T-cell responses to ancestral SARS-CoV-2 and Omicron in unvaccinated hospitalised adults living with and without HIV in South Africa

T-cell responses to ancestral SARS-CoV-2 and Omicron in unvaccinated hospitalised adults living with and without HIV in South Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article T-cell responses to ancestral SARS-CoV-2 and Omicron in unvaccinated hospitalised adults living with and without HIV in South Africa William C. McMahon, Gaurav Kwatra, Alane Izu, Natali Serafin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7588105/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract HIV-associated immune dysfunction may impact SARS-CoV-2–specific T-cell responses, yet data in COVID-19–unvaccinated people living with HIV (PLWH) remain limited. We evaluated virus-specific T-cell responses one month after COVID-19–related hospitalisation in antiretroviral-treated PLWH and HIV-uninfected adults recovering from ancestral (Wuhan-Hu-1), Beta (B.1.351), or Delta (B.1.617.2) variant infection. Flow cytometry assessed the magnitude, polyfunctionality, and activation (HLA-DR, CD38, and CD26) of CD4 + , CD8 + , and CD4 + CD8 + (double positive, DP) T-cell subsets, as well as cross-reactivity to Omicron (BA.4/BA.5). Seventeen PLWH and 21 HIV-uninfected black African adults were enrolled. SARS-CoV-2–specific CD4 + , CD8 + , and DP T-cell response magnitudes, responder frequencies, and cytokine production profiles (IFN-γ, IL-2, and TNF-α) were comparable between groups. Spike- and nucleocapsid-specific responses correlated strongly in PLWH (CD4 + : r = 0.914, p < 0.001; CD8 + : r = 0.789; p < 0.001), whereas correlations were weaker in HIV-uninfected participants (CD4 + : r = 0.512, p < 0.05; CD8 + : r = 0.427; p = 0.069). CD26 expression and most activation phenotypes (HLA-DR/CD38 subsets) did not differ by HIV status, though PLWH had fewer CD8 + HLA-DR + CD38 - T cells (adjusted p = 0.013). Both groups demonstrated cross-recognition of Omicron, irrespective of the infecting SARS-CoV-2 variant. Our results demonstrate comparable SARS-CoV-2–specific T-cell responses and activation profiles between PLWH on antiretroviral therapy and HIV-uninfected adults, with preserved cross-reactive T-cell responses to Omicron. Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Biological sciences/Microbiology HIV and COVID-19 Omicron cross-reactivity SARS-CoV-2 T-cell immunity T-cell activation (HLA-DR CD38 and CD26) Figures Figure 1 Figure 2 Figure 3 Introduction The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), disproportionately affected populations with advanced age and underlying comorbidities such as chronic lung disease, hypertension, diabetes, obesity, and people living with human immunodeficiency virus type 1 (PLWH; HIV-1) [ 1 ]. People with uncontrolled HIV infection, characterised by CD4 + T-cell counts < 200 cells/µL and/or unsuppressed viraemia, have a higher risk of severe COVID-19, hospitalisation and death compared with virally suppressed PLWH and HIV-uninfected people [ 2 – 5 ]. Although PLWH on effective antiretroviral therapy (ART) are less severely affected by COVID-19 and mount immune responses to COVID-19 vaccines comparable to those of HIV-uninfected individuals, the extent of their immune reconstitution remains highly variable [ 5 ]. Despite HIV-1 viral suppression by ART, chronic immune activation and inflammation may contribute to immune dysfunction in PLWH [ 6 ]. Lower absolute CD4 + T-cell counts in PLWH impairs cellular immunity, particularly CD4 + T helper (Th) cell responses, increasing their susceptibility to respiratory infections, including SARS-CoV-2 [ 7 – 9 ]. PLWH with suppressed viraemia generate SARS-CoV-2–specific T-cell responses that are functionally comparable to those of HIV-uninfected individuals, including the production of key Th1 cytokines such as IFN-γ, IL-2, and TNF-α [ 10 – 13 ]. In contrast, individuals with advanced HIV disease exhibit undetectable or impaired CD4 + and CD8 + T-cell responses compared with either HIV-uninfected people or PLWH who are virally suppressed [ 13 – 15 ]. CD4 + CD8 + (double positive, DP) T cells represent a minor subset of circulating lymphocytes and have been implicated in various pathological conditions, including autoimmune diseases, cancer, and viral infections [ 16 – 18 ]. Although DP T-cell proportions are often elevated during chronic viral infections – particularly in advanced HIV – their functional role in immune modulation and inflammation remains unclear [ 19 ]. Some studies suggest that DP T cells differentiate from CD4 + and CD8 + T cells in the thymus into effector memory and central memory phenotypes in response to chronic inflammation, whereas others propose a regulatory role bridging innate and adaptive immunity [ 16 , 18 ]. However, there is a paucity of studies describing their involvement in SARS-CoV-2 infection, especially in the context of HIV-1 co-infection. Severe COVID-19 is characterised by lymphopenia and increased co-expression of HLA-DR (human leukocyte antigen – DR isotype) and CD38 on CD4 + and CD8 + T cells, which are indicative of heightened T-lymphocyte activation [ 20 ]. Similarly, PLWH with high HIV-1 viral loads and low CD4 + T-cell counts also have high levels of CD4 + and CD8 + T-cells expressing HLA-DR and CD38 [ 6 , 11 , 14 ]. Although effective ART reduces immune activation, PLWH typically retain higher levels of HLA-DR + and CD38 + T cells compared with HIV-uninfected people. CD26 (dipeptidyl peptidase 4, DPP4), a host target of anti-diabetic medication and a T-cell activation antigen, has been associated with increased COVID-19 severity in high-risk populations [ 21 – 25 ]. Immune activation associates with shedding of CD26 from the cell surface, which occurs to a greater extent with HIV-1 progressive infection with accompanying high HLA-DR and CD38 expression, while PLWH with better HIV-1 control have higher CD26 expression and reduced expression of HLA-DR and CD38 on their T-cells [ 26 , 27 ]. There is a paucity of studies on SARS-CoV-2–specific T-cell responses in PLWH who are naïve to COVID-19 vaccination, which is of particular relevance in sub-Saharan Africa where two-thirds of PLWH live and COVID-19 vaccine coverage is low [ 28 , 29 ]. In this study, we characterised SARS-CoV-2–specific T-cell responses and activation profiles in PLWH and HIV-uninfected adults one month after being hospitalised for COVID-19–related illness. Participants had been infected during three COVID-19 waves when the circulating strains were: ancestral SARS-CoV-2 (Wuhan-Hu-1), Beta variant (B.1.351), or Delta variant (B.1.617.2). Furthermore, we also evaluated cross-reactive T-cell responses against the Omicron variant (BA.4/BA.5), which harbours multiple spike mutations associated with reduced sensitivity to neutralising antibodies induced by infection with earlier circulating variants [ 30 ]. Results Characteristics of study