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• W4295679984 abstract "•Targeted proteomics reveal upregulation of inflammatory proteins in PLHIV•Upregulated inflammatory proteins in PLHIV are involved in specific pathways•Unsupervised clustering differentiated PLHIV with high and low inflammation•PLHIV with high inflammation had increased risk of malignancy and trend for developing CVD Despite antiretroviral therapy (ART), people living with HIV (PLHIV) display persistent inflammation leading to non-AIDS-related co-morbidities. To better understand underlying mechanisms, we compared targeted plasma inflammatory protein concentration (n = 92) between a cohort of 192 virally suppressed PLHIV, who were followed-up for five years, and 416 healthy controls (HC). Findings were validated in an independent cohort of 649 virally suppressed PLHIV and 98 HC. Compared to HC, PLHIV exhibited distinctively upregulated inflammatory proteins, including mucosal defense chemokines, CCR5 and CXCR3 ligands, and growth factors. Unsupervised clustering of inflammatory proteins clearly differentiated PLHIV with low (n = 123) and high inflammation (n = 65), the latter having a 3.4 relative risk (95% confidence interval 1.2–9.8) to develop malignancies and trend for cardiovascular events during a 5-year follow-up. The best protein predictors discriminating the two inflammatory endotypes were PD-L1, VEGFA, LAP TGF β-1, and TNFRSF9. Our data provide insights into co-morbidities associated inflammatory changes in PLHIV on long-term ART. Despite antiretroviral therapy (ART), people living with HIV (PLHIV) display persistent inflammation leading to non-AIDS-related co-morbidities. To better understand underlying mechanisms, we compared targeted plasma inflammatory protein concentration (n = 92) between a cohort of 192 virally suppressed PLHIV, who were followed-up for five years, and 416 healthy controls (HC). Findings were validated in an independent cohort of 649 virally suppressed PLHIV and 98 HC. Compared to HC, PLHIV exhibited distinctively upregulated inflammatory proteins, including mucosal defense chemokines, CCR5 and CXCR3 ligands, and growth factors. Unsupervised clustering of inflammatory proteins clearly differentiated PLHIV with low (n = 123) and high inflammation (n = 65), the latter having a 3.4 relative risk (95% confidence interval 1.2–9.8) to develop malignancies and trend for cardiovascular events during a 5-year follow-up. The best protein predictors discriminating the two inflammatory endotypes were PD-L1, VEGFA, LAP TGF β-1, and TNFRSF9. Our data provide insights into co-morbidities associated inflammatory changes in PLHIV on long-term ART. IntroductionCombination antiretroviral therapy (cART) has dramatically increased the life expectancy of people living with HIV (PLHIV). Still, PLHIV have a higher risk of developing non-AIDS-related comorbidities, such as cardiovascular diseases (CVD) and malignancies than uninfected peers (Antiretroviral Therapy Cohort, 2017Trickey A. May M.T. Vehreschild J.J. Obel N. Gill M.J. Crane H.M. Boesecke C. Patterson S. Grabar S. Cazanave C. et al.Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies.Lancet HIV. 2017; 4: e349-e356https://doi.org/10.1016/S2352-3018(17)30066-8Abstract Full Text Full Text PDF PubMed Scopus (567) Google Scholar; Marcus et al., 2020Marcus J.L. Leyden W.A. Alexeeff S.E. Anderson A.N. Hechter R.C. Hu H. Lam J.O. Towner W.J. Yuan Q. Horberg M.A. Silverberg M.J. Comparison of overall and comorbidity-free life expectancy between insured adults with and without HIV infection, 2000-2016.JAMA Netw. Open. 2020; 3: e207954https://doi.org/10.1001/jamanetworkopen.2020.7954Crossref PubMed Scopus (125) Google Scholar). Persistent