Molecular signatures and causal factors underlying latent cytomegalovirus infection among people living with HIV (PLHIV)

Domain: biology
Published: 2026-03-25
Editorial outcome: major_revision

Abstract
CMV seropositivity contributes to medical complications in people living with HIV (PLHIV). This study provides a comprehensive evaluation of how CMV seropositivity shapes the immune system of 1,887 PLHIV, by utilizing multi-omics and deep immune phenotyping datasets. The study measured the immune cell profiles from whole blood, and the cytokine production of PBMCs exposed to various ex-vivo stimuli. We observed an increase in pro-inflammatory cytokine production of circulating immune cells and differences in phenotype of innate-like lymphocyte populations associated with CMV seropositivity. This study also measured 5-omics layers, including genomics, DNA methylation, transcriptomics, and plasma protein and metabolites. The DNA methylome and transcriptome demonstrated prominent CMV-induced signatures related to immune functions in PLHIV. Particularly, high FCRL6 expression is a promising biomarker for immune activation, underlined by the demethylation of FCRL6 and up-regulation of gene expression and plasma protein concentrations in CMV-seropositive PLHIV. Host genetics-driven elevation in both gene and protein expression of FCRL6 was also associated with latent CMV infection. A significant CMV-seroprevalence locus was associated with cytokine production capacity and protein abundance. Mendelian randomization analyses demonstrated a causal relationship between elevated FCRL6 expression and CMV seropositivity.

Introduction
Cytomegalovirus (CMV), a ubiquitous member of the Herpesviridae family, is a double-stranded DNA virus with a ~236 kB genome. After primary infection, CMV establishes lifelong latency in early myeloid cells, including CD34+ haematopoietic stem cells and monocyte progenitor cells, as well as in blood monocytes1 and possibly also in other immune cells. CMV can reactivate when host immunity wanes, which threatens pregnant women (leading to congenital CMV) and immunocompromised patients. Estimated CMV seroprevalence ranges between 50 and 90% of the global population, depending on geography, behavior, and co-infection patterns2,3.

Before combination antiretroviral therapy (cART) became standard for people living with HIV (PLHIV), CMV disease, such as retinitis, colitis, pneumonitis, and encephalitis, occurred in up to 40% of patients with advanced HIV infection4. Modern cART suppresses HIV replication and partly restores immunity, yet many virally suppressed PLHIV still exhibit chronic immune activation, accelerated immune ageing, and higher rates of non-AIDS comorbidities5. Hence, asymptomatic virally-suppressed PLHIV using ART may still face some clinical consequences of a CMV co-infection, as recently reviewed by Freeman6.

Previous studies have investigated the effects of CMV infection, with a primary focus on specific biological processes such as DNA methylation7, gene expression8, or metabolism9. However, these single-layer studies cannot provide a comprehensive understanding of molecular signatures and driving factors of CMV infection. Such a systems-level picture is particularly important for PLHIV, who show both a very high CMV prevalence and persistent immune dysfunction despite suppressive cART5.

Thus, the 2000HIV study10, including 1895 HIV-suppressed PLHIV from the Netherlands, aims to unravel biological processes associated with HIV clinical phenotypes using functional immunological assays, deep immune phenotyping, and multi-omics analysis. Around 6% of 2000HIV study participants were CMV-seronegative (CMV-), allowing the opportunity to comprehensively investigate how latent CMV may shape the immune system of PLHIV.

Here, we pinpoint the molecular signatures that distinguish CMV-seropositive (CMV+) from CMV- PLHIV. We first show that CMV seropositivity associates with enhanced cytokine-production capacity and altered immune-cell composition. At the molecular level, CMV seropositivity is associated with widespread DNA methylation alterations that lead to substantial changes in gene expression and affect immune-related processes. Across layers, the Fc receptor-like gene (FCRL6) emerges as a consistent marker of CMV co-infection in PLHIV. It is demethylated in whole blood cells of CMV+ PLHIV, leading to upregulation at both gene expression in circulating immune cells and plasma protein abundance levels. Furthermore, Mendelian Randomization (MR) analyses reveal that genetic variants increasing FCRL6 expression or protein levels may causally raise CMV seropositivity risk. Complementary genome-wide association analysis uncovers a separate chromosome-15 locus linked to CMV status, cytokine production, and NK-cell receptor abundance. Together, these data provide the first multi-omics, functional, and genetic map of CMV co-infection in a large cohort of treated PLHIV, laying the groundwork for therapeutic strategies aimed at mitigating CMV-driven immune activation in HIV infection (Supplementary Fig. S1).

Results
A large PLHIV cohort with comprehensive multi-omics and immune profiling data
People living with HIV (PLHIV) on antiretroviral therapy (ART) may still experience long-term immune dysregulation. To dissect the underlying mechanisms, the 2000HIV study adopts a systems-immunology approach that combines multi-omics with functional immune assays. The study comprises a discovery (n=1553) cohort across three Dutch HIV treatment centers and an entirely independent validation cohort (n=334) from a fourth center, enabling immediate replication of key findings in an independent sample set (Fig. 1A)10. The combined cohort was predominantly male (84.7%) with a mean age of 51.6 years. Baseline cytomegalovirus (CMV) IgG was measured in all 1887 participants by ELISA, revealing seronegativity in around 6% of the discovery cohort (n=86) and around 8% of the validation cohort (n=28, Supplementary Data S1).

The resulting dataset spans genome-wide single-nucleotide polymorphisms (SNPs), whole-blood DNA-methylation profiles, bulk transcriptomes generated from peripheral blood mononuclear cells (PBMCs), targeted plasma proteomes, and untargeted plasma metabolomes. Whole-blood, high-dimensional flow-cytometry immunophenotyping provided cell counts of major innate and adaptive immune-cell subsets, while ex vivo stimulation of PBMCs with innate and adaptive ligands followed by cytokine quantification at 24 h and 7 days captured functional immune capacity (Fig. 1A). Comparative analyses in the discovery cohort uncovered significant CMV-associated differences across multiple omics layers and immunophenotyping readouts (Fig. 1B, Supplementary Data S2–S12), suggesting that latent CMV infection imprints a distinct molecular and functional signature on the immune system of PLHIV. The present work, therefore, focuses on defining the core genomic, epigenomic, transcriptomic, cellular, and cytokine-response features that characterize CMV latency within chronic, treated HIV infection.

Latent CMV infection heightens pro-inflammatory recall responses in PLHIV
Among the 90 cytokine-stimulus read-outs measured, the most pronounced CMV-associated effects appeared after exposing PBMCs to the CMV antigen pp65 for 24 h (Fig. 1C, Supplementary Data S2–S3). In both the discovery and validation cohorts, CMV-seropositive (CMV+) donors secreted significantly higher amounts of the monocyte-derived cytokines IL-1β, IL-1Ra, IL-8, and MCP1 than CMV-seronegative (CMV-) donors (FDR<0.05 discovery; P<0.05 validation). This agrees with previous studies where heightened inflammatory responses in PBMCs to CMV infection for PLHIV are already well-documented11–13.

As pp65 is an internal CMV protein that does not directly engage pattern-recognition receptors, a possible explanation for the elevated innate-cytokine output is that the antigen is processed and presented to pre-existing pp65-specific T cells. Activated CD8+ T cells are known to release TNF-α and IFN-γ upon pp65 recognition, a phenomenon termed memory inflation, and the resulting IFN-γ allows monocytes to amplify IL-1β, IL-1Ra, IL-8, and MCP-1 production14,15. Consistent with this prior knowledge, whole-blood immunophenotyping revealed higher frequencies of HLA-DR+ CD4+ and CD8+ T cells, NK cells, and gamma-delta (γδ) T cells in CMV+ PLHIV (Fig. 1E; Supplementary Data S4, S5). This mirrors the well-documented CMV-driven reshaping of CD4+, CD8+, NK-cell, and γδ-T-cell compartments in healthy adults15,16 and the capacity of CMV to upregulate HLA-DR expression on T cells17. Together, these findings establish that latent CMV infection primes a T-cell-driven, monocyte-dominated inflammatory response that distinguishes CMV+ from CMV- PLHIV.

Multi-layer molecular overview of CMV seropositivity in PLHIV
Across multi-omics levels, we observed substantial differences in molecular signatures associated with CMV seropositivity (Fig. 1B, Supplementary Data S6–S12). In particular, >16,000 CpG sites showed significant DNA methylation differences associated with the CMV seropositivity in PLHIV (FDR<0.05). A previous study has also highlighted that CMV infection elicits extensive changes in the DNA methylome in the blood of healthy adults18. Along with the steady changes in methylome, the expression of 1442 genes was significantly associated with CMV seropositivity in PLHIV (FDR<0.05), using bulk PBMC transcriptomics data. By contrast, only 5 out of 1720 plasma metabolites were significantly associated with CMV seropositivity in PLHIV at the discovery cohort (FDR<0.05, Supplementary Data S11), even though some studies found changes in metabolic pathways related to CMV infection19. Furthermore, these metabolites did not show statistically significant differences associated with CMV seropositivity in the validation cohort (Supplementary Data S12). Out of 2367 measured plasma proteins, the abundance of 38 proteins was significantly associated with the CMV seropositivity in the discovery cohort (Supplementary Data S9). Notably, the abundance of 18 out of these 38 proteins has shown similar differences in the validation cohort (Supplementary Data S10). The inflammatory markers include granzymes (GZM), Killer cell lectin-like receptor D1 (KLRD1), Adhesion G protein-coupled receptor G1 (ADRG1), and Fc receptor-like 6 (FCRL6), which are produced by NK cells, γδ T cells, and CD8+ T cells20, suggesting increased activation of both innate and adaptive immune cells in the context of CMV-HIV co-infections. Together with the concordant gene expression shifts and the underlying DNA-methylation differences that likely drive them, this protein signature supports a model in which latent CMV amplifies systemic inflammation in treated HIV infection, a mechanism that may contribute to the higher burden of CMV-related complications observed in this population. The following sections dissect these three layers in turn, beginning with DNA methylation, then the transcriptome and proteome.

CMV seropositivity is strongly associated with DNA methylation changes
To identify the epigenetic modifications associated with CMV infection, we performed epigenome-wide association studies (EWAS) of the whole blood DNA methylation data in both discovery and validation cohorts. We observed massive DNA methylation changes related to CMV seropositivity status across the entire genome; the nonparametric multidimensional scaling approach (NMDS)21 illustrates the significant methylation difference associated with CMV infection (Fig. 2A, adonis, R2=0.005, P=0.0005, with 1999 permutations). In the discovery cohort, we identified 16,245 differentially methylated CpG sites (DMSs) from 793,767 CpG sites (Fig. 2B; Supplementary Fig. S2A-C; FDR<0.05) associated with CMV seropositivity in PLHIV, of which 14,536 DMSs were validated in the validation cohort (Fig. 2C), and more than 85% could be replicated in the downsampled analysis (Supplementary Fig. S3). Notably, 98.7% of the validated DMSs can be replicated in EWAS based on data generated from people without HIV18 (Fig. 2D), and 78.6% of the significant CMV-associated DMSs detected from a large healthy French cohort (23,616 DMSs with FDR<0.05) could be replicated in the 2000HIV discovery cohort (Fig. 2E). This finding suggests a high degree of consistency in DNA methylation differences caused by CMV seropositivity in PLHIV and healthy individuals.

To understand the impact of CMV-induced epigenetic modification, we explored potential TF motifs near validated DMSs using HOMER22,23 motif enrichment analysis. We identified significantly enriched TF motifs, including FOS24, FRA124,25, BATF26, and ATF327 (Fig. 2F), which play roles in response to stress signals, apoptosis, inflammation, immune modulation, cell proliferation, and cell differentiation. From the validated DMSs, we diagnosed cis-expression Quantitative Trait Methylation (cis-eQTM) significant genes (Method, Supplementary Data S6, 4333 genes, FDR<0.05) and performed functional enrichment analysis with the GREAT database28 (Supplementary Fig. S2D). Thereby, we recognized that genes related to CMV-associated DMSs are significantly enriched in crucial biological processes for immune function and disease pathogenesis, such as mononuclear cell differentiation, T-cell differentiation and activation, B-cell activation, extracellular matrix organization, and cell motility (Fig. 2G, Supplementary Fig. S2D, E; FDR<0.05).

To investigate the interaction between validated DMSs and immune response changes associated with CMV seropositivity, we conducted mediation tests29 between CMV serostatus and the CMV-associated cytokines from PBMCs (Fig. 1D; Methods). Mediation effects from many DMSs were detected, which suggests that DNA methylation may partially contribute to the elevated innate immune responses (IL1β, IL1Ra, and IL8 production capacity) upon CMV stimulation, and make a direct contribution to adaptive immune response (IL-1ra, IL-10, and IL-22). Such effects may be conducted by the DMS-influenced genes (Fig. 2G). Among these DMSs, cg15843262 was associated with DNMT3A30,31 (Fig. 2H, I; Supplementary Fig. S2F) and can mediate approximately 23% of IL1Ra response to CMV stimulation (Fig. 2J, upper panel). Further mediation analysis suggests that cg15843262 mediated CMV’s impact on DNMT3A expression (Fig. 2J, lower panel). DNMT3A encodes DNA methyltransferase 3A, which plays a pivotal role in de novo DNA methylation during development. The CMV-associated DMS in the DMNT3A region could alter the transcription of DMNT3A (Fig. 2H) and thus potentially elucidate the widespread DNA methylation changes associated with CMV seropositivity (Fig. 2A). Hence, latent CMV infection induces DNA methylation alterations that influence immune-related processes and mediate immune response changes.

CMV-related molecular signatures at the transcriptome and proteome levels
We analyzed the bulk RNA sequencing data of 1496 PBMC samples in the discovery cohort and identified 1442 genes significantly associated with the CMV serostatus in PLHIV (DEGs, FDR<0.05, Supplementary Data S6). Among them, 700 DEGs (48.54%, FDR<0.05) were validated in the validation cohort (Supplementary Data S7). We performed pathway analysis in 807 DEGs validated in the validation cohort (P<0.05), including 390 up-regulated and 417 down-regulated DEGs (Fig. 3A), resulting in 155 and 11 enriched pathways, respectively (Fig. 3B, Supplementary Data S8). A downsampling analysis was performed 100 times with random sample selections to evaluate the potential bias due to the imbalance between the CMV+ and CMV- patient group size. On average, around 71.49% of 1442 DEGs in the discovery cohort were replicated in the downsampling analysis (FDR<0.05) (Supplementary Fig. S4). Upregulated DEGs are involved in various immune processes such as different NK cell functions, cytotoxicity, innate defense response, signaling, and regulation pathways (Fig. 3C, D). Cell-mediated immunity plays a crucial role in CMV control, especially CD8+ T cells, CD4+ T cells, NK cells, and γδ T cells32,33. The increased immune function found in gene expression data of PBMCs (Fig. 3D) is in line with the elevated proportion of HLA-DR+ immune cell subsets (Fig. 1E). While CMV-specific cell-mediated immune assays via interferon gamma signaling can be used to predict the development of clinical manifestations of CMV or identify transplant recipients with low risk of CMV infection treatment after transplantation34–36, other significant pathways arise from transcriptomic analysis, such as phosphatidylinositol 3 kinase signaling or CD4-positivity, alpha-beta T cell lineage commitment, maybe used to further support the prediction tests. Our finding of upregulated DEGs in innate and adaptive immune responses (Fig. 3D), as well as the increased expression of plasma inflammatory proteins in CMV seropositive PLHIV, suggests that CMV seropositivity has a significant impact in individuals with a dysfunctional immune system33. After the infection phase, CMV may continuously impact both innate and adaptive immunity during persistent and latent phases37.

