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Role of myeloid cells in system-level immunometabolic dysregulation during prolonged successful HIV-1 treatment a a a Sara Svensson Akusja¨rvi , Shuba Krishnan , Anoop T. Ambikan , a b Flora Mikaeloff , Sivasankaran Munusamy Ponnan , c d,e a,c Jan Vesterbacka , Magda Lourda , Piotr Nowak , a,c a Anders Sonnerborg and Ujjwal Neogi See related paper on page 1171 Objective: Why people with HIV-1 on ART (PWH ) display convoluted metabolism ART and immune cell functions during prolonged suppressive therapy is not well evaluated. In this study, we aimed to address this question using multiomics methodologies to investigate immunological and metabolic differences between PWH and HIV-1 ART negative individuals (HC). Design: Cross-sectional study Methods: Untargeted and targeted metabolomics was performed using gas and liquid chromatography/mass spectrometry, and targeted proteomics using Olink inflammation panel on plasma samples. The cellular metabolic state was further investigated using flow cytometry and intracellular metabolic measurement in single-cell populations isolated by EasySep cell isolation. Finally, flow cytometry was performed for deep- immunophenotyping of mononuclear phagocytes. Results: We detected increased levels of glutamate, lactate, and pyruvate by plasma metabolomics and increased inflammatory markers (e.g. CCL20 and CCL7) in PWH ART compared to HC. The metabolite transporter detection by flow cytometry in T cells and monocytes indicated an increased expression of glucose transporter 1 (Glut1) and monocarboxylate transporter 1 (MCT-1) in PWH . Single cell-type metabolite mea- ART surement identified decreased glucose, glutamate, and lactate in monocytic cell populations in PWH . Deep-immunophenotyping of myeloid cell lineages subpo- ART pulations showed no difference in cell frequency, but expression levels of CCR5 were increased on classical monocytes and some dendritic cells. Conclusions: Our data thus suggest that the myeloid cell populations potentially contribute significantly to the modulated metabolic environment during suppressive HIV-1 infection. Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. AIDS 2023, 37:1023–1033 Keywords: flow cytometry, HIV-1, inflammatory markers, metabolomics, myeloid cells Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, ANA Futura, Campus Flemingsberg, Stockholm, Sweden, HIV Vaccine Trials Network, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research c d Centre, Seattle, USA, Department of Medicine Huddinge (MedH), Karolinska Institutet, Stockholm, Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, ANA Futura, Campus Flemingsberg, and Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden. Correspondence to Sara Svensson Akusja¨rvi, Karolinska Institutet, Stockholm, Sweden. Tel: +46 73 713 00 47; e-mail: sara.svensson.akusjarvi@ki.se Received: 21 November 2022; revised: 19 January 2023; accepted: 1 February 2023. DOI:10.1097/QAD.0000000000003512 ISSN 0269-9370 Copyright Q 2023 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1023 1024 AIDS 2023, Vol 37 No 7 Table 3, Supplemental Digital Content, http://links.lww. Introduction com/QAD/C814 number of samples used per experi- Successful long-term antiretroviral therapy (ART) inhi- mental method is specified. The HC samples were bits human immunodeficiency virus type-1 (HIV-1) collected at the Karolinska University Hospital, Hud- replication for a prolonged period in people with HIV-1 dinge, and the inclusion criteria were no known (PWH). We and others have earlier reported metabolic conditions or infections at the time of sampling. modifications involving dysregulation of the amino acid (AA) and central carbon metabolism in PWH on The study was approved by the Regional Ethics successful long-term ART (PWH ) [1–9]. Our recent Committee of Stockholm and performed according to ART study also reported a system-level upregulation of the Declaration of Helsinki. All participants gave oxidative phosphorylation (OXPHOS) and glycolysis informed consent before inclusion in the study. in PWH compared to HIV-1 positive elite controllers ART who naturally control viral replication [10]. These Metabolomics alterations can play a role in latent reservoir dynamics Plasma un-targeted metabolomics was performed using and immunosenescence in PWH . In our earlier the HD4 Platform (Metabolon, Morrisville, North ART studies from India [1], Denmark [2], and Cameroon [3], Carolina, USA) in a cohort including HC (n¼ 22), we reported persistent metabolic reprogramming in PWH (n¼ 29), and PWH (n¼ 11). The samples ART VP patients with successful therapy. A subset of these were selected randomly. The method was performed as PWH also showed an altered metabolic profile linked previously described [11]. Plasma targeted metabolomics ART with the development of age-related comorbidities that was performed towards central carbon metabolism can potentiate accentuated aging or age-related diseases. (CCM) and sugars using gas chromatography–mass spectrometry (GC–MS) and amino acids by liquid In this study, we performed a system-level plasma chromatography (LC)–MS/MS in a cohort of HC