Campos-Godínez José Fabio, Villegas-Campos Mauricio, Molina-Mora Jose Arturo
Centro de Investigación en Enfermedades Tropicales, Centro de Investigación en Hematología y Trastornos Afines, Facultad de Microbiología, Universidad de Costa Rica, San José 30305, Costa Rica.
Pathogens. 2025 Aug 7;14(8):788. doi: 10.3390/pathogens14080788.
The core perturbome is defined as a central response to multiple disturbances, functioning as a complex molecular network to overcome the disruption of homeostasis under stress conditions, thereby promoting tolerance and survival under stress conditions. Based on the biological and clinical relevance of and , we characterized their molecular responses to multiple perturbations. Gene expression data from (8815 target genes-based on a pangenome-across 132 samples) and (3312 target genes across 156 samples) were used. Accordingly, this study aimed to identify and describe the functionality of the core perturbome of these two prokaryotic models using a machine learning approach. For this purpose, feature selection and classification algorithms (KNN, RF and SVM) were implemented to identify a subset of genes as core molecular signatures, distinguishing control and perturbation conditions. After verifying effective dimensional reduction (with median accuracies of 82.6% and 85.1% for and , respectively), a model of molecular interactions and functional enrichment analyses was performed to characterize the selected genes. The core perturbome was composed of 55 genes (including nine hubs) for and 46 (eight hubs) for . Well-defined interactomes were predicted for each model, which are jointly associated with enriched pathways, including energy and macromolecule metabolism, DNA/RNA and protein synthesis and degradation, transcription regulation, virulence factors, and other signaling processes. Taken together, these results may support the identification of potential therapeutic targets and biomarkers of stress responses in future studies.
核心扰动组被定义为对多种干扰的一种核心反应，作为一个复杂的分子网络在应激条件下克服体内平衡的破坏，从而促进在应激条件下的耐受性和生存能力。基于[具体内容1]和[具体内容2]的生物学和临床相关性，我们表征了它们对多种扰动的分子反应。使用了来自[数据集1]（基于泛基因组的8815个靶基因，涵盖132个样本）和[数据集2]（156个样本中的3312个靶基因）的基因表达数据。因此，本研究旨在使用机器学习方法识别和描述这两种原核模型的核心扰动组的功能。为此，实施了特征选择和分类算法（KNN、RF和SVM）以识别作为核心分子特征的基因子集，区分对照和扰动条件。在验证有效降维后（[数据集1]和[数据集2]的中位数准确率分别为82.6%和85.1%），进行了分子相互作用模型和功能富集分析以表征所选基因。[数据集1]的核心扰动组由55个基因（包括9个枢纽基因）组成，[数据集2]的核心扰动组由46个基因（8个枢纽基因）组成。为每个模型预测了明确的相互作用组，它们共同与富集的途径相关，包括能量和大分子代谢、DNA/RNA和蛋白质合成与降解、转录调控、毒力因子和其他信号传导过程。综上所述，这些结果可能支持在未来研究中识别应激反应的潜在治疗靶点和生物标志物。
