Whole-body positive synchronization pattern of metabolic transcriptomes

Understanding the relationship between transcriptomes in the entire body is crucial for comprehending health and disease. To achieve this, we have developed a framework that allows us to measure the general coordination pattern of functionally similar transcriptomes at a whole-body scale. Our approach employs meta-network level analysis to determine the interconnectivity level of metabolic co-expression modules throughout the body as opposed to randomly selected modules. We utilize dimensionality reduction of co-expression modules, inter-tissue connectivity, and clique community analysis. We applied our methodology to 19 distinct human tissues obtained from the GTEx project and detected 40/52 metabolic co-expression modules out of 609/652 cross-tissue generated modules in Cohort 1 and 2 respectively. We detected that the ratio of positive to negative inter-tissue co-expression of metabolic modules was significantly higher than control. We further validated these results for an additional cohort. Moreover, to detect a global concurrent synchronization pattern we performed graph theoretical analysis, specifically a clique community analysis of metabolic transcriptomes showing a significantly larger inter-tissue metabolic transcriptome community that control with a significantly higher connectivity for each metabolic module within the community. Additionally, we performed gene-level validation of our results. In summary, our findings reveal a significant global synchronization pattern of metabolic modules throughout the body. Our framework can be further used for other type of transcriptomes and serve as a measure detecting change in this global coordination pattern across conditions.