Back

APOBEC3G Splicing Defects in Nonhuman Primate Models Result in Disparate Viral Mutational Profiles Relative to Humans

Mendez, A. D.; Springman-Rodriguez, R.; Bokani, A.; Carter-Tod, F.; Haghjoo, N.; Rzhetskaya, M.; Rorex, C.; Lehle, J. D.; Soleimanpour, M.; Ferrandez-Peral, L.; Yang, H.; Carpenter, M. A.; Thippeshappa, R.; Kutluay, S.; McLaughlin, R. N.; Mohan, M.; Ling, B.; Giavedoni, L.; Rodriguez-Barradas, M.; Harris, R.; Chen, X.; Weintraub, S.; Hultquist, J. F.; Ebrahimi, D.

2026-08-12 genomics
10.64898/2026.08.06.743345 bioRxiv
Show abstract

Nonhuman primates (NHPs), particularly macaques, are indispensable models for studying human infectious diseases due to their close immunological and physiological similarities. Understanding species-specific molecular differences is essential for maximizing the translational value of these models. Here we report that APOBEC3G (A3G), a potent antiviral restriction factor and the major source of genetic variations in HIV, exhibits a widespread mRNA splicing defect in the Cercopithecinae subfamily, which includes the commonly used NHP models. Driven by intronic polymorphisms, this splicing defect substantially reduces A3G protein levels and consequently results in a markedly reduced A3G-mediated mutation signatures, fewer defective viral genomes, and greater viral diversification in SIV compared to HIV. This species-specific effect is not restricted to lentiviruses: reduced A3G signatures have also been reported in simian foamy virus and simian T-cell leukemia virus, suggesting broader effects across primate retroviruses. These findings reveal a lineage-specific alteration in a major antiviral restriction factor, with important implications for viral restriction, evolution, drug resistance, and immune evasion. They also highlight the importance of incorporating naturally occurring genetic variation into NHP model selection to improve the reproducibility, translational fidelity, and biological relevance of preclinical research.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.