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The influence of HIV on the evolution of Mycobacterium tuberculosis

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posted on 27.08.2020 by Anastasia S Koch, Daniela Brites, David Stucki, Joanna C Evans, Ronnett Seldon, Alexa Heekes, Nicola Mulder, Mark Nicol, Tolu Oni, Digby F Warner, Valerie Mizrahi, Julian Parkhill, Sebastien Gagneux, Darren P Martin, Robert J Wilkinson
HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates.

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