We analyzed the plasma proteome of 1523 samples in the discovery cohort and detected the protein abundances of 38 proteins that were significantly associated with the CMV seropositivity in PLHIVs (DEPs, FDR<0.05, Supplementary Data S9). The 18 proteins among those differential abundance proteins (47.37%) were up-regulated in CMV+ compared to CMV- PLHIV, and were validated in the validation cohort (P<0.05) (Fig. 3E, Supplementary Data S10). To evaluate the potential bias due to the imbalance between the CMV+ and CMV- patient group size, we performed a downsampling analysis with random sample selections 100 times. On average, around 80.13% significant proteins in the discovery cohort were replicated in the downsampling analysis (FDR<0.05) (Supplementary Fig. S5). Importantly, eight validated proteins were also significantly upregulated at gene expression levels in CMV+ PLHIV across both discovery and validation cohorts and are also recognizable inflammatory markers (Fig. 3F; Supplementary Fig. S6A–C). Thus, CMV infection may not only impact gene expression of circulating immune cells but also influence systemic inflammation status.

To determine whether the CMV-associated proteomic changes we observed in PLHIV are HIV specific, we analysed the 300BCG cohort, including CMV+ (n=74) and CMV- (n=241) healthy individuals, which measured 73 proteins using an inflammatory Olink panel38. Nine proteins out of the 273 significant proteins (P<0.05) were shared between datasets, of which three were nominally significant in both cohorts and all with concordant direction of effect (Supplementary Fig. S6D). Though the sample size in the healthy cohort is relatively small, this result indicates that the CMV-driven protein alterations appear to be independent of HIV status.

CMV-responsive signals converge on cardiopulmonary and HIV-progression factors
Building on our previously published Multi-omics Factor Integration (MOFA) model of the European discovery subset (n=996)39, we asked whether CMV-associated molecular traits map onto the latent pathways already linked to comorbidities in PLHIV. We tested for over-representation of the CMV+ and CMV- differentially expressed molecules among the highest-weight features of the four disease-associated latent factors (LF6 plaque, LF8 hypertension & myocardial infarction, LF11 COPD, LF20 rapid progressors). We excluded DNA methylation from this analysis due to the direction of effect being more ambiguous than the other layers. Loadings were classified as risk or protective according to the direction of each factor’s correlation with its disease phenotype.

Three of the four clinical latent factors (LF8, LF11, and LF20) show a clear CMV imprint (FDR<0.05 across several top-weight cut-offs; Supplementary Fig. S7). LF11 is particularly striking. This factor, originally linked to both COPD risk and higher “latest CD8” counts, shows a significant positive correlation with CMV serostatus and a linear relationship with circulating CMV-IgG titres (Supplementary Fig. S7A, B). Its risk weights are enriched for CMV-upregulated genes and plasma proteins, whereas the protective tail is over-represented by CMV-down-regulated transcripts (Supplementary Fig. S7C). This polarity suggests that latent CMV infection drives transcriptional programs that feed into cytotoxic profiles captured by LF11, thereby predisposing to COPD pathogenesis. By contrast, transcripts reserved in CMV- individuals may buffer against excessive CD8+ activation and lung inflammation. This is not surprising, as COPD pathogenesis is tightly coupled to excessive CD8+ T-cell-driven inflammation40. In addition, while the impact of CMV co-infection on COPD has not yet been explored in PLHIV, chronic CMV infection itself has been associated with COPD in HIV-uninfected populations41.

For LF8, the factor linked to cardiovascular diseases, the risk features were enriched for CMV-upregulated protein expression (Supplementary Fig. S7D). This agrees with earlier reports linking CMV co-infection to cardiovascular comorbidity in PLHIV5. LF20, capturing rapid-progressor HIV phenotype, showed a similar CMV+ enrichment in its risk weights for gene expression signatures (Supplementary Fig. S7E), echoing evidence that CMV accelerates HIV disease progression42. All in all, the patterns shown here suggest that CMV infection amplifies molecular programs promoting heart and lung inflammatory diseases and HIV disease progression in PLHIV. Latent CMV amplifies systemic inflammation in treated HIV infection, a mechanism that may contribute to the higher burden of CMV-related complications observed in this population. The following sections dissect these three layers in turn, beginning with DNA methylation, then the transcriptome and proteome.

FCRL6 emerges as a promising biomarker of CMV seropositivity, evidenced by demethylation and increased expression and protein levels
Across multiple omics layers, we identified consistent markers that were significantly associated with CMV status in PLHIV in the discovery cohort, which were further replicated in the validation cohort (Supplementary Fig. S6A). Specifically, 508 markers were significantly validated at both methylation and gene expression levels, while 8 markers were even validated at plasma protein abundance levels (KIR2DL3, ITGAL, FCRL6, ADGRG1, GZMH, GBP1, GNLY, KLRD1) (Fig. 3, Supplementary Fig. S6B). KIR2DL3 is a natural killer (NK) cell receptor interacted with HLA-C1, and is strongly associated with the primary control of CMV infection in solid organ transplant recipients43. ADGRFG1 is a differentiation marker and inhibitory receptor on NK cells, and is expressed in a long-lived memory-like NK cell subset associated with prior human CMV infection44.

Fc receptor-like 6 molecule (FCRL6) emerged as a significant signature associated with CMV serostatus at methylation (Supplementary Data S13), gene expression, and plasma protein abundances. Particularly, FCRL6 demethylation in CMV-seropositive PLHIV corresponds to upregulation in gene expression and plasma protein abundance (Fig. 3F, G). The association of FCRL6 with CMV seropositivity was also replicated in all the downsampling analyses (FDR<0.05) (Supplementary Figs. S3–6). FCRL6 is an MHC class II receptor, and its expression in the peripheral blood of healthy individuals is mostly limited to T and NK cells20. Interestingly, whereas the MHC class II molecule HLA-DR is a ligand for FCRL6, CMV+ PLHIV have an elevated number of HLA-DR+ immune cell subsets (Fig. 1E). The significant upregulation of FCRL6 expression on T cells of PLHIV, also in those with undetectable HIV viral load or normal CD4 cell counts, has been previously reported20, including in hematopoietic stem cell-transplanted children with CMV reactivation45. Other markers associated with CMV serostatus at gene expression and protein levels, such as GNLY46 are also related to cytotoxic T lymphocyte differentiation and functions (Supplementary Fig. S3). These findings show that FCRL6, together with GNLY, is upregulated at various biological layers in CMV-seropositive PLHIV, underscoring its potential as a comprehensive biomarker for the effect of CMV on the immune system.

To pinpoint which immune cells contribute to the elevated plasma FCRL6 signal, we performed multicolor flow cytometry on PBMCs from four PLHIV in our cohort (two CMV+ and two CMV-). Cells were stained for CD3, CD4, CD8, CD14, CD19, CD45, CD56, TCRγδ and FCRL6. CMV+ individuals showed higher proportions of FCRL6-expressing CD8+ T cells and γδ T cells than CMV+ cells than CMV- counterparts (Supplementary Fig. S8). The frequency of FCRL6+ cells was also increased within CD14+ monocytes and CD56+ natural-killer cells. These data confirm that multiple cytotoxic and innate subsets upregulate FCRL6 in vivo during CMV co-infection and provide a cellular source for the elevated protein we detect in plasma.

GWAS identifies a significant CMV-seroprevalence locus linked to cytokine production and protein abundance
We hypothesized, therefore, the presence of a protective genotype and explored host genetics by performing a genome-wide association (GWAS) studies using 1338 donors of European ancestry in the discovery cohort (Supplementary Fig. S1A, B). The GWAS identified one genome-wide significant locus on chromosome 15, with the leading variant rs7180928 (Fig. 4A; Supplementary Fig. S9A, B). Notably, the rs7180928-G allele confers protection against CMV seroprevalence in PLHIV, an effect also observed in the validation cohort (Fig. 4B). Zooming into this locus to assess its regulation potentials, we evaluated cis-expression quantitative traits loci (cis-eQTL) and found that the rs7180928-G allele was significantly associated with expression for neuronal acetylcholine receptor subunit beta-4 (CHRNB4) (P=2.13e-05 by linear regression, Fig. 4C). However, the top cis-eQTL SNP of CHRNB4 is rs11629784, but this SNP is excluded in the GWAS analysis due to low frequency (MAF=6.96×10−3) in the discovery cohort. To further confirm the role of CHRNB4 in CMV infection in PLHIV, we performed colocalization and MR analyses to verify if the gene mediates PLHIV’s resistance to CMV. The colocalization resulted in a PPH4=0.34, PPH3=0.23, PPH2=0.39, PPH1=0.02, and PPH0=0.03 (Bayesian factor analysis), suggesting a gentle but potential shared locus between CHRNB4 expression and CMV-outcome. Subsequently, we performed a one-sample MR analysis to further support the observation using eQTL and GWAS summary statistics. The result indeed supports the colocalization analysis (Supplementary Fig. S9C, Supplementary Data S16). However, due to the limited expression of CHRNB4 in whole blood, future work is required to confirm these observations (Supplementary Fig. S9D).

Furthermore, we estimated the correlation of the protective allele with immune-related molecules, including proteins and cytokines (Fig. 4D, E). Of note, the protective allele was associated with decreased abundance of KIR2DS4 in the plasma (Fig. 4F, P=1.3×10−4, rho=−0.11, Spearman correlation test, one-way test, less than 0) and also correlated with the decreased IL22 production upon M. tuberculosis stimulation (Fig. 4G, P=2.7×10−2, rho=−0.07, Spearman correlation test, one-way test, less than 0). As KIR2DS4 was significantly highly expressed in CMV+ PLHIV in the discovery cohort (P=5.76×10−3), we observed the same direction in the validation cohort (Supplementary Fig. S9E). Meanwhile, IL22 was also significantly higher in CMV+ compared to the CMV- PLHIV (P=7.12×10−5, Fig. 4G) and was replicated in the validation cohort (Supplementary Fig. S9F). To summarize, we identified a genome-wide significant locus associated with CMV seropositivity in PLHIVs; multiple QTLs integration analysis from the same cohort suggest that this locus is involved in the regulation of immune responses, potentially contributing to the susceptibility to CMV infection in PLHIV (Fig. 4J).

Elevated gene and protein expression of FCRL6, driven by host genetics, is linked to CMV seropositivity
The consistency in FCRL6 expression associated with CMV seropositivity across multi-omics layers (Fig. 3F, G) prompted a hypothesis on whether gene expression and the plasma protein concentration of FCRL6 have a causal effect on CMV latent infection. We first performed QTL mapping for FCRL6 gene expression and protein abundance using linear regression models (Methods). Mendelian randomization (MR) leverages genetic variants (SNPs) associated with FCRL6 gene expression (eQTL) or protein abundance (protein QTL; pQTL) as instrumental variables (IVs) to infer causal relationships between these exposures and CMV seropositivity. We found significant positive causal relationships between both the gene expression (OR=1.11; P=2.98×10−3) and protein abundance (OR=0.66; P=2.77×10−4) with CMV infection risk (Fig. 5A). We primarily used the Inverse Variance Weighted (IVW) method to assess causal effects and conducted MR-Egger analysis to evaluate the robustness of these estimates against pleiotropy. The MR-Egger results for the protein closely aligned with those from IVW, suggesting minimal pleiotropic bias (Fig. 5B, D). Given the broader (pleiotropic) effects of trans-QTLs, we filtered out genetic SNPs (instrumental variables) associated with multiple genes and proteins to address potential violations of MR assumptions (Fig. 5C, E). Notably, we observed no overlap between the IVs for FCRL6 gene expression in PBMCs (Fig. 5C) and those for protein abundance in whole blood (Fig. 5E). This is consistent with previous studies showing that eQTLs and pQTLs commonly involve distinct genetic variants, reflecting differences in post-transcriptional and translational regulation47. However, we did identify two SNPs located in close proximity on chromosome 14 (rs140233210 at position 20,061,131 for gene expression and rs1959651 at position 20,080,570 for protein expression). Although these SNPs are not in linkage disequilibrium (LD), their close genomic proximity suggests that some shared regulatory mechanisms influencing FCRL6 expression at different biological levels might exist, warranting further investigation.

FCRL6 is mostly present on mature NK cells and effector CD8+ T cells, and effector memory CD8+ T cells in healthy subjects, as well as on γδ T cells and some CD4+ T cells48. FCRL6 is known to interact with MHC-II/HLA-DR molecules, inducing tolerance, while its effect on cytotoxicity and cytokine production is less clear49. Non-classical HLA molecules can modulate NK-cell checkpoints during CMV infection, especially HLA-E, which presents CMV-derived peptides to NK-cell receptor NKG2C50. We therefore surveyed all omics layers for HLA-E signals. HLA-E mRNA in PBMCs was modestly higher in CMV+ individuals (P=0.03, Supplementary Data S6), whereas circulating HLA-E protein and DNA methylation at the locus were unaltered. In the CMV GWAS, a sub-genome-wide signal lay adjacent to HLA-E (lead SNP rs2021368, p=4.25×10⁻⁵; Supplementary Fig. S9G). By contrast, whole-blood methylation profiling identified a robust CMV-associated CpG at the HLA-F/HLA-F-AS1 locus (Supplementary Data S14; cg08755130, FDR=5.7×10⁻⁷), hinting that epigenetic regulation of another non-classical HLA gene that engages NK receptors contributes to CMV-driven immune modulation51,52. Finally, we imputed classical HLA alleles with an HLA-specific reference panel to test whether MHC-I variation influences FCRL6 expression. We identified 4 HLA alleles that might affect the FCRL6 expression (P<0.05); however, these alleles had not passed the multiple testing correction (Supplementary Data S15).

Together, these genetic, epigenetic, and expression data converge on a single theme: host-driven up-regulation of FCRL6, modulated further by variation in non-classical HLA genes, forms a key axis that governs susceptibility to latent CMV infection in antiretroviral-treated PLHIV.

Methods
Study design and data sources
The 2000HIV study is a prospective multicentric observational longitudinal cohort of PLHIV on stable cART10. Participants were recruited from October 2019 until October 2021. The 2000HIV study was approved by the Independent Review Board Nijmegen (NL68056.091.81) and published at ClinicalTrials.gov (NCT03994835). Written informed consent was received from participants before inclusion in the study. All experiments with human samples were conducted following the principles of the Declaration of Helsinki. The sample sizes were comprehensively discussed in our previous publication, which announced the 2000HIV cohort10. The cohort was made up of the discovery cohort and the validation cohort. Participants in the discovery cohort were recruited from three specialized Dutch HIV treatment centers, two university medical centers, and one large general hospital (Radboudumc Nijmegen, Erasmus MC Rotterdam, and OLVG Amsterdam). Participants in the validation cohort were recruited in a separate medical center, a large general hospital (Elisabeth-TweeSteden Ziekenhuis Tilburg). Although the samples of the two sub-cohorts were collected separately, processing and measurements were identical. Inclusion criteria were: proven HIV-1 infection, age of 18 years or older, receiving cART for at least 6 months, and with a latest HIV-1 RNA of less than 200 copies/mL. Additionally, individuals with spontaneous control of HIV-1 without cART could participate if viral loads had been lower than <10,000 copies/mL for at least 5 years, during which CD4+ T-cell counts were stable >500 cells/mm3. Exclusion criteria were: no informed consent, insufficient communication because of language barriers or other problems, current pregnancy, detectable viral hepatitis B or C DNA by polymerase chain reaction (PCR), or signs of any current acute infection. In the 2000HIV cohort, we assessed the CMV status of 1887 PLHIV by performing an ELISA assay on serum samples according to the manufacturer’s protocol (GenWay, San Diego, California, US) (Supplementary Data S1).