inflammation, possibly induced by low-level viremia, cART toxicity, co-infections, microbial dysbiosis, and translocation (Brenchley et al., 2006Brenchley J.M. Price D.A. Schacker T.W. Asher T.E. Silvestri G. Rao S. Kazzaz Z. Bornstein E. Lambotte O. Altmann D. et al.Microbial translocation is a cause of systemic immune activation in chronic HIV infection.Nat. Med. 2006; 12: 1365-1371https://doi.org/10.1038/nm1511Crossref PubMed Scopus (2660) Google Scholar; Dinh et al., 2015Dinh D.M. Volpe G.E. Duffalo C. Bhalchandra S. Tai A.K. Kane A.V. Wanke C.A. Ward H.D. Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection.J. Infect. Dis. 2015; 211: 19-27https://doi.org/10.1093/infdis/jiu409Crossref PubMed Scopus (282) Google Scholar; Gianella and Letendre, 2016Gianella S. Letendre S. Cytomegalovirus and HIV: a dangerous pas de Deux.J. Infect. Dis. 2016; 214: S67-S74https://doi.org/10.1093/infdis/jiw217Crossref PubMed Scopus (78) Google Scholar; Llibre et al., 2012Llibre J.M. Buzón M.J. Massanella M. Esteve A. Dahl V. Puertas M.C. Domingo P. Gatell J.M. Larrouse M. Gutierrez M. et al.Treatment intensification with raltegravir in subjects with sustained HIV-1 viraemia suppression: a randomized 48-week study.Antivir.Ther. 2012; 17: 355-364https://doi.org/10.3851/imp1917Crossref PubMed Scopus (0) Google Scholar; van der Heijden et al., 2021van der Heijden W.A. Van de Wijer L. Keramati F. Trypsteen W. Rutsaert S. Horst R.T. Jaeger M. Koenen H.J. Stunnenberg H.G. Joosten I. et al.Chronic HIV infection induces transcriptional and functional reprogramming of innate immune cells.JCI Insight. 2021; 6: e145928https://doi.org/10.1172/jci.insight.145928Crossref PubMed Scopus (19) Google Scholar), have been reported to contribute to the development of these long-term complications (Borges Á et al., 2013Borges Á.H. Silverberg M.J. Wentworth D. Grulich A.E. Fätkenheuer G. Mitsuyasu R. Tambussi G. Sabin C.A. Neaton J.D. Lundgren J.D. et al.Predicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers.AIDS. 2013; 27: 1433-1441https://doi.org/10.1097/QAD.0b013e32835f6b0cCrossref PubMed Scopus (126) Google Scholar; Breen et al., 2011Breen E.C. Hussain S.K. Magpantay L. Jacobson L.P. Detels R. Rabkin C.S. Kaslow R.A. Variakojis D. Bream J.H. Rinaldo C.R. et al.B-cell stimulatory cytokines and markers of immune activation are elevated several years prior to the diagnosis of systemic AIDS-associated non-Hodgkin B-cell lymphoma.Cancer Epidemiol. Biomarkers Prev. 2011; 20: 1303-1314https://doi.org/10.1158/1055-9965.Epi-11-0037Crossref PubMed Scopus (0) Google Scholar; Hunt et al., 2016Hunt P.W. Lee S.A. Siedner M.J. Immunologic biomarkers, morbidity, and mortality in treated HIV infection.J. Infect. Dis. 2016; 214: S44-S50https://doi.org/10.1093/infdis/jiw275Crossref PubMed Scopus (145) Google Scholar). Increased concentrations of circulating inflammation markers, such as high-sensitivity C-reactive-protein (hsCRP), interleukin-6 (IL-6), tumor necrosis factor (TNF-α), and immune activation markers, including soluble (s)CD14 and sCD163 have been reported in virally suppressed PLHIV (Bastard et al., 2015Bastard J.-P. Fellahi S. Couffignal C. Raffi F. Gras G. Hardel L. Sobel A. Leport C. Fardet L. Capeau J. ANRS CO8 APROCO-COPILOTE Cohort Study GroupIncreased systemic immune activation and inflammatory profile of long-term HIV-infected ART-controlled patients is related to personal factors, but not to markers of HIV infection severity.J. Antimicrob. Chemother. 