Table 1.
Characteristics of GEO datasets utilized for investigating the common pathological mechanisms of CMV and HIV infections.
Fig 1.
Workflow of the dtudy.
Fig 2.
ASNS expression and enrichment in CMV/HIV infections.
(A) CMV infection upregulates ASNS. (B) HIV-1 infection upregulates ASNS. (C) 42 genes overlap in CMV/HIV. (D) PPI network shows ASNS as top hub. (E) MCC ranks ASNS first. (F) Centrality analysis highlights ASNS. (G) Random Forest confirms ASNS influence. (H) ASNS has high Gini scores. (I) ASNS linked to biosynthesis in CMV. (J) GSEA shows ASNS enrichment in CMV. (K) ASNS associated with biosynthesis in HIV. (L) ASNS role in co-infections underscored.
Fig 3.
ASNS and PI3K-AKT-mTOR pathway expression analysis.
(A) ASNS upregulation at 48h post-CMV infection. (B) AKT2 upregulation at 48h post-CMV infection. (C) AKT3 moderate increase at 48h post-CMV infection. (D) MTOR upregulation at 48h post-CMV infection. (E) Co-expression of ASNS with PI3K-AKT-mTOR pathway molecules. (F-G) Random Forest model shows AKT2 as key node. (H) Immune checkpoint gene expression changes post-CMV infection.
Fig 4.
ASNS and PI3K-AKT-mTOR expression in CMV/HIV infections.
(A) PIK3CA up in CMV-positive. (B) AKT1 up in CMV-primary. (C) ASNS up in acute and AIDS groups. (D) AKT1 up in acute HIV. (E-H) AKT2/3, MTOR, and LAG3 up in acute and AIDS. (I-L) ASNS, AKT2/3, and MTOR up in HIV virologic failure. (M) ASNS co-expresses with PI3K-AKT-mTOR in HIV. (N) Random Forest model performance in HIV. (O) Machine learning identifies AKT2 as key node in HIV. (P) LAG3 up in primary HCMV infection.
Fig 5.
Single-cell transcriptomics analysis of HIV infection.
(A-C) PCA and UMAP show cellular populations. (D-E) HIV alters immune cell proportions. (F-H) HIV upregulates PI3K-AKT-mTOR pathway and ASNS/AKT2 in plasma cells.
Fig 6.
Transcription factors regulating ASNS in CMV and HIV infections.
(A) Predicted transcription factors for IGJ, IGLL5, MZB1, and ASNS. (B) RUNX1 key in CMV. (C) RUNX1 accuracy in CMV. (D) RUNX1 importance in HIV-1. (E) RUNX1 motif prediction. (F) RUNX1 binding sites on ASNS promoter. (G) MDM2 up in HCMV. (H-J) ATF4, MDM2, RUNX1 dynamics across CMV stages. (K-M) ATF4, MDM2, RUNX1 down in HIV carriers. (N-P) ATF4, MDM2, RUNX1 up in HIV-resistant. (Q-R) MDM2 in HIV plasma cells.
Fig 7.
Validation of RUNX1 as a key biomarker for HIV resistance in GSE33580.
(A) Normalized expression distribution of GSE33580 (high-quality microarray data); (B) Normalized avg feature importance (3 models), confirming RUNX1’s top influence; (C) ROC analysis of candidates: RUNX1 as sole moderate diagnostic marker (AUC = 0.714); (D) Clinical impact curve: risk stratification without excessive false positives at 0.2–0.6 thresholds; (E) Decision curve analysis: positive net benefit at 0.1–0.7 thresholds (outperforming “treat all”/”treat none”); (F) Clinical prediction nomogram (5 features, RUNX1 highest weight): nomogram’s moderate diagnostic performance (AUC = 0.737) for HIV-resistant/negative distinction.
Fig 8.
LDA Model with RUNX1 for HIV Treatment Resistance Diagnosis and RUNX1-Mediated Clustering (A) Optimal LDA model (RUNX1): balanced clinical metrics (accuracy = 0.622, sensitivity = 0.535, specificity = 0.709, F1 = 0.586); (B) PCA biplot: RUNX1 as key driver of sample clustering (Dim1:34.6%, Dim2:23%); (C) AUC comparison: LDA model with highest AUC = 0.728.
Fig 9.
ASNS small molecule interactions.
(A) ONL forms 5 hydrogen bonds with ASNS. (B) Bisabosqual A forms 3 hydrogen bonds with ASNS. (C) Cidofovir forms 9 hydrogen bonds with ASNS. (D) Binding energy and RMSD for ligands. (E) ONL’s binding mode with ASNS residues. (F) Cidofovir’s binding mode with ASNS residues.
Fig 10.
Molecular dynamics of ASNS-ligand complexes.
(A) RMSD stability. (B) RMSF fluctuation. (C) COM distance consistency. (D) RG compactness. (E) SASA buried surface area. (F) Hydrogen bond formation. (G and I) Interaction diagrams of ASNS with ONL (G) and cidofovir (I). (H and J) Role of ASN-74 in hydrogen bonding for ASNS-ONL (H) and ASNS-cidofovir (J).