Ex-vivo cytokine production measurement and analysis
Peripheral blood mononuclear cells (PBMCs) were stimulated with a panel of whole (inactivated) pathogens, pattern recognition receptor ligands, pathogen-derived antigens, and viral stimuli to assess cytokine production capacity. Stimulations were performed in round-bottom 96-well plates (Greiner Bio-One) using 500,000 cells per well. Cells were incubated either for 24 h at 37°C and 5% CO₂ or for 7 days in the presence of 10% human pooled serum.

For the 24-h stimulations, the stimuli included poly-inosinic:polycytidylic acid (poly I:C), lipopolysaccharide (LPS), imiquimod, interleukin-1α (IL-1α), HIV viral envelope, cytomegalovirus (CMV), and Streptococcus pneumoniae. The cytokines measured in supernatants after 24 h were IL-1β, IL-1Ra, IL-6, IL-8, IL-10, MCP-1, MIP-1α, and TNF. For the 7-day stimulations, the stimuli included Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis, Candida albicans (conidia and hyphae), and phytohemagglutinin (PHA). The cytokines measured after 7 days were IL-5, IL-10, IL-17, IL-22, and IFNγ. After stimulation, supernatants were collected and stored at −20°C until cytokine concentrations were quantified using enzyme-linked immunosorbent assay (ELISA). Details regarding stimulant concentrations, manufacturers, and microbial strains are provided in the cohort description.

The cytokine production abundance underwent inverse rank transformations to normalize their distributions across samples. We identified cytokine with differential production abundance between CMV-seropositive (CMV+) and CMV-seronegative (CMV-) individuals in the discovery cohort (FDR<0.05) using Robust Linear Regression model (RLM) with adjustment for the confounding effect of age, sex, BMI, center, season (season_sin, season_cos), and ethnicity (top5 genetic PCs) for the discovery cohort. We then validated the significant cytokine production abundances in the validation cohort. Since the validation cohort had a smaller sample size and was collected in one center site, we performed a similar differential cytokine production abundance analysis between CMV+ and CMV- individuals in the validation cohort (P<0.05) with adjustment for the confounding effect of age and sex. Confounders were selected based on prior knowledge and by inspecting the leading principal components in Principal Component Analysis (PCA). The cytokine production upon stimulation, which had significant differences between CMV+ and CMV- and had the same direction in the effect size in both discovery and validation cohorts, was considered significantly replicated.

Immune cell profiling and analysis
Whole blood samples were immunophenotyped by using three flow cytometry panels containing 17–20 markers each and custom-made tubes with dry antibodies from DURA Innovations Technology (Beckman Colter). Cells were acquired in a 21-color, six-laser CytoFLEX-LX (Beckman Colter) and using Cytexpert software 2.3 (Beckman Colter). Instrument quality control and standardization were performed daily using CytoFLEX Daily QC Fluorospheres (Beckman Colter catalog #B53230), CytoFLEX Daily IRQC Fluorospheres beads (Beckman Colter catalog #C06147), and SPHEROtm Rainbow calibration particles 6-peak (Spherotech Inc, catalog #RCP-30-5A-6)(21). Data analysis was performed using Kaluza V2.1.2 and Cytobank Platform V9.0 (Beckman Colter)10.

The absolute cell count underwent inverse rank transformations to normalize their distributions across samples. We identified cell subsets with different cell counts between CMV+ and CMV- individuals in the discovery cohort (FDR<0.05) using RLM models with adjustment for the confounding effect of age, sex, BMI, center, season (season_sin, season_cos), and ethnicity (top5 genetic PCs) for the discovery cohort. We then validated these differential cell count subsets in the validation cohort. Since the validation cohort had a smaller sample size and was collected in one center site, we performed a similar cell count analysis between CMV+ and CMV- individuals (P<0.05) with adjustment for the confounding effect of age and BMI. Confounders were selected based on prior knowledge and by inspecting the leading principal components in PCA. The cell type subsets, which were significantly different in cell counts between CMV+ and CMV- individuals and had the same direction in the effect size in both discovery and validation cohorts, were considered significantly replicated.

Methylation measurement and analysis
DNA methylation was performed on a total of 1914 samples. DNA was isolated from EDTA whole blood by the Radboudumc Genetics Department using Chemagic Star automated configuration (consisting of the Microlab STAR and Chemagen Magnetic Separation Module 1, Hamilton Robotics), combined with Chemagen nucleic acid extraction technology with magnetic polyvinyl alcohol (M-PVA) beads, which follows a standard and automated bind-wash-elute procedure. The concentration of the DNA and 260/280nm ratio were determined using a NanoDrop spectrophotometer, after which samples were normalized to 50 ng/µL in TE buffer and randomly distributed amongst plates. High-quality DNA was selected for genome-wide DNA methylation profiling using the Illumina Infinium Methylation EPIC BeadChip array (MethylEPIC v1 manifest B5). Standard sample- and probe-based quality control were performed.

As previously described, the DNA methylation dataset was divided into a discovery cohort (n=1546) and a validation cohort (n=322), and each cohort was analyzed separately. DNA methylation values were estimated from the raw IDAT files using the minfi package in R (v.4.2.0). Preprocessing steps were done to discard two gender mismatch samples from the discovery cohort, one bad-quality sample from the validation cohort (call rate <99%). Probes (Discovery: n=2743 and Validation: n=2641) with methylation value missing (detection P>0.01) at >10% samples and probes within the sex chromosome (n=19,627) were also excluded from the downstream analysis. Since the majority of the participants are European, we also removed the probes containing SNPs at the target CpG sites with a MAF>5% in European populations, as well as the probes that mapped to multiple loci, i.e., polymorphic probes, as suggested in (Both Discovery and Validation: n=52,173). Next, we implemented stratified quantile normalization. Methylation value was also utilized for estimating the proportion of six immune cell types, namely neutrophils, monocytes, B-Cells, NK cells, CD8-T cells, and CD4-T cells, using modified Housman’s method available within the estimateCellCounts2 function of the FlowSorted.Blood.EPIC package of R. Methylation β-values was calculated as a percentage: β=M/(M+U+100), where M and U represent methylated and unmethylated signal intensities, respectively, and β-values were then transformed to M-values as log2(β/(1−β)), and M-values were used in all downstream analyses.

To mitigate the effect of extreme outliers in data, we trimmed the methylation set using: (25th percentile − 3*IQR) and (75th percentile + 3*IQR), where IQR=interquartile range. Differentially methylated CpG sites associated with latent CMV infection by fitting a robust linear regression model. The methylation M value was used as the outcome variable, and the model was corrected for age, sex, season effect (using sin(2πt/T) and cos(2πt/T) (t is the time, T is the period), batch effects, and immune cell proportions. For EWAS with all ethnicity, the top five PCs extracted from the genotype of the same individuals were included in the model for the correction of ethnicity. For validation, only age and sex were adjusted in the model.

To assess the relationship between DNA methylation and gene expression, we calculated the Pearson correlation coefficient (r) between methylation levels at specific CpG sites and the expression of corresponding genes. The correlation analysis was performed using the cor() function in R, and statistical significance was evaluated using cor.test(). The correlation coefficient (r) and associated p-values were used to determine the strength and direction of the association.

CpGs were considered significantly replicated only if they have (i) discovery cohort: FDR<0.05, (ii) validation cohort: same direction as the discovery cohort, and FDR<0.05. eQTM was performed to explore the correlation between CpG methylation and gene expression. Replicated CpGs associated genes were subjected to enrichment analysis using the clusterProfiler package in R. Cis-eQTM was performed by the Limma67 package. Genes within 250k up- and down stream of the CpG sites were tested. The mediation effects of the CpG sites were tested by the mediation R package29. The threshold for partial mediation is Effect Size of prop_mediated greater than 0.2.

Gene expression measurement and analysis
Bulk RNA-seq of PBMCs was performed on Illumina platforms (>30 million reads per sample). Sequencing reads were aligned to the human reference genome (NCBI build 38) using STAR. Key QC metrics (read quality, duplication rates, alignment rates, gene-body coverage, etc.) were aggregated via MultiQC, and all samples passed standard thresholds. We then applied the following sample-level filters to the initial set of 1899 libraries: (i) excluding 2 libraries per Nijmegen team decision, (ii) removed 1 library with no uniquely mapped reads, (iii) discarded 3 duplicate-donor libraries, retaining the replicate with higher read depth, (iv) excluded 29 libraries with <5 million uniquely mapped reads, (v) excluded 7 libraries with discordant reported sex versus Y-chromosome read counts. This resulted in 1857 final samples (Supplementary Fig. S10). Importantly, no samples were removed as PCA outliers, in order to retain biologically relevant variation (Supplementary Fig. S10). For gene-level QC, 58,347 transcripts were detected, of which those with >=10 counts in the smallest comparison group were retained, yielding 16,000 to 20,000 genes per analysis. The reads underwent rlog transformation and normalization using the DESeq2 package68.

After normalization, we identified differentially expressed genes between CMV+ and CMV- individuals in the discovery cohort (FDR<0.05) using the DESeq2 package68 with adjustment for the confounding effect of age, sex, BMI, center, season (season_sin, season_cos), and ethnicity (top5 genetic PCs). We then validated these differentially expressed genes in the validation cohort. Since the validation cohort had a smaller sample size and was collected in one center site, we performed a similar differential gene expression analysis between CMV+ and CMV- individuals (P<0.05) with adjustment for the confounding effect of age and season (season_sin, season_cos). Confounders were selected based on prior knowledge and by inspecting the leading principal components in PCA. The gene expression, which were significant differences between CMV+ and CMV- and had the same direction in the log fold change in both discovery and validation cohorts, was considered significantly replicated.

Plasma protein measurement and analysis
Circulating plasma protein expression was assessed with the use of a commercially available multiplex proximity extension assay (PEA) from Olink® proteomics AB (Uppsala, Sweden). Plasma samples from PLHIV were measured in three batches. The first batch (n=692 samples) was measured using the library Olink® Explore 1536, consisting of 1472 proteins divided into four 384-plex panels focused on inflammation, oncology, cardiometabolic, and neurology proteins (panels I). The second batch (n=692 samples) was measured using the Olink® Explore Expansion 1536, consisting of 1472 proteins divided into four 384-plex panels focused on additional inflammation II, oncology II, cardiometabolic II, and neurology II proteins (panels II). The third batch was measured using the full library (Olink® Explore 3072), consisting of ~3000 proteins divided into eight 384-plex panels focused on inflammation, oncology, cardiometabolic, and neurology proteins (panels I and II). Protein measurements are delivered as Normalized Protein expression (NPX) values, which are Olink’s relative protein quantification unit on a log2 scale. Olink has developed a built-in quality control (QC) system using internal controls to control the technical performance of assays and samples. In addition, Olink services performed bridging normalization between oncology panel II and neurology panel II from batches two and three using 27 bridging samples. In addition to Olink’s QC, we performed bridging normalization to remove the batch effect between inflammation, oncology, cardiometabolic, and neurology panels from batch one and three using 32 bridging samples. In addition, we performed bridging normalization between cardiometabolic panel II and inflammation panel II from batches two and three using 23 bridging samples. Bridging normalization was performed by following the next steps for each protein: (1) We first calculated the median of the bridging samples for each protein in the two batches. (2) Then, we calculated the median difference, keeping one batch as a reference. (3) We finally subtracted the median difference from each protein in the non-reference batch. For bridging normalization between batch one and three, we used 1461 unique proteins measured in 692 samples that were common in batch one and three. For normalization between cardiometabolic II and inflammation II panel from batch two and three, we used 736 proteins measured in 1268 samples. Limit of detection (LOD) values per protein were re-adjusted by the same adjustment factor as the respective protein measurements after bridging normalization.

After removing batch effect using bridging normalization, we performed standard quality control per protein and sample. In each of the eight panels from the Olink@Explore 3072 platform, IL6, TNF, CXCL8, LMOD1, SCRIB, and IDO1 were measured as technical duplicates for quality control purposes. Strong correlations were observed between the technical duplicates among panels, and therefore, we selected the measurements from the inflammatory panel. Next, we excluded proteins with LOD>=25 of the samples (n=547 proteins were excluded), resulting in 2367 proteins for follow-up analysis. During QC per sample, we excluded 20 samples that were recorded as taking immunomodulatory drugs. Finally, to detect outliers, we performed PCA using the NPX values from 1938 samples. Outliers were defined as those samples falling above or below four standard deviations (SD) from the mean of principal component one (PC1) and/or two (PC2). In total, seven samples were excluded based on PCA, resulting in 1910 samples.

The plasma protein abundance underwent inverse rank transformations to normalize their distributions across samples. We identified proteins with differential abundance profiles between CMV+ and CMV- individuals in the discovery cohort (FDR<0.05) using RLM models with adjustment for the confounding effect of age, sex, BMI, center, season (season_sin, season_cos), and ethnicity (top5 genetic PCs). We then validated these differential abundance proteins in the validation cohort. Since the validation cohort had a smaller sample size and was collected in one center site, we performed a similar differential abundance analysis between CMV+ and CMV- individuals (P<0.05) with adjustment for the confounding effect of sex and season (season_sin, season_cos). Confounders were selected based on prior knowledge and by inspecting the leading principal components in PCA. The protein abundances, which were significant differences between CMV+ and CMV- and had the same direction in the effect size in both discovery and validation cohorts, was considered significantly replicated.

For a comparative analysis in healthy individuals, we used Olink data from the Dutch 300BCG study38 (CMV+=74, CMV−=241). Out of the 273 significantly differentially expressed proteins in the PLHIV discovery set, nine were also measured in the inflammatory panel used for this cohort. For these nine proteins, we fitted a linear model, correcting for age and sex effects.

Plasma metabolite measurement and analysis
We measured the abundance of 1720 circulated metabolites in 1902 serum samples by using General Metabolic’s untargeted metabolic platform. Untargeted metabolome profiling was performed on plasma samples using flow injection electrospray-time-of-flight mass spectrometry in collaboration with General Metabolics LLC69. Metabolites were identified based on the mass-to-charge ratio (ion m/z). The duplicate peak intensities in raw metabolome data were averaged and normalized using moving median normalization before further analysis. Principal component analysis was then applied to identify possible outlier samples. Metabolites are annotated and further categorized based on metabolomics source (endogenous, food, or drugs) and chemical taxonomy using publicly available data from The Human Metabolome Database70.

According to the Human Metabolome Database (HMDB), 851 out of these 1720 metabolites are endogenous metabolites and were used for further analysis. The plasma metabolite abundance underwent inverse rank transformations to normalize their distributions across samples. Thereafter, we identified metabolites with differential abundance profiles between CMV+ and CMV− individuals in the discovery cohort (FDR<0.05) using RLM models with adjustment for the confounding effect of age, sex, BMI, center, season (season_sin, season_cos), and ethnicity (top5 genetic PCs). We then validated these differential abundance metabolites in the validation cohort. Since the validation cohort had a smaller sample size and was collected in one center site, we performed a similar differential abundance analysis between CMV+ and CMV- individuals (P<0.05) with adjustment for the confounding effect of age and season (season_sin, season_cos). Confounders were selected based on prior knowledge and by inspecting the leading principal components in PCA. The metabolite abundances, which were significant differences between CMV+ and CMV- and had the same direction in the effect size in both discovery and validation cohorts, were considered significantly replicated.

Downsampling analysis
Since there is a limited number of CMV individuals (n=86) in the discovery cohort, to assess whether our larger CMV+ sample introduced bias, we repeated the seropositivity association analysis 100 times on downsampled datasets for DNA methylation, gene expression, and protein abundance. In each iteration, we randomly selected 86 CMV+ individuals to match the existing 86 CMV- individuals in the discovery cohort (n=172). We then compared the effect sizes from these 100 downsampled analyses to those from the original full-cohort analysis (n=1553). Finally, we calculated the proportion of molecules (FDR<0.05) whose associations were consistently replicated across the downsampling iterations.

