Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai rice

Association of a Specific OsCULLIN3c Haplotype with Salt Stress Responses in Local Thai Rice

We previously found that OsCUL3c is involved in the salt stress response. However, there are no definitive reports on the diversity of OsCUL3c in local Thai rice. In this study, we showed that the CUL3 group was clearly separated from the other CUL groups; next, we focused on OsCUL3c, the third CUL3 of the CUL3 family in rice, which is absent in Arabidopsis. A total of 111 SNPs and 28 indels over the OsCUL3c region, representing 79 haplotypes (haps), were found. Haplotyping revealed that group I (hap A and hap C) and group II (hap B1 and hap D) were different mutated variants, which showed their association with phenotypes under salt stress. These results were supported by cis-regulatory elements (CREs) and transcription factor binding sites (TFBSs) analyses. We found that LTR, MYC, [AP2; ERF], and NF-YB, which are related to salt stress, drought stress, and the response to abscisic acid (ABA), have distinct positions and numbers in the haplotypes of group I and group II. An RNA Seq analysis of the two predominant haplotypes from each group showed that the OsCUL3c expression of the group I representative was upregulated and that of group II was downregulated, which was confirmed by RT-qPCR. Promoter changes might affect the transcriptional responses to salt stress, leading to different regulatory mechanisms for the expression of different haplotypes. We speculate that OsCUL3c influences the regulation of salt-related responses, and haplotype variations play a role in this regulation.

Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

Identification of major candidate genes for multiple abiotic stress tolerance at seedling stage by network analysis and their validation by expression profiling in rice (Oryza sativa L.)

A wealth of microarray and RNA-seq data for studying abiotic stress tolerance in rice exists but only limited studies have been carried out on multiple stress-tolerance responses and mechanisms. In this study, we identified 6657 abiotic stress-responsive genes pertaining to drought, salinity and heat stresses from the seedling stage microarray data of 83 samples and used them to perform unweighted network analysis and to identify key hub genes or master regulators for multiple abiotic stress tolerance. Of the total 55 modules identified from the analysis, the top 10 modules with 8-61 nodes comprised 239 genes. From these 10 modules, 10 genes common to all the three stresses were selected. Further, based on the centrality properties and highly dense interactions, we identified 7 intra-modular hub genes leading to a total of 17 potential candidate genes. Out of these 17 genes, 15 were validated by expression analysis using a panel of 4 test genotypes and a pair of standard check genotypes for each abiotic stress response. Interestingly, all the 15 genes showed upregulation under all stresses and in all the genotypes, suggesting that they could be representing some of the core abiotic stress-responsive genes. More pertinently, eight of the genes were found to be co-localized with the stress-tolerance QTL regions. Thus, in conclusion, our study not only provided an effective approach for studying abiotic stress tolerance in rice, but also identified major candidate genes which could be further validated by functional genomics for abiotic stress tolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03182-7.

Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks

Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.

Combining Genome and Gene Co-expression Network Analyses for the Identification of Genes Potentially Regulating Salt Tolerance in Rice

Salinity stress tolerance is a complex polygenic trait involving multi-molecular pathways. This study aims to demonstrate an effective transcriptomic approach for identifying genes regulating salt tolerance in rice. The chromosome segment substitution lines (CSSLs) of "Khao Dawk Mali 105 (KDML105)" rice containing various regions of DH212 between markers RM1003 and RM3362 displayed differential salt tolerance at the booting stage. CSSL16 and its nearly isogenic parent, KDML105, were used for transcriptome analysis. Differentially expressed genes in the leaves of seedlings, flag leaves, and second leaves of CSSL16 and KDML105 under normal and salt stress conditions were subjected to analyses based on gene co-expression network (GCN), on two-state co-expression with clustering coefficient (CC), and on weighted gene co-expression network (WGCN). GCN identified 57 genes, while 30 and 59 genes were identified using CC and WGCN, respectively. With the three methods, some of the identified genes overlapped, bringing the maximum number of predicted salt tolerance genes to 92. Among the 92 genes, nine genes, OsNodulin, OsBTBZ1, OsPSB28, OsERD, OsSub34, peroxidase precursor genes, and three expressed protein genes, displayed SNPs between CSSL16 and KDML105. The nine genes were differentially expressed in CSSL16 and KDML105 under normal and salt stress conditions. OsBTBZ1 and OsERD were identified by the three methods. These results suggest that the transcriptomic approach described here effectively identified the genes regulating salt tolerance in rice and support the identification of appropriate QTL for salt tolerance improvement.

Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Long-term transcriptional memory in rice plants submitted to salt shock

A first salt shock event alters transcriptional and physiological responses to a second event, being possible to identify 26 genes associated with long-term memory. Soil salinity significantly affects rice cultivation, resulting in large losses in growth and productivity. Studies report that a disturbing event can prepare the plant for a subsequent event through memory acquisition, involving physiological and molecular processes. Therefore, genes that provide altered responses in subsequent events define a category known as "memory genes". In this work, the RNA-sequencing (RNA-Seq) technique was used to analyse the transcriptional profile of rice plants subjected to different salt shock events and to characterise genes associated with long-term memory. Plants subjected to recurrent salt shock showed differences in stomatal conductance, chlorophyll index, electrolyte leakage, and the number of differentially expressed genes (DEGs), and they had lower Na⁺/K⁺ ratios than plants that experienced only one stress event. Additionally, the mammalian target of rapamycin (mTOR) pathways, and carbohydrate and amino acid-associated pathways were altered under all conditions. Memory genes can be classified according to their responses during the first event (+ or -) and the second shock event (+ or -), being possible to observe a larger number of transcripts for groups [+ /-] and [-/ +], genes characterised as "revised response." This is the first long-term transcriptional memory study in rice plants under salt shock, providing new insights into the process of plant memory acquisition.