Short communication: selection of thymidine analogue resistance mutational patterns in children infected from a common HIV type 1 subtype G source

HLA-B*13, B*35 and B*39 Alleles Are Closely Associated With the Lack of Response to ART in HIV Infection: A Cohort Study in a Population of Northern Brazil

Introduction

Immune reconstitution failure after HIV treatment is a multifactorial phenomenon that may also be associated with a single polymorphism of human leukocyte antigen (HLA); however, few reports include patients from the Brazilian Amazon. Our objective was to evaluate the association of the immunogenic profile of the “classical” HLA-I and HLA-II loci with treatment nonresponse in a regional cohort monitored over 24 months since HIV diagnosis.

Materials and Methods

Treatment-free participants from reference centers in the state of Pará, Brazil, were enrolled. Infection screening was performed using enzyme immunoassays (Murex AG/AB Combination DiaSorin, UK) and confirmed by immunoblots (Bio-Manguinhos, FIOCRUZ). Plasma viral load was quantified by real-time PCR (ABBOTT, Chicago, Illinois, USA). CD4⁺/CD8⁺ T lymphocyte quantification was performed by immunophenotyping and flow cytometry (BD Biosciences, San Jose, CA, USA). Infection was monitored via test and logistics platforms (SISCEL and SICLOM). Therapeutic response failure was inferred based on CD4⁺ T lymphocyte quantification after 1 year of therapy. Loci A, B and DRB1 were genotyped using PCR-SSO (One Lambda Inc., Canoga Park, CA, USA). Statistical tests were applied using GENEPOP, GraphPad Prism 8.4.3 and BioEstat 5.3.

Results

Of the 270 patients monitored, 134 responded to treatment (CD4⁺ ≥ 500 cells/µL), and 136 did not respond to treatment (CD4⁺ < 500 cells/µL). The allele frequencies of the loci were similar to heterogeneous populations. The allelic profile of locus B was statistically associated with treatment nonresponse, and the B*13, B*35 and B*39 alleles had the greatest probabilistic influence. The B*13 allele had the highest risk of treatment nonresponse, and carriers of the allele had a detectable viral load and a CD4+ T lymphocyte count less than 400 cells/µL with up to 2 years of therapy. The B*13 allele was associated with a switch in treatment regimens, preferably to efavirenz (EFZ)-based regimens, and among those who switched regimens, half had a history of coinfection with tuberculosis.

Conclusions

The allelic variants of the B locus are more associated with non-response to therapy in people living with HIV (PLHIV) from a heterogeneous population in the Brazilian Amazon.

HIV-1 Sub-Subtype A6: Settings for Normalised Identification and Molecular Epidemiology in the Southern Federal District, Russia

Russia has one of the largest and fastest growing HIV epidemics. However, epidemiological data are scarce. Sub-subtype A6 is most prevalent in Russia but its identification is challenging. We analysed protease/reverse transcriptase-, integrase-sequences, and epidemiological data from 303 patients to develop a methodology for the systematisation of A6 identification and to describe the HIV epidemiology in the Russian Southern Federal District. Drug consumption (32.0%) and heterosexual contact (27.1%) were the major reported transmission risks. This study successfully established the settings for systematic identification of A6 samples. Low frequency of subtype B (3.3%) and large prevalence of sub-subtype A6 (69.6%) and subtype G (23.4%) were detected. Transmitted PI- (8.8%) and NRTI-resistance (6.4%) were detected in therapy-naive patients. In therapy-experienced patients, 17.3% of the isolates showed resistance to PIs, 50.0% to NRTI, 39.2% to NNRTIs, and 9.5% to INSTIs. Multiresistance was identified in 52 isolates, 40 corresponding to two-class resistance and seven to three-class resistance. Two resistance-associated-mutations significantly associated to sub-subtype A6 samples: A62V_RT and G190S_RT. This study establishes the conditions for a systematic annotation of sub-subtype A6 to normalise epidemiological studies. Accurate knowledge on South Russian epidemiology will allow for the development of efficient regional frameworks for HIV-1 infection management.

Personalized HIV therapy to control drug resistance

The therapy of HIV patients is characterized by both the high genomic diversity of the virus population harbored by the patient and a substantial volume of therapy options. The virus population is unique for each patient and time point. The large number of therapy options makes it difficult to select an optimal or near optimal therapy, especially with therapy-experienced patients. In the past decade, computer-based support for therapy selection, which assesses the level of viral resistance against drugs has become a mainstay for HIV patients. We discuss the properties of available systems and the perspectives of the field.

HIV-1 mutational pathways under multidrug therapy

BACKGROUND

Genotype-derived drug resistance profiles are a valuable asset in HIV-1 therapy decisions. Therapy decisions could be further improved, both in terms of predicting length of current therapy success and in preserving followup therapy options, through better knowledge of mutational pathways- here defined as specific locations on the viral genome which, when mutant, alter the risk that additional specific mutations arise. We limit the search to locations in the reverse transcriptase region of the HIV-1 genome which host resistance mutations to nucleoside (NRTI) and non-nucleoside (NNRTI) reverse transcriptase inhibitors (as listed in the 2008 International AIDS Society report), or which were mutant at therapy start in 5% or more of the therapies studied.

METHODS

A Cox proportional hazards model was fit to each location with the hazard of a mutation at that location during therapy proportional to the presence/absence of mutations at the remaining locations at therapy start. A pathway from preexisting to occurring mutation was indicated if the covariate was both selected as important via smoothly clipped absolute deviation (a form of regularized regression) and had a small p-value. The Cox model also allowed controlling for non-genetic parameters and potential nuisance factors such as viral resistance and number of previous therapies. Results were based on 1981 therapies given to 1495 distinct patients drawn from the EuResist database.

RESULTS

The strongest influence on the hazard of developing NRTI resistance was having more than four previous therapies, not any one existing resistance mutation. Known NRTI resistance pathways were shown, and previously speculated inhibition between the thymidine analog pathways was evidenced. Evidence was found for a number of specific pathways between NRTI and NNRTI resistance sites. A number of common mutations were shown to increase the hazard of developing both NRTI and NNRTI resistance. Viral resistance to the therapy compounds did not materially effect the hazard of mutation in our model.

CONCLUSIONS

The accuracy of therapy outcome prediction tools may be increased by including the number of previous treatments, and by considering locations in the HIV genome which increase the hazard of developing resistance mutations.