2015; 70: 1816-1824https://doi.org/10.1093/jac/dkv036Crossref PubMed Scopus (37) Google Scholar; Hunt et al., 2016Hunt P.W. Lee S.A. Siedner M.J. Immunologic biomarkers, morbidity, and mortality in treated HIV infection.J. Infect. Dis. 2016; 214: S44-S50https://doi.org/10.1093/infdis/jiw275Crossref PubMed Scopus (145) Google Scholar; Neuhaus et al., 2010Neuhaus J. Jacobs Jr., D.R. Baker J.V. Calmy A. Duprez D. La Rosa A. Kuller L.H. Pett S.L. Ristola M. Ross M.J. et al.Markers of inflammation, coagulation, and renal function are elevated in adults with HIV infection.J. Infect. Dis. 2010; 201: 1788-1795https://doi.org/10.1086/652749Crossref PubMed Scopus (607) Google Scholar; Novelli et al., 2020Novelli S. Lécuroux C. Goujard C. Reynes J. Villemant A. Blum L. Essat A. Avettand-Fenoël V. Launay O. Molina J.-M. et al.Persistence of monocyte activation under treatment in people followed since acute HIV-1 infection relative to participants at high or low risk of HIV infection.EBioMedicine. 2020; 62: 103129https://doi.org/10.1016/j.ebiom.2020.103129Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar; van der Heijden et al., 2021van der Heijden W.A. Van de Wijer L. Keramati F. Trypsteen W. Rutsaert S. Horst R.T. Jaeger M. Koenen H.J. Stunnenberg H.G. Joosten I. et al.Chronic HIV infection induces transcriptional and functional reprogramming of innate immune cells.JCI Insight. 2021; 6: e145928https://doi.org/10.1172/jci.insight.145928Crossref PubMed Scopus (19) Google Scholar).Assessment of the profile of plasma inflammatory proteins allows the study of complex biological pathways that may lead to the identification of novel therapeutic targets and disease biomarkers. Previous studies that assessed plasma protein profiles were limited by small sample size and/or lack of a proper validation cohort (Babu et al., 2019aBabu H. Ambikan A.T. Gabriel E.E. Svensson Akusjärvi S. Palaniappan A.N. Sundaraj V. Mupanni N.R. Sperk M. Cheedarla N. Sridhar R. et al.Systemic inflammation and the increased risk of inflamm-aging and age-associated diseases in people living with HIV on long term suppressive antiretroviral therapy.Front. Immunol. 2019; 10: 1965https://doi.org/10.3389/fimmu.2019.01965Crossref PubMed Scopus (38) Google Scholar, Babu et al., 2019bBabu H. Sperk M. Ambikan A.T. Rachel G. Viswanathan V.K. Tripathy S.P. Nowak P. Hanna L.E. Neogi U. Plasma metabolic signature and abnormalities in HIV-infected individuals on long-term successful antiretroviral therapy.Metabolites. 2019; 9: 210https://doi.org/10.3390/metabo9100210Crossref PubMed Scopus (23) Google Scholar; deFilippi et al., 2020deFilippi C. Toribio M. Wong L.P. Sadreyev R. Grundberg I. Fitch K.V. Zanni M.V. Lo J. Sponseller C.A. Sprecher E. et al.Differential plasma protein regulation and statin effects in human immunodeficiency virus (HIV)-Infected and non-HIV-infected patients utilizing a proteomics approach.J. Infect. Dis. 2020; 222: 929-939https://doi.org/10.1093/infdis/jiaa196Crossref PubMed Scopus (7) Google Scholar; Lemma et al., 2020Lemma M. Petkov S. Bekele Y. Petros B. Howe R. Chiodi F. Profiling of inflammatory proteins in plasma of HIV-1-Infected children receiving antiretroviral therapy.Proteomes. 2020; 8: 24https://doi.org/10.3390/proteomes8030024Crossref PubMed Scopus (4) Google Scholar; Sperk et al., 2018Sperk M. Zhang W. Nowak P. Neogi U. Plasma soluble factor following two decades prolonged suppressive antiretroviral therapy in HIV-1-positive males: a cross-sectional study.Medicine (Baltim.). 2018; 97: e9759https://doi.org/10.1097/MD.0000000000009759Crossref PubMed Scopus (9) Google Scholar; Vos et al., 2021Vos A.G. Dodd C.N. Delemarre E.M. Nierkens S. Serenata C. Grobbee D.E. Klipstein-Grobusch K. Venter W.D.F. Patterns of immune activation in HIV and non HIV subjects and its relation to cardiovascular disease risk.Front. Immunol. 