School of Grain Science and Technology, Jiangsu University of Science and Technology.
School of Food and Biological Engineering, Jiangsu University.
Environ Health Prev Med. 2024;29:41. doi: 10.1265/ehpm.24-00095.
Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear.
In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in.
Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress.
In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.
砷是一种有毒的类金属，会导致急性和慢性健康问题。不幸的是，大米是全世界一半以上人口的主要主食，通常被认为是一种典型的砷积累作物。有证据表明，砷胁迫会影响水稻植株的生长和发育，并导致水稻籽粒中砷浓度升高。但潜在机制尚不清楚。
本研究采用生物信息学方法探讨了砷胁迫下水稻根系可能涉及的分子和途径。从基因表达综合数据库（GEO）中选择并下载了涉及砷处理水稻根系的数据集，以及“研究类型”限制为“基因芯片表达谱”的数据集。使用在线网络工具 GEO2R 从砷处理组和对照组中获得差异表达基因（DEGs）。使用基因本体论（GO）功能和京都基因与基因组百科全书（KEGG）通路富集分析对 DEGs 的功能进行分析。使用 STRING 和 Cystoscope 分别对 DEGs 的蛋白质-蛋白质相互作用（PPI）网络和分子复合物检测算法（MCODE）进行分析。使用 Cytoscape-cytoHubba 插件预测和探索 PPI 网络中的重要节点和枢纽基因。
从基因表达综合数据库（GEO）下载了两个数据集 GSE25206 和 GSE71492。然后从两个数据集中选择了 80 个常见的 DEGs，包括 63 个上调基因和 17 个下调基因。经过功能富集分析，这些常见的 DEGs 主要富集在 10 个 GO 项目中，包括谷胱甘肽转移酶活性、谷胱甘肽代谢过程、毒素代谢过程和 7 个与代谢相关的 KEGG 通路。经过 PPI 网络和 MCODE 分析，从 DEGs 的 PPI 网络中鉴定出 49 个节点，筛选出两个显著模块。接下来，使用 Cytoscape-cytoHubba 插件预测重要节点和枢纽基因。最后，验证并确定了 5 个基因[Os01g0644000、PRDX6（Os07g0638400）、PRX112（Os07g0677300）、ENO1（Os06g0136600）、LOGL9（Os09g0547500）]可能与水稻根系对砷胁迫的反应有关，可作为最佳候选基因。
综上所述，我们通过全面的生物信息学分析阐明了水稻根系对砷胁迫的潜在途径和基因。
