Cross-omics mapping of the human HIV restrictome across clinical states, immune-cell compartments, and protein responses

Host restriction factors can block HIV at multiple stages, but they are often studied as individual genes or within a single molecular layer. I performed an evidence-structured computational reanalysis of three independent public datasets to determine how the human HIV restrictome varies across clinical states, immune-cell compartments, and protein responses. The analysis used bulk monocyte RNA sequencing (GSE157198), PBMC single-cell/CITE-seq data (GSE256089), and primary CD4 T-cell proteomics (PXD012263) within a prespecified restrictome-immunometabolism framework. In GSE157198, ART-naive monocytes had higher core-restrictome activity than healthy controls (difference = 1.154, p = 0.000746), and core scores were strongly correlated with interferon/innate-sensing scores (Spearman r = 0.800, p = 7.47 × 10^-11). Core-lipid/cholesterol correlation was weaker and was not independent of interferon after conditional analysis. In GSE256089, participant-paired core-restrictome scores were enriched in NK cells relative to B cells, monocytes, and T cells, and matched random-gene-set analyses supported enrichment beyond background expression structure. FCGR3A monocytes also had higher participant-paired core scores than CD14 monocytes. Exploratory sensing/death-gene analysis identified cell-type-specific expression patterns but did not establish pathway activation, pyroptosis, or apoptosis. In PXD012263, the historical q < 0.05 proteomic selection contained 19 curated genes and retained mixed response polarity; sensitivity thresholds q < 0.01 and q < 0.10 selected 10 and 25 genes, respectively. Together, the results show that HIV restrictome activity varies across clinical and immune-cell contexts and is embedded within broader interferon-linked and immunometabolic states. Because the datasets are independent and not subject matched, the findings are observational and hypothesis-generating rather than causal.

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PLoS ONE
Published
2026-10-13
DOI
https://doi.org/10.1371/journal.pone.0356591
Primary Topic
HIV Research and Treatment
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article
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article

Cross-omics mapping of the human HIV restrictome across clinical states, immune-cell compartments, and protein responses

Reuben S. Maghembe
PLoS ONE
HIV Research and Treatment
article

Cross-omics mapping of the human HIV restrictome across clinical states, immune-cell compartments, and protein responses

Reuben S. Maghembe
article en

Abstract

Host restriction factors can block HIV at multiple stages, but they are often studied as individual genes or within a single molecular layer. I performed an evidence-structured computational reanalysis of three independent public datasets to determine how the human HIV restrictome varies across clinical states, immune-cell compartments, and protein responses. The analysis used bulk monocyte RNA sequencing (GSE157198), PBMC single-cell/CITE-seq data (GSE256089), and primary CD4 T-cell proteomics (PXD012263) within a prespecified restrictome-immunometabolism framework. In GSE157198, ART-naive monocytes had higher core-restrictome activity than healthy controls (difference = 1.154, p = 0.000746), and core scores were strongly correlated with interferon/innate-sensing scores (Spearman r = 0.800, p = 7.47 × 10^-11). Core-lipid/cholesterol correlation was weaker and was not independent of interferon after conditional analysis. In GSE256089, participant-paired core-restrictome scores were enriched in NK cells relative to B cells, monocytes, and T cells, and matched random-gene-set analyses supported enrichment beyond background expression structure. FCGR3A monocytes also had higher participant-paired core scores than CD14 monocytes. Exploratory sensing/death-gene analysis identified cell-type-specific expression patterns but did not establish pathway activation, pyroptosis, or apoptosis. In PXD012263, the historical q < 0.05 proteomic selection contained 19 curated genes and retained mixed response polarity; sensitivity thresholds q < 0.01 and q < 0.10 selected 10 and 25 genes, respectively. Together, the results show that HIV restrictome activity varies across clinical and immune-cell contexts and is embedded within broader interferon-linked and immunometabolic states. Because the datasets are independent and not subject matched, the findings are observational and hypothesis-generating rather than causal.

PLoS ONEVol. 21(10)
St. Francis University College of Health and Allied Sciences (TZ)
Openalex Percentile: Top 16%
HIV Research and Treatment
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