Cross-referencing CMV-responsive signatures with published MOFA latent factors
To assess the role of CMV-associated molecules, we utilized a previously published Multi-Omics Factor Analysis (MOFA) model of the European discovery subset (n=996)39, which integrated matched whole-blood RNA-seq, plasma proteomics (Olink), untargeted metabolomics, and ex vivo cytokine responses39. The complete factor-feature weight matrix from that study served as the reference for all enrichment analyses described below; no additional factor training or tuning was performed in the present work.

Within four latent factors linked to clinical phenotypes, loadings were ranked separately by absolute positive and negative weights. Based on the direction of the correlation to the clinical phenotype, we labeled loadings as risk or protective for the respective disease. E.g., if the LF correlated positively with the phenotype, positive weights were deemed risk, if the LF correlated negatively, negative weights were deemed risk.

Eleven percentile cut-offs (5%-95% in 10% steps) defined nested top-N feature lists for both directions. At each cut-off, we performed one-tailed hypergeometric tests comparing the selected features against the CMV discovery lists, restricted to the view-specific universe. Four tests were evaluated per percentile (risk-up, risk-down, protective-up, protective-down). Benjamini-Hochberg correction was applied per LF across all percentiles and test combinations. Significant enrichments (adjusted P<0.05) were depicted as dot plots: point size represents percent overlap, color encodes -log10(padj), facets separate risk vs protective weights and CMV+/CMV- discovery sets.

Genotyping, imputation, and quality control
DNA was extracted from the whole blood of each participant. Genotyping for the multi-ethnic 2000HIV cohort was performed using the Illumina Infinium Global Screening Array. To ensure quality, raw variants and samples underwent rigorous quality control using PLINK v1.90b71. Variants with over 5% missing genotype data and those deviating from Hardy-Weinberg equilibrium (HWE) with a P value <1×10−6 were excluded. The HWE exact test was conducted on variants categorized by ethnicity. Samples with a call rate below 97.5% and those with heterozygosity rates deviating more than three standard deviations (SD) from the mean for their reported ethnicity were removed. Variants that passed quality control were mapped from GRCh37 to GRCh38 using the UCSC liftOver tool. Subsequently, TOPMed Freeze5 on genome build GRCh38 was utilized to align strands to the TOPMed reference panel. The McCarthy group’s tools were employed for alignment. Post quality control, 582,404 variants from 1864 individuals were retained for imputation. These filtered variants were uploaded to the TOPmed Imputation server and matched against the TOPMed (version r2 on GRCh38) reference panel. Using BCF tools, the imputed variants were filtered by ethnicity, excluding those with low imputation quality scores (R2<0.3 or ER2<0.7) and MAF<1%, resulting in 10,810,841 variants from 1864 participants in the 2000HIV multi-ancestry cohort.

Genome-wide association study of CMV infection
To understand the genetic determinants of CMV infection in the current study cohort, we performed a GWAS analysis using genome-wide SNPs (Genotype and quality control) to explain the CMV serostatus, i.e., negative or positive. The GWAS analysis follows the standard discovery-validation strategy, where the discovery and validation cohorts include 1546 and 322 donors, respectively. The GWAS was performed using the PLINK tool (version 1.90b) with the following options: --glm allow-no-covars firth-fallback-−1 --covar-variance-standardize --no-sex --maf 0.05. Age, gender, ethnicity, BMI, smoking, risk behaviors, CD4 nadir, CD4/CD8 ratio, HIV duration, cART duration, center, and top 20 genotype PCs (calculated by PLINK using default parameters) were included as covariables to account for potential confounding effects. The obtained summary statistics were visualized by a Manhattan plot to show the identified loci at p-value threshold of 5×10−8 using the R package topr (version 1.1.0). A fair lambda value (1.08) of the summary statistics indicate less risk of inflation, which was visualized by QQ plot using topr (Supplementary Fig. S9B). The top association signal was explored by regional plot using topr with default parameters to prioritize potential causal variants and genes. The obtained summary statistics are publicly available on Zenodo (https://doi.org/10.5281/zenodo.12580771).

Quantitative Loci (QTL) mapping
Following imputation, PLINK 2.072 was used for a final round of quality control. Variants failing the HWE test at P<1×10^−12, those with a MAF<1%, and an R2<0.05 were excluded. After this process, 8,944,122 imputed SNP variants were retained for subsequent analysis. We performed QTL mapping for gene expression (n=1048), protein levels (n=1064), and cytokine response (n=1014) using the MatrixEQTL R package (v2.3)73. We applied a linear model to map the inverse-rank transformed values to the genotype data, accounting for age, sex, BMI, seasonality, pre-COVID-19 inclusion, COVID-19 vaccination status, and recruitment center. Seasonality was modeled using a sine and cosine wave with a period of 365.25 days, which can combine to form a sine wave with any phase and a yearly frequency74. The full summary statistics can be found at https://lab-li.ciim-hannover.de/apps/hiv_xqtl_atlas/.

Mendelian randomization
We performed Mendelian Randomization (MR) analyses to explore the association between FCRL6 gene expression and protein levels (exposures) with latent CMV infection (outcome) using TwoSampleMR (v0.5.7)75. For cis-MR, instrumental variables (IVs) were selected within a 2Mb window around the FCRL6 gene with stringent clumping (kb=10,000, r2=0.001) and a significance threshold of P<1×10−3, yielding one significant SNP for gene expression and two for protein levels. Additionally, we identified 19 and 17 trans-IVs (P<1×10−5) for gene expression (eQTL) and protein levels (pQTL), respectively. To ensure specificity and reduce pleiotropy, we retained only trans-eQTLs associated with fewer than five genes (P<1×10−5). For trans-pQTLs, we retained those exclusively associated with the FCRL6 protein (P<1×10−5), yielding 14 SNPs. The cis SNPs were also included in the trans-MR analysis. We applied the Wald-ratio for a single instrumental SNP and inverse-variance weighted (IVW) for multiple SNPs, followed by sensitivity checks for heterogeneity (P>0.05) and pleiotropy (P>0.05), and a leave-one-out analysis (maximum P<0.05) to assess the robustness of the findings. This comprehensive approach allowed for a careful assessment of the genetic drivers of CMV in the context of FCRL6 expression and its protein levels.

MHC genetics related to FCRL6 expression analysis
The Michigan imputation server was used to impute SNPS, alleles, and amino acids in the MHC region (6:27,970,031-33,965,553) on GRCh37 build using the four-digit multi-ethnic HLAv2 reference panel (ref). After the standard QC per SNP and sample as described above, genotyped variants from chromosome 6 from 1864 individuals of all ethnicities from the 2000HIV cohort were submitted for imputation. Genetic variants were lifted from GRCh38 to GRCh37 using the Michigan server. After imputation, we obtained 22,733 SNPs, alleles, and amino acids within the MHC region, of which 570 were HLA classical alleles at two and four-digit resolution (HLA-A, HLA-B, HLA-C, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1, HLA-DRB1) and 3449 amino acids, 4023 SNPs within HLA, and 14691 scaffold SNPs.

Then, we mapped expression-QTLs (eQTLs) using MHC markers (HLA alleles, imputed amino acids, SNPs within the MHC region, etc, in total 19792 markers) with a minor allele count of at least 10 (MAC>10) in the discovery cohort and the gene counts of the FCRL6 gene. HLA alleles at two-digit resolution were removed from eQTL mapping. In total, we used 986 samples of European ancestry from the discovery cohort and 251 samples from the validation cohort that have both imputed genetic and transcriptomic data.

We applied a linear model to map the FCRL6 gene counts to the MHC data, accounting for age, sex, BMI, seasonality, and the first five PCs to account for population stratification. The gene counts were transformed using variance stabilizing transformation (VST) as implemented in the DESeq package.

