Inter-species Transcriptomic Analysis Reveals a Constitutive Adaptation Against Oxidative Stress for the Highly Virulent Leptospira Species.

Transcriptomic analyses across large scales of evolutionary distance have great potential to shed light on regulatory evolution but are complicated by difficulties in establishing orthology and limited availability of accessible software. We introduce here a method and a graphical user interface wrapper, called Annotator-RNAtor, for performing interspecies transcriptomic analysis and studying intragenus evolution. The pipeline uses third-party software to infer homologous genes in various species and highlight differences in the expression of the core-genes. To illustrate the methodology and demonstrate its usefulness, we focus on the emergence of the highly virulent Leptospira subclade known as P1+, which includes the causative agents of leptospirosis. Here, we expand on the genomic study through the comparison of transcriptomes between species from P1+ and their related P1- counterparts (low-virulent pathogens). In doing so, we shed light on differentially expressed pathways and focused on describing a specific example of adaptation based on a differential expression of PerRA-controlled genes. We showed that P1+ species exhibit higher expression of the katE gene, a well-known virulence determinant in pathogenic Leptospira species correlated with greater tolerance to peroxide. Switching PerRA alleles between P1+ and P1- species demonstrated that the lower repression of katE and greater tolerance to peroxide in P1+ species was solely controlled by PerRA and partly caused by a PerRA amino-acid permutation. Overall, these results demonstrate the strategic fit of the methodology and its ability to decipher adaptive transcriptomic changes, not observable by comparative genome analysis, that may have been implicated in the emergence of these pathogens.

Whole genome sequencing and de novo assembly of three virulent Indian isolates of Leptospira.

Leptospirosis is a re-emerging bacterial zoonosis caused by pathogenic Leptospira, with a worldwide distribution and becoming a major public health concern. Prophylaxis of this disease is difficult due to several factors such as non-specific variable clinical manifestation, presence of a large number of serovar, species and asymptomatic reservoir hosts, lack of proper diagnostics and vaccines. Despite its global importance and severity of the disease, knowledge about the molecular mechanism of pathogenesis and evolution of pathogenic species of Leptospira remains limited. In this study, we sequenced and analyzed three highly pathogenic species of Indian isolates of Leptospira (interrogans, santarosai, and kirschneri). Additionally, we identified some virulence-related and CRISPR-Cas genes. The virulent analysis showed 232 potential virulence factors encoding proteins in L. interrogans strain Salinem and L. santarosai strain M-4 genome. While the genome of L. kirschneri strain Wumalasena was predicted to encode 198 virulence factor proteins. The variant calling analysis revealed 1151, 19,786, and 22,996 single nucleotide polymorphisms (SNPs) for L. interrogans strain Salinem, L. kirschneri strain Wumalasena and L. santarosai strain M-4, respectively, with a maximum of 5315 missense and 12,221 synonymous mutations for L. santarosai strain M-4. The structural analyses of genomes indicated potential evidence of inversions and structural rearrangment in all three genomes. The availability of these genome sequences and in silico analysis of Leptospira will provide a basis for a deeper understanding of their molecular diversity and pathogenesis mechanism, and further pave a way towards proper management of the disease.

Screening of mungbean for drought tolerance and transcriptome profiling between drought-tolerant and susceptible genotype in response to drought stress.

Mungbean, is a widely cultivated pulse crop in India, experiences severe drought stress during the cultivation period. The mechanism of drought tolerance in mungbean is not well understood. In this presents study we screened 7 widely cultivated mungbean genotypes towards their drought sensitivity at seedling stage and transcriptome sequencing of drought-tolerant and susceptible genotype to understand the drought tolerance mechanism. Our physiological data such as increase in root length, shoot length, fresh weight, dry weight, relative water content (RWC), proline content, MDA content and molecular data in terms of quantitative expression of drought stress responsive genes under 3-d drought stress in mungbean suggests that, K851 seems to be most drought tolerant and PDM-139 as drought susceptible genotype. The transcriptomic study between K-851 and PDM-139 revealed 22,882 differentially expressed genes (DEGs) which were identified under drought stress, and they were mainly mapped to phytohormone signal transduction, carbon metabolism and flavonoid biosynthesis. Out of these, 10,235 genes were up-regulated and 12,647 genes were down-regulated. Furthermore, we found that, the DEGs related to, phytohormone signal transduction, carbon metabolism and flavonoid biosynthesis and they were more induced in K-851. Our data suggested that, the drought tolerant genotype K-851, scavenges the damage of drought stress by producing more amount of osmolytes, ROS scavengers and sugar biosynthesis.

miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification.

Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.

Differential gene expression analysis of HNSCC tumors deciphered tobacco dependent and independent molecular signatures.