2021; 12: 647805https://doi.org/10.3389/fimmu.2021.647805Crossref PubMed Scopus (2) Google Scholar). In addition, other studies included only patients with central obesity and insulin resistance using statins (deFilippi et al., 2020deFilippi C. Toribio M. Wong L.P. Sadreyev R. Grundberg I. Fitch K.V. Zanni M.V. Lo J. Sponseller C.A. Sprecher E. et al.Differential plasma protein regulation and statin effects in human immunodeficiency virus (HIV)-Infected and non-HIV-infected patients utilizing a proteomics approach.J. Infect. Dis. 2020; 222: 929-939https://doi.org/10.1093/infdis/jiaa196Crossref PubMed Scopus (7) Google Scholar) or children with HIV infection (Lemma et al., 2020Lemma M. Petkov S. Bekele Y. Petros B. Howe R. Chiodi F. Profiling of inflammatory proteins in plasma of HIV-1-Infected children receiving antiretroviral therapy.Proteomes. 2020; 8: 24https://doi.org/10.3390/proteomes8030024Crossref PubMed Scopus (4) Google Scholar).In the present study, we identified signatures of 92 inflammation-related plasma proteins in virally suppressed PLHIV (n = 841) and compared them to healthy controls (HC) (n = 514). Using a discovery cohort and an independent validation cohort, we found mostly upregulation of plasma inflammatory protein concentrations in PLHIV compared to HC. Furthermore, stratification of PLHIV based on the inflammatory proteome revealed two distinct clusters, one with a high- and the other with a low-inflammation profile.ResultsCharacteristics of the study populationThe discovery cohort consisted of 192 PLHIV and 416 HC that passed the quality control (QC) procedures (Table 1). Most (178/192 [93%]) of PLHIV were males, with a median (IQR) age of 52.4(13.3) years. PLHIV had median (IQR) CD4+ counts of 660 (330) cells/mL and were on ART for a median (IQR) 6.6 (7.2) years. Healthy controls were more often female (214/416 [51%], pvalue<0.0001), younger (median [IQR] age 23 [5] years, pvalue<0.0001), and leaner (median [IQR] BMI 22.3 [3.5] kg/m2, pvalue<0.0001) compared to PLHIV.Table 1General characteristics of the discovery cohortCharacteristicPLHIV (n = 192)HC (n = 416)P-valueAge, years52.4 (13.3)23 (5.0)<0.0001Sex, female, n/N (%)14/192 (7.3)214/416 (51.4)<0.0001BMI, kg/m224.1 (3.9)22.3 (3.5)<0.0001Time since HIV diagnosis, years8.4 (8.5)––Time on ART, years6.6 (7.2)––Nadir CD4+ cell count, cells/μl250 (212.5)––Latest CD4+ count, cells/μl660 (330)––Zenith HIV-RNA, copies/mL100,000 (335,591)––Latest HIV-RNA, copies/mL0 (40)Ratio CD4/CD80.7 (0.5)––HIV RNA blips 1 yearaViral blips defined as HIV-RNA >50 and <200 copies/mL preceded and followed by HIV-RNA ≤50 copies/mL in the 1 or 5 years before visit.5/192 (2.6)––HIV RNA blips 5 yearsaViral blips defined as HIV-RNA >50 and <200 copies/mL preceded and followed by HIV-RNA ≤50 copies/mL in the 1 or 5 years before visit.33/192 (17.3)––ART classes, n/N (%) NNRTI57/192 (29.7)–– PI28/192 (14.6)–– INSTI128/192 (66.7)––Co-medication, n/N (%) Cholesterol lowering drugs51/192 (26.6)–– Antihypertensive drugs45/192 (23.4)–– Antidiabetic drugs9/192 (4.7)–– Anti-inflammatory drugs26/192 (13.5)–– Anticoagulant24/192 (12.5)–– Vitamin D44/192 (22.9)–– Psychopharmaca23/192 (12.0)––Co-morbidities, n/N (%) Cardiovascular disease18/192 (9.4)–– Hypertension52/192 (27.2) Endocrine and metabolic disease70/192 (36.5)–– Respiratory disease27/192 (14.1)–– Gastrointestinal disease23/192 (12.0)–– Psychiatric conditions47/192 (24.5)–– Previously diagnosed malignancies30/192 (15.7)–– Fracture and bone disease34/192 (17.8)–– Lipodystrophy30/192 (15.7)––Co-morbidities (5-year follow-up), n/N (%) Cardiovascular disease18/192 (9.4)–– Hypertension25/192 (13.1)–– Malignancies14/192 (7.3)–– Fracture and bone disease29/192 (15.2)––Active smoking, n/N (%)55/192 (28.6)57/416 (13.7)<0.0001Data are depicted as median (IQR) unless stated otherwise. Data