Tissue-Specific Multi-Omics Integration Demonstrates Molecular Signatures Connecting Obesity to Immune Vulnerability

Background: Adipose tissue surrounds organs and tissues in the body and can alter their function. It could secrete diverse biological molecules, including lipids, cytokines, hormones, and metabolites. In light of all this information, obesity can influence many tissues and organs in the body, and this situation makes obesity a central contributor to multiple disorders. It is very important to investigate the crosstalk between tissues and organs in the body to clarify the key mechanisms of obesity. Methods: In this study, we analyzed the gene expression profiles of the liver, skeletal muscle, blood, visceral, and subcutaneous adipose tissue. Differentially expressed genes (DEGs) were identified for each tissue, and functional enrichment and protein–protein interaction network analyses were performed on genes commonly identified across tissues. Priority candidate genes were identified using network-based centrality measures, and potential molecular intersection points were explored through host-pathogen interaction network analysis. This study provides an integrative framework for characterizing inter-tissue molecular patterns associated with obesity at the network level. Results: The muscle, subcutaneous adipose tissue, and blood have the highest number of DEGs. The subcutaneous adipose tissue and blood stand out due to the number of DEGs they possess, although liver and visceral adipose tissue have lower amounts. Cancer ranks first in terms of diseases associated with obesity, and this association is accompanied by leukemia, lymphoma, and gastric cancer. RPL15 and RBM39 are the top genes in both degree and betweenness metrics. The host–pathogen interaction network consists of 13 unique-host proteins, 54 unique-pathogen proteins, and 27 unique-pathogen organisms, and the Influenza A virus had the highest interaction. There were a small number of common metabolites in all tissues: 2-Oxoglutarate, Adenosine, Succinate, and D-mannose. Conclusions: In this study, we aimed to identify candidate molecules for obesity using an integrative approach, examining the gene profiles of different organs and tissues. The findings of this study suggest a possible link between obesity and immune-related biological processes. The network obtained from the host-pathogen interaction analysis, and especially the pathways associated with viral infections that stand out in the functional enrichment analysis, may overlap with molecular signatures linked to obesity. Furthermore, the co-occurrence of cytokine signaling, insulin, and glucose metabolism pathways in the enrichment results indicates that the response of cells to insulin may be affected in obese individuals, suggesting a potential interaction between immune and metabolic processes; however, further experimental validation is needed to reveal the direct functional effects of these relationships.