participants A total of 21 HIV-uninfected participants (male: n = 10, female: n = 11) and 17 PLWH (male: n = 7, female: n = 10) were recruited, with median ages of 54 years (IQR: 45–56) and 50 years (IQR: 44–56), respectively (Table 1 ). All participants were hospitalised with severe respiratory infections and presented with respiratory symptoms of any duration, with or without systemic features, including those with suspected tuberculosis or gastroenteritis. The majority (39.5%, 15/38) had been infected during the third COVID-19 wave, when the Delta variant (B.1.617.2) predominated, followed by 31.6% (12/38) during the first wave (ancestral Wuhan-Hu-1 SARS-CoV-2), and 28.9% (11/38) during the second wave (Beta variant, B.1.351). The distribution of infections across variant waves was comparable between PLWH and HIV-uninfected participants. The median duration of hospitalisation was 8 days (IQR: 6–12) for HIV-uninfected participants and 10 days (IQR: 7–12) for PLWH. PBMCs were collected from participants between 14–50 days post-positive SARS-CoV-2 NAAT diagnosis, after hospital discharge. HIV-uninfected participants had earlier PBMC isolation times (34 days, IQR: 30–38) compared to PLWH (39 days, IQR: 34–45; p = 0.039). Other clinical characteristics can be found in Table 1 . Table 1 Characteristics of SARS-CoV-2–infected participants, stratified by HIV status Variable HIV-uninfected Participants Participants living with HIV P -value e Number tested N = 21 N = 17 - Sex, no. (%): Male 10 (48) 7 (41) 0.691 Female 11 (52) 10 (59) Age (years), median (IQR) 54 (45–56) 50 (44–56) 0.977 COVID-19 wave (variant) recruitment period, no. (%) a : 1st wave (Wuhan-Hu-1) 4 (19) 8 (47) 0.195 2nd wave (Beta) 8 (38) 3 (18) 3rd wave (Delta) 9 (43) 6 (35) SARS-CoV-2 N-gene NAAT CT-value, median (IQR) b 34.3 (31.2–38.0) 34.3 (27.4–36.6) 0.718 PBMC isolation post-positive SARS-CoV-2 NAAT (days), median (IQR) 34 (30–38) 39 (34–45) 0.039 Hospital duration (days), median (IQR) 8 (6–12) 10 (7–12) 0.453 Smoker, no. (%) 0 (0) 2 (12) - Comorbidities, no. (%): Asthma 1 (5) 2 (12) - Tuberculosis 2 (10) 1 (6) - COPD/Emphysema 0 (0) 2 (12) - Hypertension 14 (67) 6 (35) - Diabetes 12 (57) 3 (18) - Anaemia 1 (5) 0 (0) - Epilepsy 1 (5) 0 (0) - Obesity 7 (33) 2 (12) - Covid-19-related symptoms at recruitment, no. (%): Cough 19 (90) 11 (65) - Shortness of breath 19 (90) 14 (82) - Sore throat 1 (5) 1 (6) - Fever (≥ 37.5°C) 8 (38) 7 (41) - Diarrhoea 1 (5) 5 (29) - Vomiting 1 (5) 4 (24) - Myalgia/Arthralgia 5 (24) 3 (18) - Malaise 2 (10) 1 (6) - Diminished taste 6 (29) 1 (6) - Diminished smell 6 (29) 1 (6) - Headache 1 (5) 2 (12) - Lethargy 0 (0) 2 (12) - Chest pain 6 (29) 2 (12) - Loss of appetite 3 (14) 0 (0) - Absolute CD4 + T-cell count (cells/µL), median (IQR) N/A 400 (156–610) - CD4 + T-cell count ranges, no. (%): <200 cells/µL N/A 5 (29) - ≥200–750 cells/µL N/A 6 (35) - ≥750 cells/µL N/A 2 (12) - Not recorded* N/A 4 (24) - HIV-1 viral loads, no. (%) c : Lower than detectable limit N/A 8 (47) - ≥1000 RNA copies/mL N/A 4 (24) - Not recorded* N/A 5 (29) - ART regimens, no. (%): PI + 2 NRTIs N/A 5 (29) - INSTI + 2 NRTIs N/A 5 (29) - NNRTI + 2 NRTIs N/A 5 (29) - Not recorded* N/A 2 (12) - Percentage T cells, median (IQR) d : CD4 + T cells 64.4 (42.9–70.1) 29.9 (14.1–50.3) 0.002 CD8 + T cells 29.4 (20.7–36.4) 60.5 (43.1–75.1) < 0.001 CD4 + CD8 + (double positive) T cells 0.59 (0.42–0.80) 0.37 (0.19–0.51) 0.109 CD4 + /CD8 + T-cell ratio, median (IQR): 2.09 (0.94–2.66) 0.48 (0.19–1.26) < 0.001 a Recruitment periods during three COVID-19 waves in South Africa, individually driven by Wuhan-Hu-1 (ancestral) SARS-CoV-2, the Beta variant (B.1.351), and the Delta variant (B.1.617.2). b The cycle threshold (CT) values for the SARS-CoV-2 N-gene. c HIV-1 viral loads as determined by the Cobas® 6800/8800 or Cobas® AmpliPrep/Cobas® TaqMan® HIV-1 Tests (Roche Diagnostics International Ltd, Rotkreuz, Switzerland) with linear detection ranges of 50–10 7 RNA copies/mL (lower limit of detection: 50 RNA copies/mL) and 20–10 7 RNA copies/mL (lower limit of detection: 20 RNA copies/mL), respectively. d The percentage CD4 + and CD8 + T cells as determined by flow cytometry staining after acquiring 100,000 live CD3 + cells. Refer to Supplementary Fig. S1 and S2 for additional information. e Statistical analyses using the Mann–Whitney U -test between two unpaired groups. The P -value represents direct comparisons between HIV-uninfected participants and participants living with HIV (PLWH). *There are no recent medical records available with the variable’s exact information. Denotations: ART, antiretroviral treatment; COPD, chronic obstructive pulmonary disease; CT, cycle threshold value; HIV-1, human immunodeficiency virus type 1; INSTI, integrase strand transfer inhibitor (Dolutegravir); IQR, interquartile range; N/A, not applicable; NAAT, nucleic acid amplification test; N-gene, nucleocapsid protein gene; NNRTI, nonnucleoside reverse transcriptase inhibitor (Efavirenz); NRTI, nucleoside or nucleotide reverse transcriptase inhibitor (Tenofovir and Lamivudine/Emtricitabine); PBMC, peripheral blood mononuclear cells; PI, protease inhibitor (Lopinavir/Ritonavir); RNA, ribonucleic acid; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. Among PLWH, the median CD4 + T-cell count was 400 cells/µL (IQR: 156–610), and nearly half (47%, 8/17) of PLWH had undetectable HIV-1 viral loads (< 50 RNA copies/mL), while 24% (4/17) had HIV-1 viral loads ≥ 1000 RNA copies/mL. Medical records for five participants lacked recent absolute CD4 + T-cell counts and HIV-1 viral load data. The majority (88%, 15/17) of PLWH were receiving fixed-dose combination ART. Overall, the CD4 + /CD8 + T-cell ratio, determined by the proportion of live CD3 + T cells expressing CD4 or CD8 (Supplementary Fig. S1 and S2), was lower in PLWH compared with HIV-uninfected participants (0.48 vs. 2.09; p < 0.001). This was due to a higher proportion of CD3 + T cells with a CD4 + phenotype in HIV-uninfected participants compared with PLWH (64.4% vs. 29.9%; p = 0.002), and a higher proportion of CD3 + T cells with a CD8 + phenotype in PLWH compared with HIV-uninfected participants (60.5% vs. 29.4%; p 0.05). T-cell immunity to ancestral SARS-CoV-2 SARS-CoV-2–specific T-cell responses were characterised based on the ability of T-lymphocytes to produce IFN-γ, IL-2, or TNF-α following PBMC stimulation with peptide antigens spanning the FLS and N proteins of ancestral SARS-CoV-2 (Fig. 1 ). PLWH and HIV-uninfected participants mounted comparable CD4 + T-cell response magnitudes to FLS (1.35% [IQR: 1.20–2.00%] vs. 1.73% [IQR: 1.19–2.20%]; p = 0.255) and N (1.95% [IQR: 1.52–2.15%] vs. 2.11% [IQR: 1.61–2.66%]; p = 0.636) proteins when adjusted for age, sex, SARS-CoV-2 variant, hypertension, diabetes, obesity, and the number of days between SARS-CoV-2 diagnosis and PBMC isolation (Fig. 1 a). CD8 + T-cell response magnitudes were similarly comparable among PLWH and