Advancing Variant Phenotyping in Myelodysplastic Syndromes via Computational Genomics of Mitochondrial Enzyme Complexes

Abstract

Mitochondria are essential cellular organelles that play critical roles in hematological disorders. Recurrent mutations in mitochondrial DNA (mtDNA) have been identified in patients with myelodysplastic syndromes (MDS) and serve as significant prognostic indicators for their outcomes following allogeneic hematopoietic stem-cell transplantation (allo-HCT). However, the biological mechanisms of mtDNA mutations remain unclear. The current study utilizes computational structural genomics to improve our understanding of pathogenic variants in mitochondria-encoded genes. This emerging genomics discipline employs structural models, molecular mechanic calculations, and accelerated molecular dynamic simulations to analyze gene products, focusing on their structures and motions that determine their function. We applied this methodology to perform deep variant phenotyping of entire mitochondria-encoded protein complexes associated with the pathobiology of MDS and their prognosis after HCT. Our results demonstrate that this approach significantly outperforms conventional analytical methods, providing enhanced and more accurate information to support the potential pathogenicity of these variants and better infer their dysfunctional mechanisms. We conclude that the adoption and further expansion of computational structural genomics approaches, as applied to the mitochondrial genome, have the potential to significantly increase our understanding of molecular mechanisms underlying the disease. Our study lays a foundation for translating mitochondrial biology into clinical applications, which is of significant mechanistic and biomedical relevance and should be considered in modern biomedical research.