The human body comprises organs with specialized functions, such as the organs in the digestive system that are responsible for food intake and the lungs, which provide oxygen. The organs work together to maintain homeostasis in the body. When this homeostasis is perturbed, the body is prone to disease. Inter-tissue networks of communication and cross-talk are responsible for preserving whole-body homeostasis. These networks are orchestrated and affected by the nervous system, the blood circulation systems, and circadian rhythms. Circadian rhythms standardize the body’s sleep-wake cycle. 

Given the variety of human organs’ functions, we sought to develop a quantifiable measure for inter-organ communication. This measure could also rank the relative level of communication among organs. We developed two data science methods that use tens of thousands of gene expression levels across multiple tissues to measure inter-tissue cross-talk. One measure was the average coordination of similar biological processes in different organs at a network level. The second measure was a detailed gene activity-based comparison. 

Metabolic processes are functions that all cells in the body perform regardless of cell type or organ. Metabolic processes are responsible for converting food into cellular energy and chemicals necessary to maintain cell function, as well as removing chemical waste. 

We discovered a systems-level inter-organ synchronization across the whole body. In general, the direct and inverse synchronization rate is nearly the same. However, when we focused explicitly on metabolic function, we found that positive synchronization far outpaces inverse synchronization rates. Furthermore, among the 19 organs in our research, the liver organ was the most metabolically synchronized with other tissues. Specifically, the liver was inversely synchronized with the fat underneath the skin and the primary artery that pumps oxygen-rich blood into the rest of the body. Indeed, the liver is a significant player in the whole-body metabolic network.

Our new computational approach can test the synchronicity levels of human organs and may predict disease conditions in advance.

This research is supported by a seed grant from the Data Science Research Center (DSRC) at the University of Haifa, Israel. For related studies see our lab web-site.