were analyzed using Mann-Whitney U or χ2 (or Fisher’s exact) where applicable.PLHIV, people with HIV; HC, healthy controls; BMI, body mass index; ART, antiretroviral therapy; INSTI, integrase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor.a Viral blips defined as HIV-RNA >50 and <200 copies/mL preceded and followed by HIV-RNA ≤50 copies/mL in the 1 or 5 years before visit. Open table in a new tab In the validation cohort, samples of 649 PLHIV and 98 HC passed the QC procedures (Table 2). PLHIV were slightly older than controls (median [IQR] age of 53 [15] years in PLHIV versus 49.5 [20.7] years in HC, pvalue = 0.001), and, although both groups predominantly consisted of males, the number of males was higher in the PLHIV group compared to the HC group (592/649 [91%] of PLHIV versus 74/98 [76%] of HC, pvalue<0.0001). All PLHIV in the validation cohort were on stable ART for a median (IQR) of 10 (9) years, with median (IQR) CD4+ counts of 700 (400) cells/uL.Table 2General characteristics of the validation cohortCharacteristicPLHIV (n = 649)HC (n = 98)P-valueAge, years53 (15)49.5 (20.8)0.001Sex, female, n/N (%)57/649 (8.7)24/98 (24.5)<0.0001BMI, kg/m224.9 (4.8)24.85 (3.6)0.343Time since HIV diagnosis, years12 (12)––Time on ART, years10 (9)––Latest CD4+ count, cells/μl,700 (400)––Latest HIV-RNA, copies/mL0 (2)––Data are depicted as median (IQR) unless stated otherwise. Data were analyzed using Mann-Whitney U or χ2 (or Fisher’s exact test) where applicable.PLHIV, people with HIV; HC, healthy controls; BMI, body mass index; ART, antiretroviral therapy. Open table in a new tab Age, sex, and BMI influence plasma inflammatory protein concentrations in PLHIV and HCWe first explore the relationship among plasma inflammatory proteins in PLHIV and HC from the discovery cohort and found general strong positive correlations among plasma inflammatory proteins (Figure S3). The strongest associations were found among intracellular proteins (4E-BP1, STAMBP, AXIN1, ST1A1, SIRT2, and CASP8), chemokines associated with neutrophil (CXCL1, CXCL5, and CXCL6) and monocytes chemotaxis (MCP-2 and MCP4), natural killer cell surface receptor (CD244), and immune mediators related with T and B cells development and activation (IL7, CXCL11, CD40/TNRSF5 and TNFSF14).Next, we examined the influence of host factors on plasma inflammatory protein concentrations in PLHIV and HC from the discovery cohort. Advancing age was associated with an overall higher concentration of inflammatory proteins in both groups (Figure S4). Female sex was significantly associated with reduced inflammatory proteins in HC, but not in PLHIV. The latter may have resulted from insufficient statistical power because of the small number of females in PLHIV (9%). BMI was positively associated with increased plasma inflammatory proteins concentrations, but to a lesser extent than age (Figure S4).Increased plasma inflammatory protein concentrations in PLHIVTo compare the inflammatory profile between virally suppressed PLHIV and HC, we first performed differential expression (DE) analysis using 78 circulating inflammatory proteins measurements from the discovery cohort of PLHIV (n = 192) and HC (n = 416). Subsequently, we validated the significant results from the discovery cohort using a second independent cohort of PLHIV (n = 649) and HC (n = 98). The analytical process of DE plasma inflammatory protein analysis is depicted in Figure 1A .First, we performed an unsupervised hierarchical clustering analysis using 78 circulating inflammatory proteins measurements from the discovery cohort (Figure 1B). We observed a distinct separation between the majority of PLHIV and HC individuals, suggesting an overall difference in their