Circular RNA circPLOD2 regulates pericyte function by targeting the transcription factor KLF4.

Circular RNAs are generated by backsplicing and control cellular signaling and phenotypes. Pericytes stabilize capillary structures and play important roles in the formation and maintenance of blood vessels. Here, we characterize hypoxia-regulated circular RNAs (circRNAs) in human pericytes and show that the circular RNA of procollagen-lysine,2-oxoglutarate 5-dioxygenase-2 (circPLOD2) is induced by hypoxia and regulates pericyte functions. Silencing of circPLOD2 affects pericytes and increases proliferation, migration, and secretion of soluble angiogenic proteins, thereby enhancing endothelial migration and network capability. Transcriptional and epigenomic profiling of circPLOD2-depleted cells reveals widespread changes in gene expression and identifies the transcription factor krüppel-like factor 4 (KLF4) as a key effector of the circPLOD2-mediated changes. KLF4 depletion mimics circPLOD2 silencing, whereas KLF4 overexpression reverses the effects of circPLOD2 depletion on proliferation and endothelial-pericyte interactions. Together, these data reveal an important function of circPLOD2 in controlling pericyte proliferation and capillary formation and show that the circPLOD2-mediated regulation of KLF4 significantly contributes to the transcriptional response to hypoxia.

Alterations in the hepatocyte epigenetic landscape in steatosis.

Fatty liver disease or the accumulation of fat in the liver, has been reported to affect the global population. This comes with an increased risk for the development of fibrosis, cirrhosis, and hepatocellular carcinoma. Yet, little is known about the effects of a diet containing high fat and alcohol towards epigenetic aging, with respect to changes in transcriptional and epigenomic profiles. In this study, we took up a multi-omics approach and integrated gene expression, methylation signals, and chromatin signals to study the epigenomic effects of a high-fat and alcohol-containing diet on mouse hepatocytes. We identified four relevant gene network clusters that were associated with relevant pathways that promote steatosis. Using a machine learning approach, we predict specific transcription factors that might be responsible to modulate the functionally relevant clusters. Finally, we discover four additional CpG loci and validate aging-related differential CpG methylation. Differential CpG methylation linked to aging showed minimal overlap with altered methylation in steatosis.

A Supervised Ensemble Approach for Sensitive microRNA Target Prediction.

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

Elucidating the gene regulatory networks modulating cancer stem cells and non-stem cancer cells in high grade serous ovarian cancer.

The origin and pathogenesis of epithelial ovarian cancer have perplexed investigators for decades. The most prevalent type of it is the high-grade serous ovarian carcinoma (HGSOv) which is a highly aggressive disease with high relapse rates and insurgence of chemo-resistance at later stages of treatment. These are driven by a rare population of stem cell like cancer cells called cancer stem cells (CSCs). We have taken up a systems approach to find out the common gene interaction paths between non-CSC tumor cells (CCs) and CSCs in HGSOv. Detailed investigation reveals a set of 17 Transcription Factors (named as pivot-TFs) which can govern changes in the mode of gene regulation along these paths. Overall, this work highlights a divergent road map of functional information relayed by these common key players in the two cell states, which might aid towards designing novel therapeutic measures to target the CSCs for ovarian cancer therapy.

ParStream-seq: An improved method of handling next generation sequence data.

The exponential growth of next generation sequencing (NGS) data has put forward the challenge for its storage as well as its efficient and faster analysis. Storing the entire amount of data for a particular experiment and its alignment to the reference genome is an essential step for any quantitative analysis of NGS data. Here, we introduce streaming access technique 'ParStream-seq' that splits the bulk sequence data, accessed from a remote repository into short manageable packets followed by executing their alignment process in parallel in each of the compute core. The optimal packet size with fixed number of reads is determined in the stream that maximizes system utilization. Result shows a reduction in the execution time and improvement in the memory footprint. Overall, this streaming access technique provides means to overcome the hurdle of storing the entire volume of sequence data corresponding to a particular experiment, prior to its analysis.

Integrated miRNA and mRNA expression profiling reveals the response regulators of a susceptible tomato cultivar to early blight disease.

