The interaction strength between 2 proteins is not constant but variable under different conditions. For a given biological process, identification of protein-protein interactions (PPIs) undergoing dynamic change in interaction strength is highly valuable but never achieved before. In this work, we presented a computational approach to identify changed PPIs (cPPIs) on a global scale by analyzing the coexpression level of genes encoding the interacting protein pairs. This approach stemmed from the biological con-ception that the change of protein-protein interaction bore imprint at the gene coexpression level. We applied this method to identify cPPIs in cells treated with a cytokine TGFβ, as well as cPPIs in rheumatoid arthritis (RA) patients. The accuracy of identification was evaluated by comparing our results with data from the high-throughput experiment and literature mining. Our analysis demonstrated that this is a simple and effective method to infer cPPIs from a given set of PPIs or even from the whole interactome. Further analysis uncovered the biological functions of the cPPIs in RA patients, which included muscle contraction and antigen presentation. Our method could help to elucidate molecular mechanisms of dynamic biological processes.
The long asymptomatic stage of HIV infection poses a great challenge in identifying recent HIV infections. This is a bottleneck for monitoring HIV epidemic trends and evaluating the effectiveness of national AIDS control programs. Several serological methods were used to address this issue with some success. Because of high false-positive rates in patients with advanced infection or in ART treatment, UNAIDS still hesitates to recommend their use in routine surveillance. We developed a new pattern-based method for measuring intra-patient viral genetic diversity for determination of recent infections and estimation of population incidence. This method is verified by using several datasets (424 subtype B and 77 CRF07_BC samples) with clearly identified HIV-1 infection times. Pattern-based diversities of recent infections are significantly lower than that of chronic ones. With larger window periods varying from 200 to 350 days, a higher accuracy (90% 95%) not affected by advanced disease nor ART treatment could be obtained. The pattern-based genetic method is supplementary to the existing serology-based assays, both of which could be suitable for use in low and high epidemic regions, respectively.
YANG JingXIA XiaYuHE XiangYANG SenLinRUAN YuHuaZHAO QuanBiWANG ZhiXinSHAO YiMingPAN XianMing