HIV-uninfected participants for the FLS (1.10% [IQR: 0.89–1.55%] vs. 1.16% [IQR: 0.75–1.80%]; p = 0.729) and N (1.19% [IQR: 0.85–1.57%] vs. 1.12% [IQR: 0.87–1.46%]; p = 0.522) proteins. All participants mounted detectable FLS- and N-specific CD4 + and CD8 + T-cell responses, except for one PLWH who lacked a detectable CD4 + T-cell response. Fewer than 75% of participants mounted detectable DP T-cell responses to the FLS and N proteins of ancestral SARS-CoV-2. However, DP T-cell response magnitudes were comparable among PLWH and HIV-uninfected participants for both the FLS (0.66% [IQR: 0–1.21%] vs. 0.14% [IQR: 0–1.03%]; p = 0.733) and N (0.40% [IQR: 0–0.94%] vs. 1.53% [IQR: 0–2.37%]; p = 0.114) proteins. Additional comparisons of FLS-specific responses with N-specific responses within the three different T-cell subsets are displayed in Fig. 1 a. Various combinations of T-lymphocyte cytokine production were investigated to assess potential differences in T-cell responses between participants living with and without HIV (Fig. 1 b and 1 c; Supplementary Fig. S3). SARS-CoV-2–specific CD4 + and CD8 + T cells among PLWH and HIV-uninfected participants were predominantly IL-2 monofunctional, with fewer cells producing IFN-γ or TNF-α. The highest proportion of polyfunctional T cells co-produced IL-2 and TNF-α. The overall FLS-specific polyfunctional cytokine production profiles were similar between participants living with and without HIV for CD4 + , CD8 + and DP T cells. A similar pattern was observed for N-specific polyfunctional cytokine production profiles of CD4 + and CD8 + T cells. However, DP T cells exhibited an increased N-specific polyfunctional cytokine production profile in PLWH compared to HIV-uninfected participants (p = 0.039). Furthermore, within-group analyses revealed that FLS- and N-specific CD8 + T-cell responses were predominantly lower than CD4 + T-cell responses among HIV-uninfected participants (p 0.05; Supplementary Fig. S4). A positive correlation was observed between FLS- and N-specific response magnitudes in both CD4 + (r = 0.640; p < 0.001) and CD8 + (r = 0.622; p < 0.001) T cells, irrespective of HIV status (Fig. 1 d). While this correlation was present in both groups, it was strongest among PLWH for CD4 + (r = 0.914; p < 0.001) and CD8 + (r = 0.789; p < 0.001) T cells. HIV-uninfected participants exhibited a moderate correlation between CD4 + (r = 0.512; p < 0.05) T cells and a weaker correlation in CD8 + (r = 0.427; p = 0.069) T cells. No significant correlation was observed for DP T cells (r = 0.205; p = 0.229). Since T-lymphocytes were predominantly monofunctional, we further assessed the correlation between FLS- and N-specific response magnitudes among single-cytokine-producing cells (i.e. cells that only produce IFN-γ, IL-2, or TNF-α; Fig. 1 e). Positive correlations were observed among single-cytokine-producing CD4 + T cells for IFN-γ (r = 0.722; p < 0.001), IL-2 (r = 0.641; p < 0.001), and TNF-α (r = 0.593; p < 0.001). Similar trends were observed in CD8 + T cells for IFN-γ (r = 0.674; p < 0.001), IL-2 (r = 0.471; p < 0.01), and TNF-α (r = 0.505; p < 0.01). While most correlations were maintained when stratifying by HIV status, the correlation between FLS- and N-specific TNF-α production was not sustained in HIV-uninfected participants for CD4 + nor CD8 + T cells. DP T cells exhibited a weak overall correlation for IFN-γ production between FLS- and N-specific responses (r = 0.390; p < 0.05); this correlation was lost when stratifying by HIV status. Markers of T-cell activation T-cell activation was evaluated by assessing the extracellular expression of CD26 (DPP4) on CD4 + , CD8 + , and DP T cells in relation to the HLA-DR and CD38 activation markers (Fig. 2 and Supplementary Fig. S5). PLWH and HIV-uninfected participants expressed comparable levels of CD26 on their CD4 + (73.4% [IQR: 57.3–82.8%] vs. 81.5% [IQR: 75.6–89.3%]; p = 0.155), CD8 + (21.2% [IQR: 5.5–36.6%] vs. 27.9% [IQR: 20.7–42.3%]; p = 0.982), and DP (58.2% [IQR: 26.5–75.2%] vs. 55.9% [IQR: 32.0–72.7%]; p = 0.759) T cells (Fig. 2 a). Among HIV-uninfected participants, the proportion of CD4 + T cells expressing CD26 was higher than CD8 + (81.5% vs. 27.9%; p < 0.001) and DP (55.9%; p < 0.001) T cells. Similar results were observed in PLWH (73.4% CD4 + T cells vs. 21.2% CD8 + T cells; p < 0.001 and vs. 58.2% DP T cells; p = 0.049). CD26-expressing CD8 + T cells were, however, consistently lower than DP T cells among HIV-uninfected participants and PLWH. Overall, the proportions of CD26-expressing CD4 + , CD8 + , and DP T cells did not differ by HIV status in adjusted analysis (p > 0.05). However, among females living with HIV, CD4 + T cells exhibited decreased CD26 expression (57.4% [IQR: 41.0–73.4%]) compared with their male counterparts (83.4% [IQR: 79.5–88.1%]; p = 0.003) and HIV-uninfected females (84.5% [IQR: 76.2–89.4%]; p = 0.004; Supplementary Fig. S5a). The proportion of activated but non-proliferating CD4 + T cells (CD4 + HLA-DR + CD38 - ) was similar between PLWH and HIV-uninfected participants (3.35% [IQR: 2.32–12.3%] vs. 2.81% [IQR: 1.64–5.44%]; p = 0.536; Fig. 2 b). In contrast, PLWH had lower proportions of CD8 + HLA-DR + CD38 - T cells compared with HIV-uninfected participants (12.9% [IQR: 7.78–17.4%] vs. 15.0% [IQR: 8.32–18.6%]; p = 0.013). The proportion of highly activated and proliferating T cells (HLA-DR + CD38 + ), as well as the proportion of proliferating but less activated T cells (HLA-DR - CD38 + ) was also similar between participants living with and without HIV. CD8 + T cells consistently exhibited higher proportions of both HLA-DR + CD38 - and HLA-DR + CD38 + phenotypes than CD4 + T cells (p < 0.001), irrespective of HIV status. However, the proportion of CD8 + HLA-DR - CD38 + T cells was consistently lower than that of CD4 + HLA-DR - CD38 + T cells in both PLWH and HIV-uninfected participants (p < 0.001). An inverse relationship was observed between CD26 expression and both CD4 + HLA-DR + CD38 - (r=-0.588; p < 0.001) and CD4 + HLA-DR + CD38 + T cells (r=-0.596; p < 0.001; Fig. 2 c). These negative correlations remained consistent when stratified by HIV status. In contrast, CD26 expression did not correlate with CD4 + HLA-DR - CD38 + T cells, nor with any of the three activation states assessed in CD8 + and DP T cells. Furthermore, in both PLWH and HIV-uninfected participants, there were no significant associations between T-cell response magnitudes and activation states (Fig. 2 d). Stimulation with FLS- and N-specific peptides had no measurable effect on CD4 + and CD8 + T-cell activation states (Supplementary Fig. S5b-d). T-cell cross-reactivity to Omicron (BA.4/BA.5) Participants were enrolled during earlier waves of the COVID-19 pandemic, prior to the emergence of the Omicron variant, and were therefore presumed unexposed to Omicron. To assess T-cell cross-reactivity, we evaluated whether participants’ T-lymphocytes could recognise