Polyamine metabolism impacts T cell dysfunction in the oral mucosa of people living with HIV

Metabolic changes in immune cells contribute to both physiological and pathophysiological outcomes of immune reactions. Here, by comparing protein expression, transcriptome, and salivary metabolome profiles of uninfected and HIV+ individuals, we found perturbations of polyamine metabolism in the oral mucosa of HIV+ patients. Mechanistic studies using an in vitro human tonsil organoid infection model revealed that HIV infection of T cells also resulted in increased polyamine synthesis, which was dependent on the activities of caspase-1, IL-1β, and ornithine decarboxylase-1. HIV-1 also led to a heightened expression of polyamine synthesis intermediates including ornithine decarboxylase-1 as well as an elevated dysfunctional regulatory T cell (TregDys)/T helper 17 (Th17) cell ratios. Blockade of caspase-1 and polyamine synthesis intermediates reversed the TregDys phenotype showing the direct role of polyamine pathway in altering T cell functions during HIV-1 infection. Lastly, oral mucosal TregDys/Th17 ratios and CD4 hyperactivation positively correlated with salivary putrescine levels, which were found to be elevated in the saliva of HIV+ patients. Thus, by revealing the role of aberrantly increased polyamine synthesis during HIV infection, our study unveils a mechanism by which chronic viral infections could drive distinct T cell effector programs and Treg dysfunction.

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For Metabolome : Metabolite identifications were made with Compound Discoverer 3.1 SP1 and MZmine2 software. MS1 and MS2 spectra were matched using a mass tolerance of m/z =0.1. The raw data were acquired and aligned using the Compound Discover based on the m/z value and the retention time of ion signals. Ions from both ESI-or ESI+ were merged and imported into the SIMCA-P program (version 14.1) for multivariate analysis. Further data processing was performed by using DecoID (DecoID v0.3.0) to deconvolute chimeric MS2 spectra and increase the identification rate. Metabolite identifications were made with level 2 confidence according to the Metabolomics Standards Initiative.
For polyamine concentration: SoftMax Pro 6.1 software Data analysis Flowjo versions 9.8,9.9.6,10.5.3,and 10.7 Differential gene expression analysis: The gene reads count data from HOIL and PBMC samples were normalized with Edge R Package limma (version 3.26.8) and analyzed with an unpaired t-test. Reactome Pathway Database (https://reactome.org), and Gene Ontology enrichment analysis (http:// geneontology.org) databases Metabolome analysis: Metaboanalyst 5.0. Graphs were generated via Metascape For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.

Data
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Other source data will be provided as a source file with this manuscript. The raw LC/MS data as well as the processed metabolic profiles and corresponding metadata for the human (deidentified) samples are publicly available on the Metabolomics Workbench repository (NMRD: ST002328).

Human research participants
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Reporting on sex and gender
Participants of both sexes were recruited.

Population characteristics
Informed consent were obtained from healthy individuals (n = 32) and Cleveland HIV+ cohort (n = 46). Healthy control subjects were at least 18 years of age and in good general health. HIV+ participants were 18 years or older, and were HIV positive with cART treatment for at least 1 year. Characteristics of enrolled participants were provided in supplementary table 1.

Recruitment
Participants were recruited after obtaining informed consents. Healthy control subjects were at least 18 years of age and in good general health. Exclusion criteria were oral inflammatory lesions (including gingivitis and periodontitis), oral cancer diagnosis, soft tissue lesions, and the use of tobacco in the past month. HIV+ participants were 18 years or older, and were HIV positive with cART treatment for at least 1 year. Exclusion criteria were oral cancer diagnosis and the use of tobacco in the past month. The inclusion and exclusion criteria were the same for periodontitis study, except that the the inclusion criteria for the periodontitis group included the presence of periodontitis. HIV+ individuals volunteered based on their HIV positivity. Studying the HIV+ patients in this Case-Control study was the goal of the investigation and this self-selection did not impact the results negatively.

Ethics oversight
University Hospitals Cleveland Medical Center Institutional Review Board (IRB); IRB# 05-17-02 Note that full information on the approval of the study protocol must also be provided in the manuscript.

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Sample size
Power analyses was done based on estimates. For the power and sample size calculation, we used https://www.stat.ubc.ca/~rollin/stats/ ssize/n2.html We used relevant population values for mu1 (mean of population 1), mu2 (mean of population 2), and sigma value of 0.5 (common standard deviation) 0.05 as α (type I error rate) and a 2 sided test for power calculation. Because our preliminary data showed that HIV+ group had atleast 1.4 fold increase in Treg proportions, we used 1 and 1.4 as mu1 and mu2 respectively. Our desired power was set to 0.8.

Replication
We performed at least triplicate repeats of the in vitro experiments with independent biological replicates in each experiment. We confirm that all replicate experiments showed reproducibly similar data and were successful except for a 2-3 episodes of contamination in which cases data were not collected. For ex vivo analyses of human samples, samples from each patient were used as biologically independent replicates.
Randomization In vitro experiments were also performed using randomly allocated tonsils.Human participants were randomly recruited for control and HIV+ groups, with representation of males and females in each group.

Blinding
Salivary metabolome analyses were done by two of the co-authors who were blinded to the identity of the saliva. The investigators were also blinded to group allocation during RNA-seq data collection and/or analysis. It was not possible to do complete blinding for the in vitro and flow cytometry experiments, as the same research associate performed the cell culture and the staining. However, the person who did the flow-cytometry data analysis was blinded on the groups until the final analysis of all the replicate experiments at which point the research associate released the codes for the cell-culture groups.
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Flow Cytometry
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Methodology
Sample preparation For single-cell flow cytometry staining, cells isolated and processed ex vivo from tissues or lymphoid organs as well as cultured cells were washed in PBS or PBS/BSA, and blocked by Fc receptor blocking, before surface staining using the antibodies. For Foxp3 and other intracellular marker staining, the cells were fixed with Foxp3 fix-perm set (eBioSciences/ Thermofisher) after surface staining. Live-Dead viability staining was used to remove dead cells in the analyses. Appropriate un-stain, isotype, secondary antibody, single stain and FMO controls were used and representative data are shown in supplementary figures. Before intracellular cytokine staining, cultures were re-stimulated with PMA (50 ng/ml) and Ionomycin (500 ng/ml) for 4 hours, with brefeldin-A (10 μg/ml) added in last 2 hours. For phospho staining, the cells were washed, fixed and were stained with Phosflow staining kit from BD Biosciences using manufacturer's protocol.

Instrument BD Fortessa
Software Flowjo versions 9.8,9.9.6,10.5.3 and 10.7.1 Cell population abundance CD4+ T ells were more abundant in tonsils than in the oral tissues.

Gating strategy
Gating strategy: Preliminary FSC/SSC gates for the starting leukocyte cell population, and subsequent gating to include singlets, and CD3+ T cells were used. Boundaries of the "positive" gates were assigned based on the unstained controls, PBMC/d0 negative controls, and FMO controls. We have shown data exemplifying the gating strategies and the controls where appropriate in the supplementary Information.
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