Dominant CD4+ T cell receptors remain stable throughout antiretroviral therapy-mediated immune restoration in people with HIV

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical-demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Comparative efficacy of antifungal drugs for the treatment of oral candidiasis in HIV-positive patients: A Bayesian network meta-analysis

Oral candidiasis infection is particularly prevalent among individuals in HIV-positive patients. Antifungal drugs have shown promising therapeutic effects in treating oral candidiasis in HIV-positive patients. However, the selection of specific antifungal drugs for the treatment of oral candidiasis in HIV-positive patients lacks evidence-based guidelines. This study aims to address this gap by conducting a comprehensive review of relevant randomized controlled trials (RCTs) and performing a network meta-analysis to assess the efficacy of different antifungal drugs in treating oral candidiasis in HIV-positive patients. A systematic search was conducted in databases including EMBASE, Web of Science, Medline, and Cochrane databases to identify relevant articles. Additionally, key pertinent sources in the literatures were also reviewed. All studies published prior to August 2023 were eligible for inclusion. Two researchers independently conducted the screening of literature, extraction of data, and evaluation of quality. Pairwise and network meta-analysis were then performed to assess the primary outcomes of the randomized controlled trials (RCTs) included. The protocol was registered on the PROSPERO database (CRD42024513912). Twenty-six RCTs were included in this meta-analysis, involving a total of 3145 patients and evaluating seven interventions (placebo, fluconazole, itraconazole, nystatin, clotrimazole, ketoconazole, miconazole). Pairwise meta-analysis and network meta-analysis showed fluconazole was significantly efficacy in increasing mycological cure rates when compared with placebo, clotrimazole, and nystatin. Ketoconazole and miconazole were significantly efficacy in increasing mycological cure rates when compared with nystatin. Network meta-analysis also suggested the efficacy of the seven interventions in increasing mycological cure rates was ranked as follows: placebo (35.3%), fluconazole (95.2%), itraconazole (61.6%), nystatin (17.0%), clotrimazole (52.7%), ketoconazole (69.2%), miconazole (69.1%). The available evidence indicates that fluconazole had the greatest possibility to increase mycological cure rates in HIV-positive patients, while, nystatin was the least effective antifungal drug in increasing mycological cure rates in HIV-positive patients.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A Leave-One-Covariate-Out (LOCO) inference approach identified specific HIV reservoir and clinical-demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, machine learning algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA. The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells

The latent reservoir of HIV persists for decades in people living with HIV (PWH) on antiretroviral therapy (ART). To determine if persistence arises from the natural dynamics of memory CD4+ T cells harboring HIV, we compared the clonal dynamics of HIV proviruses to that of memory CD4+ T cell receptors (TCRβ) from the same PWH and from HIV-seronegative people. We show that clonal dominance of HIV proviruses and antigen-specific CD4+ T cells are similar but that the field's understanding of the persistence of the less clonally dominant reservoir is significantly limited by undersampling. We demonstrate that increasing reservoir clonality over time and differential decay of intact and defective proviruses cannot be explained by mCD4+ T cell kinetics alone. Finally, we develop a stochastic model of TCRβ and proviruses that recapitulates experimental observations and suggests that HIV-specific negative selection mediates approximately 6% of intact and 2% of defective proviral clearance. Thus, HIV persistence is mostly, but not entirely, driven by natural mCD4+ T cell kinetics.