and respond to spike mutations of the Omicron variant (BA.4/BA.5) compared to the ancestral virus, using partial spike-specific peptide antigens (Fig. 3 ). PLWH and HIV-uninfected participants mounted comparable Omicron-specific CD4 + (1.85% [IQR: 1.35–2.04%] vs. 1.74% [IQR: 1.27–2.37%]; p = 0.884), CD8 + (1.08% [IQR: 0.57–1.68%] vs. 1.10% [IQR: 0.93–1.41%]; p = 0.678), and DP (0.47% [IQR: 0–2.66%] vs. 0.89% [IQR: 0.27–1.71%]; p = 0.373) T-cell response magnitudes (Fig. 3 a). Ancestral-specific response magnitudes were similarly comparable among PLWH and HIV-uninfected participants for CD4 + (1.50% [IQR: 1.27–2.30%] vs. 1.90% [IQR: 1.24–2.47%]; p = 0.613), CD8 + (1.31% [IQR: 0.77–1.73%] vs. 1.14% [IQR: 0.79–1.45%]; p = 0.594), and DP (0.55% [IQR: 0–1.48%] vs. 0.96% [IQR: 0.41–2.20%]; p = 0.809) T cells. Additional comparisons showed no differences in T-cell reactivity between ancestral and Omicron spike peptides in either HIV-uninfected participants or PLWH and are displayed in Fig. 3 a. The median fold change in spike-specific T-cell responses to Omicron relative to the ancestral virus was comparable between HIV-uninfected participants and PLWH for both CD4 + (0.96 [IQR: 0.79–1.05] vs. 0.99 [IQR: 0.78–1.07]; p = 0.894) and CD8 + (1.01 [IQR: 0.94–1.18] vs. 0.86 [IQR: 0.82–1.10]; p = 0.316) T cells (Fig. 3 b). Median fold changes in both groups remained close to 1.0 and within the 0.5–1.5 range. However, one HIV-uninfected participant exhibited an elevated CD4 + T-cell response fold change of 2.52, suggesting a more than 2.5-fold increase in reactivity to Omicron compared to the ancestral virus. Polyfunctional profiling further demonstrated that CD4 + and CD8 + T cells were predominantly monofunctional, with no significant differences in cytokine production between PLWH and HIV-uninfected participants, irrespective of ancestral- or Omicron-specific peptides (Fig. 3 c and 3 d). Furthermore, correlation analysis revealed a positive relationship between partial spike responses to ancestral SARS-CoV-2 and the Omicron variant in both CD4 + (r = 0.856; p < 0.001) and CD8 + (r = 0.888; p < 0.001) T cells (Fig. 3 e), which remained consistent upon stratification by HIV status. In contrast, DP T cells demonstrated only a weak correlation (r = 0.396; p < 0.05), which persisted among HIV-uninfected participants (r = 0.463; p < 0.05) but not in PLWH (r = 0.272; p = 0.341). Discussion In this study, we demonstrated that HIV-1 infection did not substantially alter SARS-CoV-2–specific T-cell responses and activation profiles, which were largely comparable between ART-treated PLWH and HIV-uninfected black African adults in South Africa, all of whom were naïve to COVID-19 vaccination and had been hospitalised for COVID-19–related illness. We observed that CD8 + T cells exhibited greater activation (HLA-DR + CD38 - and HLA-DR + CD38 + ) than CD4 + T cells, while CD4 + HLA-DR - CD38 + proportions exceeded those of their CD8 + counterparts. Elevated CD26 (DPP4) expression was predominantly observed among CD4 + T cells and inversely correlated with CD4 + T-cell activation (HLA-DR + CD38 - and HLA-DR + CD38 + ). Furthermore, we demonstrated that SARS-CoV-2–specific T cells in HIV-uninfected people and PLWH effectively cross-recognised the Omicron variant (BA.4/BA.5) and to a similar degree, suggesting preserved T-cell immunity following infection with earlier variants. Comparable incidence of SARS-CoV-2 infection has been reported among PLWH and HIV-uninfected people; however, caution is warranted when interpreting the independent effects of HIV infection on COVID-19 outcomes [ 5 ]. While effective ART improves clinical outcomes, immune reconstitution remains highly variable and is influenced by confounding factors such as age, sex, comorbidities, and socio-economic status [ 1 – 5 , 31 , 32 ]. Although HIV-uninfected participants and PLWH in our study exhibited minimal variability in their clinical and demographic characteristics – except for their T-cell counts and PBMC isolation days post-positive diagnosis – we adjusted the analyses for confounders (i.e. age, sex, SARS-CoV-2 variant, comorbidities, and PBMC isolation days post-positive diagnosis). This relatively homogenous setting strengthens the internal validity of our findings by reducing confounding factors that could have influenced the observed T-cell responses. The CD4 + /CD8 + T-cell ratio serves as a key biomarker for assessing immune competence, often providing a more accurate indication of immune dysfunction than absolute CD4 + T-cell counts and HIV-1 viral loads [ 33 ]. In our study, PLWH exhibited a CD4 + /CD8 + T-cell ratio lower than 1.0, a threshold generally associated with adverse clinical outcomes [ 34 ]. This imbalance is a consequence of chronic HIV-1–induced immune activation, which drives persistent CD8 + T-cell expansion and CD4 + T-cell depletion, even in participants receiving ART. However, immune reconstitution remains incomplete in up to 30% of ART-treated PLWH [ 35 , 36 ], which may impair the development of functional SARS-CoV-2–specific CD4 + and CD8 + T-cell responses [ 5 , 7 , 37 ]. SARS-CoV-2–specific T-cell responses typically peak between 14–30 days post-diagnosis [ 14 ]. To maximise sample size, we extended this window to include participants with PBMC samples collected one month following hospital discharge (between 14–50 days post-positive diagnosis), aligning with a previous study by our group investigating T-cell immunity in pregnant and postpartum women living with and without HIV [ 12 ]. Despite the lower CD4 + /CD8 + T-cell ratio, our findings demonstrate that most participants mounted strong SARS-CoV-2–specific CD4 + and CD8 + T-cell responses against the FLS and N proteins of ancestral SARS-CoV-2, which were comparable between PLWH and HIV-uninfected participants. Only one PLWH lacked detectable CD4 + T-cell responses to both proteins. The magnitude, functionality, and correlation of FLS- and N-specific T-cell responses were largely comparable between HIV-uninfected participants and PLWH, aligning with previous studies showing that ART-treated PLWH with suppressed viraemia maintain SARS-CoV-2–specific T-cell immunity comparable to those of HIV-uninfected people [ 5 , 12 , 13 ]. N-specific T-cell responses consistently exceeded FLS-specific responses, suggesting that SARS-CoV-2 infection leading to severe disease may preferentially drive more robust and durable N-specific immunity. In line with previous research, we also observed that CD4 + T-cell responses were mostly higher than CD8 + T-cell responses in HIV-uninfected participants [ 12 ]. Conversely, PLWH exhibited comparable magnitudes of CD4 + and CD8 + T-cell responses, diverging from our previous findings among pregnant women [ 12 ], possibly reflecting the combined effects of COVID-19 severity and chronic HIV-associated immune activation on T-cell responses. In our study, both HIV-uninfected participants and PLWH