Genome-wide identification of novel intergenic enhancer-like elements: implications in the regulation of transcription in Plasmodium falciparum.

BACKGROUND: The molecular mechanisms of transcriptional regulation are poorly understood in Plasmodium falciparum. In addition, most of the genes in Plasmodium falciparum are transcriptionally poised and only a handful of cis-regulatory elements are known to operate in transcriptional regulation. Here, we employed an epigenetic signature based approach to identify significance of previously uncharacterised intergenic regions enriched with histone modification marks leading to discovery of enhancer-like elements. RESULTS: We found that enhancer-like elements are significantly enriched with H3K4me1, generate unique non-coding bi-directional RNAs and majority of them can function as cis-regulators. Furthermore, functional enhancer reporter assay demonstrates that the enhancer-like elements regulate transcription of target genes in Plasmodium falciparum. Our study also suggests that the Plasmodium genome segregates functionally related genes into discrete housekeeping and pathogenicity/virulence clusters, presumably for robust transcriptional control of virulence/pathogenicity genes. CONCLUSIONS: This report contributes to the understanding of parasite regulatory genomics by identification of enhancer-like elements, defining their epigenetic and transcriptional features and provides a resource of functional cis-regulatory elements that may give insights into the virulence/pathogenicity of Plasmodium falciparum.

Multi-omics Data Integration, Interpretation, and Its Application.

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Loss of Hepatic Oscillatory Fed microRNAs Abrogates Refed Transition and Causes Liver Dysfunctions.

Inability to mediate fed-fast transitions in the liver is known to cause metabolic dysfunctions and diseases. Intuitively, a failure to inhibit futile translation of state-specific transcripts during fed-fast cycles would abrogate dynamic physiological transitions. Here, we have discovered hepatic fed microRNAs that target fasting-induced genes and are essential for a refed transition. Our findings highlight the role of these fed microRNAs in orchestrating system-level control over liver physiology and whole-body energetics. By targeting SIRT1, PGC1α, and their downstream genes, fed microRNAs regulate metabolic and mitochondrial pathways. MicroRNA expression, processing, and RISC loading oscillate during these cycles and possibly constitute an anticipatory mechanism. Fed-microRNA oscillations are deregulated during aging. Scavenging of hepatic fed microRNAs causes uncontrolled gluconeogenesis and failure in the catabolic-to-anabolic switching upon feeding, which are hallmarks of metabolic diseases. Besides identifying mechanisms that enable efficient physiological toggling, our study highlights fed microRNAs as candidate therapeutic targets.

Profiling conserved biological pathways in Autosomal Dominant Polycystic Kidney Disorder (ADPKD) to elucidate key transcriptomic alterations regulating cystogenesis: A cross-species meta-analysis approach.

PURPOSE: Initiation and progression of fluid filled cysts mark Autosomal Dominant Polycystic Kidney Disease (ADPKD). Thus, improved therapeutics targeting cystogenesis remains a constant challenge. Microarray studies in single ADPKD animal models species with limited sample sizes tend to provide scattered views on underlying ADPKD pathogenesis. Thus we aim to perform a cross species meta-analysis to profile conserved biological pathways that might be key targets for therapy. METHODS: Nine ADPKD microarray datasets on rat, mice and human fulfilled our study criteria and were chosen. Intra-species combined analysis was performed after considering removal of batch effect. Significantly enriched GO biological processes and KEGG pathways were computed and their overlap was observed. For the conserved pathways, biological modules and gene regulatory networks were observed. Additionally, Gene Set Enrichment Analysis (GSEA) using Molecular Signature Database (MSigDB) was performed for genes found in conserved pathways. RESULTS: We obtained 28 modules of significantly enriched GO processes and 5 major functional categories from significantly enriched KEGG pathways conserved in human, mice and rats that in turn suggest a global transcriptomic perturbation affecting cyst - formation, growth and progression. Significantly enriched pathways obtained from up-regulated genes such as Genomic instability, Protein localization in ER and Insulin Resistance were found to regulate cyst formation and growth whereas cyst progression due to increased cell adhesion and inflammation was suggested by perturbations in Angiogenesis, TGF-beta, CAMs, and Infection related pathways. Additionally, networks revealed shared genes among pathways e.g. SMAD2 and SMAD7 in Endocytosis and TGF-beta. CONCLUSION: Our study suggests cyst formation and progression to be an outcome of interplay between a set of several key deregulated pathways. Thus, further translational research is warranted focusing on developing a combinatorial therapeutic approach for ADPKD redressal.

Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.