Immune cell subset profiling and metabolic dysregulation define the divergent immune microenvironments in HIV immunological non-responders
Qingfei Chu , Ningye Fang , Huanhuan Chen , Abdur Rashid , Xia Luo , Jianjun Li , Kang Li
Clinical and Translational Medicine ›› 2025, Vol. 15 ›› Issue (10) : e70498
Immune cell subset profiling and metabolic dysregulation define the divergent immune microenvironments in HIV immunological non-responders
Background: A subset of people living with HIV (PLWH) exhibit poor immune recovery despite effective antiretroviral therapy (ART), remaining at risk of disease progression. The immunometabolic mechanisms underlying this immunological non-response remain unclear.
Methods: We integrated transcriptomic and immunophenotypic approaches to characterise immune differences between immunological responders (IRs) and non-responders (INRs). Public datasets were analysed to identify differentially expressed genes (DEGs), followed by enrichment analysis, predictive modelling, immune infiltration assessment, and regulatory network construction. In parallel, flow cytometry was performed to assess T and B cell subsets in an independent cohort including IRs, INRs, treatment-naïve patients (TNPs), and healthy controls (HCs).
Results: DEGs between IRs and INRs were enriched in mitochondrial and ribosomal pathways. INRs showed reduced Th1, Th17, and Tfh cells, alongside increased markers of immune activation and exhaustion. Predictive modelling identified five hub genes (ATP5O, PIGY, UQCRQ, COX7C, and BLVRB) associated with immune recovery, and clustering based on their expression defined two transcriptionally distinct subtypes. Flow cytometry further confirmed that INRs exhibited diminished CD4+ T cell counts, increased PD-1+ and HLA-DR+ expression, and reduced resting memory B cells, reflecting persistent immune dysfunction.
Conclusions: This study underscores the pivotal role of immunometabolic dysregulation in shaping heterogeneous immune responses to ART. By integrating computational and experimental data, we identified key biomarkers and regulatory pathways associated with immune recovery. Our findings highlight the central influence of metabolic processes on immune restoration outcomes and propose personalised metabolic interventions as a promising strategy to enhance therapeutic efficacy in HIV-infected individuals.
acquired immunodeficiency syndrome / hub genes / immune cell infiltration / immunological non-responders / immunological responders / therapeutic targets
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.
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