ETENLNC: An end to end lncRNA identification and analysis framework to facilitate construction of known and novel lncRNA regulatory networks.

Long non-coding RNAs (lncRNAs) play crucial roles in the regulation of gene expression and maintenance of genomic integrity through various interactions with DNA, RNA, and proteins. The availability of large-scale sequence data from various high-throughput platforms has opened possibilities to identify, predict, and functionally annotate lncRNAs. As a result, there is a growing demand for an integrative computational framework capable of identifying known lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding), a user-friendly, integrative, open-source, scalable, and modular computational framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC employs six stringent filtration steps to identify novel lncRNAs, performs differential expression analysis of mRNA and lncRNA transcripts, and predicts regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked ETENLNC against six existing tools and optimized it for desktop workstations and high-performance computing environments using data from three different species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.

Integrative ceRNA network analysis identifies unique and shared molecular signatures in Bipolar Disorder and Schizophrenia.

Bipolar Disorder (BPD) and Schizophrenia (SCZ) are complex psychiatric disorders with shared symptomatology and genetic risk factors. Understanding the molecular mechanisms underlying these disorders is crucial for refining diagnostic criteria and guiding targeted treatments. In this study, publicly available RNA-seq data from post-mortem samples of the basal ganglia's striatum were analyzed using an integrative computational approach to identify differentially expressed (DE) transcripts associated with SCZ and BPD. The analysis aimed to reveal both shared and distinct genes and long non-coding RNAs (lncRNAs) and to construct competitive endogenous RNA (ceRNA) networks within the striatum. Furthermore, the functional implications of these identified transcripts are explored, alongside their presence in established databases such as BipEx and SCHEMA. A significant outcome of our analysis was the identification of 21 DEmRNAs and 1 DElncRNA shared between BPD and SCZ across the Caudate, Putamen, and Nucleus Accumbens. Another noteworthy finding was the identification of Hub nodes within the ceRNA networks that were linked to major psychosis. Particularly, MED19, HNRNPC, MAGED4B, KDM5A, GOLGA7, CHASERR, hsa-miR-4778-3p, hsa-miR-4739, and hsa-miR-4685-5p emerged as potential biomarkers. These findings shed light on the common and unique molecular signatures underlying BPD and SCZ, offering significant potential for the advancement of diagnostic and therapeutic strategies tailored to these psychiatric disorders.

Integrative network-based approaches identified systems-level molecular signatures associated with gallbladder cancer pathogenesis from gallstone diseases.

Gallbladder cancer (GBC) is one of the most fatal malignancies of the biliary tract system and is ranked sixth among the neoplasms of the gastrointestinal tract. Gallstone disease (GSD) is considered the major risk factor for GBC. However, the underlying molecular mechanism of GBC pathogenesis from different stages of GSD is not yet clearly understood. We analyzed transcriptomic datasets of GBC with reference to GSD of three different follow-up periods, i.e.,GBC vs. GSD3 (1-3 years), GBCvs. GSD5 (5-10 years), andGBC vs. GSD10 (more than 10 years). We identified overlapping and specific molecular signatures in GBC compared with GSD at three different follow-up periods. Using integrative network biology approaches, such as protein-protein interaction network analysis, transcriptional regulatory network analysis, and miRNA-target gene network analysis, we have identified a few hub genes. The hub genes identified from GBC vs. GSD3, GBC vs. GSD5, and GBC vs. GSD10 were directly or indirectly associated with cancer progression and initiation from GSD. Functional enrichment analysis indicated significant correlation between GSD and GBC pathogenesis. The identified hub genes can be used for future targeted validation to develop potential diagnostic, prognostic, or therapeutic biomarkers in GBC.

LncRNAs in neuropsychiatric disorders and computational insights for their prediction.

Long non-coding RNAs (lncRNAs) are 200 nucleotide extended transcripts that do not encode proteins or possess limited coding ability. LncRNAs epigenetically control several biological functions such as gene regulation, transcription, mRNA splicing, protein interaction, and genomic imprinting. Over the years, drastic progress in understanding the role of lncRNAs in diverse biological processes has been made. LncRNAs are reported to show tissue-specific expression patterns suggesting their potential as novel candidate biomarkers for diseases. Among all other non-coding RNAs, lncRNAs are highly expressed within the brain-enriched or brain-specific regions of the neural tissues. They are abundantly expressed in the neocortex and pre-mature frontal regions of the brain. LncRNAs are co-expressed with the protein-coding genes and have a significant role in the evolution of functions of the brain. Any deregulation in the lncRNAs contributes to disruptions in normal brain functions resulting in multiple neurological disorders. Neuropsychiatric disorders such as schizophrenia, bipolar disease, autism spectrum disorders, and anxiety are associated with the abnormal expression and regulation of lncRNAs. This review aims to highlight the understanding of lncRNAs concerning normal brain functions and their deregulation associated with neuropsychiatric disorders. We have also provided a survey on the available computational tools for the prediction of lncRNAs, their protein coding potentials, and sub-cellular locations, along with a section on existing online databases with known lncRNAs, and their interactions with other molecules.

"KRiShI": a manually curated knowledgebase on rice sheath blight disease.

