesting proximity to topologically associating domain boundaries and enhancer clusters. Spatial interactions between enhancer-promoter pairs detected both known and previously unknown mechanisms. Addiction and analgesia SNPs were common in relevant populations and exhibited large effect sizes, whereas a SNP located in the promoter of the SLC1A2 gene exhibited a moderate effect size for lithium response in bipolar disorder in patients of European ancestry. SNPs associated with drug-induced organ injury were rare but exhibited the largest effect sizes, consistent with the published literature.

CONCLUSION

This work demonstrates that an in silico bioinformatics-based approach using integrative analysis of a diversity of molecular and morphological data types can discover pharmacoepigenomic variants that are suitable candidates for further validation in cell lines, animal models and human clinical trials.

Bayesian pathway analysis of cancer microarray data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in "Data Preprocessing and Discretization", "Scoring", "Significance Assessment", and "Software and Web Application". We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA).