HIV-1 drug resistance and genetic diversity in a cohort of people with HIV-1 in Nigeria

HIV-1 drug resistance and genetic diversity in people with HIV-1 in Cape Verde

OBJECTIVES

To characterize the genetic diversity and drug resistance profiles of people with HIV-1 failing ART in Cape Verde (CV).

DESIGN

Cross-sectional study conducted between January 2019 and December 2021 in 24 health centres on the islands of Santiago and São Vicente.

METHODS

The HIV-1 pol gene was sequenced in individuals with a detectable viral load. HIV-1 genetic diversity was determined by phylogenetic analysis. Drug resistance mutation patterns and resistance phenotypes were estimated using the Stanford algorithm.

RESULTS

Viral load was detected in 73 of 252 (29%) enrolled participants and sequencing data were produced for 58 (79%) participants. CRF02 AG strains predominated (46.5%), followed by subtype G (22.4%). Most patients (80%) had mutations conferring resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs) (67%), nucleoside reverse transcriptase inhibitors (55%), integrase inhibitors (10%) and/or protease inhibitors (7%) used in Cape Verde, a significant increase compared with a study conducted in 2010-2011. The most common mutations were M184V/I (43%), K103N/S (36%) and G190A/S (19%). NNRTI resistance was associated with younger age and exposure to two or more drug regimens.

CONCLUSION

The HIV-1 epidemic in Cape Verde is mainly driven by CRF02_AG and subtype G. Resistance to NNRTIs and/or NRTIs is highly prevalent and resistance to LPV/r and DTG is emerging. Our results support the use of DTG-based first-line ART and protease inhibitor-based regimens for patients with virological failure, but emerging resistance to LPV/r and DTG is a concern. Continued monitoring of drug resistance is essential to ensure adequate healthcare for PWH in Cape Verde.

Immunological and molecular assessment of HIV-1 mutations for antiretroviral drug resistance in Saudi Arabia

Human Immunodeficiency Virus (HIV) is a significant threat to public health. HIV genotyping and antiretroviral resistance testing may have contributed to improved non-treated management. Immune markers might assist HIV-1 diagnosis and drug-resistant variant identification. HIV-1 immunogenicity and molecular characteristics of antiretroviral drug resistance are evaluated in 56 treatment-naive HIV patients. DNA sequencing and retroviral resistance testing identified HIV-1 genotypes. 55.4% of patients were susceptible to protease inhibitors (PI), nucleoside reverse transcriptase inhibitors (NRTI), and non-nucleoside reverse transcriptase inhibitors (NNRTI) antiretroviral drugs, whereas 44.6% had drug-resistance mutations against at least one antiretroviral drug. 3.6% of cases had PI-resistant mutations, while 30.4% had NRTI-resistant mutations, and 30.4% had NNRTI-resistant mutations. In patients who are susceptible to PI, the mean value of human plasma sCD80 is 2.11 ± 0.65 ng/mL; in patients with mutations, it is 3.93 ± 2.91 ng/mL. Individuals who are susceptible to PI have plasma sCD27 levels of 78.7 ± 63.2 U/mL, whereas individuals who are mutant have levels of 56.5 ± 32.1 U/mL. IP-10's mean value was 363 ± 109.2 pg/mL for the susceptible patients and 429 ± 20.7 pg/mL for the mutated patients. In susceptible patients, the plasma sCD4 level is 0.163 ± 0.229 ng/mL; in mutant patients, it is 0.084 ± 0.012 ng/mL. The data showed a relative relation between immunological parameters such as sCD80, sCD27, sCD4, and IP-10 and mutation for drug resistance.

A unified classification system for HIV-1 5' long terminal repeats

The HIV-1 provirus mainly consists of internal coding region flanked by 1 long terminal repeats (LTRs) at each terminus. The LTRs play important roles in HIV-1 reverse transcription, integration, and transcription. However, despite of the significant study advances of the internal coding regions of HIV-1 by using definite reference classification, there are no systematic and phylogenetic classifications for HIV-1 5' LTRs, which hinders our elaboration on 5' LTR and a better understanding of the viral origin, spread and therapy. Here, by analyzing all available resources of 5' LTR sequences in public databases following 4 recognized principles for the reference classification, 83 representatives and 14 consensus sequences were identified as representatives of 2 groups, 6 subtypes, 6 sub-subtypes, and 9 CRFs. To test the reliability of the supplemented classification system, the constructed references were applied to identify the 5' LTR assignment of the 22 clinical isolates in China. The results revealed that 16 out of 22 tested strains showed a consistent subtype classification with the previous LTR-independent classification system. However, 6 strains, for which recombination events within 5' LTR were demonstrated, unexpectedly showed a different subtype classification, leading a significant change of binding sites for important transcription factors including SP1, p53, and NF-κB. The binding change of these transcriptional factors would probably affect the transcriptional activity of 5' LTR. This study supplemented a unified classification system for HIV-1 5' LTRs, which will facilitate HIV-1 characterization and be helpful for both basic and clinical research fields.

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.