Knowledgebase for rice sheath blight information (KRiShI) is a manually curated user-friendly knowledgebase for rice sheath blight (SB) disease that allows users to efficiently mine, visualize, search, benchmark, download, and update meaningful data and information related to SB using its easy and interactive interface. KRiShI collects and integrates widely scattered and unstructured information from various scientific literatures, stores it under a single window, and makes it available to the community in a user-friendly manner. From basic information, best management practices, host resistance, differentially expressed genes, proteins, metabolites, resistance genes, pathways, and OMICS scale experiments, KRiShI presents these in the form of easy and comprehensive tables, diagrams, and pictures. The "Search" tab allows users to verify if their input rice gene id(s) are Rhizoctonia solani (R. solani) responsive and/or resistant. KRiShI will serve as a valuable resource for easy and quick access to data and information related to rice SB disease for both the researchers and the farmers. To encourage community curation a submission facility is made available. KRiShI can be found at http://www.tezu.ernet.in/krishi .

Identifying Signal-Crosstalk Mechanism in Maize Plants during Combined Salinity and Boron Stress Using Integrative Systems Biology Approaches.

Combined stress has been seen as a major threat to world agriculture production. Maize is one of the leading cereal crops of the world due to its wide spectrum of growth conditions and is moderately sensitive to salt stress. A saline soil environment is a major factor that hinders its growth and overall yield and causes an increase in the concentration of micronutrients like boron, leading to excess over the requirement of the plant. Boron toxicity combined with salinity has been reported to be a serious threat to the yield and quality of maize. The response signatures of the maize plants to the combined effect of salinity and boron stress have not been studied well. We carried out an integrative systems-level analysis of the publicly available transcriptomic data generated on tolerant maize (Lluteño maize from the Atacama Desert, Chile) landrace under combined salt and boron stress. We identified significant biological processes that are differentially regulated in combined salt and boron stress in the leaves and roots of maize, respectively. Protein-protein interaction network analysis identified important roles of aldehyde dehydrogenase (ALDH), galactinol synthase 2 (GOLS2) proteins of leaf and proteolipid membrane potential regulator (pmpm4), metallothionein lea protein group 3 (mlg3), and cold regulated 410 (COR410) proteins of root in salt tolerance and regulating boron toxicity in maize. Identification of transcription factors coupled with regulatory network analysis using machine learning approach identified a few heat shock factors (HSFs) and NAC (NAM (no apical meristem, Petunia), ATAF1-2 (Arabidopsis thaliana activating factor), and CUC2 (cup-shaped cotyledon, Arabidopsis)) family transcription factors (TFs) to play crucial roles in salt tolerance, maintaining reactive oxygen species (ROS) levels and minimizing oxidative damage to the cells. These findings will provide new ways to design targeted functional validation experiments for developing multistress-resistant maize crops.

Time-course transcriptome analysis identifies rewiring patterns of transcriptional regulatory networks in rice under Rhizoctonia solani infection.

Sheath Blight (SB) disease in rice is caused by the infection from the fungal pathogen Rhizoctonia solani (R. solani). SB is one of the most severe rice diseases that can cause up to 50% yield losses in rice. Naturally occurring rice varieties resistant to SB have not been reported yet. We have performed a Time-Series RNA-Seq analysis on a widely cultivated rice variety BPT-5204 for identifying transcriptome level response signatures during R. solani infection at 1st, 2nd and 5th day post infection (dpi). In total, 428, 3225 and 1225 genes were differentially expressed in the treated rice plants on 1, 2 and 5 dpi, respectively. GO and KEGG enrichment analysis identified significant processes and pathways differentially altered in the rice plants during the fungal infection. Machine learning and network based integrative approach was used to construct rice Transcriptional Regulatory Networks (TRNs) for the three time points. TRN analysis identified SUB1B, MYB30 and CCA1 as important regulatory hub transcription factors in rice during R. solani infection. Jasmonic acid, salicylic acid, ethylene biogenesis and signaling were induced on infection. SAR was up regulated, while photosynthesis and carbon fixation processes were significantly down regulated. Involvement of MAPK, CYPs, peroxidase, PAL, chitinase genes were also observed in response to the fungal infection. The integrative analysis identified seven putative SB resistance genes differentially regulated in rice during R. solani infection.

Transcriptomic data of MCF-7 breast cancer cells treated with G1, a G-protein coupled estrogen receptor (GPER) agonist.

Besides short-term non-genomic effects, the G-protein coupled estrogen receptor (GPER) also mediates long-term genomic effects of estrogen. The genomic effects of GPER activation are not completely understood. G1 is a selective GPER agonist, which is popularly used for addressing the effects of GPER activation. Here, we present transcriptomic (RNA-seq) data on MCF-7 cells treated with 100 nM, or 1 µM G1 for a period of 48 h. The data are available from GEO (accession number GSE188706).

SNMRS: An advanced measure for Co-expression network analysis.

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

An Integrative Systems Biology Approach Identifies Molecular Signatures Associated with Gallbladder Cancer Pathogenesis.

Gallbladder cancer (GBC) has a lower incidence rate among the population relative to other cancer types but is a major contributor to the total number of biliary tract system cancer cases. GBC is distinguished from other malignancies by its high mortality, marked geographical variation and poor prognosis. To date no systemic targeted therapy is available for GBC. The main objective of this study is to determine the molecular signatures correlated with GBC development using integrative systems level approaches. We performed analysis of publicly available transcriptomic data to identify differentially regulated genes and pathways. Differential co-expression network analysis and transcriptional regulatory network analysis was performed to identify hub genes and hub transcription factors (TFs) associated with GBC pathogenesis and progression. Subsequently, we assessed the epithelial-mesenchymal transition (EMT) status of the hub genes using a combination of three scoring methods. The identified hub genes including, CDC6, MAPK15, CCNB2, BIRC7, L3MBTL1 were found to be regulators of cell cycle components which suggested their potential role in GBC pathogenesis and progression.