The effects of feedback loops on disease comorbidity in human signaling networks

Abstract

In general, diseases are more likely to be comorbid if they share associated genes or molecular interactions in a cellular process. However, there are still a number of pairs of diseases which show relatively high comorbidity but do not share any associated genes or interactions. This observation raises the need for a novel factor which can explain the underlying mechanism of comorbidity. We here consider a feedback loop (FBL) structure ubiquitously found in the human cell signaling network as a key motif to explain the comorbidity phenomenon, since it is well known to have effects on network dynamics. For every pair of diseases, we examined its comorbidity and length of all FBLs involved by the disease-associated genes in the human cell signaling network. We found that there is a negative relationship between comorbidity and length of involved FBLs. This indicates that a disease pair is more likely to comorbid if they are connected with FBLs of shorter length. We additionally showed that such a negative relationship is more obvious when the number of positive involved FBLs is larger than that of negative involved FBLs. Moreover, we observed that the negative relationship between comorbidity and length of involved FBLs holds especially for disease pairs that do not share any disease-associated genes. Finally, we proved all these results through intensive simulations, based on a Boolean network model. kwonyk@ulsan.ac.kr Supplementary data are available at BioInformatics online.