This study aimed at updating previous data on HIV-1 integrase variability, by using effective bioinformatics methods combining different statistical instruments from simple entropy and mutation rate to more specific approaches such as Hellinger distance. A total of 2133 HIV-1 integrase sequences were analyzed in: i) 1460 samples from drug-naive [DN] individuals; ii) 386 samples from drug-experienced but INI-naive [IN] individuals; iii) 287 samples from INI-experienced [IE] individuals. Within the three groups, 76 amino acid positions were highly conserved (<= 0.2% variation, Hellinger distance: <0.25%), with 35 fully invariant positions; while, 80 positions were conserved (>0.2% to <1% variation, Hellinger distance: <1%). The H12-H16-C40-C43 and D64D116-E152 motifs were all well conserved. Some residues were affected by dramatic changes in their mutation distributions, especially between DN and IE samples (Hellinger distance >= 1%). In particular, 15 positions (D6, S24, V31, S39, L74, A91, S119, T122, T124, T125, V126, K160, N222, S230, C280) showed a significant decrease of mutation rate in IN and/or IE samples compared to DN samples. Conversely, 8 positions showed significantly higher mutation rate in samples from treated individuals (IN and/or IE) compared to DN. Some of these positions, such as E92, T97, G140, Y143, Q148 and N155, were already known to be associated with resistance to integrase inhibitors; other positions including S24, M154, V165 and D270 are not yet documented to be associated with resistance. Our study confirms the high conservation of HIV-1 integrase and identified highly invariant positions using robust and innovative methods. The role of novel mutations located in the critical region of HIV-1 integrase deserves further investigation.

Vergni, D., Santoni, D., Bouba, Y., Lemme, S., Fabeni, L., Carioti, L., et al. (2022). Evaluation of HIV-1 integrase variability by combining computational and probabilistic approaches. INFECTION GENETICS AND EVOLUTION, 101, 105294 [10.1016/j.meegid.2022.105294].

Evaluation of HIV-1 integrase variability by combining computational and probabilistic approaches

Bertoli, Ada;Perno, Carlo Federico;Ceccherini-Silberstein, Francesca;Santoro, Maria
2022

Abstract

This study aimed at updating previous data on HIV-1 integrase variability, by using effective bioinformatics methods combining different statistical instruments from simple entropy and mutation rate to more specific approaches such as Hellinger distance. A total of 2133 HIV-1 integrase sequences were analyzed in: i) 1460 samples from drug-naive [DN] individuals; ii) 386 samples from drug-experienced but INI-naive [IN] individuals; iii) 287 samples from INI-experienced [IE] individuals. Within the three groups, 76 amino acid positions were highly conserved (<= 0.2% variation, Hellinger distance: <0.25%), with 35 fully invariant positions; while, 80 positions were conserved (>0.2% to <1% variation, Hellinger distance: <1%). The H12-H16-C40-C43 and D64D116-E152 motifs were all well conserved. Some residues were affected by dramatic changes in their mutation distributions, especially between DN and IE samples (Hellinger distance >= 1%). In particular, 15 positions (D6, S24, V31, S39, L74, A91, S119, T122, T124, T125, V126, K160, N222, S230, C280) showed a significant decrease of mutation rate in IN and/or IE samples compared to DN samples. Conversely, 8 positions showed significantly higher mutation rate in samples from treated individuals (IN and/or IE) compared to DN. Some of these positions, such as E92, T97, G140, Y143, Q148 and N155, were already known to be associated with resistance to integrase inhibitors; other positions including S24, M154, V165 and D270 are not yet documented to be associated with resistance. Our study confirms the high conservation of HIV-1 integrase and identified highly invariant positions using robust and innovative methods. The role of novel mutations located in the critical region of HIV-1 integrase deserves further investigation.
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore MED/07
English
Bioinformatics
Genetic variability
HIV-1
Hellinger distance
Integrase
Mutational rate
Drug Resistance, Viral
Humans
Mutation
HIV Infections
HIV Integrase
HIV Integrase Inhibitors
HIV-1
Vergni, D., Santoni, D., Bouba, Y., Lemme, S., Fabeni, L., Carioti, L., et al. (2022). Evaluation of HIV-1 integrase variability by combining computational and probabilistic approaches. INFECTION GENETICS AND EVOLUTION, 101, 105294 [10.1016/j.meegid.2022.105294].
Vergni, D; Santoni, D; Bouba, Y; Lemme, S; Fabeni, L; Carioti, L; Bertoli, A; Gennari, W; Forbici, F; Perno, Cf; Gagliardini, R; Ceccherini-Silberstein, F; Santoro, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/303923
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