RESUMEN
Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.
Asunto(s)
Cardiotoxicidad , Perfilación de la Expresión Génica , Células Madre Pluripotentes Inducidas , Miocitos Cardíacos , Transcriptoma , Humanos , Cardiotoxicidad/genética , Transcriptoma/efectos de los fármacos , Transcriptoma/genética , Miocitos Cardíacos/efectos de los fármacos , Miocitos Cardíacos/metabolismo , Células Madre Pluripotentes Inducidas/efectos de los fármacos , Células Madre Pluripotentes Inducidas/metabolismo , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos , Aprendizaje Automático , Cardiotoxinas/toxicidad , Fibroblastos/efectos de los fármacos , Fibroblastos/metabolismo , Células Endoteliales/efectos de los fármacos , Células Endoteliales/metabolismoRESUMEN
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.