Flow cytometry for FCRL6 in PLHIV PBMCs
The PBMCs were cultured in RPMI 1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% human pool serum overnight and the stained for the following markers: LIVE/DEAD fixable viability dye viakrome (Beckman Colter-Brea, CA, USA), CD3 (BV605-Biolegend-San Diego, CA, USA), CD4 (BUV395-Biolegend), CD8 (BV785-Biolegend), CD14 (AF700-Biolegend), CD19 (PE-Cy5-Biolegend), CD45 (Krome Orange-Beckman Colter-Brea, CA, USA), CD56 (BUV737 - Biolegend), TCRγδ (BV421 - Biolegend), and FCRL6 (APC-Biolegend). Antibodies were diluted in a total staining volume of 50 µL, which included Brilliant Stain Buffer Plus (BD Biosciences, Franklin Lakes, NJ, USA) and PBS supplemented with 2% bovine serum albumin (BSA- Sigma Aldrich, USA) and 2mM EDTA (Gibco) (PBS-F) as the staining/washing buffer. For FcRL6-APC, a fluorescence minus one (FMO) control was included to define the positive populations. Staining was performed for 30 min at 4°C in the dark, followed by washing with PBS-F+EDTA. Data acquisition was carried out using the CytoFLEX flow cytometer (Beckman Colter, Brea, CA, USA) (Supplementary Fig. S8A).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study comprehensively investigated the CMV-induced molecular phenotypes in a PLHIV cohort. The authors identified\nseveral immune signatures related to CMV seropositivity, especially FRCL6. There are several concerns listed as below.\nMajor points:\n1. The first big concern is the strong imbalance of the CMV+ and CMV- data size. The CMV+ group has much more samples\nthan CMV- group in both discovery and validation cohort. How did the authors make sure the results are not biased by the\nimbalance?\nFor Figure1B, for example, within the CMV+ group, there might be also heterogeneity, if the authors take 500 random CMV+\nsamples and compare with other 500 random CMV+ samples, how does the effect size distribution look?\nIf the authors try to downsample the CMV+ group to the same size of CMV- group and re-perform the analysis, would the\nresults be still similar?\n2. How much of the CMV + vs CMV- results are specific to PLHIV?\nDo the healthy CMV+ individuals also have the similar immune activation as observed in the PLHIV? How do these\nmolecular phenotypes connect to the HIV complications?\n3.Could the authors provide quality control information for the bulk RNA-seq data? For example, a PCA plot of all the\nsamples.\n4.Could the author perform colollization analysis to see if the GWAS and eQTL are sharing causal variants? Please include\nthe LD information in Fig4C. Is CHRNB4 the gene that mediates this protective effect? More biological insights (such as\ntissue/cell type expression patterns) might be added in addition to the current discussion.\n5.Please provide the statistical test results for Fig4F and G.\n6.Could the authors provide the complete results of pQTL and cQTL analysis instead of just showing 1 example in Figure\n4F,G,J)?\nA comprehensive multiple QTLs integration analysis should be performed and could serve as an independent figure itself.\n7.Is there pQTL/eQTL information for FCRL6?\nMinor points:\n1.Could the authors arrange the supp Figures in the chronological order?\n2.Line 243, il1ra should be Il1ra, the first letters should be capital.\n3.In Fig2I, what is the correlation coefficient between methylation and gene expression?\n4.Line249, FigS1H is wrong, there is no FigS1H.\n5.Figure4 A bottom got covered by 4C.\n6.Line 347, where is the Figure 5E?\n7.The row names in Fig1C are not easy to read, maybe the authors could try to remove the ‘pbmc_’ and also somehow\nrearrange the oder?\n8.Line257, is the validation using p value instead of adjusted p value?", "reviewer_2": "This study investigates the immunological, molecular, epigenetic and genetic impacts of cytomegalovirus (CMV)\nseropositivity in people living with HIV (PLHIV). CMV+ individuals exhibited heightened immune activation following PBMC\nexposure to CMV antigen. CMV seropositivity was also associated with significant DNA methylation changes affecting\nimmune-related gene expression, highlighting eight consistent markers, including FCRL6, which emerged as a potential\nmarker for immune activation. GWAS revealed a CMV-seroprevalence locus on chromosome 15 (rs7180928-G) linked to\ncytokine production and plasma protein abundance. Mendelian randomization analyses suggested that increased FCRL6\nexpression is genetically driven and associated with latent CMV infection. These findings underscore the role of CMV in\nimmune modulation in PLHIV and suggest broader implications for other immunocompromised populations.\nThe study has generated significant results with sound methodology. Here are a couple of recommendations for the paper.\n1. Methylation measured on bulk cells. Could the authors perform a deconvolution analysis and show if the specific cells that\nare infected with CMV show the overall pattern observed.\n2. It has been demonstrated that the non-classical HLA molecule, HLA-E, is responsible for CMV control. The results from\nthe manuscript indicate an NK related function, which is interesting since HLA-E is linked to NK cell function. Could the\nauthors examine the HLA-E region specifically to determine a direct methylation/transcriptome/GWAS effect on CMV. This\nwill identify a new pathway and future validate the data generated in this manuscript with the previously published HLA-E\ngene linked to CMV.", "reviewer_3": "This is a comprehensive multi-omic analysis of the relationship of CMV seropositivity (latent CMV infection) with the immune\nsystem in people with HIV, as part of the 2000HIV project. The authors have observed profound effects of having CMV on\ngene expression, cytokine production, plasma proteome and metabolome, and DNA methylation in the peripheral blood and\nin peripheral blood cells, using a discovery and a validation cohort approach. While some of the differences are expected\nfrom previous literature, this study is notable for its very meticulous approach, the large numbers of individuals analyzed by\neach method, and the connections among the different assays, which demonstrate regulation at several levels – for instance,\nCMV is associated with DNA de-methylation of FCRL6 (consistent with its upregulated gene expression and protein\nabundance) in CMV-seropositive individuals with HIV. Finally, the authors use GWAS and mendelian randomization\nanalysis to posit that the FCRL6 gene is causally linked to CMV infection, although how the authors think FCRL6 leads to\nCMV susceptibility is not well described.\nMajor Concerns:\n• The data is Figure 1C, showing increased cytokines produced by PBMCs of CMV-seropositive donors after CMV pp65\nstimulation for 24h seems to be a bit confusing to me. The authors don’t really justify why CMV pp65 peptide (an internal\nmatrix protein) should elicit innate cytokines directly. And an obvious alternative - that pp65 protein is processed and\npresented to CMV-reactive T cells in the culture, which then release cytokines (such as TNF or IFNg) to activate the innate\ncells – is seemingly not considered. Indeed, this may explain the reduced TNF in the validation cohort after CMV pp65\nstimulation (the darkest blue cell): innate cells are taking up the TNF.\n• The authors go to a lot of effort to investigate the role of CMV on FCRL6 (and vice versa), but there is no data on which\ncells are expressing it in vivo, and whether cellular FCRL6 levels are different in CMV seropositive and seronegative\nindividuals. They say from the literature that it is expressed on NK cells and T cells – could the authors stain PBMCs from\nCMV disparate individuals and show a difference in FCRL6 surface expression?\n• Could the authors discuss why none of the SNPs (instrumental variables) shown in Figure 5C (causal for gene expression)\noverlap with the SNPs in Figure 5E (causal for protein expression), and vice versa. Is this an expected finding or not?\nMinor Concerns:\n• Please refrain from using terms like “HIV-infected” or “non-HIV-infected”. Just use people (living) with HIV or people without\nHIV. The term “patients” is also discouraged.\n• Table S7 only seems to contain 700 DEGs (FDR <0.05), not 807 as stated in the text (line 257).\n• Figure 5 panel E is not labeled within Figure 5.\n• Line 587 in the Methods says “(show figures)” ???\n• Figure 2B legend (lines 769-771) the x-axis and y-axis designations are reversed from what is shown in Figure 2B.\n• Figure 2F legend (line 780) says “MOMER” instead of “HOMER”.", "reviewer_4": "This is an interesting manuscript that assesses CMV serostatus in relation to a large study of 'omics data in a cohort of\npeople living with HIV. The work develops hypotheses related to the impact of CMV on the immune system.\nPoints\n-The clinical cohort has a very high rate of CMV seroprevalance and this may reflect demographic factors related to infection\nrisk. The relevance of the data to the general population is thus not clear and this should be stated as a limitation.\n-I find the manuscript to be rather poorly written for the scientific reader. I would advise a considerable rewriting that\naddresses the cohort strucutre, experiments that were done in each section and the specific outcomes. The abstract also\nneeds to be succinct and clear\n-The cytokine functional assessment is in relation to the short term response to challenge with a CMV protein. CMV\nmodulates the immune systems in substantial ways and as such this differential response is to be expected.\n-the work uses bulk transcriptomics and the increase in many cytotoxic transcripts is not surprising.\n-CMV induces major changes in the immune cell repertoire - has this been corrected for in relation to the relative prevalence\nof genes and methylation profiles associated with this transition?\n-The work also seeks to assess genomic risk factors related to CMV infection. There has been prior work in this regard and\nthe incremental advance here seems modest. The potentially unusual features of the residual CMV-seronegative donors\nwithin this cohort should be discussed.\nThe FCRL6 association is interesting and the most important feature of the manuscript. The magnitude of this effect, and as\nsuch its potential use in assessment of CMV status within individual donors, could be expanded."}, "author_response": "We thank Reviewer #1 for recognising the comprehensive nature of our multi-omic investigation into\nCMV-induced molecular phenotypes in PLHIV and for highlighting FCRL6 as a key immune signature.\nMajor points:\n1.1. The first big concern is the strong imbalance of the CMV+ and CMV- data size. The CMV+ group\nhas much more samples than CMV- group in both discovery and validation cohort. How did the\nauthors make sure the results are not biased by the imbalance?\nFor Figure1B, for example, within the CMV+ group, there might be also heterogeneity, if the authors\ntake 500 random CMV+ samples and compare with other 500 random CMV+ samples, how does the\neffect size distribution look?\nIf the authors try to downsample the CMV+ group to the same size of CMV- group and re-perform\nthe analysis, would the results be still similar?\nWe thank the reviewer for this important comment regarding the imbalance between CMV+ and\nCMV- groups.\nWe agree a downsampling analysis would be a good way to ensure that our findings are not biased\nby the larger sample size in the CMV+ group. Therefore, we conducted the association analysis 100\ntimes, each time randomly selecting 86 CMV+ individuals to match the 86 CMV- individuals in the\ndiscovery cohort. We then compared the effect sizes from these 100 downsampled analyses to those\nobtained from the full discovery cohort.\nThe results were highly consistent. The associations retained the same direction of effect across\ndownsampling iterations.\n● DNA methylation (Figure R-1): more than 85% of the top associations were replicated in the\ndownsampled datasets.\n● Transcriptomics (Figure R-2): on average, around 71.49% significant genes (FDR < 0.05) were\nreplicated in the downsampled dataset. Importantly, the gene of interest (FRCL6) was\nsignificant in all the downsampling analysis.\n● Proteomics (Figure R-3): on average, around 80.13% significant proteins (FDR < 0.05) were\nreplicated in the downsampled dataset. Importantly, the protein of interest (FRCL6) was also\nsignificant in all the downsampling analysis.\nFigure R-1. Correlation between effect sizes from the full DNA methylation analysis (x-axis) and the\ndownsampled analyses (y-axis). The darker colour indicates if the CpG site was also significant in the respective\ndownsampling analysis (FDR < 0.05). The percentages of significant CpG sites (FDR < 0.05) replicated in the\ndownsampling iterations are in the range between 85% - 98%. This figure has been included in the revised\nmanuscript as Supplementary Fig. S3.\niterations in the discovery cohort. The percentages of significant genes (FDR < 0.05) replicated in the\ndownsampling iterations are in the range between 48.89% - 86.55% with mean = 71.49%. The x-axis is the\neffect size from original analysis. The y-axis is the effect size from downsampling analysis. This figure has been\nincluded in the revised manuscript as Supplementary Fig. S4.\niterations in the discovery cohort. The percentages of significant proteins (FDR < 0.05) replicated in the\ndownsampling iterations are in the range between 44.74% - 100.00 % with mean = 80.13%. The x-axis is the\neffect size from original analysis. The y-axis is the effect size from downsampling analysis. This figure has been\nincluded in the revised manuscript as Supplementary Fig. S5.\nThese findings demonstrate that the key results are robust and not driven by group size imbalance.\nWe included the downsampling analyses methods and outcomes in our revised manuscript as\nfollowed:\nPage 9, line 219: “and more than 85% could be replicated in the downsampled analysis\n(Supplementary Fig. S3).”\nPage 10, line 262: “A downsampling analysis was performed 100 times with random sample\nselections to evaluate the potential bias due to the imbalance between the CMV+ and CMV- patient\ngroup size. On average, around 71.49% of 1442 DEGs in the discovery cohort were replicated in the\ndownsampling analysis (FDR < 0.05) (Supplementary Fig. S4).“\nPage 17, line 499: “To evaluate the potential bias due to the imbalance between the CMV+ and CMV-\ngroup size, we performed a downsampling analysis with random sample selections 100 times. On\naverage, around 80.13% significant proteins in the discovery cohort were replicated in the\ndownsampling analysis (FDR < 0.05) (Supplementary Fig. S5)”\n“To assess whether our larger CMV+ sample introduced bias, we repeated the seropositivity\nassociation analysis 100 times on downsampled datasets for DNA methylation, gene expression, and\nprotein abundance. In each iteration, we randomly selected 86 CMV+ individuals to match the 86\nCMV- individuals in the discovery cohort (n = 172). We then compared the effect sizes from these 100\ndownsampled analyses to those from the original full-cohort analysis (n = 1553). Finally, we\ncalculated the proportion of molecules (FDR < 0.05) whose associations were consistently replicated\nacross the downsampling iterations.”\n1.2. How much of the CMV + vs CMV- results are specific to PLHIV?\nDo the healthy CMV+ individuals also have the similar immune activation as observed in the PLHIV?\nHow do these molecular phenotypes connect to the HIV complications?\nThank you for your questions. We have answered them in subparts below.\n1.2a. How much of the CMV + vs CMV- results are specific to PLHIV?\nIn addition to the previously reported 98.7% concordance of CMV-associated DNA methylation\nchanges with an independent healthy cohort, we have now extended our analysis to protein\nabundance in the Dutch young healthy (300BCG) cohort (DOI: 10.1172/JCI133935). Out of the 273\nproteins showing P < 0.05 in our PLHIV analysis, nine were included in the healthy cohort. All of these\nhad concordant direction of effect, and six were also nominally significant. This high level of\nreplication at the protein level, together with our initial methylation concordance, indicates that\nCMV-driven molecular alterations are robust and occur independently of HIV infection.\nWe have revised our manuscript and added the new Results on page 11, lines 294-300:\n“To determine whether the CMV-associated proteomic changes we observed in PLHIV also occur\nHIV-specific, we analysed the 300BCG healthy cohort (CMV+ = 74, CMV- = 241), which measured 73\nproteins using an inflammatory Olink panel38. Nine proteins out of the 273 significant proteins (P <\n0.05) were shared between datasets, of which three were nominally significant in both cohorts and\nall with concordant direction of effect (Supplementary Fig. S6D). Though the sample size in the\nhealthy cohort is relatively small, this result indicates that the CMV-driven protein alterations appear\nto be independent of HIV status.”\nCMV- in PLHIV and matching proteins in a healthy cohort. This figure has been included in the revised\nmanuscript as Supplementary Fig. S6D.\nAs well as new Methods on page 24, lines 732-735:\n“For a comparative analysis in healthy individuals, we used Olink data from the Dutch 300BCG study38\n(CMV+ = 74, CMV- = 241). Out of the 273 significantly differentially expressed proteins in the PLHIV\ndiscovery set, nine were also measured in the inflammatory panel used for this cohort. For these\nnine proteins we fitted a linear model, correcting for age and sex effects.”\n1.2b. Do the healthy CMV+ individuals also have the similar immune activation as observed in the\nPLHIV?\nWe appreciate the reviewer’s insightful question. While cytokine response data are available for the\nhealthy cohort mentioned in the previous question, these responses were not measured following\nCMV-specific stimulation. As such, a direct comparison to the CMV-specific immune activation\nobserved in PLHIV is not currently feasible. We acknowledge this as a limitation and hope to explore\nthis question in future studies with matched antigen-specific assays.\n1.2c. How do these molecular phenotypes connect to the HIV complications?\nWe thank the reviewer for this question. Building on our recently published Multi-Omics Factor\nAnalysis (MOFA) analysis in the European discovery subset (n = 996; integrating gene expression,\nplasma proteins, metabolites, cytokine responses and DNA methylation; DOI:\n10.1038/s41591-025-03887-1), we performed an over-representation analysis (ORA) between the\nCMV-associated molecular traits identified here and the trait loadings that define each latent factor\n(LF). We excluded DNA methylation as the direction of effect can be more ambiguous than the other\nlayers.