metabolomics profiling using untargeted and targeted (n¼ 37), PWH (n¼ 55), and PWH (n¼ 24) at the ART VP metabolomics in a well defined Swedish cohort of Swedish Metabolomics Centre (Umea, Sweden). PWH , together with matched HIV-negative controls ART (HC). We also performed immune phenotyping of Proteomics metabolite transporters for some critical metabolites Targeted plasma proteomics was performed using the altered in PWH and intercellular measurements of the Olink inflammation panel (Olink, Sweden) as previously ART key metabolites in different cell populations. Finally, we described [12]. In brief, a proximity extension assay was phenotyped myeloid cell lineages, focusing on the used to evaluate the plasma levels of a set panel of proteins. expression levels of chemokine receptors that play a Cycle threshold (Ct) values were normalized to an significant role in cellular trafficking during inflamma- extension control and inter-plate control, and all proteins tion. We observed that intercellular metabolites and were reported as normalized protein expression levels metabolite transporters are altered in monocytes but not (NPX). T cells, potentially linked with the systemic metabolic environment. Our study thus provides a comprehensive Bioinformatic analysis understanding of immuno-metabolic regulation during Differential enrichment analysis was performed using R long-term successful therapy. package limma v3.50.0 [13]. Mann–Whitney U test was performed using R package stats v4.1.2. Heatmaps were created using R package ComplexHeatmap v2.10.0 [14]. UMAP dimensionality reduction was done with R package umap v2.7.0. Boxplots, bar plots, and bubble Methods plots were generated using the R package ggplot2 v3.3.5/ Cohort description . Network and community analysis was performed as This study included one group of HIV-1 infected previously described by us [15]. individuals with suppressed viremia (PWH , n¼ 64), ART one group with viremia (PWH , n¼ 24), and HIV-1- Isolation of CD4 T cells and monocytes VP þ þ þ negative controls (HC, n¼ 37) (total cohort character- Cell subpopulations of CD4 T cells (CD3 CD4 ) and istics can be viewed in Table 1, Supplemental Digital monocytes (CD3 CD14 ) were isolated using EasySep Content, http://links.lww.com/QAD/C814). The magnets (STEMCELL Technologies, Vancouver, British median suppressive treatment of PWH was 7 years Columbia, Canada). First, PBMCs were washed in FACS ART [interquartile range (IQR) 6–13]. We also included buffer (PBS þ 2% FBS þ 2 mmol/l EDTA) and treated paired samples between PWH (n¼ 11) and PWH with DNAse (100 ug/ml) (StemCell, #7900) for 15 min VP ART (n¼ 11) after a median of 8 years (IQR 6–8 years) of at room temperature. Subsequently, Easysep Human suppressive therapy. Clinical parameters for the longitu- CD4 positive selection kit II (StemCell, #17852) was dinal cohort can be viewed in Table 2, Supplemental used according to the manufacturer’s protocol, and the Digital Content, http://links.lww.com/QAD/C814. In flowthrough used for Easysep Human monocyte isolation Immunometabolism in HIV-1 Svensson Akusja¨rvi et al. 1025 kit (StemCell, #19359), according to the manufacturer’s between PWH and HC. Therefore, a pronounced host VP protocol. metabolic alteration was detected during the initial viraemic phase. Since our primary aim was to identify Intracellular metabolite detection the metabolic dysregulation in PWH compared to ART From isolated CD4 T cells and monocytes, 30 000 cells matched HC, we restricted the analysis to these two were used for metabolite detection in duplicates for groups. Heatmap visualization of significantly different each sample. Lactate-Glo assay (Promega, #J5022; Pro- metabolites between PWH and HC showed a distinct ART mega, Madison, Wisconsin, USA), Glutamate-Glo assay enrichment pattern of metabolites between the groups (Promega, #J7022), and Glucose-Glo assay (Promega, (Fig. 1b). This included a significant enrichment of #J6022) were used to measure intracellular metabolites glutamate, pyruvate, and lactate in PWH (Fig. 1c). ART according to the manufacturer’s instructions. Luminescence Uniform manifold approximation and projection (UMAP) was measured using Varioskan microplate reader (Thermo- could clearly distinguish clusters for PWH and HC ART Fisher Scientific, Waltham, Massachusetts, USA). based on these three metabolites (Fig. 1d). For 11 individ- uals, we had paired samples from the initial viraemic phase Flow cytometry without therapy and after a median duration of eight years Purity evaluation of isolated cell populations was acquired of treatment. In five of these patients, the median levels of on BD Fortessa (BDBiosciences, Franklin Lakes, New glutamate, both before and during treatment, were higher Jersey, USA). Expression levels of metabolite transporters than in HC, while glutamate levels increased following was acquired on BD Symphony (BdBiosciences). The treatment initiation. In most of the patients, lactate levels myeloid lineage panel was acquired on BD Symphony decreased (9 of 11) while pyruvate levels increased (7 of 11) (BdBiosciences). Staining was complemented with Dead following therapy (Fig. 1e). Next, we tried to identify