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Introduction: The naturally occurring dipeptide Tryptophylglycine (WG) is enhanced in human immunodeficiency virus (HIV-1) infected Elite Controllers (EC). We have shown that this dipeptide has an anti-HIV-1 effect and evaluated now its synergistic antiretroviral activity, in combination with current antiretrovirals against multi-drug resistant HIV-1 isolates. Methods: Drug selectivity assay with WG-am and ARVs agains HIV-1 resistant isolates were carried out. Subsequently, two methods, Chou-Talalay's Combination Index (CI) and ZIP synergy score (SS), were used to quantify the synergism. Results: WG-am had a moderate/strong synergism with the four tested antiretrovirals: raltegravir, tenofovir, efavirenz, darunavir. WG-am:TDF had strong synergism at ED50, ED75, ED90 (CI: <0.2) in isolates resistant to protease inhibitors or integrase strand inhibitors (INSTI), and a slightly less synergism in isolates resistant to non-nucleoside or nucleotide reverse transcriptase inhibitors. WG-am combined with each of the four drugs inhibited all drug-resistant isolates with over 95% reduction at maximum concentration tested. The highest selectivity indexes (CC50/ED50) were in INSTI-resistant isolates. Conclusion: Our data suggest that WG, identified as occurring and enhanced in Elite Controllers has a potential to become a future treatment option in patients with HIV-1 strains resistant to any of the four major categories of anti-HIV- 1 compounds. [ABSTRACT FROM AUTHOR]

Motivation In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). Results The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. Availability and implementation This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network. [ABSTRACT FROM AUTHOR]

Purpose of Review: To unravel the current knowledge and possible link between the gut microbiome and HIV-1 virological control in elite controllers (EC), who can suppress viral replication in the absence of antiretroviral therapy. In addition, to discuss the limitations of current research and propose future research directions., Recent Findings: EC possess a different gut bacterial microbiota profile in composition and functionality from that of treatment-naive HIV-1 viremic progressors (VP). Specifically, EC have a richer bacterial microbiota as compared to VP, which closely resembles the microbiota in HIV-1 negative healthy controls (HC). Differentially abundant bacteria are found between EC and VP or HC, though results vary among the few existing studies. These data imply that the gut microbiome could contribute to the natural suppression of HIV-1 infection., Summary: An association between the gut microbiome and HIV-1 virological control is evidenced by recent studies. Yet, there are substantial knowledge gaps, and the underlying mechanism of how the microbiome influences the EC phenotype is far from clarified. Future research should consider diverse microbial communities, the complex microbe-host interactions, as well as yet-unidentified causal links between microbiome alterations and HIV-1 disease progression., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: With integrase strand transfer inhibitor (INSTI) use associated with increased body mass index (BMI) and BMI increases associated with higher diabetes mellitus (DM) risk, this study explored the relationship between INSTI/non-INSTI regimens, BMI changes, and DM risk., Methods: RESPOND participants were included if they had CD4, HIV RNA, and ≥ 2 BMI measurements during follow up. Those with prior DM were excluded. DM was defined as a random blood glucose ≥ 11·1 mmol/L, HbA1c ≥ 6·5%/48 mmol/mol, use of antidiabetic medication, or site reported clinical diagnosis. Poisson regression assessed the association between natural log (ln) of time-updated BMI, current INSTI/non-INSTI, and their interactions, on DM risk., Results: Among 20,865 people with HIV included, most were male (74%) and White (73%). Baseline median age was 45 years (IQR 37-52), with a median BMI of 24 kg/m2 (IQR 22-26). There were 785 DM diagnoses with a crude rate of 0·73 (95%CI 0·68-0·78)/100 PYFU. Ln(BMI) was strongly associated with DM (adjusted incidence rate ratio (aIRR) 16·54 per log increase, 95%CI 11·33-24·13; p<0·001). Current INSTI use associated with increased DM risk (IRR 1·58, 95%CI 1·37-1·82; p<0·001) in univariate analyses, only partially attenuated when adjusted for variables including ln(BMI) (aIRR 1·48, 95%CI 1·29-1·71; p<0·001). There was no interaction between ln(BMI), INSTI and non-INSTI use, and DM (p=0·130)., Conclusions: In RESPOND, compared with non-INSTIs, current use of INSTIs was associated with an increased DM risk, which partially attenuated when adjusted for BMI changes and other variables., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The gut and oral microbiome is altered in people living with HIV (PLWH). While antiretroviral treatment (ART) is pivotal in restoring immune function in PLWH, several studies have identified an association between specific antiretrovirals, particularly integrase inhibitors (INSTI), and weight gain. In our study, we explored the differences in the oral and gut microbiota of PLWH under different ART regimens, and its correlation to Body Mass Index (BMI). Fecal and salivary samples were collected from PLWH (n = 69) and healthy controls (HC, n = 80). We performed taxonomy analysis to determine the microbial composition and relationship between microbial abundance and ART regimens, BMI, CD4 + T-cell count, CD4/CD8 ratio, and ART duration. PLWH showed significantly lower richness compared to HC in both the oral and gut environment. The gut microbiome composition of INSTI-treated individuals was enriched with Faecalibacterium and Bifidobacterium, whereas non-nucleotide reverse transcriptase inhibitor (NNRTI)-treated individuals were enriched with Gordonibacter, Megasphaera, and Staphylococcus. In the oral microenvironment, Veillonella was significantly more abundant in INSTI-treated individuals and Fusobacterium and Alloprevotella in the NNRTI-treated individuals. Furthermore, Bifidobacterium and Dorea were enriched in gut milieu of PLWH with high BMI. Collectively, our findings identify distinct microbial profiles, which are associated with different ART regimens and BMI in PLWH on successful ART, thereby highlighting significant effects of specific antiretrovirals on the microbiome., (© 2024. The Author(s).)