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Coexpression analysis of tomato genes and experimental verification of coordinated expression of genes found in a functionally enriched coexpression module.

ABSTRACT: Gene-to-gene coexpression analysis is a powerful approach to infer the function of uncharacterized genes. Here, we report comprehensive identification of coexpression gene modules of tomato (Solanum lycopersicum) and experimental verification of coordinated expression of module member genes. On the basis of the gene-to-gene correlation coefficient calculated from 67 microarray hybridization data points, we performed a network-based analysis. This facilitated the identification of 199 coexpression modules. A gene ontology annotation search revealed that 75 out of the 199 modules are enriched with genes associated with common functional categories. To verify the coexpression relationships between module member genes, we focused on one module enriched with genes associated with the flavonoid biosynthetic pathway. A non-enzyme, non-transcription factor gene encoding a zinc finger protein in this module was overexpressed in S. lycopersicum cultivar Micro-Tom, and expression levels of flavonoid pathway genes were investigated. Flavonoid pathway genes included in the module were up-regulated in the plant overexpressing the zinc finger gene. This result demonstrates that coexpression modules, at least the ones identified in this study, represent actual transcriptional coordination between genes, and can facilitate the inference of tomato gene function.

SUBMITTER: Ozaki S

PROVIDER: S-EPMC2853382 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Coexpression analysis of tomato genes and experimental verification of coordinated expression of genes found in a functionally enriched coexpression module.

Ozaki Soichi S Ogata Yoshiyuki Y Suda Kunihiro K Kurabayashi Atsushi A Suzuki Tatsuya T Yamamoto Naoki N Iijima Yoko Y Tsugane Taneaki T Fujii Takashi T Konishi Chiaki C Inai Shuji S Bunsupa Somnuk S Yamazaki Mami M Shibata Daisuke D Aoki Koh K

DNA research : an international journal for rapid publication of reports on genes and genomes 20100203 2

Gene-to-gene coexpression analysis is a powerful approach to infer the function of uncharacterized genes. Here, we report comprehensive identification of coexpression gene modules of tomato (Solanum lycopersicum) and experimental verification of coordinated expression of module member genes. On the basis of the gene-to-gene correlation coefficient calculated from 67 microarray hybridization data points, we performed a network-based analysis. This facilitated the identification of 199 coexpressio ...[more]

PMID: 20130013

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Project description:BACKGROUND: Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed. RESULTS: We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and Arabidopsis roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest. CONCLUSIONS: TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.

Project description:BackgroundInflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD.MethodsGene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified.ResultsCombined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant.ConclusionsThe hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD.

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Coexpression analysis of tomato genes and experimental verification of coordinated expression of genes found in a functionally enriched coexpression module.

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Coexpression analysis of tomato genes and experimental verification of coordinated expression of genes found in a functionally enriched coexpression module.

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