cell (Near-IR or Aqua) viability stain (Invitrogen, biomarkers that could differentiate HC from successfully ThermoFisher, Scientific). Specifics about antibodies treated PWH using multivariate methods with unbi- ART can be found in Table 4, Supplemental Digital Content, ased variable selection in R (MUVR). This analysis http://links.lww.com/QAD/C814. All flow cytometry identified glutamate and g-carboxyglutamate as key analysis was performed using FlowJo 10.8.1 (TreeStar biomarkers (Fig. 1f). We further performed weighted Inc., Ashland, Oregon, USA). co-expression analysis using the Leiden algorithm, where coordination between pyruvate, lactate, and glutamate was Statistics observed (Fig. 1g). Therefore, our data indicate a Statistical analysis was performed using MannWhitney dysregulation of glutamate metabolism in PWH with ART U-test in Prism 9.3.0 (GraphPad, Software, San Diego, long-term successful HIV-1 treatment. This modulation California, USA) or Rstudio (v.1.3.1056; R Foundation could play a significant coordinating role in pyruvate and for Statistical Computing, Vienna, Austria). In omics lactate metabolism. data, the multiple hypothesis corrections were performed using BenjaminiHochberg (BH) method. Dysregulated central carbon and amino acid metabolism together with elevated levels of inflammatory markers in people with HIV-1 on antiretroviral therapy Next, to validate our findings in a larger cohort of Results PWH (n¼ 55), PWH (n¼ 24), and HC (n¼ 37), ART VP Increased levels of glutamate, pyruvate and we performed targeted metabolomic analysis for AA and lactate in people with HIV-1 on antiretroviral metabolites of the central carbon metabolism (CCM) and therapy compared to HIV-negative controls sugars. Among the quantified AA (n¼ 26), CCM We performed untargeted metabolomic analysis using the metabolites, and sugars (n¼ 15), 19 metabolites were HD4 Platform (Metabolon) on plasma samples obtained significantly different (adjusted P< 0.05) between from PWH (n¼ 29), untreated PWH with viremia PWH and HC (Fig. 2a). Of these, the majority of ART ART (PWH , n¼ 11), and matched HIV-negative controls AA (tryptophan, methionine, lysine, glutamine, glycine, VP (HC, n¼ 22). A total of 841 metabolites were detected, of arginine, threonine, and kynurenine) decreased in which 143 (17%), belonging toxenobiotics, were excluded PWH while glutamate was increased (Fig. 2a). ART from further analysis as these are not naturally produced Amongst the CMM metabolites and sugars, we detected (Fig. 1a). After adjusting for gender, body mass index a decrease in glucose while its isoform beta-glucose was (BMI), and age, known to influence the plasma increased in PWH. Furthermore, a similar trend with an metabolite profile, 14 metabolites were significantly increase in lactate and pyruvate was seen for the untargeted different (adjusted P< 0.1) between PWH and HC metabolomics, strengthening the result (Fig. 2a). As the ART (Fig. 1a and Supplementary File 1, Supplemental Digital proportion of metabolites can be a consequence of clinical Content, http://links.lww.com/QAD/C813). We also parameters or confounders, we next employed UMAP to detected 36 (adjusted P< 0.1) metabolites that differed evaluate if gender, HIV-positive status, BMI, or length of between PWH and PWH and 70 (adjusted P< 0.1) treatment could separate PWH from HC. The UMAP VP ART ART 1026 AIDS 2023, Vol 37 No 7 Identified Differentially (b) (a) Metabolites Abundant (n=841) Metabolites* Z−Score Group HC 100 Pyruvate PWH Lactate ART PWH VP −2 Gender −4 Female 75 Glutamate 46% 7% Male Log2 FC BMI 10 20 30 40 50 Age −1 22% −2 30 40 50 60 70 6% 17% (c) (d) Glutamate Pyruvate Lactate 1% HC PWH ** * ** ART 1.3 0 0 1.3 0.6 *Excluding Xenobiotics 1 0.6 1 Super pathway 0 -0.1 -0.1 Lipid Peptide Amino Acid Carbohydrate -0.8 -1 -0.8 Xenobiotics Cofactors & Vitamins -1 Nucleotide Other PWH PWH **p<0.001, adjusted p<0.05 *p<0.05, adjusted p<0.1 HC ART VP -2 (e) -4 -2 0 2 Pyruvate Glutamate Lactate UMAP1 1.5 1.0 (g) 1.0 Glutamate 1 0.5 0.5 0.0 0.0 -0.5 -0.5 -1 γ-carboxyglutamate -1.0 -1.0 N-acetylleucine (f) Linoleate 1-stearoyl-2- Linoleate arachidonoyl-GPC Cysteine-glutathione disulfide MUVR Rank N-acetylleucine Heptanoate Leucylglycine Heptanoate 1-stearoyl-2-arachidonoyl-GPC 400 Cysteine- Glutamate glutathione γ-Carboxyglutamate disulfide Leucylglycine Fig. 1. Untargeted metabolomics showed that PWH have increased levels of glutamate, pyruvate, and lactate. (a) Proportion ART of total metabolites identified and differentially abundant metabolites between HC and PWH from untargeted metabolomics ART data. Xenobiotic metabolites were excluded from both datasets. (b) Heatmap showing metabolite abundance pattern of significantly changed metabolites in PWH compared to HC (adj. P< 0.1). Column annotation represents the study cohort, ART and the bottom annotation includes the age, gender, and BMI of each patient. Log FC representation denotes log2 scaled fold change in PWH compared to HC. (c) Boxplot of glutamate, pyruvate, and lactate levels in HC, PWH and PWH .