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Near normalization of peripheral blood markers in HIV-infected patients on long-term suppressive antiretroviral therapy: a case–control study

Brochado-Kith, Oscara; Martinez, Isidoroa; Berenguer, Juanb,c; Medrano, Luz Mariaa; González-García, Juand; Garcia-Broncano, Pilara,e; Jiménez-Sousa, María Ángelesa; Carrero, Anaa,b; Hontañón, Victord; Muñoz-Fernández, María Ángelesc,f,g; Fernández-Rodríguez, Amandaa,∗; Resino, Salvadora,∗

Author Information
doi: 10.1097/QAD.0000000000002645



HIV-infection promotes gradual destruction of CD4+ T cells and persistent immune activation, but the combination antiretroviral therapy (cART) has largely transformed HIV infection into a chronic disease [1]. The cART allows the control of HIV replication and achievement of a CD4+ T-cell counts at least 500/μl, which remains a commonly used marker of immune restoration. However, complete HIV suppression does not imply freedom from comorbidities, as HIV persists in the body and promotes a permanent status of immune activation and inflammation, leading to non-AIDS comorbidities in patients on cART [2,3]. In addition, CD4+/CD8+ values lower than one after cART are linked to immune activation [4], even in individuals with CD4+ T-cell counts more than 500/μl (failure of normalization) [5].

The deregulation of the immune system is not entirely restored with long-term suppressive cART, since cART reduces all the sources of immune activation and inflammation, but it does not abolish them [6,7]. The deficits in the immune response in CD4+ T helper (Th) and regulatory CD4+ T cells (Tregs) are also not entirely reversed by suppressive cART [8–13]. The relation between the persistence of T-cell activation and senescence in patients on cART has been widely reported [3]. In addition, immune activation also promotes bacterial translocation and increased inflammation [2,14]. During this process, immune cells may be stimulated by microbial components, such as lipopolysaccharides, which bind to the cellular receptors (CD14/TLR4), activating the synthesis of proinflammatory cytokines such as TNF-α, IL-1, IL-6, etc., and the overexpression of chronic activation markers [15–17]. The immune activation and inflammation also upregulate the production of TGF-β, which promotes fibrosis of lymphoid tissue, with the progressive loss of naïve T cells [18]. Another critical factor to consider, due to the influence of long-term cART, is the development of metabolic syndrome, which is related to inflammation, endothelial dysfunction, and cardiovascular disease [2].

Several reports have explored the impact of suppressive cART on biomarkers of the immune system in HIV-infected patients [2,6]. The vast majority of these studies found decreases in some of the analyzed biomarkers, but do not usually make a global analysis of this phenomenon. In this report, we evaluated the normalization, respect to healthy individuals, of gene expression in peripheral blood mononuclear cells (PBMCs), and peripheral blood biomarkers (plasma and T-cells) in HIV-infected patients on long-term suppressive cART and normalized CD4+ T-cell counts.



We carried out a case–control study in individuals enrolled between July 2015 and February 2016 at two Hospitals in Spain (Hospital General Universitario ‘Gregorio Marañón’ and Hospital Universitario ‘La Paz’). HIV-infected patients (HIV-group) had the following selection criteria: first, HIV infection detectable by PCR; second, long-term suppressive cART (undetectable HIV viral load (<50 copies/ml) and CD4+ T-cell counts at least 500 cells/μl more than 1 year before sampling); third, CD4+/CD8+ ratio at least 1 at time of sampling. We excluded patients with hepatitis C or hepatitis B virus (HBV) active coinfection. The healthy control group had the following selection criteria: first, individuals negative for HIV, hepatitis C virus, and HBV infection; second, sex and age-matched with HIV-infected patients.

The study has been carried out under the Declaration of Helsinki and it has been approved by the Research Ethics Committee of the Instituto de Salud Carlos III (CEI PI 23_2011). All the participants signed a written consent before registration.

