Genetics and immunological recovery with antiretroviral treatment for HIV

HIV-Induced Thymic Insufficiency and Aging-Related Immunosenescence on Immune Reconstitution in ART-Treated Patients

The mechanisms underlying unsatisfactory immune reconstitution in HIV-1 positive patients under ART have not been fully elucidated, even after years of investigation. Thus, this study aimed to assess the correlation between age and thymic production profile, and its influence on inadequate immunological recovery. Here, 44 ART-treated patients with undetectable plasma HIV-1 load (<40 copies/mL) were classified as 31 immunological responders (IR) and 13 immunological non-responders (INR), according to their CD4+ T-cell count after 18 months of ART. The thymic function was assessed by identifying recent thymic emigrants (RTEs) CD4+ T cells (CD4+/CD45RA+CD31+) in PBMCs using flow cytometry. Clinical data were also analyzed from medical records. The INR group showed a higher age at ART initiation (41 ± 3.0) compared to the IR (33.7 ± 2.1) group (p = 0.041). Evaluating RTE CD4+ T-cells, we observed a lower percentage in the INR group (19.5 ± 6.3) compared to the IR group (29.9 ± 11.5) (p = 0.012). There was a strong negative correlation between age at ART initiation and RTE CD4+ T-cells in INRs (r = -0.784, p = 0.004). Our study has highlighted the thymic insufficiency and aging-related immunosenescence with unsatisfactory immunological recovery during ART in HIV-1 positive patients.

Thymic Exhaustion and Increased Immune Activation Are the Main Mechanisms Involved in Impaired Immunological Recovery of HIV-Positive Patients under ART

Decades of studies in antiretroviral therapy (ART) have passed, and the mechanisms that determine impaired immunological recovery in HIV-positive patients receiving ART have not been completely elucidated yet. Thus, T-lymphocytes immunophenotyping and cytokines levels were analyzed in 44 ART-treated HIV-positive patients who had a prolonged undetectable plasma viral load. The patients were classified as immunological non-responders (INR = 13) and immunological responders (IR = 31), according to their CD4+ T cell levels. Evaluating pre-CD4+ levels, we observed a statistically significant trend between lower CD4+ T cell levels and INR status (Z = 3.486, p < 0.001), and during 18 months of ART, the CD4+ T cell levels maintained statistical differences between the INR and IR groups (WTS = 37.252, p < 0.001). Furthermore, the INRs were associated with an elevated age at ART start; a lower pre-treatment CD4+ T cell count and a percentage that remained low even after 18 months of ART; lower levels of recent thymic emigrant (RTE) CD4+ T cell (CD45RA + CD31+) and a naïve CD4+ T cell (CD45RA + CD62L+); higher levels of central memory CD4+ T cells (CD45RA-CD62L+); and higher immune activation by CD4+ expressing HLA-DR+ or both (HLA-DR+ and CD38+) when compared with IRs. Our study demonstrates that thymic exhaustion and increased immune activation are two mechanisms substantially implicated in the impaired immune recovery of ART-treated HIV patients.

Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses

Objective

Numerous studies have reported on the pathogenesis of poor immune reconstitution (PIR) after antiretroviral treatment in human immunodeficiency virus (HIV) patients. However, fewer studies focused on both immune-related genes (IRGs) and immune cells, and the correlation between IRGs and immune cells was evaluated via bioinformatics analyses.

Methods

Gene expression profiling of GSE143742 from the Gene Expression Omnibus (GEO) database was analyzed to get differentially expressed immune-related genes (DEIRGs). The enrichment analysis and protein-protein interaction (PPI) networks of DEIRGs were established. The relative fractions of 22 immune cell types were detected using the "CIBERSORT". The correlation analysis between DEIRGs and immune cells was constructed to discover the potential IRGs associated with immune cells. A logistic regression diagnostic model was built, and a receiver operating characteristic (ROC) curve was performed to evaluate the model's diagnostic efficacy. The CMap database was used to find molecules with therapeutic potential. RT-qPCR was used to verify the expression of the hub DEIRGs.

Results

We identified eight types of significantly changed immune cells and five hub IRGs in INRs. The DEIRGs were mainly enriched in lymphocyte activation, receptor-ligand activity, and T cell receptor signaling pathway. The correlation analysis showed that the expression of TNF, CXCR4 and TFRC correlate with CD8 cells, resting mast cells, activated NK cells, and naïve CD4 cells in INRs. Meanwhile, TFRC and IL7R relate to activated NK cells and resting memory CD4 cells respectively in IRs. A diagnostic model was constructed using multiple logistic regression and nine small molecules were identified as possible drugs.

Conclusion

In this study, we suggested that the process of PIR might be related to TNF, CXCR4, TFRC, CD48, and IL7R. And these IRGs play roles in regulating immune-competent cells. And our constructed diagnostic model has excellent effectiveness. Moreover, some small-molecule drugs are screened to alleviate PIR.

HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.