exhibited comparable DP T-cell proportions, as well as similar response magnitudes and functionality profiles. Furthermore, while moderate to strong correlations were observed between FLS- and N-specific CD4 + and CD8 + T-cell responses, these associations were absent – or even inversely related – when assessing single-cytokine production by DP T cells. This suggests that DP T cells may be subject to distinct regulatory mechanisms during SARS-CoV-2 infection. Given their emerging role in chronic immune activation and regulation, further studies are needed to delineate whether DP T cells contribute to protective immunity or are indicative of immune dysfunction in the context of SARS-CoV-2 and HIV co-infection. CD26 (DPP4) is a multifunctional glycoprotein expressed on various cell types, including T cells, and has been suggested as a potential biomarker for severe COVID-19, particularly in elderly individuals and those with high-risk comorbidities, such as diabetes [ 21 , 27 ]. While its role in T-cell immunity during SARS-CoV-2 infection in PLWH remains poorly characterised, CD26 expression has been linked to immune regulation and HIV control [ 27 , 38 ]. CD26 is highly expressed on CD4 + T cells compared to CD8 + T cells [ 27 , 38 ]. In our study, CD4 + T cells exhibited higher CD26 expression compared to CD8 + and to DP T cells, with no differences observed between HIV-uninfected participants and PLWH. Furthermore, we identified a strong inverse correlation between CD26 expression and both the CD4 + HLA-DR + CD38 - and CD4 + HLA-DR + CD38 + T-cell activation phenotypes, irrespective of HIV status. Since chronic immune activation is a hallmark of HIV infection and can contribute to immune dysfunction, further investigation is needed to determine whether CD26 may play a protective role in modulating SARS-CoV-2–specific T-cell responses in PLWH. HLA-DR is a major histocompatibility complex (MHC) class II cell surface receptor involved in the immune system’s ability to recognise foreign molecules and is crucial for presenting antigens to CD4 + T-cells [ 39 ]. CD38 is a type II transmembrane glycoprotein and its expression on CD8 + T cells correlates with HIV-1 viral loads and disease progression [ 40 ]. Both HLA-DR and CD38 are associated with immune activation and have been shown to be elevated in PLWH indicating chronic immune activation [ 6 , 11 , 14 ]. During SARS-CoV-2 infection, these markers are further heightened, particularly in severe cases [ 6 , 20 ]. In general, we found no differences in CD4 + or CD8 + T-cell activation phenotypes (HLA-DR + CD38 - , HLA-DR + CD38 + , and HLA-DR - CD38 + ) between HIV-uninfected participants and PLWH, suggesting that immune activation was driven by SARS-CoV-2 infection in both groups rather than being solely attributable to HIV infection. This is particularly relevant given that PLWH typically exhibit elevated activation markers despite effective ART. The only exception was a decreased proportion of CD8 + HLA-DR + CD38 - T cells in PLWH compared with HIV-uninfected participants. We additionally observed that CD8 + T cells were more activated than CD4 + T cells, characterised by increased proportions of activated but non-proliferating (HLA-DR + CD38 - ) phenotypes, as well as highly activated and proliferating (HLA-DR + CD38 + ) phenotypes, consistent with other studies reporting heightened T-cell dysfunction in severe COVID-19 [ 41 ]. In contrast, CD4 + T cells exhibited increased proportions of proliferating, but less activated (HLA-DR - CD38 + ) phenotypes compared with CD8 + T cells. Activation markers did not correlate with SARS-CoV-2–specific T-cell response magnitudes to the FLS and N proteins of the ancestral virus, nor did it change following antigen stimulation. This may be due to the kinetics of activation marker modulation, as significant alterations are typically observed 24–72 hours post-stimulation rather than within the 16-hour stimulation period used in this study [ 42 ]. Further longitudinal investigations are warranted to better characterise the dynamics of T-cell activation over time, particularly in the context of HIV and SARS-CoV-2 co-infection. Lastly, we demonstrated that CD4 + , CD8 + , and DP T-cell responses not only cross-react with Omicron but also exhibit comparable magnitudes and functionality profiles to those induced by ancestral SARS-CoV-2, irrespective of HIV status or the specific SARS-CoV-2 variant that caused infection. These findings align with previous studies showing that SARS-CoV-2–specific T-cell immunity remains largely preserved across diverse populations, despite spike-specific mutations in Beta, Delta, and Omicron variants that can facilitate immune evasion by disrupting MHC class I and II epitope presentation [ 12 , 43 – 46 ]. While our analysis only focussed on partial spike protein regions containing Omicron-specific mutations and their corresponding ancestral counterparts, a more comprehensive characterisation of T-cell responses targeting other immunodominant epitopes across the entire viral proteome would have provided a broader assessment of T-cell immunity and its overall preservation across SARS-CoV-2 variants. Our study had several limitations. First, the availability of PBMC samples was restricted to a single time point, limiting the power of our analyses and preventing us from assessing the kinetics of SARS-CoV-2–specific T-cell responses over time. The absence of longitudinal samples for these participants precluded analyses of long-term immunity, including memory responses and the potential interplay between SARS-CoV-2 and HIV during immune recovery. Second, we lacked recent medical records for some PLWH, preventing confirmation of absolute CD4 + T-cell counts and HIV-1 viral loads at the time of enrolment, which may introduce variability in immune response interpretations. Third, we did not include a non-hospitalised control group, which could have provided a comparative baseline to better contextualise T-cell responses and activation profiles observed in our cohort. A fixed threshold of 0.02% was applied to define positive T-cell responders, chosen to balance sensitivity in the absence of standardised guidelines. Although not empirically derived or externally validated, this approach ensured analytical consistency. Finally, our manuscript only focuses on T-cell immunity. While our flow cytometry panel included extracellular markers to assess SARS-CoV-2–specific innate immune responses from monocytes (i.e. CD14 and CD16) and natural killer cells (i.e. CD16 and CD56), these findings will be reported separately. In conclusion, our study provides evidence that SARS-CoV-2–specific T-cell immunity is comparable among unvaccinated, hospitalised black African adults living with and without HIV. Despite chronic immune activation associated with HIV, we observed comparable SARS-CoV-2–specific T-cell responses in PLWH on ART, further strengthening the