(d) ART ART, VP Sample distribution based on significantly changed metabolites between HC and PWH using UMAP dimensionality reduction. ART Ellipses denote 95% confidence interval. (e) Boxplot showing glutamate, pyruvate, and lactate levels in longitudinal metabolomics data. Paired samples are connected by the dotted line. (f) Metabolites were identified as biomarkers of PWH compared with HC ART using MUVR biomarker prediction. Color gradient of bubbles is relative to MUVR rank. (g) Weighted co-expression analysis using Leiden algorithm and represented as a network. Metabolites identified as predicted biomarkers from F and dysregulated metabolites identified from B are labelled. Statistical significance was determined using MannWhitney U-test (P< 0.05). HC, HIV-1 negative controls; MUVR, multivariate methods with unbiased variable selection in R; PWH , people with HIV on ART antiretroviral therapy; UMAP, uniform manifold approximation and projection. analysis identified that alterations in CCM metabolites and Inflammation panel. Of the 92 proteins identified, CC sugars were associated with the duration of therapy motif chemokine ligand 20 [CCL20/macrophage inflam- (Fig. 2b). Finally, to evaluate the inflammation level, we matory protein 3 alpha (MIP-3a)] and CCL7 [monocyte performed secretome analysis in plasma using the Olink chemotactic protein 3 (MCP-3)] were significantly higher log2(Measurement) Percentage Percentage log2(Measurement) UMAP2 logFC Immunometabolism in HIV-1 Svensson Akusja¨rvi et al. 1027 CCM & Sugers (a) (b) Z−Score1 Lactate** 2 HC Pyruvate** 1 PWH Fumarate* ART β-Glucose* UMAP1 Maltose* Male Citrate* −1 Female Glycerate** Oxoglutarate* −2 Long ART Group Short ART PWH ART HC HC UMAP1 UMAP1 Glucose* PWH Amino Acids VP Change Normal weight Overweight High in ART Low in ART Obese Methionine* Lysine* NS Tryptophane* UMAP1 UMAP1 Z−Score2 Ornithine* Glutamine* Glycine* Threonine** Arginine** −1 Kynurenine* Glutamate** UMAP1 UMAP1 −2 **Adjusted (BH) p <0.001, *Adjusted (BH) p <0.05 (c) CCL28 LIF CCL20 CCL7 CDCP1 CCL25 IL−20 ** ● ● ● ● ● ● ● ● ** ** 2.1 ● ● ● ● 4.7 ● * ● ● ● 7.5 ● ● 2.1 ● ● 3.8 10.5 ● ● ● 5.9 ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● HC ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.7 3.9 ● ● ● ● ● ● ● ● ● 6.5 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● PWH 8.5 ● 3.0 4.4 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ART ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3.1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● PWH ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● VP ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 0.9 ●● ● ● 6.5 ● ● ● ● ● ● 2.2 ●● ● ● 2.9 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.9 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.4 0.3 4.5 1.4 CCL11 CST5 HGF TGF-α SLAMF1 4E−BP1 CASP−8 * * ● ● ● ● ● ● ● ● 11.5 4.3 ● ● 6.5 ● ● ● ● ● ● * 8 ● ● ●● ● * ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● * ● ● ● ● 5.9 ● ● 11.0 ● ● ● ● ● * ● ● ● ● ● ● ●● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10.5 ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● 3.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5.5 ● ● ● ● ● ● ● ● 7 ● ● ● ● ● ● ● 9.5 ● ● ● ● ● ● ● ● ● ● ●● ● ● 4.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● 4 ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 9.5 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● 8.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 6 4.5 ● ● ● ● ● 2.3 ● ●● ● ● ● ● 2.9 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● 8.5 ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● 6.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 1.4 5 1.3 3.5 **Adjusted (BH) p <0.001, *Adjusted (BH) p <0.05 Fig. 2. Dysregulated central carbon and amino acid metabolism together with elevated levels of inflammatory markers in PWH . (a) Heatmap showing metabolite abundance pattern of targeted metabolomics data. Normalized levels of central carbon ART metabolism (CCM), sugar metabolites and amino acids are shown from the cohorts. Column annotation denotes the study cohorts and right annotation shows the metabolite levels in PWH compared to HC. Significantly changed metabolites in PWH ART ART compared to HC are labelled. (b) Sample distribution based on targeted metabolomics data of CCM, sugar metabolites and amino acids by UMAP dimensionality reduction. Individual UMAPs are coloured in respective category for cohort, gender, duration of treatment, and BMI. (c) Boxplot of significantly regulated proteins in PWH compared to HC (adj.P< 0.05). Statistical ART significance was determined using MannWhitney U-test (adjusted P< 0.05). HC, HIV-1 negative controls; PWH , people ART with HIV on antiretroviral therapy; UMAP, uniform manifold approximation and projection. (adjusted P< 0.001) in PWH compared to HC factor (HGF) (Fig. 2c). Only caspase-8 (CASP-8) showed a ART (Fig. 2c). Another 12 inflammatory markers were significant decrease in PWH compared to HC. Thus, ART significantly higher in PWH than HC, using a our data suggest that PWH has a dysregulated energy ART ART significance cut-off adjusted P< 0.05. These markers metabolism, in AA, CCM, and sugars, together with included CCL25 [thymus expressed chemokine (TECK)], increased markers of inflammation like MIP-3a or MCP- CCL28 [mucosa-associated epithelial chemokine (MEC)], 3. CCL11 (eotaxin-1), cystatin-D (CST5), interleukin 20 (IL-20), signaling lymphocytic activation molecule 1 Intracellular metabolites and transport are (SLAMF1), CUB domain-containing protein 1 (CDCP1), dysregulated in monocytes during suppressive eukaryotic translation initiation factor 4E-binding protein HIV-1 infection 1 (4EBP1), transforming growth factor-alpha (TGF-a), Viral infections hijack the cellular metabolic environ- leukemia inhibitory factor (LIF), and hepatocyte growth ment, mostly AAs, CCM, and sugars, for their support Amino Acids (LC-MS/MS) CCM & Sugers (GC-MS) Normalized Protein Expression (NPX) UMAP2 UMAP2 UMAP2 UMAP2 UMAP2 UMAP2 UMAP2 UMAP2 1028 AIDS 2023, Vol 37 No 7 and propagation [11,16,17]. As the significant systemic cell populations can be viewed in Fig. 4b. Even as the alteration of the metabolites was glucose, pyruvate/ proportion of DC2/DC3 showed a slight reduction in lactate, and glutamate, next we performed flow PWH (P¼ 0.085), no significant differences were ART cytometry analysis of metabolite transporters, glucose detected in the frequency of cell populations except for þ þ transporter-1 (Glut1), pyruvate and lactate transporter the frequency of CD11b CD33 low density granulo- monocarboxylate transporter 1 (MCT-1), and cysteine/ cytes (LDGs) (Fig. 2B, Supplemental Digital Content, glutamate antiporter (xCT) in a cohort of HC (n¼ 9) http://links.lww.com/QAD/C814). Additionally, we and PWH (n¼ 27) (Fig. 1A, Supplemental looked at chemokine receptor expression CCR2, ART Digital Content, http://links.lww.com/QAD/C814). CCR5, and CX3CR1 as they are known markers of þ þ The CD4 T cells were decreased while CD8 T cells migration and activation of myeloid lineages (Fig. 4c). were increased in PWH compared to HC (Fig. 3a). CM expressing CCR5 were increased in frequency, while ART No significant differences were observed in classical CCR5 granulocytic (G)-MDSC were decreased and (CM), intermediate (IM), or nonclassical monocytes CX3CR1 G-MDSC were increased in PWH ART (NCM). Receptor expression analysis showed that the compared to HC (Fig. 4d). In DC lineages, the frequency þ þ þ þ percentage of CM Glut1 increased in PWH of CX3CR1 plasmacytoid (p)DC and CCR2 DC1 ART compared to HC (Fig. 3b and c). Analysis of the median was decreased, while the frequency of CX3CR1 DC1 fluorescence intensity (MFI) showed no difference in and CCR5 DC2/DC3 was increased in PWH ART þ þ CD4 or CD8 T cells (Fig. 3d). Even though there was compared to HC (Fig. 4d). No other significant no significant difference in MCT-1 CM, the MFI of differences in the proportion of cells expressing any of MCT-1 was increased on CM in PWH compared to the receptors were identified between the groups (Fig. ART HC (Fig. 3c and d). To further evaluate the intracellular 3A, Supplemental Digital Content, http://links.lww. metabolite levels in blood cell populations, a cohort of com/QAD/C814). Within the cell populations, the MFI HC (n¼ 10) and PWH (n¼ 29) was used to isolate was decreased for CCR2 on DC1, pDC, and G-MDSC ART CD4 T cells and monocytes (Fig. 3e and Fig. 1B, in PWH compared to HC (Fig. 4E). Furthermore, an ART Supplemental Digital Content, http://links.lww.com/ increase in MFI for CCR5 on DC2/DC3 and CX3CR1 QAD/C814). One PWH sample was excluded due on G-MDSC was observed in PWH compared to ART ART to low cell viability. After CD4 T-cell isolation, two HC HC (Fig. 4e and Fig. 3B, Supplemental Digital Content, samples were excluded from the analysis due to low cell http://links.lww.com/QAD/C814). Collectively, this count. The purity of isolated CD4 T cells had a median data shows the relevance of chemokine receptors on of 97% (IQR 93.9–97.575) (Fig. 1C, Supplemental myeloid lineages as the key regulators of myeloid cell Digital Content, http://links.lww.com/QAD/C814). trafficking during HIV-1 infection, rather than the Intracellular measurement of glucose, lactate, and distribution of cell populations during suppressive glutamate showed no significant difference within the therapy. CD4 T-cell subset (Fig. 3f). After monocyte isolation, four HC samples and one PWH sample were ART excluded from the analysis due to low cell count. The purity of the monocytes had a median of 93.15% Discussion [interquartile range (IQR) 95.55–90.55] (Fig. 1D, Supplemental Digital Content, http://links.lww.com/ Our comprehensive immuno-metabolic study in QAD/C814). Intracellular measurement of glucose, PWH identified system-level alterations in amino ART lactate, and glutamate showed a significant decrease of acid metabolism during long-term treatment. We all three metabolites in PWH compared to HC identified a coordinated role of pyruvate, glutamate, ART (Fig. 3g). These data indicate that the main differences in and lactate in HIV-1 infection, indicating central carbon the metabolic profile occur in the monocytes during and energy metabolism dysregulation. Furthermore, the long-term suppressive therapy. plasma inflammatory markers MIP-3a and MCP-3 increased in PWH . Macrophages and monocytes ART Characterization of monocytic subpopulations in mainly produce the MIP-3a, while MCP-3 is a people with HIV-1 on