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

Suratanee, Apichat; Chokrathok, Chidchanok; Chutimanukul, Panita; Khrueasan, Nopphawitchayaphong; Buaboocha, Teerapong; Chadchawan, Supachitra; Plaimas, Kitiporn

doi:10.3390/genes9120594

Cited by 11 publications

(15 citation statements)

References 133 publications

(97 reference statements)

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“…These influential genes could send some alert signals to activate the regulatory factors in these pathways to stimulate or inhibit the translation, transcription, or the expression of other genes, to generate a physiological and biochemical adaptation in response to the environmental stress [3]. An essential point in the network analysis is the highest degree rates represented for the clusters with more influential genes, which could indicate their regulatory role in controlling their other neighbors, whose expressions are correlated in the network [14].…”

Section: Discussionmentioning

confidence: 99%

“…This type of network allows for the understanding of the regulatory mechanisms and processes in the biological system [11,13]. The CEN shows the relationship between genes and the regulatory processes by following the central dogma of regulatory control; network analysis on the CEN can identify significant genes possessing stronger influence or causality in the network [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

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

Franco

Rana

Cavalcante

et al. 2020

Genes

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Franco

Rana

Cavalcante

et al. 2020

Genes

View full text Add to dashboard Cite

show abstract

“…These influential genes could send some alert signals to activate the regulatory factors in these pathways to stimulate or inhibit the translation, transcription or the expression of other genes, to generate a physiological and biochemical adaptation in response to the environmental stress [3]. An essential point in the network analysis is the highest degree rates represented for the clusters with more influential genes, which could indicate the regulating role to control their other neighbors whose expressions are correlated in the network [14].…”

Section: Discussionmentioning

confidence: 99%

“…This type of network allows the understanding of the regulatory mechanisms and processes in the biological system [11,13]. The CEN shows the relationship between genes and the regulatory processes by following the central dogma of regulatory control; network analysis on the CEN can identify significant genes possessing stronger influence or causality in the network [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

Co-Expression Networks for Causal Gene Identification Based on RNA-seq Data of C<em>orynebacterium Pseudotuberculosis</em>

Franco¹,

Rana²,

Cavalcante³

et al. 2020

Preprint

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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 is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on 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 data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and 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 have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with 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.

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“…The read count of the RNA-Seq was analyzed and normalized using the DESeq package in software R (Anders and Huber, 2010). We constructed the gene co-expression network of the rice lines under normal and salt stress conditions at the growth stages following the method of Suratanee et al (2018), and these constructs were combined as whole-state networks. The expression levels of whole-state networks were mixed.…”

Section: Identification Of Marker Genes By Gcn and CC Analysismentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 11 publications

References 133 publications

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

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

Co-Expression Networks for Causal Gene Identification Based on RNA-seq Data of C<em>orynebacterium Pseudotuberculosis</em>

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

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