McCulloch, A. | bioRxiv Science

bioRxiv2022

Integration of mass-spectrometry-based metabolomics and proteomics to characterise different senescence induced molecular sub-phenotypes

Cellular senescence is a key driver of ageing and its related disease. Thus, targeting and eliminating senescent cells is a major focus in biogerontology to predict and ameliorate age-related malady. Many studies have focused on targeting senescence through the identification of its molecular biomarkers. However, these are not specific for senescence and have different expression patterns across various senescence phenotypes. Here we report a combination of molecular studies ({beta}-galactosidase expression, DNA damage and replication immunodetection) with a mass spectrometry analysis integrating intra and extracellular global metabolomics to reveal small molecules differentially expressed across multiple senescence phenotypes (replicative senescence, x-ray, and chemical-induced senescence). Altered key intracellular metabolic changes were identified, depending on the stress stimuli, which were consistent with the presence of pro-inflammatory metabolites in the cellular secretome. Our work shows the advantage of combining molecular and metabolomics studies for the detailed analysis of cellular senescence and that senescence phenotype changes upon induction method.

molecular biology↗

bioRxiv2021

Systems analysis of the familial cardiomyopathy signaling network

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

systems biology↗