Benzo[a]pyrene-induced DNA adducts and gene expression profiles in target and non-target organs for carcinogenesis in mice

Background: Gene expression changes induced by carcinogens may identify differences in molecular function between target and non-target organs. Target organs for benzo[a]pyrene (BaP) carcinogenicity in mice (lung, spleen and forestomach) and three non-target organs (liver, colon and glandular stomach) were investigated for DNA adducts by 32P-postlabelling, for gene expression changes by cDNA microarray and for miRNA expression changes by miRNA microarray after exposure of animals to BaP. Results: BaP-DNA adduct formation occurred in all six organs at levels that did not distinguish between target and non-target. cDNA microarray analysis showed a variety of genes modulated significantly by BaP in the six organs and the overall gene expression patterns were tissue specific. Gene ontology analysis also revealed that BaP-induced bioactivities were tissue specific; eight genes (Tubb5, Fos, Cdh1, Cyp1a1, Apc, Myc, Ctnnb1 and Cav) showed significant expression difference between three target and three non-target organs. Additionally, several gene expression changes, such as in Trp53 activation and Stat3 activity suggested some similarities in molecular mechanisms in two target organs (lung and spleen), which were not found in the other four organs. Changes in miRNA expression were generally tissue specific, involving, in total, 21/54 miRNAs significantly up- or down-regulated. Conclusions: Altogether, these findings showed that DNA adduct levels and early gene expression changes did not fully distinguish target from non-target organs. However, mechanisms related to early changes in p53, Stat3 and Wnt/β-catenin pathways may play roles in defining BaP organotropism

Aberrantly Expressed CeRNAs Account for Missing Genomic Variability of Cancer Genes via MicroRNA-Mediated Interactions

There is growing evidence that RNAs compete for binding and regulation by a finite pool of microRNAs (miRs), thus regulating each other through a competing endogenous RNA (ceRNA) mechanism. My dissertation work focused on systematically studying ceRNA interactions in cancer by reverse-engineering context-specific miR-RNA interactions and ceRNA regulatory interactions across multiple tumor types and study the effects of these interactions in cancer. I attempted to use ceRNA interactions to explain how genetic and epigenetic alterations are propagated to target established drivers of tumorigenesis. Using bioinformatics analysis of primary tumor samples and experimental validation in cell lines, I have investigated the roles that mRNAs and noncoding RNAs can play in tumorigenesis via ceRNA interactions. Specifically, I studied how RNAs target tumor-suppressors and oncogenes as ceRNAs, and attempted to accounting for some of the missing genomic variability in tumors

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Integrative methods for analysing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

The Number Of Titrated Microrna Species Dictates Cerna Regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were coregulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor-and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent

Module Network Inference from a Cancer Gene Expression Data Set Identifies MicroRNA Regulated Modules

Background: MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. Methodology/Principal Findings: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. Conclusions/Significance: Our results show that a robust module network analysis of expression data can provide nove

A New System for Human MicroRNA functional Evaluation and Network

MicroRNAs are functionally important endogenous non-coding RNAs that silence host genes in animal and plant via destabilizing the mRNAs or preventing the translation. Given the far-reaching implication of microRNA regulation in human health, novel bioinformatics tools are desired to facilitate the mechanistic understanding of microRNA mediated gene regulation, their roles in biological processes, and the functional relevance among microRNAs. However, most state-of-the-art computational methods still focus on the functional study of microRNA targets and there is no e ective strategy to infer the functional similarity among microRNAs. In this study, we developed a new method to quantitatively measure the functional similarity among microRNAs based on the integrated functional annotation data from Gene Ontology, human pathways, and PFam databases. Through analyzing human microRNAs, we further demonstrated the use of the derived microRNA pairwise similarities to discover the cooperative microRNA modules and to construct the genome-scale microRNAmediated gene network in human. The complete results and the similarity assessment system can be freely accessed at (http://sbbi.unl.edu/microRNASim). Adviser: Juan Cu