Dysregulation of microRNAs (miRNAs) plays a critical role in tumor growth and progression. In this study, we sought to explore the expression and biological roles of miR-539 in hepatocellular carcinoma (HCC).The expression of miR-539 in human HCC tissues and cell lines was examined. The effects of miR-539 overexpression on cell growth, tumorigenicity, arsenic trioxide resistance of HCC cells were determined. The signaling pathways involved in the action of miR-539 in HCC were also investigated.miR-539 was downregulated in HCC tissues and cells, relative to corresponding controls. Overexpression of miR-539 inhibited HCC cell viability and colony formation in vitro and impaired tumorigenesis of HCC cells in vivo. Transfection with miR-539 mimic significantly induced apoptosis in HepG2 cells, which was coupled with reduced expression of anti-apoptotic proteins Bcl-2 and Bcl-xL and decreased phosphorylation of Stat3. Overexpression of a constitutively active form of Stat3 partially blocked miR-539-mediated apoptosis. Enforced expression of miR-539 resensitized arsenic trioxide-resistant HCC cells to arsenic trioxide. Intratumoral delivery of miR-539 mimic significantly retarded the growth of xenograft tumors from arsenic trioxide-resistant HCC cells by about 35%, compared to delivery of control miRNA (P < 0.05). In combination with arsenic trioxide, miR-539 mimic yielded about 80% decrease in tumor burden.miR-539 functions as a tumor suppressor in HCC and reexpression of this miRNA offers a potential therapeutic strategy for this disease.

Persistent organic pollutants (POPs) and heavy metals are toxic compounds with established adverse effects on human health. POPs and heavy metal exposure not only affects gene expression profiles but also epigenetic mechanisms. To investigate the integrative molecular effects of chronic human exposure to these compounds, we applied multi-layer nonnegative matrix factorization to integrate gene expression and DNA methylation profiles with blood levels of POPs (PCBs, DDE, and HCB), lead and cadmium, from two prospective European cohort studies (EPIC Italy and NSHDS). This integration strategy generates different genomic profiles for the obtained clusters that potentially explain exposure-induced molecular effects. We combined those genomic profiles into genomic interaction networks to investigate their biological relevance. We identified clusters of subjects within the combined EPIC and NSHDS cohorts showing distinct exposure profiles for DDE and lead. These genomic profiles demonstrated involvement of POP and heavy metal exposure in various biological processes including signaling processes, immune-related processes, DNA repair, mRNA translation, and mRNA transcription. The simulation on the NSHDS cohort revealed sex-specific exposure-responses of male gamete formation in males, and of cytochrome P450 genes and bile acid synthesis in females. In the EPIC cohort, methylation differences for sex-specific CpG sites connected to mantle cell lymphoma were associated with lead exposure. In conclusion, exposure-related changes in transcriptome and epigenome in the EPIC and NSHDS cohorts enabled us to identify communities in our interaction network that relate to POP and lead exposure that could explain the development of certain diseases. In general, we could not identify clear epigenetic alterations that directly associate with observed changes in the transcriptome. However, the combination of the epigenetic and transcriptomic alterations provided us with genomic profiles that relate to the exposure profiles. Hence, both omics layers can be used to cluster samples into distinct groups that have a mixture of exposures.

Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene–gene and gene—DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.

Circulating microRNAs (cimiRNAs) are a class of non-encoding RNAs found in bodily fluids such as blood, cerebrospinal fluid (CSF) and tears. CimiRNAs have been implicated as promising biomarkers for central nervous system (CNS) disorders because they are actively secreted as messengers and are profoundly involved in fine-tuning of developmental and differentiation processes. Furthermore, they are attractive biomarkers because they are extremely stable, tissue enriched and can be determined in a quantitative manner. This review aims to provide a comprehensive assessment on the current progress regarding the potential value of cimiRNAs as CNS biomarkers. Within this framework five CNS disorders are explored which share a common pathological hallmark namely cognitive impairment. The CNS disorders include Major depression disorder (MDD), Bipolar disorder (BD), Schizophrenia (SZ), Alzheimer's disease (AD) and Parkinson disease (PD). The similarities and differences between altered cimiRNAs in the different disorders are described. The miR-29 family, miR-34a-5p and miR-132-3p are discussed as common dysregulated cimiRNAs found in the CNS disorders. Furthermore, it is shown that the type of bodily fluid used for measuring cimiRNAs is important as inconsistencies in cimiRNAs expression directions are found when comparing CSF, blood cell-free and blood cell-bound samples.

Valproic acid (VPA) is a frequently prescribed anti-epileptic drug which is known to cause liver toxicity and steatosis through mitochondrial dysfunction. Nevertheless the mechanisms underlying these adverse effects are incompletely understood. In this study, we determined the effect of relatively short (3 h) or prolonged (72 h) exposure to VPA on mitochondrial function in primary human hepatocytes (PHHs). While 3 h VPA exposure did not affect oxygen consumption rates (OCRs) in PHHs, prolonged exposure (24–72 h) significantly reduced basal and maximal OCRs. Given that in particular prolonged VPA exposure is required to cause mitochondrial dysfunction, we investigated gene expression data after VPA exposure for 24, 48, 72 h and 72 h VPA followed by a 72 h washout period. We were able to reduce the comprehensive gene expression changes into a more comprehensible set of 18 TFs that were predicted to be persistently activated after 72 h of VPA exposure. Lentiviral knock-down of one of the candidate TFs, C/EBPα, partly rescued VPA-induced mitochondrial dysfunction. Furthermore, RNA-Seq analysis of shC/EBPα and shGFP control PHHs identified 24 genuine C/EBPα target genes that are regulated in response to prolonged VPA exposure in PHHs. Altogether this provides new insights on the involvement of C/EBPα in driving VPA-induced mitochondrial dysfunction in human liver cells. This hub gene, with its downstream regulators involved in this deregulation, thus represent potential new biomarkers for VPA-induced mitochondrial dysfunction.

Inflammation is one of the factors that may increase the sensitivity of hepatic cells to acetaminophen (APAP) induced toxicity. To investigate the mechanisms, we exposed 3-dimensional (3D) Human Liver Microtissues, a co-culture of primary human hepatocytes (PHH) and Kupffer cells (KCs), to 0, 0.5 (low), 5 (median) and 10 mM (high dose) APAP for 24 h, with/without lipopolysaccharide (LPS). Microarray-technology was used to evaluate the transcriptome changes. In the presence of LPS, the median-dose of APAP is sufficient to inhibit the expression of respiratory chain-and antioxidant-related genes, suggesting the involvement of reactive oxygen species (ROS) and oxidative stress. Furthermore, the median- and high-dose of APAP inhibited the expression of Fc fragment receptor (Fc gamma R)-coding genes, regardless of the presence of LPS. The toll-like receptor 4 (TLR4) expression, however, was continuously elevated after the LPS/APAP co-exposures, which may result in reduced KC-phagocytosis and unbalanced cytokine patterns. Compared to the treatment with LPS only, LPS/APAP co-exposures induced the production of interleukin (IL)-8, a pro-inflammatory cytokine, but suppressed the secretion of IL-6, a cytokine regulating hepatic regeneration, along with the increase in APAP dosages. In addition to the disrupted mitochondrial functions, the presence of LPS exacerbated APAP toxicity. These findings suggest that 3D Microtissues are a suitable model for the mechanistic exploration of inflammation-associated drug toxicity.

BACKGROUND:The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem.RESULTS:Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database.CONCLUSIONS:DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.