understanding of cell-mediated immunity’s ability to contribute to protection against severe COVID-19. These findings are particularly important in high HIV-burdened settings, where disruptions in vaccine access, immunological heterogeneity, and the ongoing risk of emerging SARS-CoV-2 variants pose substantial public health challenges. Future research should focus on the durability and long-term quality of T-cell immunity to inform immunisation strategies and pandemic preparedness efforts in vulnerable populations. Methods Study participants Hospitalised adults (≥ 18 years) admitted with severe respiratory infections at Chris Hani Baragwanath Academic Hospital (CHBAH) and Bheki Mlangeni District Hospital (BMDH) in Johannesburg, South Africa, were invited to participate in this study. CHBAH and BMDH are located in Soweto, a densely populated, low-middle–income urban settlement (or ‘township’) with a predominantly black African population and diverse socio-economic backgrounds [ 47 ]. SARS-CoV-2 infection was screened using a nucleic acid amplification test (NAAT), as previously described [ 48 ]. Participants with confirmed SARS-CoV-2 infection were requested to return to the study clinic for a follow-up visit approximately one month after hospital discharge, at which time venous blood was collected, peripheral blood mononuclear cells (PBMCs) were isolated and stored at the Vaccines and Infectious Diseases Analytics (VIDA) Research Unit located at CHBAH. Enrolment occurred during three periods: 1 April to 31 October 2020; 1 November 2020 to 30 April 2021; and 1 May to 30 November 2021, corresponding to South Africa’s first, second, and third COVID-19 waves, driven by the ancestral (Wuhan-Hu-1) SARS-CoV-2, Beta variant (B.1.351), and Delta variant (B.1.617.2), respectively. The infecting SARS-CoV-2 variant was inferred based on the timing of infection and not confirmed by viral genome sequencing [ 12 , 46 ]. Clinical data were collected and managed electronically as previously described [ 12 , 46 ]. All participants provided written informed consent to participate and for the anonymised publication of their results. Peripheral blood mononuclear cell processing PBMCs were isolated, cryopreserved, and thawed as previously described [ 12 , 46 ]. In this study, PBMCs with a viability of ≥ 70% were rested for 4 h at 37°C and 5% CO 2 in R10 medium before antigen stimulation. All PBMCs were isolated after hospital discharge. SARS-CoV-2 peptide antigens Peptide pools (Miltenyi Biotec, Bergisch Gladbach, Germany) used for the in vitro stimulation of SARS-CoV-2–specific T cells were prepared as previously described [ 12 , 46 ]. Briefly, PepTivator® peptide pools Prot_S1 and Prot_S were combined to represent the near full-length spike (FLS) glycoprotein of ancestral SARS-CoV-2. Prot_N was included to represent the complete nucleocapsid (N) protein of ancestral SARS-CoV-2, and Prot_S B.1.1.529/BA.5 was used to represent the Omicron variant. A corresponding ancestral reference pool was included as a control for the variant pool. Cell stimulation and immunofluorescent staining Cell stimulation and immunofluorescent staining procedures were performed as previously described [ 12 , 46 ], with minor modifications. Briefly, additional extracellular markers were included: CD14 APC (1:10, clone M5E2; BD Pharmingen™, BD Biosciences, San Jose, California, USA), CD16 APC-Cy7 (1:67, clone 3G8; BD Pharmingen™), CD26 PE (1:100, clone BA5b; BioLegend, San Diego, California, USA), CD38 BV421 (1:40, clone S17015A; BioLegend), CD56 BV650 (1:100, clone NCAM16.2; BD Horizon™), and HLA-DR FITC (1:20, clone L243; BD Biosciences). Furthermore, fixed and permeabilized cells were stained with CD3 PerCP (1:40, clone SK7; BD Biosciences). Flow cytometry Flow cytometry was performed as previously described [ 12 , 46 ], with data acquired on a 4-laser BD LSRFortessa™ X-20 flow cytometer (BD Biosciences) and analysed using FlowJo™ software (v.10.10.0; FlowJo LLC., Ashland, Oregon, USA). In addition to standard gating [ 12 , 46 ], this study incorporated gating for CD4 + CD8 + (double positive, DP) T cells, and assessment of activation marker expression (CD26, CD38, and HLA-DR). A representative gating strategy is provided in Supplementary Fig. S1 . Functionality data are presented as percentages after background subtraction from unstimulated controls. Statistical analysis Statistical analyses were performed as previously described [ 12 , 46 ], with a few additions. Briefly, a fixed threshold of 0.02% was applied to define positive CD4 + and CD8 + T-cell responses through the individual or combined production of any of the cytokines (IFN-γ, IL-2, or TNF-α) [ 46 ]. Participants with T-cell responses below the 0.02% threshold were classified as non-responders and were assigned a value of 0.015% in log-scaled figures solely for visualisation purposes. T-cell responses and activation statuses (CD26, HLA-DR, and CD38) were compared between PLWH and HIV-uninfected participants using multivariate log-linear regression, adjusted for age, sex, SARS-CoV-2 variant, hypertension, diabetes, obesity, and number of days between SARS-CoV-2 NAAT diagnosis and PBMC isolation. The Mann–Whitney U -test and the Wilcoxon signed-rank test were used for comparisons between unpaired and paired groups, respectively [ 12 , 46 ]. Pearson’s chi-squared test was used for comparing proportions. Spearman’s rank correlation coefficient (two-sided; α = 0.05) was used to describe the linear relationship between two continuous variables (e.g. total FLS and N responses) [ 12 ]. Polyfunctional T-cell response differences were determined by a permutation test with 10,000 iterations using SPICE (v.6.1, https://niaid.github.io/spice/ ; Vaccine Research Center, NIAID, NIH, Bethesda, Maryland, USA) [ 12 , 46 , 50 ]. All other statistical analyses and graphical representations were performed using STATA® (v.18.5; StataCorp LLC, College Station, Texas, USA) and GraphPad Prism® (v.10.4.1; GraphPad Software Inc., San Diego, California, USA), respectively. P -values < 0.05 were considered statistically significant. Declarations Competing Interests Authors S.A.M., M.C.N. and G.K. report receiving grant support, paid to their institution, from the Bill & Melinda Gates Foundation. No competing interests were declared for the remaining authors. Ethics declaration This is a sub-study of a parent study, entitled “Sentinel, hospital-based surveillance for the investigation of SARS-Coronavirus-2 and other respiratory pathogens” which was performed in line with the principles of the Declaration of Helsinki. The parent study was approved by the Human Research Ethics Committee of the University of the Witwatersrand (Reference number: 200313), as well as this sub-study (Reference number: M220285). Disclaimer The authors acknowledge that the opinions, findings, and conclusions expressed in this manuscript are that of the authors alone, and that the National