bioRxivBioengineeringPreprint
Ancient human mitochondrial genomes encode antimicrobial peptides
Marcelo D. T. TorresiD, Abdullah Ali, Hyun-Su Lee, Fangping WaniD, César de la Fuente‐NúñeziD
Abstract
Mitochondria are bacteria-derived organelles that coordinate major innate immune pathways, but whether mitochondrial genomes themselves encode direct antimicrobial functions is underexplored. Here, we test the hypothesis that ancient human mitochondrial DNA is not only an evolutionary record, but also contains encrypted peptide sequences with the capacity to contribute to host defense. Because mitochondria descend from a bacterial endosymbiont and retain bacterial-like molecular features, we reasoned that their compact genomes might preserve sequence fragments capable of engaging bacterial-like membranes or bacterial physiology. We mined 2,025 ancient human mitochondrial genomes using a computational pipeline that couples ORF extraction and deep learning, identifying 65 candidate peptides that we term mitochondrins. We synthesized 38 candidates and experimentally validated 14 as antimicrobials against clinically relevant Gram-negative and Gram-positive bacteria. Active mitochondrins were not defined by length, charge, or helicity alone; instead, potency depended on precise hydrophobic-cationic patterning, with nested peptide families revealing how single motif-level changes can switch activity on or off. Mechanistic assays showed that mitochondrins span multiple antibacterial modalities, from strong membrane disruption to potent activity with limited membrane perturbation, suggesting noncanonical or multi-step killing mechanisms. Several peptides displayed low cytotoxicity toward human cells, and one representative mitochondrin reduced bacterial burden in a murine skin abscess model. These findings provide a biochemical basis for the hypothesis that mitochondrial genomes may contribute to innate immunity.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- Citations
- 0
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