González-Morelo Kevin J, Galán-Vásquez Edgardo, Melis Felipe, Pérez-Rueda Ernesto, Garrido Daniel
Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigación en Matemáticas Aplicadas y en Sistemas. Universidad Nacional Autónoma de México, Ciudad Universitaria, México City, México.
Front Mol Biosci. 2023 Jan 26;10:1040721. doi: 10.3389/fmolb.2023.1040721. eCollection 2023.
Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for . Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for and , in contrast with . Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.
生物系统通过基因相互作用对环境扰动和多种化合物做出反应,这些遗传因素构成复杂的网络。转录组学研究的实验信息有助于识别基因网络,从而增进我们对微生物适应性的理解。在本研究中,我们使用加权基因共表达分析(WGCNA)分析了三种双歧杆菌对不同类型人乳寡糖(HMO)的基因共表达网络。从基因表达综合数据库(Geo Datasets)获取RNA测序数据。每个数据集获得了10到20个共表达模块。与HMO相关的基因出现在具有更多基因的模块中,而与之形成对比。在每个模块中鉴定出枢纽基因,总体而言,它们参与保守的基本过程。某些模块富含LacI样转录因子,而其他模块则富含某些代谢途径,如次生代谢物的生物合成。三种双歧杆菌转录组对HMO利用呈现出不同的调控模式。根据其参与半乳糖或N-乙酰葡糖胺利用的情况,双歧杆菌属中的HMO相关基因在两个模块中共表达。相反,双歧杆菌属在参与HMO利用的基因共表达方面结构较少。最后,双歧杆菌属中的这类基因聚集在一个小模块中,表明与主要细胞过程缺乏共表达,提示是最近获得的。本研究突出了这些双歧杆菌基因组在消耗HMO过程中不同的共表达结构,有助于理解这些肠道微生物物种中的基因调控和共表达。