Early blight, caused by the fungus Alternaria solani, is a devastating foliar disease of tomatoes, causes massive yield loss each year worldwide. Molecular basis of the compatible host-pathogen interaction was elusive. We adopted next generation sequencing approach to decipher miRNAs and mRNAs that are differentially expressed during Alternaria-stress in tomato. Some of the interesting findings were also validated by alternative techniques. Our analysis revealed 181 known-miRNAs, belonging to 121 miRNA families, of which 67 miRNAs showed at least 2-fold change in expression level with the majority being downregulated. Concomitantly, 5,450 mRNAs were significantly regulated in the same diseased tissues. Differentially expressed genes were most significantly associated with response to stimulus process, photosynthesis, biosynthesis of secondary metabolites, plant-pathogen interaction and plant hormone signal transduction pathways. GO term enrichment-based categorization of gene-functions further supported this observation, as terms related to pathogen perception, disease signal transduction, cellular metabolic processes including oxidoreductase and kinase activity were over represented. In addition, we have discovered 102 miRNA-mRNA pairs which were regulated antagonistically, and careful study of the targeted mRNAs depicted that multiple transcription factors, nucleotide-binding site leucine-rich repeats, receptor-like proteins and enzymes related to cellular ROS management were profoundly affected. These studies have identified key regulators of Alternaria-stress response in tomato and the subset of genes that are likely to be post-transcriptionally silenced during the infection.

Deciphering the cross-talking of human competitive endogenous RNAs in K562 chronic myelogenous leukemia cell line.

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder characterized by increased proliferation or abnormal accumulation of granulocytic cell line without the depletion of their capacity to differentiate. A reciprocal chromosomal translocation proceeding to the 'Philadelphia chromosome', involving the ABL proto-oncogene and BCR gene residing on Chromosome 9 and 22 respectively, is observed to be attributed to CML pathogenesis. Recent studies have been unraveling the crucial role of genomic 'dark matter' or the non-coding repertoire in cancer initiation and progression. The intricate cross-talk between competitive endogenous RNAs (ceRNAs) provides a scaffold to systematically functionalize the miRNA response element harboring non-coding RNAs and incorporate them with the protein-coding RNA dimension in complex ceRNA networks. This network of coding and non-coding transcriptome linked by shared miRNAs evidently offers a platform to elucidate the complex regulatory interactions at the post-transcriptional level in human cancers. In this context, analyzing CML, from the perspective of the ceRNA hypothesis, surely craves intensive attention and a comprehensive discussion. Here, we performed RNA-seq data analysis to retrieve Lymphoblastoid and CML coding as well as non-coding repertoire and constructed a ceRNA network for the CML cell line, considering the non-cancer lymphoblastoid cell line as the control. We investigated if any alteration exists in the ceRNA landscape of the transcripts which are exhibiting differential expression across the two cell lines and observed that the major ceRNA regulators vary in cancer network when compared with the Lymphoblastoid network. The top ranked significant functional modules in the ceRNA network display cancer associated attributes and reveal putative regulators in CML pathogenesis.

MicroRNA let-7 modulates the immune response to Mycobacterium tuberculosis infection via control of A20, an inhibitor of the NF-κB pathway.

The outcome of the interaction between Mycobacterium tuberculosis (Mtb) and a macrophage depends on the interplay between host defense and bacterial immune subversion mechanisms. MicroRNAs critically regulate several host defense mechanisms, but their role in the Mtb-macrophage interplay remains unclear. MicroRNA profiling of Mtb-infected macrophages revealed the downregulation of miR-let-7f in a manner dependent on the Mtb secreted effector ESAT-6. We establish that let-7f targets A20, a feedback inhibitor of the NF-κB pathway. Expression of let-7f decreases and A20 increases with progression of Mtb infection in mice. Mtb survival is attenuated in A20-deficient macrophages, and the production of TNF, IL-1ß, and nitrite, which are mediators of immunity to Mtb, is correspondingly increased. Further, let-7f overexpression diminishes Mtb survival and augments the production of cytokines including TNF and IL-1ß. These results uncover a role for let-7f and its target A20 in regulating immune responses to Mtb and controlling bacterial burden.