inflammatory profiles. This observation was confirmed through PCA showing separate clusters between PLHIV and HC (Figure 1C). We next performed DE analysis to assess differences in individual plasma inflammatory protein concentrations between PLHIV and HC from the discovery cohort. Given the effect of age and sex on the inflammatory protein concentrations (Figure S4), DE analysis was performed using a linear model with age and sex as covariates. The results of the DE analysis are presented in a volcano plot (Figure 1D). In total, 64 out of 78 proteins concentrations (82%) were differentially expressed (FDR<0.05) between PLHIV and HC, and most of the statistically significant proteins were upregulated in PLHIV. We found similar results when DE analysis between PLHIV and HC was performed with age, sex, BMI, and smoking status as covariates (Figure S5).We confirmed our findings in the independent validation cohort of 649 PLHIV and 98 HC (Figure S1). In the validation cohort, PCA using relative concentrations of 62 proteins showed a different inflammatory profile between PLHIV and HC (Figure 2A ). DE analysis in the validation cohort identified 40/62 (64.5%) DEP between PLHIV and HC with pvalue<0.05 (Figure S6), of which 29/40 (72.5%) proteins were upregulated in both the discovery and validation cohort (Figure 2B and Table S1).Figure 2Validation of differentially expressed proteins between PLHIV and HCShow full caption(A) PCA of plasma inflammatory proteins (n = 64) from the validation cohort of PLHIV (n = 98) and HC (n = 649) using the first two principal components. The ellipses were centered based on the median of the PC1 and PC2 for each group (PLHIV and HC). Protein distributions across PC1 and PC2 for each group are presented in marginal histogram plots. The median differences of the protein distribution across PC1 or PC2 between PLHIV and HC were calculated by the Mann-Whitney-U test. ∗∗∗p-value<0.0001. See also Figure S6.(B) Four-quadrant plot of the fold change of DEP (n = 29) in the discovery (xaxis) and validation cohort (yaxis). DE analysis was performed using a linear regression model with age and sex as covariates. See also Table S1 and Figures S6 and S7.(C) Heatmaps showing the correlations between the relative concentration of DEP (n = 29) and absolute concentration of plasma inflammatory markers measured in PLHIV of the discovery cohort. The analysis was performed by linear regression model using age and sex as covariates.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Finally, we analyzed whether the relative concentration of the validated differentially abundant plasma inflammatory proteins (n = 29) measured by Olink was associated with the absolute concentration of plasma markers measured by ELISA. In general, DEP were positively associated with acute-phase proteins (TNF-α and hsCRP), adipokines (resistin), cytokines (IL-6, IL-1Ra, IL-18BP, IL-18, and IL-10), and monocyte activation markers (sCD14 and sCD163) (FDR<0.05) (Figure 2C). As expected, relative concentrations of IL-18 and TNF-α from the DE analysis significantly correlated with the absolute concentrations of IL-18 and TNF-α (FDR<0.005). In addition, hepatocyte growth factor (HGF) showed a significant positive correlation with other adipokines, such as resistin and leptin, and a negative correlation with adiponectin (Figure 2C).Network analysis reveals upregulation of specific inflammatory pathways in PLHIVWe further investigated the inter-relationship among the 29 significantly upregulated proteins in PLHIV compared to HC identified in the discovery and validation cohort. For this, we performed network analysis using relative concentrations of the 29 proteins from the PLHIV of the