"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 .

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.

Temperature differentially modulates the transcriptome response in Oryza sativa to Xanthomonas oryzae pv. oryzae infection.

Bacterial blight is caused by the pathogen Xanthomonas oryzae pv. oryzae (Xoo). Genome scale integrative analysis on the interaction of high and low temperatures on the molecular response signature in rice during the Xoo infection has not been conducted yet. We have analysed a unique RNA-Seq dataset generated on the susceptible rice variety IR24 under combined exposure of Xoo with low 29/21 °C (day/night) and high 35/31 °C (day/night) temperatures. Differentially regulated key genes and pathways in rice plants during both the stress conditions were identified. Differential dynamics of the regulatory network topology showed that WRKY and ERF families of transcription factors play a crucial role during signal crosstalk events in rice plants while responding to combined exposure of Xoo with low temperature vs. Xoo with high temperatures. Our study suggests that upon onset of high temperature, rice plants tend to switch its focus from defence response towards growth and reproduction.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses.

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology.

The biggest challenge for modern biology is to integrate multidisciplinary approaches towards understanding the organizational and functional complexity of biological systems at different hierarchies, starting from the subcellular molecular mechanisms (microscopic) to the functional interactions of ecological communities (macroscopic). The plant-insect interaction is a good model for this purpose with the availability of an enormous amount of information at the molecular and the ecosystem levels. Changing global climatic conditions are abruptly resetting plant-insect interactions. Integration of discretely located heterogeneous information from the ecosystem to genes and pathways will be an advantage to understand the complexity of plant-insect interactions. This review will present the recent developments in omics-based high-throughput experimental approaches, with particular emphasis on studying plant defence responses against insect attack. The review highlights the importance of using integrative systems approaches to study plant-insect interactions from the macroscopic to the microscopic level. We analyse the current efforts in generating, integrating and modelling multiomics data to understand plant-insect interaction at a systems level. As a future prospect, we highlight the growing interest in utilizing the synthetic biology platform for engineering insect-resistant plants.

Transcriptome responses to combinations of stresses in Arabidopsis.

Biotic and abiotic stresses limit agricultural yields, and plants are often simultaneously exposed to multiple stresses. Combinations of stresses such as heat and drought or cold and high light intensity have profound effects on crop performance and yields. Thus, delineation of the regulatory networks and metabolic pathways responding to single and multiple concurrent stresses is required for breeding and engineering crop stress tolerance. Many studies have described transcriptome changes in response to single stresses. However, exposure of plants to a combination of stress factors may require agonistic or antagonistic responses or responses potentially unrelated to responses to the corresponding single stresses. To analyze such responses, we initially compared transcriptome changes in 10 Arabidopsis (Arabidopsis thaliana) ecotypes using cold, heat, high-light, salt, and flagellin treatments as single stress factors as well as their double combinations. This revealed that some 61% of the transcriptome changes in response to double stresses were not predic from the responses to single stress treatments. It also showed that plants prioritized between potentially antagonistic responses for only 5% to 10% of the responding transcripts. This indicates that plants have evolved to cope with combinations of stresses and, therefore, may be bred to endure them. In addition, using a subset of this data from the Columbia and Landsberg erecta ecotypes, we have delineated coexpression network modules responding to single and combined stresses.

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.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes.

BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking. RESULTS: In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p < 0.01) in 10 ecotypes, including 498 transcription factors and 315 transposable elements. The majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. CONCLUSIONS: A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems.

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.

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.

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).

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.

Identification of Systems Level Molecular Signatures from Glioblastoma Multiforme Derived Extracellular Vesicles.

Glioblastoma multiforme (GBM) is one of the most lethal malignancies of the central nervous system characterized by high mortality rate. The complexity of GBM pathogenesis, progression, and prognosis is not fully understood yet. GBM-derived extracellular vesicles (EVs) carry several oncogenic elements that facilitate GBM progression. The purpose of this study was to identify systems level molecular signatures from GBM-derived EVs using integrative analysis of publicly available transcriptomic data generated from plasma and serum samples. The dataset contained 19 samples in total, of which 15 samples were from plasma (11 GBM patients and 4 healthy samples) and 4 samples were from serum (2 GBM and 2 healthy samples). We carried out statistical analysis to identify differentially expressed genes (DEGs), functional enrichment analysis of the DEGs, protein-protein interaction networks, module analysis, transcription factors and target gene regulatory networks analysis, and identification of hub genes. The differential expression of the identified hub genes were validated with the independent TCGA-GBM dataset. We have identified a few crucial genes and pathways associated with GBM prognosis and therapy resistance. The DEGs identified from plasma were associated with inflammatory processes and viral infection. On the other hand, the hub genes identified from the serum samples were significantly associated with protein ubiquitinylation processes and cytokine signaling regulation. The findings indicate that GBM-derived plasma and serum DEGs may be associated with distinct cellular processes and pathways which facilitate GBM progression. The findings will provide better understanding of the molecular mechanisms of GBM pathogenesis and progression. These results can further be utilized for developing and validating minimally invasive diagnostic and therapeutic molecular biomarkers for GBM.

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.

Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder.

Background: Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach. Methods: RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions. Results: We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder. Conclusions: Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ.

Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction.

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.

Arginylation regulates adipogenesis by regulating expression of PPARγ at transcript and protein level.

Protein modification by arginylation regulates protein stability, function and interaction. The loss of arginylation disrupts a diverse set of fundamental cellular processes from proliferation to death. In the current study, role of arginylation in cell differentiation is investigated. Using in vitro preadipocyte differentiation model, it was observed that the inhibition or knockout (KO) of arginyltransferase 1 (ATE1) severely hindered differentiation of preadipocytes into mature adipocytes. Absence of arginylation inhibited expression of two key adipogenic transcription factors PPARγ and C/EBPα, and their downstream adipogenic genes (FABP4, GLUT4, PLN1). Arginylation did not affect the induction of C/EBPß and C/EBPδ, the up-stream regulators of PPARγ gene. However, absence of arginylation affected PPARγ protein expression, independent of its transcript level. The constitutive expression of PPARγ1 protein in Ate1 KO cells as well as ATE1 inhibitor treated wild type cells were dampened due to increased proteasome mediated degradation of PPARγ1 in the absence of arginylation in the cells. Taken together these observations suggested arginylation mediated transcriptional regulation of PPARγ and C/EBPα was downstream of C/EBPß and C/EBPδ, and that the arginylation mediated regulation of PPARγ protein expression may play a role in this process. The inhibition of arginylation in mature adipocytes also reduced expression of lipogenesis genes and decreased fat accumulation in differentiated adipocytes. Thus, the current study shows that arginylation is essential for preadipocyte differentiation and maturation which are thought to be key factors in the maintenance of adipose tissue homeostasis.