Shaping current European mitochondrial haplogroup frequency in response to infection: the case of SARS-CoV-2 severity

Abstract

The frequency of mitochondrial DNA haplogroups (mtDNA-HG) in humans is known to be shaped by migration and repopulation. Mounting evidence indicates that mtDNA-HG are not phenotypically neutral, and selection may contribute to its distribution. Haplogroup H, the most abundant in Europe, improved survival in sepsis. Here we developed a random forest trained model for mitochondrial haplogroup calling using data procured from GWAS arrays. Our results reveal that in the context of the SARS-CoV-2 pandemic, HV branch were found to represent protective factors against the development of critical SARS-CoV-2 in an analysis of 14,349 patients. These results highlight the role of mtDNA in the response to infectious diseases and support the proposal that its expansion and population proportion has been influenced by selection through successive pandemics.

Fig. 1. Workflow followed to train machine learning model to perform mitochondrial HG calling.

The left panel shows the strategy for training a random forest model to classify subjects into mitochondrial HGs, while the right panel outlines how to perform external validation of the trained random forest model. *This process of 3-fold cross-validation (3-fold CV) is different from the 3-fold CV fine-tuning search of hyperparameters.

Fig. 2. Stability changes by respiratory complex, determined by major mitochondrial HG markers.

All HGs are compared to a reference model determined according to the Cambridge reference sequence (NC_012920). Only mitochondrial HGs that were significant risk modulators for SARS-Cov2 severity are represented. According to the developers of the rosetta Flex.ddG protocol, significant energetic changes can be considered above or below ± 1 Kcal/mol (red lines). Although CIV also contains three subunits encoded by mtDNA, this respiratory complex is not represented, as none of the described mitochondrial HG markers produce amino acidic changes.

Fig. 3. Principal component (PC) analysis summarizing genetic variability Cases Vs Controls.

A plot depicting the two main PC, cases (n=8894) Vs controls (n=5455), that gathers that main amount of overall genetic variability based in GWAS array.