LncRBase: an enriched resource for lncRNA information.

Long noncoding RNAs (lncRNAs) are noncoding transcripts longer than 200 nucleotides, which show evidence of pervasive transcription and participate in a plethora of cellular regulatory processes. Although several noncoding transcripts have been functionally annotated as lncRNAs within the genome, not all have been proven to fulfill the criteria for a functional regulator and further analyses have to be done in order to include them in a functional cohort. LncRNAs are being classified and reclassified in an ongoing annotation process, and the challenge is fraught with ambiguity, as newer evidences of their biogenesis and functional implication come into light. In our effort to understand the complexity of this still enigmatic biomolecule, we have developed a new database entitled "LncRBase" where we have classified and characterized lncRNAs in human and mouse. It is an extensive resource of human and mouse lncRNA transcripts belonging to fourteen distinct subtypes, with a total of 83,201 entries for mouse and 133,361 entries for human: among these, we have newly annotated 8,507 mouse and 14,813 human non coding RNA transcripts (from UCSC and H-InvDB 8.0) as lncRNAs. We have especially considered protein coding gene loci which act as hosts for non coding transcripts. LncRBase includes different lncRNA transcript variants of protein coding genes within LncRBase. LncRBase provides information about the genomic context of different lncRNA subtypes, their interaction with small non coding RNAs (ncRNAs) viz. piwi interacting RNAs (piRNAs) and microRNAs (miRNAs) and their mode of regulation, via association with diverse other genomic elements. Adequate knowledge about genomic origin and molecular features of lncRNAs is essential to understand their functional and behavioral complexities. Overall, LncRBase provides a thorough study on various aspects of lncRNA origin and function and a user-friendly interface to search for lncRNA information. LncRBase is available at http://bicresources.jcbose.ac.in/zhumur/lncrbase.

piRNAQuest: searching the piRNAome for silencers.

BACKGROUND: PIWI-interacting RNA (piRNA) is a novel and emerging class of small non-coding RNA (sncRNA). Ranging in length from 26-32 nucleotides, this sncRNA is a potent player in guiding the vital regulatory processes within a cellular system. Inspite of having such a wide role within cellular systems, piRNAs are not well organized and classified, so that a researcher can pool out the biologically relevant information concerning this class. DESCRIPTION: Here we present piRNAQuest- a unified and comprehensive database of 41749 human, 890078 mouse and 66758 rat piRNAs obtained from NCBI and different small RNA sequence experiments. This database provides piRNA annotation based on their localization in gene, intron, intergenic, CDS, 5/UTR, 3/UTR and repetitive regions which has not been done so far. We have also annotated piRNA clusters and have elucidated characteristic motifs within them. We have looked for the presence of piRNAs and piRNA clusters in pseudogenes, which are known to regulate the expression of protein coding transcripts by generating small RNAs. All these will help researchers progress towards solving the unanswered queries on piRNA biogenesis and their mode of action. Further, expression profile for piRNA in different tissues and from different developmental stages has been provided. In addition, we have provided several tools like 'homology search', 'dynamic cluster search' and 'pattern search'. Overall, piRNAQuest will serve as a useful resource for exploring human, mouse and rat piRNAome. The database is freely accessible and available at http://bicresources.jcbose.ac.in/zhumur/pirnaquest/. CONCLUSION: piRNAs play a remarkable role in stem cell self-renewal and various vital processes of developmental biology. Although researchers are mining different features on piRNAs, the exact regulatory mechanism is still fuzzy. Thus, understanding the true potential of these small regulatory molecules with respect to their origin, localization and mode of biogenesis is crucial. piRNAQuest will provide us with a better insight on piRNA origin and function which will help to explore the true potential of these sncRNAs.

PVT: an efficient computational procedure to speed up next-generation sequence analysis.

BACKGROUND: High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. RESULTS: We analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during 'spliced alignment' and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time. CONCLUSIONS: PVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to ~23% for the single end read dataset. Further, PVT designed for paired end reads showed an improved performance of ~41% over TopHat (for the chosen data) with respect to execution time. Moreover we propose PVT-Cloud which implements PVT pipeline in cloud computing system.