antiretroviral therapy vs. chemoattractant that plays a significant role in monocyte HIV-1 negative controls mobilization and trafficking to the inflammation sites. As we identified the main differences in intracellular Therefore, our data indicate that monocytes play a metabolite and transporter expression in monocytes, we potential role in the modulation of the inflammatory wanted to evaluate the distribution in monocytic cell profile during suppressive ART. This could also be linked populations. Therefore, we ran a panel characterizing with the metabolic alterations in the monocytic subsets myeloid cells in HC (n¼ 10) and PWH (n¼ 29) (Fig. for transporter expression and intracellular levels. Finally, ART 2A, Supplemental Digital Content, http://links.lww. our deep immune profiling of myeloid cell lineages com/QAD/C814). The panel identified subsets of indicated an altered expression of CCR5 and CCR2 in monocytes, dendritic cells (DCs), and myeloid-derived monocytes and subsets of DCs. This data further points suppressor cells (MDSCs) (Fig. 4a). The distribution of towards the role of myeloid cell lineages in cell trafficking, Immunometabolism in HIV-1 Svensson Akusja¨rvi et al. 1029 (a) CD4 CD8 CM IM NCM 100 100 100 p=0.0036 p=0.0023 20 15 80 80 60 60 HC 10 PWH ART 40 40 20 20 0 0 0 0 0 (b) (c) CD4 CD8 CM IM NCM CM CM 0.071 98.2 1.16 1.17 ART p=0.046 6 80 0.08 99.9 0.65 0.93 HC 0.074 17.6 1.41 ART 0.07 6.12 1.75 HC 0 0 9.94 12.9 94.0 42.9 ART HC PWH 7.70 10.6 90.1 46.7 ART HC (e) (d) CD4 CD8 CM IM NCM 2000 10000 3000 HC PWH 2500 ART 6000 2000 3000 1400 1500 2500 2000 + (f) CD4 T cells 1400 1500 2500 2000 Glucose Lactate Glutamate 0 0 0 0 0 p=0.32 1400 p=0.0257 10000 p=0.3993 HC p=0.3018 PWH 3500 ART 1500 4000 4 1200 1300 1100 2500 1200 1300 1100 (g) Monocytes 0 0 0 0 0 600 500 800 4000 Glucose Lactate Glutamate 400 p=0.0133 600 3000 7 p=0.0179 HC p=0.0154 PWH ART 400 2000 200 5 200 1000 0 0 0 0 0 0 Fig. 3. Intracellular metabolites and transport are dysregulated in monocytes during suppressive HIV-1 infection. Metabolite þ þ uptake and secretion in HC (n¼ 9) and PWH (n¼ 27). (a) Proportion of CD4 and CD8 T cells and classical (CM), ART intermediate (IM), and nonclassical (NCM) monocytes in PBMCs. (b) Contour plots showing percentage of cells expressing Glut1, þ þ MCT-1, and xCT in CD4 , CD8 , CM, IM, and NCM. The contour plots show a representing sample in each group with the median expression level within that group. (c) Percentage of CM expressing Glut1 or MCT-1 PWH and HC. (d) Median fluorescence ART þ þ intensity (MFI) of Glut1, MCT-1, and xCT expression on CD4 , CD8 , CM, IM, and NCM cell populations in HC and PWH . (e) ART Schematic representation of CD4 and monocyte isolation for intracellular metabolite measurement. (f) Intracellular measurement of glucose, lactate, and glutamate in a cohort of HC (n¼ 8) and PWH (n¼ 28) in CD4 T cells. (g) Intracellular measurement of ART glucose, lactate, and glutamate in a cohort of HC (n¼ 6) and PWH (n¼ 27) in monocytes. Statistical significance was evaluated ART using MannWhitney U-test (P< 0.05) and represented as median with interquartile range (IQR). See also Figure S1. HC, HIV-1 negative controls; IQR, interquartile range; MUVR, multivariate methods with unbiased variable selection in R; PWH , people ART with HIV on antiretroviral therapy; UMAP, uniform manifold approximation and projection. xCT MCT-1 Glut 1 MFI of xCT MFI of MCT-1 MFI of Glut 1 % cells of CD3 % cells of CD3 % cells of CD3 % cells of CD3 % Glut1 cells Luminescence (LOG) Luminescence (LOG) % cells of CD3 % MCT-1 cells 1030 AIDS 2023, Vol 37 No 7 (a) Monocytes/DC DC MDSC/LDG CM IM DC1 G MDSC LDG pDC CD14 PWH CD15 ART DC2/DC3 DC4/NCM DC5 NCM/MD-DC cDC M MDSC CD16 CD1c HLADR CD14 CD16 CD1c CM IM LDG DC1 pDC G MDSC CD14 HC CD15 DC4/NCM DC2/DC3 DC5 NCM/MD-DC cDC M MDSC HLADR CD16 CD1c CD1c CD14 CD16 (b) (c) CD14 CD16 CD15 CD33 HLA-DR NCM/DC4 PWH IM ART CM CD11b CD11c CD1c CD303 CD66b DC5 DC2 DC3 DC1 M MDSC G MDSC CD141 CCR2 CCR5 CX3CR1 HC pDC LDG Low High UMAP-2 (d) CM G-MDSC pDC DC1 DC1 DC2/DC3 CCR5 CCR5 CX3CR1 CX3CR1 CCR2 CX3CR1 CCR5 0.021 0.031 0.013 0.02 0.047 0.0067 0.041 10.0 0.9 7.5 0.6 5.0 0.3 2.5 0.0 0 0.0 (e) DC1 DC2/DC3 pDC G-MDSC CCR2 CCR5 CCR2 CX3CR1 CCR2 0.006 0.041 0.0028 0.036 0.016 16000 6000 18000 HC PWH ART 8000 8000 Fig. 4. Receptor expression of CCR2, CCR5, and CX3CR1 on myeloid cell subsets differentiates PWH from HC. Characteri- ART zation of myeloid lineages in HC (n¼ 10) and PWH (n¼ 29). (a) Gating defines all cell populations identified in the cohort. (b) ART UMAP representation of the distribution of classical monocytes (CM), intermediate monocytes (IM), nonclassical monocytes/ dendritic cell 4 (NCM/DC4), dendritic cell 1 (DC1), dendritic cell 2/3 (DC2/DC3), dendritic cell 5 (DC5), plasmacytoid dendritic MFI % positive cells UMAP-1 CD14 CD14 CD141 CD141 CD141 CD141 CD303 CD303 CD15 CD15 CD66B CD66B Immunometabolism in HIV-1 Svensson Akusja¨rvi et al. 1031 regulation, and persistent inflammation in PWH during PWH, indicated a prevalent disruption of glutaminolysis, successful ART. that is, lysis of glutamine to glutamate, in PWH .We ART also saw that glutaminolysis was central to comorbidities