Laboratory assays

RNA-seq analysis was performed from PBMCs, peripheral blood T-cell subsets were evaluated by flow cytometry, and plasma biomarkers by immunoassays. All these tests are described in detail in the Supplemental Digital Content (SDC) 1,

Statistical analysis

The statistical analyses were carried out with the R statistical package version v3.4.1 (R Foundation for Statistical Computing, Vienna, Austria). All P values were two-tailed and were corrected for multiple testing to reduce the risk of spurious results by using the false discovery rate (FDR) with Benjamini and Hochberg (q values) test.

The sample size for each group was calculated according to the RnaSeqSampleSize calculator [19], established a minimum of 13 samples per group to be able to reject the null hypothesis that the population means of the two groups are equal with probability (power) 0.8 using an exact test. Calculates were performed by using the following parameters: 100 minimum average read counts, an estimated dispersion of 0.3, and a minimum fold change of 2. The FDR associated with this test of this null hypothesis was 0.5.

The R-package ‘DESeq2 v1.18.1’ (Heidelberg, Germany) was used for the analysis of differentially expressed genes (DEG), and the reads were normalized by Reads Per Kilobase Million. We filtered out those RNAs with less than 100 counts among all samples. DESeq2 applies a negative binomial generalized lineal model with an empirical Bayes shrinkage for dispersion estimation using the function dds. Subsequently, we applied two screening strategies on the result of the univariate analysis by DESeq2: a first approach to identify strongly DEG (fold change ≥2 and q value ≤0.05), and a second less stringent approach without an FDR correction (fold change ≥1.5 and P value ≤0.05) to explore a broader list of genes for pathway analysis. In addition, we carried out a supervised multivariate analysis (multiple dependent variables) using a partial least squares discriminant analysis (PLS-DA) (R-packages ‘mixomics v6.3.2’). PLS-DA creates a regression model that classifies the variables according to their ability to sort each sample in the correct group, and it is capable of dealing with multicollinearity, which is common in plasma biomarkers and transcripts. The PLS-DA provides the variable importance in projection (VIP) score of each biomarker for ranking genes. After the PLS-DA analysis, the genes with VIP at least one were selected to find represented biological routes by using DAVID Functional Annotation tool (Frederick, Maryland, USA) [20].

Finally, we analyzed within the HIV group the correlation between the most significant biomarkers and the SRRM4 gene expression using the Spearman correlation test. This analysis was performed with the R-package ‘Hmisc v4.1–1’ (Nashville, Tennessee, USA).



The characteristics of the patients enrolled in the study are shown in Table 1. We only found significant differences between healthy control-group and HIV-group for the CD4+/CD8+ ratio (P value = 0.001). Regarding sex, 59.1% of patients were male, but no significant differences were found between groups.

Table 1
Table 1:
Clinical and epidemiological characteristics of HIV-infected patients and healthy controls with mRNA sequencing data.

Gene expression and pathways

A total of 27 173 different genes were identified, and 6786 fulfilled filtering criteria (see Patients and Methods sections) for subsequent analysis. Overall, samples showed an average of 23.47 million reads, 99.04% mapped to the reference genome (the mean mapping percentage was 99.04%). The raw RNA data can be accessed at the ArrayExpress repository (EMBL-EBI; under the accession number E-MTAB-8249.

DEG analysis between groups found only the serine/arginine repetitive matrix 4 (SRRM4) (q value ≤0.05 and fold-change ≥2) (SDC 2; see Supplementary Fig. 1A,, showing higher expression in HIV-group. In addition, although our cohort was sex-balanced to avoid sex bias in gene expression, we explored the possible effect of sex distribution. The analysis between HIV and healthy control men (n = 13 and 7, respectively) showed us two DEG, the ACTA2 antisense RNA 1 and the SRRM4 (q value ≤0.05 and fold-change ≥2). However, any DEG were detected between HIV and healthy control women (n = 9 and 7, respectively), probably due to the reduced sample size. Relaxed conditions in women comparison showed that the SRRM4 gene expression was higher in HIV women, but the significance was lost after adjusting by multiple comparisons.

We also explored those DEG that did not reach statistical significance after correction for multiple testing (P value ≤0.05 and fold-change ≥1.5), to identify small underlying differences between groups with all the patients. One hundred and forty-seven genes were identified (SDC 2; see Supplementary Table 1,, which collectively differentiated HIV and healthy control individuals in different clusters in the PLS-DA analysis (SDC 2; see Supplementary Fig. 1B, Moreover, we represented these 147 genes on a heatmap (SDC 2; see Supplementary Fig. 2,, which showed that healthy control-group and HIV-group clustered independently.