\nFor each LF significantly associated with a clinical phenotype, loadings were given a “risk” or\n“protective” label:\n5\n● if the LF correlated positively with the phenotype, features with positive loadings were\nclassified as risk;\n● if the LF correlated negatively, features with negative loadings were classified as risk.\nOut of four clinical phenotype-linked LFs, three were significantly enriched for genes and proteins up\nor down-regulated in CMV+ individuals (Figure R-5): Factor 8, correlated with hypertension and prior\nmyocardial infarction, (ii) Factor 11, associated with chronic obstructive pulmonary disease (COPD),\n(iii) Factor 20, associated with the rapid-progressor phenotype in HIV infection.\nWe added these new results to a new Results section titled ‘CMV-responsive signals converge on\ncardiopulmonary and HIV-progression factors’ on pages 11-12, lines 301-330:\n“Building on our previously published Multi-omics Factor Integration (MOFA) model of the European\ndiscovery subset (n = 996)39, we asked whether CMV-associated molecular traits map onto the latent\npathways already linked to comorbidities in PLHIV. We tested for over-representation of the CMV+\nand CMV- differentially expressed molecules among the highest-weight features of the four\ndisease-associated latent factors (LF6 plaque, LF8 hypertension & myocardial infarction, LF11 COPD,\nLF20 rapid progressors). We excluded DNA methylation from this analysis due to the direction of\neffect being more ambiguous than the other layers. Loadings were classified as risk or protective\naccording to the direction of each factor’s correlation with its disease phenotype.\nThree of the four clinical latent factors (LF8, LF11 and LF20) show a clear CMV imprint (FDR<0.05\nacross several top-weight cut-offs; Supplementary Fig. S7). LF11 is particularly striking. This factor,\noriginally linked to both COPD risk and higher “latest CD8” counts, shows a significant positive\ncorrelation with CMV serostatus and a linear relationship with circulating CMV-IgG titres\n(Supplementary Fig. S7A,B). Its risk weights are enriched for CMV-upregulated genes and plasma\nproteins, whereas the protective tail is over-represented by CMV-down-regulated transcripts\n(Supplementary Fig. S7C). This polarity suggests that latent CMV infection drives transcriptional\nprogrammes that feed into cytotoxic profile captured by LF11, thereby predisposing to COPD\npathogenesis. By contrast, transcripts reserved in CMV- individuals may buffer against excessive CD8+\nactivation and lung inflammation. This is not unsurprising, as COPD pathogenesis is tightly coupled to\nexcessive CD8+ T-cell driven inflammation40. In addition, while the impact of CMV co-infection on\nCOPD has not yet been explored in PLHIV, chronic CMV infection itself has been associated with\nCOPD in HIV-uninfected populations41.\nFor LF8, the factor linked to cardiovascular diseases, the risk features were enriched for\nCMV-upregulated protein expression (Supplementary Fig. S7D). This agrees with earlier reports\nlinking CMV co-infection to cardiovascular comorbidity in PLHIV5. LF20, capturing rapid-progressor\nHIV phenotype, showed a similar CMV+ enrichment in its risk weights for gene expression signatures\n(Supplementary Fig. S7E), echoing evidence that CMV accelerates HIV disease progression42. All in all,\nthe patterns shown here suggest that CMV infection amplifies molecular programmes promoting\nheart and lung inflammatory diseases and HIV disease progression in PLHIV.”\nenrichment iteratively from 5% until 95% of the top features for each LF. Only dots passing multiple testing\ncorrections are colored (FDR < 0.05). These figures have been included in the revised manuscript as\nSupplementary Fig. S7.\nMOFA latent factors’, page 25, lines 768-787):\n“To assess the role of CMV-associated molecules, we utilised a previously published Multi-Omics\nFactor Analysis (MOFA) model of the European discovery subset (n = 996) which integrated matched\nwhole-blood RNA-seq, plasma proteomics (Olink), untargeted metabolomics, and ex-vivo cytokine\nresponses39. The complete factor-feature weight matrix from that study served as the reference for\nall enrichment analyses described below; no additional factor training or tuning was performed in\nthe present work.\nWithin four latent factors linked to clinical phenotypes, loadings were ranked separately by absolute\npositive and negative weights. Based on the direction of the correlation to the clinical phenotype, we\nlabelled loadings as risk or protective for the respective disease. E.g. if the LF correlated positively\nwith the phenotype, positive weights were deemed risk, if the LF correlated negatively, negative\nweights were deemed risk.\nEleven percentile cut-offs (5%-95% in 10% steps) defined nested top-N feature lists for both\ndirections. At each cut-off we performed one-tailed hypergeometric tests comparing the selected\nfeatures against the corresponding CMV discovery lists, restricted to the view-specific universe. Four\ntests were evaluated per percentile (risk-up, risk-down, protective-up, protective-down).\nBenjamini-Hochberg correction was applied per LF across all percentiles and test combinations.\nSignificant enrichments (adjusted P < 0.05) were depicted as dot plots: point size represents percent\noverlap, colour encodes -log10(padj), facets separate risk vs protective weights and CMV+/CMV-\ndiscovery sets.“\n1.3. Could the authors provide quality control information for the bulk RNA-seq data? For example, a\nPCA plot of all the samples.\nThank you for requesting additional QC details for our bulk RNA-seq data. We have revised the\nmanuscript (Methods ‘Gene expression measurement and analysis’, page 22, lines 656-669) to\ninclude the full QC and filtering workflow as follows:\n“Bulk RNA-seq of PBMCs was performed on Illumina platforms (>30 million reads per sample).\nSequencing reads were aligned to the human reference genome (NCBI build 38) using STAR. Key QC\nmetrics (read quality, duplication rates, alignment rates, gene-body coverage, etc.) were aggregated\nvia MultiQC, and all samples passed standard thresholds. We then applied the following sample-the following sample-level\nfilters to the initial set of 1,899 libraries: (i) excluding 2 libraries per Nijmegen team decision,\n(ii) removed 1 library with no uniquely mapped reads, (iii) discarded 3 duplicate-donor libraries,\nretaining the replicate with higher read depth, (iv) excluded 29 libraries with <5 million uniquely\nmapped reads, (v) excluded 7 libraries with discordant reported sex versus Y-chromosome read\ncounts. This resulted in 1,857 final samples (Supplementary Fig. S10). Importantly, no samples were\nremoved as PCA outliers, in order to retain biologically relevant variation (Supplementary Fig. S10).\nFor gene-level QC, 58,347 transcripts were detected, of which those with >=10 counts in the smallest\n8\ncomparison group were retained, yielding 16,000 to 20,000 genes per analysis. The reads underwent\nrlog transformation and normalisation using DEseq2 package68.”\nWe have also added the requested PCA plot of all 1,857 samples (Figure R-5). We hope these\nadditions fully address your comment.\nFigure R-5. PCA was performed on normalized expression values of all genes in the discovery cohort. The first\nprincipal component (PC1) explains 22% of the variance and the second principal component (PC2) explains\n6.8% of the variance. Each point represents a single individual, color-coded by clinical condition: HIV-infected\nprogressors (HIV; red), elite controllers with transient CMV reactivation (EC_TRANSIENT; cyan), rapid\nprogressors (RP; orange), and elite controllers with persistent CMV reactivation (EC_PERSISTENT; dark red).\nShaded ellipses denote the 95% confidence interval for each group. This figure has been included in the revised\nmanuscript as Supplementary Fig. S10.\n1.4. Could the author perform colollization analysis to see if the GWAS and eQTL are sharing causal\nvariants? Please include the LD information in Fig4C. Is CHRNB4 the gene that mediates this\nprotective effect? More biological insights (such as tissue/cell type expression patterns) might be\nadded in addition to the current discussion.\nThank you for your questions. We have answered them in subparts below.\n1.4a. Could the author perform colollization analysis to see if the GWAS and eQTL are sharing causal\nvariants?\nFollowing the reviewer’s suggestion, we have performed colocalization analysis between eQTL of\nCHRNB4 and GWAS of CMV. The coloc returns a PP4 = 0.34 and PP3 = 0.23. The combination of these\ntwo posterior p-values suggest a correlation between CHRNB4 expression and CMV+/-, while they\nlikely share the same causal variant. However, limited by the sample size, we did not reach the\nempirical threshold of PP4 >= 0.5. Therefore, we move forward to perform a one sample Mendelian\nrandomization (MR) analysis. The result suggested a potential causal relationship between CHRNB4\nexpression and CMV-seronegative outcome in our cohort (Figure R-6). Collectively, the colocalization\nand MR analysis support the role of CHRNB4 in controlling CMV resistance in PLHIV. These results\nhave been added to the revised manuscript (page 14, lines 379-386):\n“To further confirm the role of CHRNB4 in CMV infection in PLHIV, we performed colocalization and\nMR analyses to verify if the gene mediates PLHIV’s resistance to CMV. The colocalization resulted in a\nPP4 = 0.34 and PP3 = 0.23 (Bayesian factor analysis), suggesting a gentle but potential shared locus\nbetween CHRNB4 expression and CMV-seronegative outcome. Subsequently, we performed a\none-sample MR analysis to further support the observation using eQTL and GWAS summary\nstatistics. The result indeed supports the colocalization analysis (Supplementary Fig. S9C). However,\ndue limited expression of CHRNB4 in whole blood, future work is required to confirm these\nobservations.”\nFigure R-6. MR analysis between CHRNB4 expression and PLHIV resistance to CMV. This figure has been\nincluded in the revised manuscript as Supplementary Fig. S9C.\n1.4b. Please include the LD information in Fig4C.\nWe thank the reviewer for the suggestion to add LD information in Fig. 4C. We feel this information\nwill definitely improve the figure. Due to the new layout, we have adjusted the figure by splitting the\ntwo summary statistics, updating the figure legend as well. The new figure can be found below\n(Figure R-7).\n1.4c. Is CHRNB4 the gene that mediates this protective effect? More biological insights (such as\ntissue/cell type expression patterns) might be added in addition to the current discussion.\nWe thank the reviewer’s suggestion to dive into the locus to gain more biological insights. From the\nresults above, we have concluded that CHRNB4 mediates the protective effect. However, the\nCHRNB4 gene is specifically expressed in the testis (Figure R-8). We did not observe expression\npatterns between CMV+ and CMV- due to limited expression of CHRNB4 in whole blood. Previous\nstudy have suggested the role of CHRNB4 in lung function (e.g., lung cancer and COPD,\n10.3389/fonc.2020.571167, 10.1038/s41467-021-26637-6), and addiction to nicotine\n(10.1038/npp.2010.95, 10.1093/hmg/ddr498, 10.1038/ng.571, 10.1038/mp.2013.158) etc, while\nthere is limited studies clearly state the relation between CMV infection and CHRNB4 expression. Of\nnote, Lunardi et al have suggested the response of CHRNB4 in fibroblasts upon the treatment of\nanti-hCMV antibodies (Table 2, 10.1371/journal.pmed.0030002), suggesting the potential role of\nCHRNB4 in CMV infection, which has been discussed in the manuscript (page 17, line 498).\nFigure R-8. Violin plots show the tissue expression of CHRNB4 across tissue from the GTEx portal.\n11\n1.5. Please provide the statistical test results for Fig4F and G.\nThank you for this suggestion. We have now added the statistical test results in Figure 4F and G,\nas\nshown below in Figure R-9.\nFigure R-9. New panels F (left) and G (right) for Fig. 4, showing the statistical tests between the violin plots.\n1.6. Could the authors provide the complete results of pQTL and cQTL analysis instead of just\nshowing 1 example in Figure 4F,G,J)? A comprehensive multiple QTLs integration analysis should be\nperformed and could serve as an independent figure itself.\nWe appreciate the reviewer’s suggestion. The full results of the pQTL and cQTL analyses (currently\nfiltered at the threshold of P < 1x10-5), as well as eQTL, are publicly available on our website. These\nanalyses were conducted as part of a comprehensive multiple QTL integration analysis by our group,\nand the corresponding manuscript is currently online at Nature Medicine.\nTo indicate that our FCRL6 QTL analysis is part of a a larger comprehensive analysis, we have added\nthe following text under the QTL mapping Methods on page 27, line 830:\n“The full summary statistics can be found at https://lab-li.ciim-hannover.de/apps/hiv_xqtl_atlas/.”\n1.7. Is there pQTL/eQTL information for FCRL6?\nThank you for your question. Yes, in fact we used the eQTL and pQTL information for FCRL6 in our\nMendelian Randomization (MR) analysis shown in Figure 5, selecting significantly associated genetic\nvariants as instrumental variables (IV). The IV for the FCRL6 gene expression MR can be found in\nFigure 5C, and the ones for the FCRL6 protein MR are shown in Figure 5E.\nTo indicate this more clearly, we have modified the MR results to mention that the IV are derived\nfrom QTL summary statistics on page 14, line 405: “Mendelian Randomization (MR) leverages\ngenetic variants (SNPs) associated with FCRL6 gene expression (eQTL) or protein abundance (protein\nCMV seropositivity.”, and by adding “(eQTL)” and “(pQTL)” to the Methods ‘Mendelian\nRandomization’ on page 27.\nMinor points:\n1. Could the authors arrange the supp Figures in the chronological order?\nThank you for this suggestion. We have now rearranged the Supplementary Figures to reflect the\nchronological order in which they are referenced in the manuscript, enhancing readability and clarity.\n2. Line 243, il1ra should be Il1ra, the first letters should be capital.\nThank you for pointing this out. We have corrected il1ra to Il1ra as suggested in the mentioned line.\n3. In Fig2I, what is the correlation coefficient between methylation and gene expression?\nThank you for your question. The correlation coefficient (r) between methylation and gene\nexpression is -0.31. The details of the calculation are described in the methods section.\n4. Line249, FigS1H is wrong, there is no FigS1H.\nThank you for pointing this out. It should be “Fig. 2H”. The manuscript has been updated accordingly.\n5. Figure4 A bottom got covered by 4C.\nThank you for pointing this out. We have adjusted the panel positions to fix this issue.\n6. Line 347, where is the Figure 5E?\nThank you for catching this. The label for Figure 5E was missing, and we have now added it in the\nrevised manuscript.\n7. The row names in Fig1C are not easy to read, maybe the authors could try to remove the ‘pbmc_’\nand also somehow rearrange the oder?\nThank you for the suggestion. To improve readability, we have removed the “pbmc_” from the figure\n(Figure R-9). Although the order does not follow an alphabetical pattern or their exposure duration,\nthe order was arranged via a clustering calculation, so the cytokines were grouped based on the\nsimilarity in their sample profiles to each other. For instance, the current plot is able to show\ncytokines that were upregulated in CMV+ grouped near each other. Thus, the current order might\nhelp readers to navigate the plot and focus on the up/down-regulated pattern in cytokines.\n8. Line257, is the validation using p value instead of adjusted p value?\nThank you for your question. In the submitted version, the validation was performed using the\np-value, not the adjusted p-value. However during this revision, we updated this to FDR < 0.05 based\non another reviewer's suggestion, resulting in 700 DEGs being validated in the validation cohort.\nThis study investigates the immunological, molecular, epigenetic and genetic impacts of\ncytomegalovirus (CMV) seropositivity in people living with HIV (PLHIV). CMV+ individuals exhibited\nheightened immune activation following PBMC exposure to CMV antigen. CMV seropositivity was\nalso associated with significant DNA methylation changes affecting immune-related gene expression,\nhighlighting eight consistent markers, including FCRL6, which emerged as a potential marker for\nimmune activation. GWAS revealed a CMV-seroprevalence locus on chromosome 15 (rs7180928-G)\nlinked to cytokine production and plasma protein abundance. Mendelian randomization analyses\nsuggested that increased FCRL6 expression is genetically driven and associated with latent CMV\ninfection. These findings underscore the role of CMV in immune modulation in PLHIV and suggest\nbroader implications for other immunocompromised populations.\nThe study has generated significant results with sound methodology. Here are a couple of\nrecommendations for the paper.\nWe appreciate Reviewer #2’s positive assessment of our study’s robust methodology and significant\nfindings across immunological, molecular, epigenetic, and genetic layers.\n2.1. Methylation measured on bulk cells. Could the authors perform a deconvolution analysis and\nshow if the specific cells that are infected with CMV show the overall pattern observed.\nWe thank the reviewer for this insightful suggestion. We fully agree that distinguishing DNA\nmethylation patterns between CMV-infected and non-infected cell types would enhance the\nresolution of our analysis. However, the methylation data in our study were generated from whole\nblood samples. Given the latent nature of CMV infection and the absence of single-cell level\nannotation or definitive markers to identify infected cells, we currently lack the resolution to reliably\ndistinguish between infected and non-infected cell populations. Moreover, to our knowledge, there\nare no established deconvolution methods capable of inferring CMV infection status at the cell-type\nlevel from bulk methylation data. Nevertheless, we consider this an important future direction and\nwill explore it further as methodological advancements become available.\n2.2. It has been demonstrated that the non-classical HLA molecule, HLA-E, is responsible for CMV\ncontrol. The results from the manuscript indicate an NK related function, which is interesting since\nHLA-E is linked to NK cell function. Could the authors examine the HLA-E region specifically to\ndetermine a direct methylation/transcriptome/GWAS effect on CMV. This will identify a new pathway\nand future validate the data generated in this manuscript with the previously published HLA-E gene\nlinked to CMV.\nWe thank the reviewer for this insightful suggestion regarding the role of HLA-E in CMV control.