Examining saliva proteomic dynamics in mitochondrial diseases from a perspective of intrinsic health

Mitochondrial disease (MitoD), a clinical condition caused by genetic mitochondrial defects, affects cellular energy transformation and alters multiple dimensions of health. Recently, we collected a longitudinal saliva proteomics data set consisting of six healthy controls and six MitoD subjects throughout the awakening response process. We undertook three independent unsupervised or inferential approaches to characterize proteome dynamics and assessed their ability to separate MitoD individuals from controls. First, we designed a permutation test to detect the global difference in the proteomic co-regulation structure between healthy and unhealthy subjects. Second, we performed non-linear embedding and cluster analysis on elasticity to capture a more complicated relationship between health and the proteome. Third, we developed a machine learning algorithm to extract low-dimensional representations of the proteome dynamic and use them to cluster subjects into healthy and unhealthy groups without any knowledge of their true status. All three methods showed clear differences between MitoD individuals and controls. Our results revealed a significant and consistent association between MitoD status and the saliva proteome at multiple levels during the awakening response, including its dynamic change, co-regulation structure, and elasticity. Pipelines such as those shown here are the first step toward establishing interpretable and accurate framework for detecting signals related to mitochondrial disease progression from proteome dynamics.