Research Foundation of South Africa accepts no liability whatsoever in this regard. Funding This study was funded by the Bill & Melinda Gates Foundation [grant number INV-016202] and the South African Medical Research Council [grant number SHIP NCD 96756]. Author W.C.M. received grants from the Poliomyelitis Research Foundation [grant number 22/82] and the National Research Foundation of South Africa [grant number PMDS2205067384] in support of this study’s research. Author C.T.T. is funded in part through the South African Chairs Initiative of the Department of Science and Innovation/National Research Foundation of South Africa [grant number 84177]. Author Contribution W.C.M. prepared this manuscript. M.C.N., N.S., and F.L. enrolled participants, as well as collected clinical data and samples. W.C.M. and members of the Wits VIDA COVID team processed PBMC samples. W.C.M. generated data by performing all other associated experiments. W.C.M. and A.I. analysed the data by performing statistical analyses. W.C.M., G.K., A.I., N.S., F.L., S.S., C.T.T., S.A.M., and M.C.N. interpreted results. All authors had full access to this study’s data and had final responsibility for the decision to submit for publication. Acknowledgement We thank the Centre for Vaccines and Immunology, as well as the Centre for HIV and STIs at the National Institute for Communicable Diseases, a division of the National Health Laboratory Services (NHLS), for the use of their laboratory facilities and flow cytometry equipment. We also thank all study participants, their clinicians, and collaborating members of the Wits VIDA COVID team. Data Availability Source data for main figures (Fig. 1-3) and for the supplementary figures (Fig. S2-S6) are provided in Supplementary Information 2. Anonymised participant-level data and raw data generated during the study will be made available upon reasonable request directed to the corresponding author. References Russell, C. D., Lone, N. I. & Baillie, J. K. 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Supplementary Files AdultCovid19CMIStudySupplementaryInformation08.09.2025.docx AdutlCovid19CMIStudySupplementaryInformation208.09.2025.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 13 Nov, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 22 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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06:43:46","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174634,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/fbec0be716587149ace14661.html"},{"id":96789439,"identity":"d2e30d47-1db5-4f27-adae-5091330ff6b0","added_by":"auto","created_at":"2025-11-26 06:43:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5422005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT-cell immunity to full-length spike and nucleocapsid proteins of ancestral SARS-CoV-2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Proportion of CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells producing IFN-γ, IL-2, or TNF-α in response to full-length spike (FLS) and nucleocapsid (N) proteins of ancestral SARS-CoV-2 among participants living with (n=17) and without HIV (n=19). The proportion of positive responders is indicated above each group, with responders defined as those with T-cell responses ≥0.02% (threshold). Non-responders (\u0026lt;0.02%) are graphically represented with a value of 0.015%. \u003cstrong\u003e(b, c)\u003c/strong\u003e Polyfunctionality profiles of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in response to FLS and N proteins, respectively. Each participant is represented by an individual point, with median responses displayed below each axis. Polyfunctional response patterns are colour-coded and summarised in pie charts with overarching legend denoting cytokine production. \u003cstrong\u003e(d, e)\u003c/strong\u003e Correlations between total FLS- and N-specific T-cell responses and responses of single-cytokine-producing T cells. Statistical comparisons between unpaired (HIV-uninfected vs. PLWH) and paired (FLS vs. N) groups were performed using adjusted multivariate log-linear regression and the Wilcoxon signed-rank test, respectively. Differences in overall polyfunctional T-cell response profiles were assessed using a permutation test (10,000 iterations). Spearman’s rank correlation coefficient (two-sided; α = 0.05) was used to determine the \u003cem\u003eP-\u003c/em\u003e and \u003cem\u003er-\u003c/em\u003evalues. Only significant \u003cem\u003eP\u003c/em\u003e-values from correlation analyses are indicated: *\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.05; **\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.01; ***\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001. Denotations: Combined: participants living with and without HIV; FLS: full-length spike glycoprotein; IFN-γ: interferon gamma; IL-2: interleukin 2; N: nucleocapsid protein; PLWH: participants living with HIV; TNF-α: tumor necrosis factor alpha.\u003c/p\u003e","description":"","filename":"Figure1Final.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/302143cf77668245d5bdecde.jpg"},{"id":96789443,"identity":"aaf8e978-d048-4c33-b777-0b735e50e277","added_by":"auto","created_at":"2025-11-26 06:43:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3410999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between T-cell activation markers HLA-DR and CD38 and expression of CD26 (DPP4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Proportion of CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells expressing CD26 (DPP4) among participants living with (n=17) and without HIV (n=18). \u003cstrong\u003e(b)\u003c/strong\u003e CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell activation, measured by the proportion of cells expressing HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e, HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, or HLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, among participants living with (n=17) and without HIV (n=21). Each participant is represented by an individual point, with median proportions displayed below each axis. Box-and-whisker plots show medians (horizontal black lines), interquartile ranges (boxes), and minimum/maximum values (whiskers). \u003cstrong\u003e(c) \u003c/strong\u003eCorrelations between CD26 (DPP4) expression on different T-cell subsets and activation markers (HLA-DR and CD38). \u003cstrong\u003e(d)\u003c/strong\u003e Correlations between total FLS- and N-specific T-cell responses and the expression of CD26 (DPP4), HLA-DR, and CD38 on different T-cell subsets. Statistical comparisons between unpaired (HIV-uninfected vs. PLWH) and paired (CD4\u003csup\u003e+\u003c/sup\u003e vs. CD8\u003csup\u003e+\u003c/sup\u003e; CD4\u003csup\u003e+\u003c/sup\u003e vs DP; CD8\u003csup\u003e+\u003c/sup\u003e vs. DP) groups were performed using adjusted multivariate log-linear regression and the Wilcoxon signed-rank test, respectively. Spearman’s rank correlation coefficient (two-sided; α = 0.05) was used to determine the \u003cem\u003eP-\u003c/em\u003e and \u003cem\u003er-\u003c/em\u003evalues. Only significant \u003cem\u003eP\u003c/em\u003e-values from correlation analyses are indicated as follows: *\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.05; **\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.01; ***\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001. Denotations: Combined: participants living with and without HIV; FLS: full-length spike glycoprotein; N: nucleocapsid protein; PLWH: participants living with HIV.