discovery cohort. Moderate to strong correlations (Spearman’s Rho>0.3) are displayed in Figure 3A . Overall, significant positive correlations were observed among DEP (FDR<0.05). Protein-protein interactions among DEP were further visualized by dendrogram based on hierarchical clustering analysis (Figure 3B). Four different clusters of proteins that shared similar functions were identified through the network and hierarchical clustering analysis (Figures 3A and 3B).Figure 3Dysregulation of distinct inflammatory pathways in PLHIVShow full caption(A) Results from the network analysis using the DEP (n = 29) between PLHIV and HC. Network analysis was performed by calculating the spearman’s rank correlation between pairs of proteins measured in PLHIV of the discovery cohort (n = 192), and only those pairs with rho≥0.3 are presented. Proteins are represented as nodes, line colors connecting the nodes represent the degree of correlation for protein linkage; the darker the color, the stronger the correlation. Nodes color represents the centrality (importance) of protein based on nodes closeness.(B) Dendrogram of the DEP (n = 29) between PLHIV and HC.(C) Heatmap showing the correlations between the two CCR5 ligands (CCL3 and CCL4) and CCR5 expression measured in a different subset of immune cells in PLHIV of the discovery cohort. The correlations were calculated using a linear regression model with age and sex as covariates.(D) Heatmap showing the correlations between DEP (n = 29) and immunophenotyping data measured in PLHIV of the discovery cohort. The color-coding key depicts the beta estimate calculated by a linear regression model with age and sex as covariates. Significance level (FDR corrected) was defined as follows: <0.05(∗), <0.005(∗∗), and <0.0001(∗∗∗).View Large Image Figure ViewerDownload Hi-res image Download (PPT)The first cluster consisted of the growth regulator oncostatin M (OSM) and several growth factors, including HGF, vascular endothelial growth factor A (VEGFA), transforming growth factor α (TGF-α). VEGFA and TGF-α were the most central proteins in the network analysis, indicating that these proteins showed the most pairwise correlations with other proteins (Figure 3A). OSM has been shown to stimulate the accumulation of immature and mature T-cells in lymph nodes, restoring immune responsiveness in immune-deficient mice (Clegg et al., 1996Clegg C.H. Rulffes J.T. Wallace P.M. Haugen H.S. Regulation of an extrathymic T-cell development pathway by oncostatin M.Nature. 1996; 384: 261-263https://doi.org/10.1038/384261a0Crossref PubMed Scopus (88) Google Scholar). Furthermore, OSM is known to play a role in the initiation and progression of Kaposi sarcoma (Miles et al., 1992Miles S.A. Martínez-Maza O. Rezai A. Magpantay L. Kishimoto T. Nakamura S. Radka S.F. Linsley P.S. Oncostatin M as a potent mitogen for AIDS-Kaposi's sarcoma-derived cells.Science. 1992; 255: 1432-1434https://doi.org/10.1126/science.1542793Crossref PubMed Scopus (239) Google Scholar; Nair et al., 1992Nair B.C. DeVico A.L. Nakamura S. Copeland T.D. Chen Y. Patel A. O'Neil T. Oroszlan S. Gallo R.C. Sarngadharan M.G. Identification of a major growth factor for AIDS-Kaposi's sarcoma cells as oncostatin M.Science. 