Of these 147 genes, we selected 67 genes with a VIP at least one in the PLS-DA analysis (SDC 2; see Supplementary Table 2, for subsequent analysis of the putative biological deregulated pathways. We identified one KEGG pathway (Ribosome) and six GO categories (q value ≤0.05) (Table 2). The identified pathways had in common six ribosomal genes (ribosomal host proteins): S27 (RPS27), L18a (RPL18A), L8 (RPL8), L26 (RPL26), L4 (RPL4), and S21 (RPS21), all of them downregulated in the HIV-group. In addition, the eukaryotic translation initiation factor 1A X-linked (EIF1AX), which belongs to the ‘translational initiation’ GO category, was also downregulated in the HIV-group.

Table 2
Table 2:
Summary of significant gene ontology categories and KEGG pathways (P value ≤0.05).

T cell subpopulation and plasma biomarkers

T-cells subset and plasma biomarkers were also analyzed (SDC 2; see Supplementary Table 3,, but none of them showed significant differences after correcting for multiple comparisons (q value >0.05) (SDC 2; see Supplementary Fig. 3A, Noncorrected analysis showed that the HIV-group had higher values of CD4+ Treg cells (CD4+CD25+CD127−/low), MCP-1, and sVEGF-R1 (P value = 0.006, 0.004 and 0.016, respectively). We also performed a PLS-DA, which showed that HIV-infected patients and healthy controls were very similar according to the profile of T-cell subpopulation and plasma biomarkers (SDC 2; see Supplementary Fig. 3B,

We evaluated the correlation between the most significant biomarkers and the SRRM4 gene expression within HIV-group. We only found a slight correlation between sVEGFR1 and SRRM4 gene expression (R2 = 0.29; P < 0.05).


In this study, when we analyzed the transcriptomic profile, only the SRRM4 gene was significantly overexpressed in HIV-group, being this difference higher between males. The SRRM4 gene is involved in alternative RNA splicing events, acting like an essential gene switcher [21]. HIV produces multiple-spliced RNA species that modulates viral protein expression, replication, and infectivity [22]. Thus, the HIV genome presents exonic splicing enhancers that are selectively bound by members of the serin-arginine rich protein family to promote the use of nearby splice sites [23]. As HIV depends on host splicing modulators for viral RNA processing [24], it may be plausible that SRRM4 could be up-regulated in HIV infected-PBMCs to modulate HIV splicing mechanisms during latency. In addition, HIV residual transcription of the reservoir occurs during ART, increasing the cell-associated HIV RNA levels [25]. In this scenario, it would have been interesting to analyze the correlation between the levels of cell-associated HIV RNA and SRRM4 expression in the HIV-group. However, all the RNA from patients was used to perform the RNA-seq analysis, precluding any additional experiments.

In addition, we found the deregulation of specific biological pathways in HIV-infected patients, compared with healthy controls, where several ribosomal genes were downregulated in HIV-infected patients. Ribosomal host proteins are known to be associated with viral infections, playing critical roles in the life cycle of viruses, both as positive viral infection factors or antiviral factors [26]. In HIV infection downregulates ribosomal host proteins genes [27], which may suppose an antiviral function of ribosomal host proteins. In this setting, the RSP21 has been detected in extracellular vesicles from HIV infected patients, indicating that the excretion of this protein could be an additional mechanism to repress it [28]. Similar to our results, an in-vitro study in HIV infection of primary CD4+ T cells showed a downregulation of ribosomal host proteins [27], particularly those implicated in regulating ribosomal RNA transcription, prerRNA processing, and ribosome maturation. The deregulation of ribosomal host proteins and other nucleolar proteins during HIV infection seems to be a viral strategy to facilitate viral production [27].