\nTo explore potential genetic regulatory effects, we examined the HLA-E genomic region in our CMV\nGWAS and found that the top SNP (rs2021368, p = 4.25e-05) lies nearby HLA-E (Figure R-10).\nThis\nindicates a suggestive association.\nmanuscript as Supplementary Fig. S9F.\nOur gene expression analysis revealed that HLA-E expression in PBMCs showed a nominally\nsignificant association with CMV seropositivity (p = 0.03, Supplementary Table S6), making it the\nstrongest association among the non-classical HLA genes. However, the effect size remains modest,\nsuggesting that while HLA-E expression by peripheral blood mononuclear cells (PBMCs) may play a\nrole, its contribution to CMV serostatus is not the primary driver.\nIn contrast to the GWAS and transcriptome, plasma protein levels of circulating HLA-E did not show\nany significant associations with CMV serostatus (p = 0.14, Supplementary Table S9).\nLastly, we analyzed whole blood DNA methylation data for associations with CMV seropositivity\nacross non-classical HLA genes. Here we also did not observe a strong association between CMV\nserostatus and methylation at HLA-E. However, we did identify a notable association at\nHLA-F/HLA-F-AS1 (Table R1).\nHLA-F is a non-classical MHC class I molecule that interacts with NK cell receptors and is known to be\nupregulated in viral infections, including HIV. HLA-F-AS1, its antisense long non-coding RNA, may play\na role in regulating HLA-F expression via epigenetic mechanisms. Although the role of HLA-F in CMV\ninfection is less well defined compared to HLA-E, our findings suggest that epigenetic regulation in\nthe HLA-F region may be involved in CMV-related immune responses, potentially through modulation\nof NK cell activity (DOI: 10.1038/ni.3513; DOI: 10.1002/eji.201040348).\nThis table has been included in the revised manuscript as Supplementary Table S14.\nCpG Gene logFC AveExpr P.Value adj.P.Val\ncg08755130 HLA-F -0.1724 7.4625 2.5361e-09 5.6940e-07\ncg08755130 HLA-F-AS1 0.2469 2.4867 5.1418e-08 6.7101e-06\nWe have added the above the info to the Results on page 15, lines 427-439:\n“Non-classical HLA molecules can modulate NK-cell checkpoints during CMV infection, especially\nHLA-E, which presents CMV-derived peptides to NK-cell receptor NKG2C50. We therefore surveyed all\nomics layers for HLA-E signals. HLA-E mRNA in PBMCs was modestly higher in CMV+ individuals (P =\n0.03, Supplementary Table S6), whereas circulating HLA-E protein and DNA methylation at the locus\nwere unaltered. In the CMV GWAS, a sub-genome-wide signal lay adjacent to HLA-E (lead SNP\nrs2021368, p = 4.25 × 10⁻⁵; Supplementary Fig. S9F). By contrast, whole-blood methylation profiling\nidentified a robust CMV-associated CpG at the HLA-F/HLA-F-AS1 locus (Supplementary Table S14;\ncg08755130, FDR = 5.7 × 10⁻⁷), hinting that epigenetic regulation of another non-classical HLA gene\nthat engages NK receptors contributes to CMV-driven immune modulation51,52. Finally, we imputed\nclassical HLA alleles with an HLA-specific reference panel to test whether MHC-I variation influences\nFCRL6 expression. We identified 4 HLA alleles that might affect the FCRL6 expression (P < 0.05);\nhowever, these alleles had not passed the multiple testing correction (Supplementary Table S15).“\nThis is a comprehensive multi-omic analysis of the relationship of CMV seropositivity (latent CMV\ninfection) with the immune system in people with HIV, as part of the 2000HIV project. The authors\nhave observed profound effects of having CMV on gene expression, cytokine production, plasma\nproteome and metabolome, and DNA methylation in the peripheral blood and in peripheral blood\ncells, using a discovery and a validation cohort approach. While some of the differences are expected\nfrom previous literature, this study is notable for its very meticulous approach, the large numbers of\nindividuals analyzed by each method, and the connections among the different assays, which\ndemonstrate regulation at several levels - for instance, CMV is associated with DNA de-methylation\nof FCRL6 (consistent with its upregulated gene expression and protein abundance) in\nCMV-seropositive individuals with HIV. Finally, the authors use GWAS and mendelian randomization\nanalysis to posit that the FCRL6 gene is causally linked to CMV infection, although how the authors\nthink FCRL6 leads to CMV susceptibility is not well described.\nWe thank Reviewer #3 for acknowledging the thoroughness and scope of our multi-omics approach\nand for pointing out the need to better articulate the mechanistic role of FCRL6 in CMV susceptibility.\nMajor Concerns:\n3.1. The data is Figure 1C, showing increased cytokines produced by PBMCs of CMV-seropositive\ndonors after CMV pp65 stimulation for 24h seems to be a bit confusing to me. The authors don’t\nreally justify why CMV pp65 peptide (an internal matrix protein) should elicit innate cytokines\ndirectly. And an obvious alternative - that pp65 protein is processed and presented to CMV-reactive T\ncells in the culture, which then release cytokines (such as TNF or IFNg) to activate the innate cells - is\nseemingly not considered. Indeed, this may explain the reduced TNF in the validation cohort after\nCMV pp65 stimulation (the darkest blue cell): innate cells are taking up the TNF.\nThe reviewer raises a very good point. The increase in innate cytokines in CMV-seropositive\nindividuals upon pp65 stimulation is further confirming the presence of CMV-specific memory\nresponses in PLHIV. It has been shown by others that CMV pp65, which is a late CMV antigen, triggers\nthe response of CD8+ T cells, therefore increasing TNF and IFN-gamma production upon stimulation\nreflecting the presence of reminiscent CMV-specific CD8+ T cell responses, named as memory\ninflation (DOI: 10.1038/nri.2016.38). Thus, although we did not measure IFN-gamma production\nupon CMV pp65 stimulation in the participants of our cohort, the cytokines TNF-α and IL-1β\nproduction to CMV are proposed to originate from CMV-specific memory responses mediated by\nCD8+ T cells but also monocytes. It remains to be addressed whether monocytes can directly\nrecognize CMV-derived peptide antigens. However, their activation is likely mediated by CMV\npp65-specific CD8+ T cells, which recognize CMV pp65 peptides presented via MHC class I. For\ninstance, IFN-gamma produced by these activated memory CD8+ T cells may, in turn, stimulate\nmonocytes to produce cytokines (DOI: 10.1038/nri2448).\nTo indicate this more clearly to the reader we have reworked the Results section (“Exposure of\nPBMCs from CMV+ PLHIV to latent CMV antigen enhances the production of inflammatory\n18\ncytokines”) on pages 7-8, lines 165-183 (renamed to: ‘Latent CMV infection heightens\npro-inflammatory recall responses in PLHIV’):\n“Among the 90 cytokine-stimulus read-outs measured, the most pronounced CMV-associated effects\nappeared after exposing PBMCs to the CMV antigen pp65 for 24 hours (Fig. 1C, Supplementary\nTables S2-S3). In both the discovery and validation cohorts, CMV-seropositive (CMV+) donors\nsecreted significantly higher amounts of the monocyte-derived cytokines IL-1β, IL-1Ra, IL-8 and\nMCP1 than CMV-seronegative (CMV-) donors (FDR < 0.05 discovery; P < 0.05 validation) . This agrees\nwith previous studies where heightened inflammatory responses in PBMCs to CMV infection for\nPLHIV is already well-documented11–13.\nAs pp65 is an internal CMV protein that does not directly engage pattern-recognition receptors, the\nsimplest explanation for the elevated innate-cytokine output is that the antigen is processed and\npresented to pre-existing pp65-specific T cells. Activated CD8+ T cells are known to release TNF-a and\nIFN-y upon pp65 recognition, a phenomenon termed memory inflation, and the resulting IFN-y\nallows monocytes to amplify IL-1β, IL-1Ra, IL-8 and MCP-1 production14,15. Consistent with this\nmodel, whole-blood immunophenotyping revealed higher frequencies of HLA-DR+ CD4+ and CD8+ T\ncells, NK cells and gamma-delta (γδ) T cells in CMV+ PLHIV (Fig. 1E; Supplementary Tables S4,S5). This\nmirrors the well-documented CMV-driven reshaping of CD4+, CD8+, NK-cell and γδ-T-cell\ncompartments in healthy adults15,16 and the capacity of CMV to upregulate HLA-DR expression on T\ncells17. Together, these findings establish that latent CMV infection primes a T-cell-driven,\nmonocyte-dominated inflammatory response that distinguishes CMV+ from CMV- PLHIV.”\n3.2. The authors go to a lot of effort to investigate the role of CMV on FCRL6 (and vice versa), but\nthere is no data on which cells are expressing it in vivo, and whether cellular FCRL6 levels are\ndifferent in CMV seropositive and seronegative individuals. They say from the literature that it is\nexpressed on NK cells and T cells - could the authors stain PBMCs from CMV disparate individuals and\nshow a difference in FCRL6 surface expression?\nWe thank the reviewer for this suggestion. To directly assess cellular FCRL6 expression, we performed\nflow cytometry on PBMCs from four PLHIV (2 CMV+, 2 CMV-) in our cohort. In short, cells were\nstained with markers for CD3, CD4, CD8, CD14, CD19, CD45, CD56, TCRγδ, and FCRL6. The resulting\npercentages of FCRL6+ cells within each subset can be found in Figure R-11, which indicate that\nCMV+ PLHIV have proportionally higher percentages of FCRL6 expressing CD8+ T cells as well as in\ntotal TCRγδ T cells. In addition, CD14+ monocytes and NK cells (CD56+) expressing FCRL6+ were also\nhigher in PLHIV CMV+ in comparison to PLHIV CMV-. Altogether, the current data provides additional\nevidence on the source of FCLR6 protein and further confirms its association with CMV seropositivity.\ndata is stratified based on the CMV status, including 2 PLHIV CMV+ and 2 PLHIV CMV-. This figure has been\nincluded in the revised manuscript as Supplementary Fig. S8B.\nWe added these new Results to the revised manuscript at page 13, lines 359-366:\n“To pinpoint which immune cells contribute to the elevated plasma FCRL6 signal, we performed\nmulticolour flow cytometry on PBMCs from four PLHIV in our cohort (two CMV+ and two CMV-).\nCells were stained for CD3, CD4, CD8, CD14, CD19, CD45, CD56, TCRγδ and FCRL6. CMV+ individuals\nshowed higher proportions of FCRL6-expressing CD8+ T cells and γδ T cells than CMV- counterparts\n(Supplementary Fig. S8). The frequency of FCRL6+ cells was also increased within CD14+ monocytes\nand CD56+ natural-killer cells. These data confirm that multiple cytotoxic and innate subsets\nup-regulate FCRL6 in vivo during CMV co-infection and provide a cellular source for the elevated\nprotein we detect in plasma.”\nAnd new Methods (‘Flow cytometry for FCRL6 in PLHIV PBMCs’, page 28, line 869-881):\n“The PBMCs were cultured in RPMI 1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA,\nUSA) supplemented with 10% human pool serum overnight and the stained for the following\nmarkers: LIVE/DEAD fixable viability dye viakrome (Beckman Coulter - Brea, CA, USA), CD3 (BV605 -\nBiolegend - San Diego, CA, USA), CD4 (BUV395 - Biolegend), CD8 (BV785 - Biolegend), CD14 (AF700 -\nBiolegend), CD19 (PE-Cy5 - Biolegend), CD45 (Krome Orange - Beckman Coulter - Brea, CA, USA),\nCD56 (BUV737 - Biolegend), TCRγδ (BV421 - Biolegend), and FCRL6 (APC - Biolegend). Antibodies\nwere diluted in a total staining volume of 50 µL, which included Brilliant Stain Buffer Plus (BD\nBiosciences, Franklin Lakes, NJ, USA) and PBS supplemented with 2% bovine serum albumin (BSA-\nSigma Aldrich, USA) and 2 mM EDTA (Gibco) (PBS-F) as the staining/washing buffer. For FcRL6-APC, a\nfluorescence minus one (FMO) control was included to define the positive populations. Staining was\nperformed for 30 minutes at 4°C in the dark, followed by washing with PBS-F + EDTA. Data\nacquisition was carried out using the CytoFLEX flow cytometer (Beckman Coulter, Brea, CA, USA)\n(Supplementary Fig. S8A).”\n20\n3.3. Could the authors discuss why none of the SNPs (instrumental variables) shown in Figure 5C\n(causal for gene expression) overlap with the SNPs in Figure 5E (causal for protein expression),\nand\nvice versa. Is this an expected finding or not?\nWe thank the reviewer for their insightful question. Indeed, the instrumental variables (IV) for FCRL6\ngene expression and protein abundance do not overlap, indicating that the genetic regulation of\nFCRL6 at the transcript and protein levels appears to be independent in this context. This is an\nexpected finding and aligns with previous studies (DOI: 10.1038/s41586-018-0175-2), showing that\neQTLs and pQTLs can be driven by different genetic variants due to post-transcriptional regulation,\ndifferences in mRNA and protein stability, etc.\nAdditionally, the differences in genetic regulation could also be explained by the fact that we\nmeasured gene expression in PBMCs and protein levels in plasma. PBMC gene expression primarily\ncaptures intracellular transcriptional regulation, whereas plasma protein levels reflect systemic\ncirculation.\nLastly, most of the IV are trans-QTLs, where the genetics variant affects the gene or protein at a\ndifferent genomic locus. Given that trans-QTLs generally have smaller effect sizes compared to\ncis-QTLs, statistical power is more limited. Despite this, we identified two SNPs within the same\ngenomic region that, although not in linkage disequilibrium (LD), suggest the possibility of shared\nregulatory mechanisms.\nImportantly, both the gene and protein level MR results for FCRL6 show significant associations with\nCMV serostatus, and the overall conclusion is supported by converging lines of evidence rather than\na single SNP or QTL.\nWe have added some text to discuss this matter to the revised manuscript (Discussion, page 15, lines\n415-423 ):\n“Notably, we observed no overlap between the IVs for FCRL6 gene expression in PBMCs (Fig. 5C) and\nthose for protein abundance in whole blood (Fig. 5E). This is consistent with previous studies\nshowing that eQTLs and pQTLs commonly involve distinct genetic variants, reflecting differences in\npost-transcriptional and translational regulation47. However, we did identify two SNPs located in close\nproximity on chromosome 14 (rs140233210 at position 20,061,131 for gene expression and\nrs1959651 at position 20,080,570 for protein expression). Although these SNPs are not in linkage\ndisequilibrium (LD), their close genomic proximity suggests that some shared regulatory mechanisms\ninfluencing FCRL6 expression at different biological levels might exist, warranting further\ninvestigation.”\nMinor Concerns:\n• Please refrain from using terms like “HIV-infected” or “non-HIV-infected”. Just use people (living)\nwith HIV or people without HIV. The term “patients” is also discouraged.\nThank you for your feedback. We have revised the manuscript to replace terms like “HIV-infected”\nand “non-HIV-infected” with “people living with HIV” and “people without HIV” throughout the text.\n“transplant recipient”.\n• Table S7 only seems to contain 700 DEGs (FDR <0.05), not 807 as stated in the text (line 257).\nThank you for this comment. The validation was originally performed using the nominal p-value,\nresulting in 807 DEGs. During this revision, we apply the FDR < 0.05 in the validation cohort, resulting\nin 700 DEGs as mentioned in the comment. We updated this information in the manuscript's main\ntext.\n• Figure 5 panel E is not labeled within Figure 5.\nThank you for catching this. We have now labeled in the revised manuscript.\n• Line 587 in the Methods says “(show figures)” ???\nWe apologize for this mistake. The entire sentence this is referring to has been removed from the\nMethods section.\n• Figure 2B legend (lines 769-771) the x-axis and y-axis designations are reversed from what is shown\nin Figure 2B.\nThank you for pointing this out. We corrected the mistake in the manuscript.\n• Figure 2F legend (line 780) says “MOMER” instead of “HOMER”.\nThank you for pointing this out. We corrected the mistake in the manuscript.\nThis is an interesting manuscript that assesses CMV serostatus in relation to a large study of 'omics\ndata in a cohort of people living with HIV. The work develops hypotheses related to the impact of\nCMV on the immune system.\nWe are grateful to Reviewer #4 for your interest in our work examining CMV serostatus within a large\nHIV-infected cohort and for noting the hypothesis-generating strength of our omics analyses.\nPoints\n4.1. The clinical cohort has a very high rate of CMV seroprevalance and this may reflect demographic\nfactors related to infection risk. The relevance of the data to the general population is thus not clear\nand this should be stated as a limitation.\nThank you for your insightful comment regarding the high rate of CMV seroprevalence in our clinical\ncohort. We agree that the unusually high CMV seroprevalence (~94 %) in our cohort likely reflects its\ndemographic make-up (predominantly middle-aged MSM living with HIV in the Netherlands) and\ncould limit external generalisability. To address this, we have added a paragraph to the Discussion\n(page 18, lines 523-533) that (i) contrasts our prevalence with national estimates, (ii) explains how\nhigh baseline exposure might affect effect-size estimates, and (iii) calls for replication in cohorts with\nlower CMV prevalence. Despite this, the high replication in our down-sampling analysis, as well as\nthe consistent signal of FCRL6 across all omics layers, lend confidence to the biological relevance of\nour findings despite the epidemiological limitation.\n“CMV seroprevalence in the 2000HIV study is ~94%, which is considerably higher than the ~50%\nreported for age-matched adults in the general Dutch population66. This elevation almost certainly\nreflects the cohort’s demographic composition, which is predominantly middle-aged men who have\nsex with men (MSM) living with HIV and using long-term ART. High baseline prevalence reduces\nstatistical power to study CMV- PLHIV and may exaggerate the magnitude of CMV-associated\nphenotypes. However, the downsampling analyses showed high concordance with the full-cohort\nresults, with the vast majority of significant associations replicated across iterations, demonstrating\nrobust reproducibility. Even so, the cytokine and multi-omic signatures reported here should be\ninterpreted with caution in settings where CMV exposure is less common, and future multi-centre\nstudies that include low-prevalence populations will be required for external validation.”\n4.2. I find the manuscript to be rather poorly written for the scientific reader. I would advise a\nconsiderable rewriting that addresses the cohort strucutre, experiments that were done in each\nsection and the specific outcomes. The abstract also needs to be succinct and clear\nWe thank the reviewer for this writing style suggestion. The 2000HIV study has been previously\npublished by our collaborators (DOI: 10.3389/fimmu.2022.982746), including study design,\nmulti-omics methods, and participant characteristics information. Since our manuscript focuses on\nbioinformatic analysis, we emphasized the data analysis steps, and only presented the key\n23\ninformation about cohort structure and wet-lab experiments while citing the original paper. In\nresponse to your suggestion, as well as the other reviewer's suggestions, we have rewritten the\nabstract and the main text to be more concrete and scientific-oriented.