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Plants have evolved with complex sensory systems to recognize signals from multiple environmental conditions. A light signal is one of the most important environmental factors that regulates not only photomorphogenesis but also the developmental strategy of plants throughout their life cycle. The molecular mechanisms of the light signaling modules and the interactions between light and other environmental signals have been studied extensively. However, to enhance plant growth, particularly in crop production, we need to gain a deeper understanding of how light regulates plant development within gene regulatory networks (GRNs). Understanding GRNs is important to identify not only the novel genes and transcription factors in light signaling pathways but also the factors that connect light signaling and other environmental signals. Weighted gene co-expression network analysis (WGCNA) has been used to study GRN. We applied WGCNA to 58 RNA-seq samples of wild-type Arabidopsis grown under different light treatments and built the gene co-expression networks. We identified 14 different modules that are significantly associated with different light treatments. Among them, the honeydew1 and ivory display significant association with the dark-grown seedlings. Many hub genes identified from these modules are significantly enriched in light responses, including responses to red, far-red, blue light, light stimulus, auxin responses, and photosynthesis. Although we found many known transcription factors in these modules, we also identified several unknown genes and transcription factors that are significantly associated with the honeydew1 module and highly differentially expressed between dark and light conditions. To examine whether the hub genes in the honeydew1 module play a role in light signaling, we isolated mutants in selected hub genes and measured hypocotyl lengths under dark, red, and far-red light conditions. These assays showed that four hub genes are involved in regulating light signaling pathways. This study provides a new approach to identifying novel genes in GRNs underlying light responses in Arabidopsis.
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Network analysis reveals dysregulated functional patterns in type II diabetic skin
Abstract Skin disorders are one of the most common complications of type II diabetes (T2DM). Long-term effects of high blood glucose leave individuals with T2DM more susceptible to cutaneous diseases, but its underlying molecular mechanisms are unclear. Network-based methods consider the complex interactions between genes which can complement the analysis of single genes in previous research. Here, we use network analysis and topological properties to systematically investigate dysregulated gene co-expression patterns in type II diabetic skin with skin samples from the Genotype-Tissue Expression database. Our final network consisted of 8812 genes from 73 subjects with T2DM and 147 non-T2DM subjects matched for age, sex, and race. Two gene modules significantly related to T2DM were functionally enriched in the pathway lipid metabolism, activated by PPARA and SREBF ( SREBP ). Transcription factors KLF10 , KLF4 , SP1 , and microRNA-21 were predicted to be important regulators of gene expression in these modules. Intramodular analysis and betweenness centrality identified NCOA6 as the hub gene while KHSRP and SIN3B are key coordinators that influence molecular activities differently between T2DM and non-T2DM populations. We built a TF-miRNA-mRNA regulatory network to reveal the novel mechanism ( miR-21-PPARA-NCOA6 ) of dysregulated keratinocyte proliferation, differentiation, and migration in diabetic skin, which may provide new insights into the susceptibility of skin disorders in T2DM patients. Hub genes and key coordinators may serve as therapeutic targets to improve diabetic skincare.
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- Award ID(s):
- 2022382
- PAR ID:
- 10337965
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-022-10652-8