There are many studies in well controlled HIV-infected patients on cART that showed a decrease in peripheral blood markers expression [6], but there is scarce information about HIV-infected patients on long-term cART with CD4+ T cells normalized. Recently, a Swedish study identified that HIV-infected patients on long-term suppressive cART clustered and networked with healthy controls and separated from HIV-naïve viremic patients on cART [29]. In our study, normalized values of T-cell subsets (naïve/memory/effector, regulation, immune activation, and senescence) and plasma biomarkers (bacterial translocation, inflammation, endothelial dysfunction, coagulopathy, metabolism, and angiogenesis) was observed in HIV-infected individuals on long-term successful cART.

HIV-group had slightly elevated plasma levels of sVEGFR1, which is a potent antiangiogenic factor derived from alternative splicing. In addition, sVEGFR1 was positively correlated with SRRM4 expression. In this scenario, SRRM4 could be involved in the higher production of this soluble factor since other SR (Serine/Arginine Repetitive) family members have been described to module its expression [30]. MCP-1 is slightly increased in HIV infection, but no previous relationship has been identified with SRRM4 or related splicing factors [31]. HIV-group also showed slightly elevated values of CD4+ Treg cells (CD4+CD25+CD127−/low), which are related to suppressive functions and the shaping of the HIV reservoir [32], which is the main barrier for an HIV cure. Several SR family members have already been reported as HIV splicing modulators [33]. Similarly, the higher SRRM4 expression in HIV-group may be related to HIV splicing events and the promotion of HIV-1 persistence in Tregs. Therefore, CD4+ Treg cells might be considered in future strategies to target the HIV reservoir, where the inhibition of SRRM4 could directly influence the latent reservoir. However, this hypothesis should be confirmed by functional assays to provide additional confirmatory data.


T-cell subsets and plasma biomarkers in peripheral blood, and gene expression in PBMCs, were close to normalization in HIV-infected patients on long-term suppressive cART compared with healthy controls. However, residual alterations remain, mainly at the gene expression, which still reveals the impact of HIV infection in these patients.


We want particularly acknowledge the patients in this study for their participation and to the Spanish HIV HGM BioBank integrated into the Spanish AIDS Research Network (RIS) and collaborating Centers for the generous gifts of clinical samples used in this work. We also want to thank the Bioinformatics Unit at the Institute of Health Carlos III for their valuable support for the bioinformatics analysis.

Declarations: Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki, and patients gave their written consent. The Institutional Review Board and the Research Ethics Committee of the Instituto de Salud Carlos III (ISCIII) approved the study (CEI PI 23_2011).

Consent for publication: Not applicable.

Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

The raw RNA data are publicly available at the ArrayExpress repository (EMBL-EBI; under the accession number E-MTAB-8249.

Author contributions: Conceptualization: S.R. and A.F.R.

Data curation: J.B., J.G.G., A.C., V.H., and P.G.B.

Formal analysis: O.B.K., S.R., I.M., and A.F.R.

Funding acquisition: J.B., J.G.G., and S.R.

Investigation and methodology: L.M.M., O.B.K., and A.F.R.

Project administration: J.B.

Supervision and visualization: S.R.

Writing – original draft preparation: I.M., A.F.R., and S.R.

Writing – review & editing: M.A.J.S. and M.A.M.F.

All authors have read and approved the final article.

The current study was supported by grants from Instituto de Salud Carlos III (ISCII; grant numbers PI14/01094 and PI17/00657 to J.B., PI14/01581 and PI17/00903 to J.G.G., CP14CIII/00010 and PI15CIII/00031 to A.F.R., and PI14CIII/00011 and PI17CIII/00003 to S.R.) and Ministerio de Sanidad, Servicios Sociales e Igualdad (grant number EC11-241). The study was also funded by the RD16CIII/0002/0002, RD16/0025/0018, and RD16/0025/0017 projects as part of the Plan Nacional R + D + I and cofunded by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER). J.B. is an investigator from the Programa de Intensificación de la Actividad Investigadora en el Sistema Nacional de Salud (I3SNS), Refs INT15/00079 and INT16/00100.

Conflicts of interest

There are no conflicts of interest.


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Amanda Fernández-Rodríguez and Salvador Resino contributed equally to this article.


antiretroviral therapy; gene expression; HIV; plasma biomarkers; ribosome proteins; T-cell subpopulations

Supplemental Digital Content

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