Divergent host adaptation in Marburg virus: a hypothesis-generating framework for prioritizing non-traditional reservoirs.

BACKGROUND

Although Rousettus aegyptiacus is the primary natural reservoir of Marburg virus (MARV), recent atypical outbreaks and viral genomic diversification suggest a highly complex host-pathogen ecology. Here, we systematically evaluated the molecular homologies between MARV and diverse mammalian taxa to identify putative reservoir hosts and guide targeted surveillance.

METHODS

A multi-dimensional molecular characterization of the Marburg virus (MARV) genome was performed using an integrative suite of computational approaches. Distant sequence homology was interrogated via BLAST in conjunction with a null model framework, supplemented by granular analyses of codon usage bias, nucleotide composition, and dinucleotide frequencies. For comparative genomic context, we evaluated a cohort of 63 mammalian species across the orders Rodentia, Primates, Chiroptera, and Perissodactyla.

RESULTS

Multidimensional molecular profiling showed varied genomic similarity between MARV and mammalian groups, with stable convergence observed in Perissodactyla and certain rodents across multiple codon usage indices. Notably, the effective number of codons (ENC) in Equus caballus (52.43) nearly matched MARV (52.44), and Equus asinus exhibited the lowest average RSCU Manhattan distance to the virus (0.25). Additionally, MARV's CpG suppression ([Formula: see text] = 0.48) was consistent with the CpG depletion levels characteristic of the studied mammalian taxa.

CONCLUSION

Through the lens of computational biology, this study identifies putative host cohorts within the orders Perissodactyla and Rodentia that share distinct genomic, codon-usage, and dinucleotide-frequency homologies with MARV. These findings establish high-priority targets for downstream ecological field monitoring, seroepidemiological surveys, and experimental validation, ultimately providing a scientific blueprint to refine zoonotic surveillance networks under the global 'One Health' paradigm.