\n4.3. The cytokine functional assessment is in relation to the short term response to challenge with a\nCMV protein. CMV modulates the immune systems in substantial ways and as such this differential\nresponse is to be expected.\nWe agree with the reviewer that this is expected. However, we also feel the present manuscript aims\nto provide further insights on the effects of CMV reactivation in the context of people living with HIV.\nFor instance, as mentioned above to Reviewer #3, the short-term increase in pro-inflammatory\ncytokine production in response to CMV pp65 could be due to specific memory responses. These\nCMV responses may predispose PLHIV to poor prognosis as it has been suggested that CMV-driven\ninflammation can lead to increased HIV replication and reservoir (DOI: 10.1093/infdis/jiw217;\n10.1089/aid.2017.0145). Therefore, a deeper understanding of the effects of CMV provides\nopportunities to identify therapeutic targets that reduce the burden of CMV-co-infections in PLHIV.\n4.4. The work uses bulk transcriptomics and the increase in many cytotoxic transcripts is not\nsurprising.\nWe agree that this pattern is not unexpected given that the data is from PLHIV. We feel our ability to\nrecapitulate them using independent omics measurements highlights both the quality of our dataset\nand the robustness of our analytical approach.\n4.5. CMV induces major changes in the immune cell repertoire - has this been corrected for in\nrelation to the relative prevalence of genes and methylation profiles associated with this transition?\nWe appreciate the reviewer’s comment regarding immune cell composition changes induced by CMV\ninfection and their potential impact on methylation and gene expression profiles. Although we do\nnot have direct measurement of the immune cell repertoire, we have corrected for the confounding\neffects for each omics layer, as mentioned in the Methods section.\nFor the methylation profile, to account for differences in immune cell proportions, we estimated the\nrelative abundance of six major immune cell types (neutrophils, monocytes, B cells, NK cells, CD8+ T\ncells, and CD4+ T cells) using DNA methylation data. Specifically, we applied a modified version of\nHouseman’s method implemented in the estimateCellCounts2 function from the\nFlowSorted.Blood.EPIC R package. These estimated cell-type proportions were included as covariates\nin downstream methylation analyses to correct for potential confounding due to shifts in immune\ncell composition associated with CMV seropositivity.\nFor gene expression profiles, we adjusted for the top confounding effect of age, sex, BMI, center\ncollection, season (season_sin, season_cos), and ethnicity (top5 genetic PCs) in the discovery cohort.\nadjusted for the confounding effect of age and season (season_sin, season_cos).\n4.6. The work also seeks to assess genomic risk factors related to CMV infection. There has been\nprior work in this regard and the incremental advance here seems modest. The potentially unusual\nfeatures of the residual CMV-seronegative donors within this cohort should be discussed.\nWe agree that a comprehensive discussion on the unusual features of the residual CMV-seronegative\nwill benefit the related field. To our knowledge, there are 21 reported genetic loci associated with\nCMV infection in different disease contexts at a p-value threshold of 5e-5, such as patients after\nallogeneic hematopoietic cell transplantation and patients who are diagnosed Schizophrenia. These\nloci are from three cohorts consisting of European participants. These studies are mainly focused on\nspecific disease populations, which is just like our scenario. In our study, we observed ultra high\nCMV-seropositive rates in people living with HIV, which suggests a high chance of co-infection of HIV\nand CMV. In contrast, a hand of PLHIV is CMV-seronegative. It indicates protective factors in this\nsmall group of donors and the underlying protective factors are extremely important to help\npatients’ resistance to CMV infection. Of note, the above mentioned studies have indicated genetic\nloci potentially involved in the CMV infection, which encouraged us to explore the potential genetic\nfactors resisting CMV infection regardless of the high risk. The GWAS results suggested a genetic\nlocus, which supported our hypothesis and could be concluded that genetic factors may play an\nimportant role in resistance to CMV infection in people living with HIV.\nFollowing the reviewer’s suggestion, we have added the following text to the revised manuscript\n(pages 17-18, lines 511-522) to address this:\n“Previous studies have suggested that genetic factors are potentially involved in the CMV infection in\ndifferent disease contexts, such as individuals after allogeneic hematopoietic cell transplantation and\nindividuals who are diagnosed Schizophrenia63–65. However, the potential roles of genetic variants in\nresistance to CMV infection in PLHIV is less known, in contrast to the observed high prevalence of\nCMV seropositivity. Therefore, we performed a GWAS to screen genetic loci that are associated with\nCMV- in our PLHIV cohort. The screening suggests a locus on chromosome 15 where rs7180928-G\nallele is protective, which is not previously reported. To confirm its role in CMV infection in our study\ncohort, we systematically estimated whether this locus bridges other molecular traits with\nCMV-sero-negativities in PLHIV. The analysis suggested interplays among the identified locus,\ncytokine production, protein abundance, and gene expression, which collectively highlighted the\nimportance of the identified locus in CMV resistance upon HIV-positive.”\n4.7. The FCRL6 association is interesting and the most important feature of the manuscript. The\nmagnitude of this effect, and as such its potential use in assessment of CMV status within individual\ndonors, could be expanded.\nWe thank the reviewer for recognising FCRL6 as a key finding in our study. As shown in Table R2,\nmultiple CpG sites within the FCRL6 locus exhibit strong and consistent hypomethylation in\nanalysis further links this epigenetic signature to increased FCRL6 expression. To display the strength\nof the epigenetic effect more clearly, we have now included this table in our revised manuscript, with\na reference to it on page 15, line 434. Importantly, we also observe elevated FCRL6 protein levels in\nCMV+ donors (Figure 3), underscoring its multi-layer biomarker potential. While the downsampling\nanalysis confirms that these associations are robust to the imbalance in CMV status, we acknowledge\nthat the limited number of CMV- individuals in the 2000HIV cohort constrains definitive conclusions\nabout clinical assay performance. Therefore, we have moderated our claims regarding FCRL6’s\nimmediate diagnostic utility and propose that future work that leverages larger, more balanced\ncohorts, will be essential to fully validate FCRL6 as a reliable marker of CMV infection.\nTable R2. Summary statistics of the association of CpG sites within FCRL6 to CMV-seropositivity. This table has\nbeen included in our revied manuscript as Supplementary Table S14.\nCpG Gene logFC AveExpr P.Value adj.P.Val\ncg23615393 FCRL6 -0.4498 5.1535 1.4797e-06 0.0001236\ncg24833981 FCRL6 -0.7956 5.1535 1.0268e-21 4.8908e-18\ncg00950718 FCRL6 -0.2619 5.1535 3.4875e-05 0.0006598\ncg05919685 FCRL6 -0.3901 5.1535 1.2677e-06 6.0838e-05\ncg27198652 FCRL6 -0.5346 5.1535 9.7049e-10 1.0750e-07\nReviewer #4 (Remarks on code availability):\nNA\nI appreciate the authors’ efforts during the revision. The manuscript has been improved\nsignificantly. Here are a few points I hope could be elaborated further.\nWe thank Reviewer #1 for noticing our efforts and finding further things that would improve the\nmanuscript.\n1)Major point 1.\nI appreciate the downsampling analysis and suppFig S3,S4,S5. Just out of curiosity, how was the\nnumber of downsample size ’86’ determined? Any intuition behind and maybe included in the\nmethods section?\nWe thank the reviewer for this question. As mentioned in the previous response letter, there were\nfor the downsampling analysis. This is also mentioned in the methods ‘Downsampling analysis’ on\npage 26. However, we see now that we don't explicitly mention the specific number in the result\nsection of the manuscript, just the percentages. We have now made additions (bolded below) to\nthe result section that describes the cohort to mention the sample sizes of the CMV- individuals\nwithin the discovery and validation cohorts to the manuscript on page 7 line 150:\n“Baseline cytomegalovirus (CMV) IgG was measured in all 1,887 participants by ELISA, revealing\nseronegativity in ~6% of the discovery cohort (n = 86) and ~8% of the validation cohort (n = 28)\n(Supplementary Table S1).”\nA minor suggestion for FigS4-S5: Maybe the FCRL6 can be highlighted using a different color or\nshape for the point. Also the ‘validate_DEGs/DEPs yes/no’ labels only need to show once for the\nwhole plot instead of in each subplots.\nWe thank the reviewer’s suggestion for improving the visualisation of Figures S4-5. The FCRL6\nwere highlighted in a different color (dark red color). The labels are grouped and shown once at\nthe bottom for the whole plot, including for S3.\n2)Major point 4.\n2.1 Could the authors provide PPH1 and PPH2 of the coloc analysis as well?\nWe thank the reviewer’s suggestion and think that supplying PPH1 and PPH2 helps improve the\nmanuscript. Following the request, we have updated the manuscript by adjusting the following\nsentence to include the PP0, PP1, and PP2 from the colocalization analysis.\n“To further confirm the role of CHRNB4 in CMV infection in PLHIV, we performed colocalization\nand MR analyses to verify if the gene mediates PLHIV’s resistance to CMV. The colocalization\nresulted in a PP4 = 0.34, PP3 = 0.23, PP2 = 0.39, PP1 = 0.02, and PP0 = 0.03 (Bayesian factor\nanalysis), …”.\n2.2 Could the author also add a box plot for CHRNB4 eQTL? Please also report the accurate\nnominal p value of the CHRNB4 eQTL for rs7180928 instead of ‘P<0.05’.\nFollowing the reviewer’s suggestion, we have added the following violin plot to display the eQTL\nof CHRNB4 as Supplementary figure S9D. We also updated the manuscript to mention the newly\nadded supplementary figure. The exact p-value (p-value = 2.13e-05) has also been updated in the\nmanuscript (at line 383 of page 14).\nFigure R-2.2. Violin plot showing the eQTL effect of rs7180928 for gene CHRNB4. The observed p-value is\n2.13e-05. Included in the revised manuscript as Supplementary Fig. S9D.\n2.3 I appreciate the new locus zoom plot with LD information. But the Figure legend for Fig4C\n(line) seems to be not updated (there is no orange dots anymore). Now the eQTL and GWAS\nsignals are not easy to distinguish (dark blue vs light blue?). Could the authors try to improve it?\nMaybe another more straightforward way is to plot them separately but just stack the GWAS on\ntop of the eQTL and use the same gene annotation panel.\nIt would be better to have 2 y axis (left and right) showing for both -log10p value of eQTL and of\nGWAS, if you still overlay the 2 plots.\nWe apologise for not updating the figure legend previously. Following the reviewer’s suggestion,\nwe have updated the figure by stacking two Manhattan plots but keeping the original gene panel,\nwhich is available as the following. And the figure legend has been updated as well.\nFigure R-2.3. Updated locuszoom plot which stacks GWAS and eQTL summary statistics of locus pinpointed\nby rs7180928. Included in the revised manuscript as Fig. 4C.\n2.4 According to the original Figure4C, the lead eQTL for CHRNB4 is rs11629784. What is the p\nvalue of this one in the GWAS?\nWe thank the reviewer’s question and also think the p-value of rs11629784 in CMV status helps to\nincrease the manuscript. However, the rs11629784 is excluded in the GWAS analysis due to low\nfrequency (MAF = 0.006963) in the discovery cohort and there is no donor that is grouped into AA\ngenotype. As stated in our method section, we ensure 3 donors in each genotype group to\nguarantee the logistic regression works smoothly, but we release this restriction in eQTL analysis\nwhen using linear regression. We added the following sentence to underscore the limitation at\nline 383 on page 14.\n“However, the top cis-eQTL SNP of CHRNB4 is rs11629784, but this SNP is excluded in the GWAS\nanalysis due to low frequency (MAF = 6.96e-3) in the discovery cohort.”\n2.5 I appreciate the author’s effort of finding CHRNB4 related studies. However, I am still not\nconvinced this is the most likely mediating gene. According to GTEX data, rs7180928 has eQTL\neffect for CRABP1(p=0.0000041) and for DNAJA4(p= 0.000016) (see\nhttps://gtexportal.org/home/snp/rs7180928), are they not significant or not detectable in your\nanalysis?\nWe respect the reviewer’s comment. As indicated in our method section, transcriptional profiles\nare from PBMCs isolated from the donors’ whole blood, while the reported eQTLs of CRABP1 and\nDNAJA4 at rs7180928 by GTEx are from thyroid and nerve (tibial), respectively. Moreover, the rest\nreported eQTL genes by GTEx for this given SNP are not from whole blood either. Therefore, we\ndid not report these eQTLs in our last manuscript. Of note, their expressions are low in whole\nblood which limits the power to detect eQTL, for instance CRABP1\n(https://gtexportal.org/home/gene/ENSG00000166426.8).\n2.6 There are quite a bunch of genes (e.g.SKIC8, DNAJA4, CRABP1, IREB2, HYKK, PSMA4, CHRNA3,\nCHRNA5) near the GWAS peak in this locus. I assume the authors have full eQTL summary\nstatistics for all these genes. I guess some of these genes in addition to CHRNB4 should also have\neQTL signal. I would suggest the authors to run coloc between each of these\neQTLs and GWAS,\nthen to check if the CHRNB4 eQTL really has the highest PPH4. This analysis should be able to\nmake sure that CHRNB4 is the most likely mediating gene.\nWe appreciate the reviewer’s suggestion to present colocalization analyses for genes in the\nidentified locus. To improve the manuscript, we added a supplementary table (Supplementary\nTable S16) to release the concerns. The following table (Table R1) shows the genes suggested by\nthe reviewer.\nTable R1. Small snippet of new Supplementary Table S16 showing the genes suggested by the reviewer.\nFeature N SNPs PPH0 PPH1 PPH2 PPH3 PPH4 Comment\nCHRNB4 579 2.61E-02 1.53E-02 0.39131 0.22945 0.337789 Reported gene by this study\nCRABP1 579 5.65E-02 3.32E-02 0.52821 0.31010 0.071985\nDNAJA4 579 1.81E-18 1.06E-18 0.59931 0.35188 0.048815\nPSMA4 579 8.03E-06 4.72E-06 0.59962 0.35206 0.048309\nWDR61 579 1.36E-12 7.98E-13 0.60097 0.35286 0.046171 a.k.a. SKIC8\nCHRNA3 579 2.34E-15 1.37E-15 0.60294 0.35401 0.043050\nCHRNA5 579 6.36E-26 3.73E-26 0.60345 0.35432 0.042235\nIREB2 579 6.43E-49 3.77E-49 0.60402 0.35465 0.041332\nHYKK 579 1.54E-01 9.04E-02 0.45188 0.26531 0.038570\n3)Major point 5.\nPlease add the KIR2DS4 and IL22 to the y axis labels of the Fig4F,G. Also for this kind of QTL box\nplots, I think the authors should have a p value for each, instead of comparing different genotypes\n3 times. Could you label that p value from the QTL analysis? Please also report the test method in\nthe figure legend.\nWe thank the reviewer’s suggestion which surely helps to improve the manuscript. We have\nupdated the figures by adding “KIR2DS4” and “IL22” to the y-axis labels. The figures are also\nupdated by adding exact p-values of Spearman correlation between genetic dosage and protein\nabundance or cytokine level , which is indicated in the figure and figure legends. The exact\np-values of Fig 4F and 4G are also updated in the manuscript. For Fig 4F, r = -0.11, p = 1.3e-4,\nSpearman correlation test (one-way, less than 0) and for Fig 4G, r = -0.07, p = 2.7e-2,\nSpearman correlation test (one-way, less than 0), respectively, which have been updated in the figures,\nwhich are also available as the following.\nFigure R-3. The updated violin plots by adding statistical tests and updating labels of y-axis. Included in the\nrevised manuscript as Fig 4F.\nAll the other points were well addressed, thanks.\nWe thank the reviewer for this comment.\nPlease also update the code for the new analysis on github.\nWe have now updated the code for the new analyses from the previous revision and this revision\non GitHub at https://github.com/CiiM-Bioinformatics-group/2000HIV_CMV.\nReviewer #2 (Remarks to the Author):\nI am satisfied with the changes the authors have made\nReviewer #2 (Remarks on code availability):\nThe code has been checked and was functional and appropriate\nWe thank Reviewer #2 for these comments.\nReviewer #3 (Remarks to the Author):\nThe authors have done an outstanding job responding in great detail to the reviewers' comments,\nincluding my own. The manuscript is much improved and I don't have any major concerns that\nwould preclude acceptance.\nMinor concerns:\n-There are some typos (words without spaces between, etc) and other English language issues,\nparticular on lines:\n52 (\"populationsassociated\")\n52 (\"seropositivity.This\")\n57 (\"PLHIV. ost\")\n103 (\"to90%\")\n240 (\"cytokines PBMCs\")\n294-295 (\"also occur HIV-specific\")\n320 (\"not unsurprising\") [I think you mean not surprising here]\n-In addition, many of the references appear to be messed up, with only initials given instead of\nnames: Refs 4, 6, 14, 16, 17, 19, 20, 21, 22, 24, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36, 43, 44, 46, 48,\n49, 53, 54, 55, 57, 58, and 72.\nWe thank Reviewer #3 for these comments, as well as noticing these typos and reference issues. It\nappears we created our references database exactly during the weekend when there was a\nwebsite maintenance ongoing at NCBI back in July. We have now refetched the metadata for these\nreferences and the issue has been resolved.\nReviewer #4 (Remarks to the Author):\nThis is a revision.\nThe authors have answered the reviewers comments very well.\nThey have maximised the power of the study cohort.\nThe demonstration of increased levels of FCRL6 on several immune cells is important.\nThere are spelling mistakes in the abstract which is surprising.\nWe thank Reviewer #4 for these comments. We also apologise for the spelling mistakes in the\nabstract. We hope we have now found and fixed all of these."}]

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