Head and neck cancer is the sixth most common cancer worldwide, with tobacco as the leading cause. However, it is increasing in non-tobacco users also, hence limiting our understanding of its underlying molecular mechanisms. RNA-seq analysis of cancers has proven as effective tool in understanding disease etiology. In the present study, RNA-Seq of 86 matched Tumor/Normal pairs, of tobacco smoking (TOB) and non-smokers (N-TOB) HNSCC samples analyzed, followed by validation on 375 similar datasets. Total 2194 and 2073 differentially expressed genes were identified in TOB and N-TOB tumors, respectively. GO analysis found muscle contraction as the most enriched biological process in both TOB and N-TOB tumors. Pathway analysis identified muscle contraction and salivary secretion pathways enriched in both categories, whereas calcium signaling and neuroactive ligand-receptor pathway was more enriched in TOB and N-TOB tumors respectively. Network analysis identified muscle development related genes as hub node i. e. ACTN2, MYL2 and TTN in both TOB and N-TOB tumors, whereas EGFR and MYH6, depicts specific role in TOB and N-TOB tumors. Additionally, we found enriched gene networks possibly be regulated by tumor suppressor miRNAs such as hsa-miR-29/a/b/c, hsa-miR-26b-5p etc., suggestive to be key riboswitches in regulatory cascade of HNSCC. Interestingly, three genes PKLR, CST1 and C17orf77 found to show opposite regulation in each category, hence suggested to be key genes in separating TOB from N-TOB tumors. Our investigation identified key genes involved in important pathways implicated in tobacco dependent and independent carcinogenesis hence may help in designing precise HNSCC diagnostics and therapeutics strategies.

De novo transcriptome of Gymnema sylvestre identified putative lncRNA and genes regulating terpenoid biosynthesis pathway.

Gymnema sylvestre is a highly valuable medicinal plant in traditional Indian system of medicine and used in many polyherbal formulations especially in treating diabetes. However, the lack of genomic resources has impeded its research at molecular level. The present study investigated functional gene profile of G. sylvestre via RNA sequencing technology. The de novo assembly of 88.9 million high quality reads yielded 23,126 unigenes, of which 18116 were annotated against databases such as NCBI nr database, gene ontology (GO), KEGG, Pfam, CDD, PlantTFcat, UniProt & GreeNC. Total 808 unigenes mapped to 78 different Transcription Factor families, whereas 39 unigenes assigned to CYP450 and 111 unigenes coding for enzymes involved in the biosynthesis of terpenoids including transcripts for synthesis of important compounds like Vitamin E, beta-amyrin and squalene. Among them, presence of six important enzyme coding transcripts were validated using qRT-PCR, which showed high expression of enzymes involved in methyl-erythritol phosphate (MEP) pathway. This study also revealed 1428 simple sequence repeats (SSRs), which may aid in molecular breeding studies. Besides this, 8 putative long non-coding RNAs (lncRNAs) were predicted from un-annotated sequences, which may hold key role in regulation of essential biological processes in G. sylvestre. The study provides an opportunity for future functional genomic studies and to uncover functions of the lncRNAs in G. sylvestre.

Cross-Kingdom Regulation of Putative miRNAs Derived from Happy Tree in Cancer Pathway: A Systems Biology Approach.

MicroRNAs (miRNAs) are well-known key regulators of gene expression primarily at the post-transcriptional level. Plant-derived miRNAs may pass through the gastrointestinal tract, entering into the body fluid and regulate the expression of endogenous mRNAs. Camptotheca acuminata, a highly important medicinal plant known for its anti-cancer potential was selected to investigate cross-kingdom regulatory mechanism and involvement of miRNAs derived from this plant in cancer-associated pathways through in silico systems biology approach. In this study, total 33 highly stable putative novel miRNAs were predicted from the publically available 53,294 ESTs of C. acuminata, out of which 14 miRNAs were found to be regulating 152 target genes in human. Functional enrichment, gene-disease associations and network analysis of these target genes were carried out and the results revealed their association with prominent types of cancers like breast cancer, leukemia and lung cancer. Pathways like focal adhesion, regulation of lipolysis in adipocytes and mTOR signaling pathways were found significantly associated with the target genes. The regulatory network analysis showed the association of some important hub proteins like GSK3B, NUMB, PEG3, ITGA2 and DLG2 with cancer-associated pathways. Based on the analysis results, it can be suggested that the ingestion of the C. acuminata miRNAs may have a functional impact on tumorigenesis in a cross-kingdom way and may affect the physiological condition at genetic level. Thus, the predicted miRNAs seem to hold potentially significant role in cancer pathway regulation and therefore, may be further validated using in vivo experiments for a better insight into their mechanism of epigenetic action of miRNA.

A snapshot of microbial communities from the Kutch: one of the largest salt deserts in the World.

Here we present the first report on the taxonomic diversity of the microbial communities of the saline desert of the Great Rann of Kutch, Gujarat, India, using a metagenomic approach. Seven samples, differing in salinity levels and covering different seasons, were analysed to investigate the dynamics of microbial communities in relation to salinity and season. Metagenomic data generated using whole metagenome sequencing revealed that despite its very high salinity (4.11-30.79 %), the saline desert's microbiota had a rich microbial diversity that included all major phyla. Notably, 67 archaeal genera, representing more than 60 % of all known archaeal genera, were present in this ecosystem. A strong positive correlation (0.85) was observed between the presence of the extremely halophilic bacterium Salinibacter and salinity level. Taxonomic and functional comparisons of the saline desert metagenome with those of other publicly available metagenomes (i.e. sea, hypersaline lagoon, solar saltern, brine, hot desert) was carried out. The microbial community of the Kutch was found to be unique yet more similar to the sea biomes followed by hypersaline lagoon.