The three main pathways of CCM and energy such as metabolic syndrome (MetS) [2,3]. Interestingly, metabolism are compartmentalized between the cyto- the level of pyruvate was high in the Danish cohort but plasm and mitochondria, namely, glycolysis/gluconeo- not in India or Cameroon. Additionally, the level of genesis, glutamine metabolism (glutaminolysis), and inflammation was high in the Indian cohort [25], while tricarboxylic acid cycle (TCA cycle) [providing electrons our earlier Swedish study on long-term treated individu- for OXPHOS]. The host dependencies for HIV-1 als (nearly two decades) indicated near normalization of include glycolysis and TCA [18]. Susceptibility to the inflammatory profile [26]. Based on these observa- HIV-1 is also regulated by a cell’s metabolic activity tions, we posit that the increased level of pyruvate in and activation stage, where CD4 T cells with elevated cohorts from high-income countries could be linked with OXPHOS and glycolysis are more permissive to infection the low level of persistent inflammation. This alteration [9,19]. In this study, we detected decreased Glut1 could be associated with using newer antiretrovirals with a expression on CD8 T cells while the percentage of better toxicity profile while indicating an enhanced CM expressing the receptor was increased. The uptake of metabolic profile and improved quality of life. glucose through Glut1 is essential for viral production [20]. In our recent study, we detected reduced Glut1 Herein, we also show how intracellular metabolic expression on T-lymphocytes in HIV-positive elite modulation mainly occurs in the monocytic cell popula- controllers compared to the HIV-negative controls, tion in PWH . We did not see any differences in the ART which could be one mechanism restricting HIV-1 frequency of myeloid cell populations, indicative of infection [21]. A recent study also indicated that latent maintained cellular frequencies during suppressive ART. HIV-1 reservoirs use glutaminolysis as an alternative fuel However, there was a variation in the receptor expression source for energy generation [22]. Our cohort detected of CCR2, CCR5, and CX3CR1 in some sub-popula- decreased glutamine and increased glutamate in plasma tions. Activation of these chemokines’ receptors mediates from PWH . Glutaminolysis is the primary pathway immune cell trafficking. Even as the general classification ART high low charging the TCA-cycle and OXPHOS in naive and of CM is CCR2 and CX3CR1 , our cohort memory T-cell subsets. Higher HIV-1 infections have exhibited an increase in CCR5 in PWH [27]. ART been shown in T cells selected for OXPHOS activity [19] CCR5 is one of the main co-receptors used for HIV-1 and compromised metabolic steps preceding OXPHOS entry into cells, and in monocytes, the dependency of result in lipid accumulation [22]. Furthermore, we CCR5 for the M-tropic virus is well described [28]. Earlier recently observed how modulation of glutaminolysis studies have also shown how activated monocytes and significantly affects the reactivation of the latent virus [3]. macrophages shift to a glycolytic metabolism by increasing This hints toward a substantial role of glutaminolysis in the expression of Glut1, one of the primary glucose HIV-1 reactivation. transporters [29]. As described here by us and others, these activated Glut1 monocytes were enriched during HIV-1 A recent seminal study also showed that senescent cells infection [30]. Therefore, the elevated levels of CCR5 on relied on glutaminolysis and proposed that inhibiting CM could indicate an increased susceptibility towards glutaminolysis in the aging body could prevent age- HIV-1 or, on a phenotype level, an increased transition associated disorders and even prolong the lifespan [23]. It into a proinflammatory phenotype to elicit heightened is known that in glutamate toxicities, pyruvate plays a inflammatory responses. Simultaneously, the evaluated significant role in quenching glutamate to keep the level immune activation seen in PWH could support an ART of inflammation low [24]. Our longitudinal data thus environment of increased glucose metabolism and higher indicates that the increase of pyruvate could result from activation-induced differentiation of monocytes, as previ- elevated glutamate levels. In our earlier cross-sectional ously reported in T cells [31,32]. studies, we observed higher glutamate levels in PWH ART in cohorts from India [1], Cameroon [3], and Denmark Even as a complete characterization of the effect of HIV-1 [2]. These cohorts, collectively including more than 500 infection on DC subsets and progenitors is limited, several cells (pDC), granulocyte-like myeloid suppressor cells (G-MDSC), monocytic myeloid-derived suppressor cells (M-MDSC), and low-density granulocytes (LDG) in PWH and HC. (c) Heatmap representation of each marker used in the UMAP. (d) Frequency ART (%) of cells expressing CCR5 (CM, G-MDSC, and DC2/DC3), CCR2 (DC1), and CX3CR1 (G-MDSC, pDC, and DC1). (e) Median fluorescence intensity (MFI) of CCR2 (DC1, pDC, and G-MDSC), CCR5 (DC2/DC3), and CX3CR1 (G-MDSC) on the indicated cells. Statistical significance was determined using MannWhitney U-test (P< 0.05) and represented with median and IQR. See also Figure S2 and Figure S3. HC, HIV-1 negative controls; IQR, interquartile range; MUVR, multivariate methods with unbiased variable selection in R; PWH , people with HIV on antiretroviral therapy; UMAP, uniform manifold approximation and ART projection. 