1992; 255: 1430-1432https://doi.org/10.1126/science.1542792Crossref PubMed Scopus (211) Google Scholar), a common Herpes virus 8 related opportunistic cancer in PLHIV.Furthermore, the second cluster consisted of the mucosal defense chemokines (chemokine (C–C motif) ligand 11 (CCL11), monocyte chemotactic protein 4 (MCP-4/CCL13), CCL20, CCL25, and CCL28)) and cystatin-D (CST5). Of interest, the concentrations of the three mucosal defense chemokines, CCL11, CCL20, and CCL25, were significantly associated with the absolute concentrations of IFABP, a marker of gut wall integrity (Figure 2C). In addition, CCL28 was associated with sCD14, a marker of monocyte activation (Figure 2C).The third cluster consisted of CCR5 ligands (CCL3 and CCL4), C-X-C Motif Chemokine Receptor 3 (CXCR3) ligand chemokines (Chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, CXCL11), MCP-2, cluster of differentiation 8A (CD8A), and TNF-α. CCR5 is known as the main HIV co-receptor, and we found a significant negative correlation between CCL4 concentrations with CCR5 expression of different CD4+ (total CD4+ cells, mTreg, and total pool effector memory cells) and CD8+ cell subsets (total CD8+ cells, total pool effector memory cells, and effector memory cells) (Figure 3C).The last cluster consisted of an assortment of cytokine (IL-18), chemokine (CCL23), cluster of differentiation proteins (CD5, CD6, CD244, and PD-L1/CD274), eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), and adenosin deaminase (ADA). Most of these proteins are known to play an important role in T cell activation, differentiation, and chemotaxis for T cell migration. Other members of this cluster were tumor necrosis factor superfamily (tumor necrosis factor superfamily member 14 (TNFSF14), and TWEAK/TNFSF12) and tumor necrosis factor receptor superfamily members (CD40). TNFSF14 is known as herpes virus entry mediator ligand (Montgomery et al., 1996Montgomery R.I. Warner M.S. Lum B.J. Spear P.G. Herpes simplex virus-1 entry into cells mediated by a novel member of the TNF/NGF receptor family.Cell. 1996; 87: 427-436https://doi.org/10.1016/S0092-8674(00)81363-XAbstract Full Text Full Text PDF PubMed Scopus (998) Google Scholar), also for cytomegalovirus, a common co-pathogen in PLHIV.To identify the cellular origin of our differentially expressed proteins (n = 29), we used single-cell transcriptomic publicly available data from the Human Proteomic Atlas (HPA) project (Karlsson et al., 2021Karlsson M. Zhang C. Méar L. Zhong W. Digre A. Katona B. Sjöstedt E. Butler L. Odeberg J. Dusart P. et al.A single-cell type transcriptomics map of human tissues.Sci. Adv. 2021; 7: eabh2169https://doi.org/10.1126/sciadv.abh2169Crossref PubMed Scopus (96) Google Scholar). HPA used consensus transcriptomics data in 76 single cell types to classify genes according to their single-cell type-specific category. We found innate (dendritic cells, macrophages, langerhans cells, natural killer (NK) cells) and adaptive immune cells (T cells) among cells that produce most of our differentially expressed proteins (Figure S7).Lastly, given that many of the identified proteins are primarily released by immune cells modulating subsequently their proliferation and migration, we investigated whether the DEP correlated with the proportion of circulating immune cells. The strongest association was found for OSM, which was positively and negatively associated with neutrophils and lymphocytes percentages respectively (Figure 3D). These findings are consistent with a previous study showing that OSM is primarily expressed in neutrophils and stored in neutrophils granules in the circulation (Uriarte et al., 2008Uriarte S.M. Powell D.W. Luerman G.C. Merchant M.L. Cummins T.D. Jog N.R" @default.
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• W4295679984 date "2022-10-01" @default.
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• W4295679984 title "Targeted plasma proteomics reveals upregulation of distinct inflammatory pathways in people living with HIV" @default.
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• W4295679984 doi "https://doi.org/10.1016/j.isci.2022.105089" @default.
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