Genome-wide analysis suggests the potential role of lncRNAs during seed development and seed size/weight determination in chickpea.

MAIN CONCLUSION: The integrated transcriptome data analyses suggested the plausible roles of lncRNAs during seed development in chickpea. The candidate lncRNAs associated with QTLs and those involved in miRNA-mediated seed size/weight determination in chickpea have been identified. Long non-coding RNAs (lncRNAs) are important regulators of various biological processes. Here, we identified lncRNAs at seven successive stages of seed development in small-seeded and large-seeded chickpea cultivars. In total, 4751 lncRNAs implicated in diverse biological processes were identified. Most of lncRNAs were conserved between the two cultivars, whereas only a few of them were conserved in other plants, suggesting their species-specificity. A large number of lncRNAs differentially expressed between the two chickpea cultivars associated with seed development-related processes were identified. The lncRNAs acting as precursors of miRNAs and those mimicking target protein-coding genes of miRNAs involved in seed size/weight determination, including HAIKU1, BIG SEEDS1, and SHB1, were also revealed. Further, lncRNAs located within seed size/weight associated quantitative trait loci were also detected. Overall, we present a comprehensive resource and identified candidate lncRNAs that may play important roles during seed development and seed size/weight determination in chickpea.

PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea.

Long non-coding RNAs (lncRNAs) make up a significant portion of non-coding RNAs and are involved in a variety of biological processes. Accurate identification/annotation of lncRNAs is the primary step for gaining deeper insights into their functions. In this study, we report a novel tool, PLncPRO, for prediction of lncRNAs in plants using transcriptome data. PLncPRO is based on machine learning and uses random forest algorithm to classify coding and long non-coding transcripts. PLncPRO has better prediction accuracy as compared to other existing tools and is particularly well-suited for plants. We developed consensus models for dicots and monocots to facilitate prediction of lncRNAs in non-model/orphan plants. The performance of PLncPRO was quite better with vertebrate transcriptome data as well. Using PLncPRO, we discovered 3714 and 3457 high-confidence lncRNAs in rice and chickpea, respectively, under drought or salinity stress conditions. We investigated different characteristics and differential expression under drought/salinity stress conditions, and validated lncRNAs via RT-qPCR. Overall, we developed a new tool for the prediction of lncRNAs in plants and showed its utility via identification of lncRNAs in rice and chickpea.

Integrative network analysis of miRNA-mRNA expression profiles during epileptogenesis in rats reveals therapeutic targets after emergence of first spontaneous seizure.

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) - short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

Integrated analysis of transcriptomic and proteomic alterations in mouse models of ALS/FTD identify early metabolic adaptions with similarities to mitochondrial dysfunction disorders.

OBJECTIVE: Sporadic and familial amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease that results in loss of motor neurons and, in some patients, associates with frontotemporal dementia (FTD). Apart from the accumulation of proteinaceous deposits, emerging literature indicates that aberrant mitochondrial bioenergetics may contribute to the onset and progression of ALS/FTD. Here we sought to investigate the pathophysiological signatures of mitochondrial dysfunction associated with ALS/FTD. METHODS: By means of label-free mass spectrometry (MS) and mRNA sequencing (mRNA-seq), we report pre-symptomatic changes in the cortices of TDP-43 and FUS mutant mouse models. Using tissues from transgenic mouse models of mitochondrial diseases as a reference, we performed comparative analyses and extracted unique and common mitochondrial signatures that revealed neuroprotective compensatory mechanisms in response to early damage. RESULTS: In this regard, upregulation of both Acyl-CoA Synthetase Long-Chain Family Member 3 (ACSL3) and mitochondrial tyrosyl-tRNA synthetase 2 (YARS2) were the most representative change in pre-symptomatic ALS/FTD tissues, suggesting that fatty acid beta-oxidation and mitochondrial protein translation are mechanisms of adaptation in response to ALS/FTD pathology. CONCLUSIONS: Together, our unbiased integrative analyses unveil novel molecular components that may influence mitochondrial homeostasis in the earliest phase of ALS.

Genome-wide profiling of miRNAs during seed development reveals their functional relevance in seed size/weight determination in chickpea.

MicroRNAs (miRNAs) are non-coding small RNAs that regulate gene expression at transcriptional and post-transcriptional levels. The role of miRNAs in seed development and seed size/weight determination is poorly understood in legumes. In this study, we profiled miRNAs at seven successive stages of seed development in a small-seeded and a large-seeded chickpea cultivar via small RNA sequencing. In total, 113 known and 243 novel miRNAs were identified. Gene ontology analysis revealed the enrichment of seed/reproductive/post-embryonic development and signaling pathways processes among the miRNA target genes. A large fraction of the target genes exhibited antagonistic correlation with miRNA expression. The sets of co-expressed miRNAs showing differential expression between the two cultivars were recognized. Known transcription factor (TF) encoding genes involved in seed size/weight determination, including SPL, GRF, MYB, ARF, HAIKU1, SHB1, KLUH/CYP78A5, and E2Fb along with novel genes were found to be targeted by the predicted miRNAs. Differential expression analysis revealed higher transcript levels of members of SPL and REVOLUTA TF families and lower expression of their corresponding miRNAs in the large-seeded cultivar. At least 19 miRNAs known to be involved in seed development or differentially expressed between small-seeded and large-seeded cultivars at late-embryogenesis and/or mid-maturation stages were located within known quantitative trait loci (QTLs) associated with seed size/weight determination. Moreover, 41 target genes of these miRNAs were also located within these QTLs. Altogether, we revealed important roles of miRNAs in seed development and identified candidate miRNAs and their target genes that have functional relevance in determining seed size/weight in chickpea.

Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer.

PURPOSE: Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers. EXPERIMENTAL DESIGN: Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment. RESULTS: Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance. CONCLUSIONS: Subtype classification systems represent canonical "termini a quo" (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors.

Machine Learning-Based Annotation of Long Noncoding RNAs Using PLncPRO.

Long noncoding RNAs (lncRNAs) are noncoding RNAs with transcript length more than 200 nucleotides. Although poorly conserved, lncRNAs are expressed across diverse species, including plants and animals, and are known to be involved in regulation of various biological processes. To understand their biological significance, we first need to identify the lncRNAs accurately. However, distinguishing lncRNAs from coding transcripts is still a challenging task. Here, we describe a machine learning-based approach to accurately identify the plant lncRNAs. We describe the usage of plant long noncoding RNA prediction by random forests (PLncPRO), which employs machine learning-based random forest algorithm to recognize the lncRNAs from the set of given transcript sequences. Stepwise instructions have been provided to use PLncPRO to annotate the lncRNA sequences.

DNA methylation reprogramming during seed development and its functional relevance in seed size/weight determination in chickpea.

Seed development is orchestrated via complex gene regulatory networks and pathways. Epigenetic factors may also govern seed development and seed size/weight. Here, we analyzed DNA methylation in a large-seeded chickpea cultivar (JGK 3) during seed development stages. Progressive gain of CHH context DNA methylation in transposable elements (TEs) and higher frequency of small RNAs in hypermethylated TEs during seed development suggested a role of the RNA-dependent DNA methylation pathway. Frequency of intragenic TEs was higher in CHH context differentially methylated region (DMR) associated differentially expressed genes (DEGs). CG context hyper/hypomethylation within the gene body was observed for most of DMR-associated DEGs in JGK 3 as compared to small-seeded chickpea cultivar (Himchana 1). We identified candidate genes involved in seed size/weight determination exhibiting CG context hypermethylation within the gene body and higher expression in JGK 3. This study provides insights into the role of DNA methylation in seed development and seed size/weight determination in chickpea.

Updates on Genomic Resources in Chickpea for Crop Improvement.

In recent years, rapid advancement has been done in generation of genomic resources for the important legume crop chickpea. Here, we provide an update on important advancements made on availability of genomic resources for this crop. The availability of reference genome and transcriptome sequences, and resequencing of several accessions have enabled the discovery of gene space and molecular markers in chickpea. These resources have helped in elucidating evolutionary relationship and identification of quantitative trait loci for important agronomic traits. Gene expression in different tissues/organs during development and under abiotic/biotic stresses has been interrogated. In addition, single-base resolution DNA methylation patterns in different organs have been analyzed to understand gene regulation. Overall, we provide a consolidated overview of available genomic resources of chickpea that may help in fulfilling the promises for improvement of this important crop.

Genome-wide bisulphite-sequencing reveals organ-specific methylation patterns in chickpea.

DNA methylation is widely known to regulate gene expression in eukaryotes. Here, we unraveled DNA methylation patterns in cultivated chickpea to understand the regulation of gene expression in different organs. We analyzed the methylation pattern in leaf tissue of wild chickpea too, and compared it with cultivated chickpea. Our analysis indicated abundant CG methylation within gene-body and CHH methylation in intergenic regions of the chickpea genome in all the organs examined. Analysis of differentially methylated regions (DMRs) demonstrated a higher number of CG context DMRs in wild chickpea and CHH context DMRs in cultivated chickpea. We observed increased preponderance of hypermethylated DMRs in the promoter regions and hypomethylated DMRs in the genic regions in cultivated chickpea. Genomic location and context of the DMRs correlated well with expression of proximal genes. Our results put forth a positive correlation of promoter hypermethylation with increased transcript abundance via identification of DMR-associated genes involved in flower development in cultivated chickpea. The atypical correlation observed between promoter hypermethylation and increased transcript abundance might be dependent on 24-nt small RNAs and transcription factors binding to the promoter region. This study provides novel insights into DNA methylation patterns in chickpea and their role in regulation of gene expression.

Genome-wide analysis of long intergenic non-coding RNAs in chickpea and their potential role in flower development.

Non-coding RNAs constitute a major portion of the transcriptome in most of eukaryotes. Long non-coding transcripts originating from the DNA segment present between the protein coding genes are termed as long intergenic non-coding RNAs (lincRNAs). Several evidences suggest the role of lincRNAs in regulation of various biological processes. In this study, we identified a total of 2248 lincRNAs in chickpea using RNA-seq data from eight successive stages of flower development and three vegetative tissues via an optimized pipeline. Different characteristic features of lincRNAs were studied and compared with those of predicted mRNAs in chickpea. Further, we utilized a method using network propagation algorithm to reveal the putative function of lincRNAs in plants. In total, at least 79% of the identified chickpea lincRNAs were assigned with a putative function. A comprehensive expression profiling revealed differential expression patterns and tissue specificity of lincRNAs in different stages of flower development in chickpea. In addition, potential lincRNAs-miRNA interactions were explored for the predicted lincRNAs in chickpea. These findings will pave the way for understanding the role of lincRNAs in the regulatory mechanism underlying flower development in chickpea and other legumes.