1032 AIDS 2023, Vol 37 No 7 studies have shown the depletion of DCs during acute signaling may subsequently affect monocyte trafficking viral infections (e.g. COVID-19 [33] and Hantavirus and polarization during persistent inflammation. In turn, [34]). Possibly, similar events occur during initial HIV-1 these aberrations might influence adaptive immunity. A viremia but normalize during suppressive ART. This better understating of immune cell trafficking and would be in accordance with our data, where we did not polarization of myeloid cell lineage in PWH on successful see any differences in the frequency of DC subsets. therapy, together with strategies to modulate the activation However, our data show that the main differences in of macrophage phenotypes, could provide adjunctive receptor expression occur in the DCs with a decrease of therapeutic targets to improve metabolic health while CCR2 and CX3CR1 in some subpopulations, while mediating the control of viral replication in PWH. CCR5 increases in DC2/DC3. The DCs belong to the mononuclear phagocytes (MNPs) together with mono- cytes and macrophages, which are crucial for linking the innate and adaptive immune systems [35]. In HIV-1, DCs Acknowledgements can transfer the virus to T cells by delivery of membrane- bound virus through a viral synapse or de novo synthesis of Author contributions: U.N conceived the study; A.S. and virus from infected cells [35]. After capturing the virus P.N. designed and responsible for the clinical cohorts, U. and antigen presentation, DCs migrate to the lymph N., S.S.A., S.M.P., and S.K. planned the experiments; S.S. nodes and spleen. This can facilitate HIV-1 spread to A. and S.K. performed the research; A.T.A. and F.M. compartments rich in target cells for infection. However, performed the bioinformatics analysis; J.V., P.N., and A.S. our data do not indicate increased dissemination of DC contributed with patient material and interpretation of subsets to distant body compartments as the frequencies of the clinical findings; S.M.P. and M.L. contributed with DC subsets are maintained during suppressive ART [36]. analysis; U.N., S.S.A., and S.K. analysed the data; U.N. Instead, the altered chemokine receptor expression could and S.S.A. wrote the manuscript. All authors reviewed hint at the function of immune cells in PWH . For the and critically revised the manuscript. Authors would like ART CX3CR1 receptor, also known as the fractalkine to thank Dr Soham Gupta, Assistant Professor Karolinska receptor, the receptor-ligand interaction is strongly Institute, for his intellectual input in the study. linked to the survival of immune cells [37]. Thus, the reduced expression in some DC subsets in PWH Data availability. The untargeted and targeted metabo- ART could indicate a decreased capacity to counteract cell lomics and OlinkTM proteomics data is available DOI: death mechanisms. This could result from the hypothe- 10.6084/m9.figshare.19589365 (upon acceptance). Dur- sized rapid DC decline during peak viremia. MNPs are ing the review process data can be obtained through potent producers of CCL2, the ligand for CCR2, https://figshare.com/s/f402e955f3308f4dff86. mediating a proinflammatory function [38]. A reduced expression of CCR2 could affect the responsiveness of Funding: This study was funded by the Swedish Research DC recruitment to the site of inflammation. Our results Council Interdisciplinary Grant 2018-06156 to UN. The show a dysregulated phenotype on some DC subsets in authors acknowledge the support received from the PWH . However, the implication on immune cell Swedish Research Council grants, 2017-01330 and ART function, metabolic reprogramming, and immunological 2021-01756 to U.N and 2017-05848 to A.S. M.L. was aging during suppressive ART remain to be elucidated. supported by the Swedish Childhood Cancer Fund (TJ20180128). Though our study used a relatively large number of samples, including the longitudinal samples, our study has Conflicts of interest limitations that merit comments. First, the study’s cross- There are no conflicts of interest. sectional nature limits the observations to associations that do not infer causation. Second, the ART regimens were Posted history: This manuscript was previously posted heterogeneous. Third, though we have a more homoge- on Research Square: doi: https://doi.org/10.21203/rs.3. nous group, CMV status, diet and lifestyle can influence rs-1574216/v1 the inflammatory status. We have initiated a large study to understand the myeloid cell dysfunction in PWH Supplementary File 1. Differential metabolite abundance ART. Finally, our analysis is more steady-state in the absence of between the groups in the untargeted metabolomics. functional assays. 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AIDS – Wolters Kluwer Health
Published: Jun 1, 2023
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