\u003c/p\u003e","description":"","filename":"Figure2Final.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/3a6358168ddff8007641c0a8.jpg"},{"id":96789447,"identity":"f4ec19af-7946-49de-a53d-85b5196406ae","added_by":"auto","created_at":"2025-11-26 06:43:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5595039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT-cell immunity and cross-reactivity to the Omicron variant (BA.4/BA.5).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Proportion of spike-specific CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells producing IFN-γ, IL-2, or TNF-α in response to peptide pools covering partial ancestral and Omicron spike proteins among participants living with (n=14) and without HIV (n=19). The proportion of positive responders is indicated above each group, with responders defined as those with T-cell responses ≥0.02% (threshold). Non-responders (\u0026lt;0.02%) are graphically represented with a value of 0.015%. \u003cstrong\u003e(b)\u003c/strong\u003e Fold change in spike-specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses. The shaded area (0.5–1.5) represents similar responses to ancestral SARS-CoV-2 and the Omicron variant. \u003cstrong\u003e(c, d)\u003c/strong\u003e CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell polyfunctional profiles in response to ancestral SARS-CoV-2 and the Omicron variant, respectively. Each participant is represented by an individual point, with median responses displayed below each axis. Polyfunctional response patterns are colour-coded and summarised in pie charts with overarching legend denoting cytokine production. \u003cstrong\u003e(e)\u003c/strong\u003e Correlations between ancestral SARS-CoV-2 and Omicron spike-specific T-cell responses. Statistical comparisons between unpaired (HIV-uninfected vs. PLWH) and paired (ancestral vs. Omicron) groups were performed using adjusted multivariate log-linear regression and the Wilcoxon signed-rank test, respectively. Differences in overall polyfunctional T-cell response profiles were assessed using a permutation test (10,000 iterations). Spearman’s rank correlation coefficient (two-sided; α = 0.05) was used to determine the \u003cem\u003eP-\u003c/em\u003e and \u003cem\u003er-\u003c/em\u003evalues. Only significant \u003cem\u003eP\u003c/em\u003e-values from correlation analyses are indicated as follows: *\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.05; **\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.01; ***\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001. Denotations: Combined: participants living with and without HIV; IFN-γ: interferon gamma; IL-2: interleukin 2; PLWH: participants living with HIV; TNF-α: tumor necrosis factor alpha.\u003c/p\u003e","description":"","filename":"Figure3Final.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/e136cf5ca38ff371eb65b1f1.jpg"},{"id":96922725,"identity":"b3396b7d-bfa7-4ccf-ae2a-0d50b198ec28","added_by":"auto","created_at":"2025-11-27 14:19:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15650759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/9b4bec94-b4b8-4b66-bcf7-32ef54a359e6.pdf"},{"id":96789438,"identity":"3c5dde2b-db5a-405e-addd-4242b9a38703","added_by":"auto","created_at":"2025-11-26 06:43:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2261358,"visible":true,"origin":"","legend":"","description":"","filename":"AdultCovid19CMIStudySupplementaryInformation08.09.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/54f1e940c32be0b761a3a48a.docx"},{"id":96789441,"identity":"f2119c8a-5d6b-4476-b4be-504b38a05382","added_by":"auto","created_at":"2025-11-26 06:43:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":60996,"visible":true,"origin":"","legend":"","description":"","filename":"AdutlCovid19CMIStudySupplementaryInformation208.09.2025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7588105/v1/1926d715bf91ec6d6082e0b2.xlsx"}],"financialInterests":"Competing interest reported. Authors S.A.M., M.C.N. and G.K. report receiving grant support, paid to their institution, from the Bill \u0026 Melinda Gates Foundation. No competing interests were declared for the remaining authors.","formattedTitle":"T-cell responses to ancestral SARS-CoV-2 and Omicron in unvaccinated hospitalised adults living with and without HIV in South Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), disproportionately affected populations with advanced age and underlying comorbidities such as chronic lung disease, hypertension, diabetes, obesity, and people living with human immunodeficiency virus type 1 (PLWH; HIV-1) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. People with uncontrolled HIV infection, characterised by CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts\u0026thinsp;\u0026lt;\u0026thinsp;200 cells/\u0026micro;L and/or unsuppressed viraemia, have a higher risk of severe COVID-19, hospitalisation and death compared with virally suppressed PLWH and HIV-uninfected people [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough PLWH on effective antiretroviral therapy (ART) are less severely affected by COVID-19 and mount immune responses to COVID-19 vaccines comparable to those of HIV-uninfected individuals, the extent of their immune reconstitution remains highly variable [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite HIV-1 viral suppression by ART, chronic immune activation and inflammation may contribute to immune dysfunction in PLWH [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Lower absolute CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts in PLWH impairs cellular immunity, particularly CD4\u003csup\u003e+\u003c/sup\u003e T helper (Th) cell responses, increasing their susceptibility to respiratory infections, including SARS-CoV-2 [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. PLWH with suppressed viraemia generate SARS-CoV-2\u0026ndash;specific T-cell responses that are functionally comparable to those of HIV-uninfected individuals, including the production of key Th1 cytokines such as IFN-γ, IL-2, and TNF-α [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, individuals with advanced HIV disease exhibit undetectable or impaired CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses compared with either HIV-uninfected people or PLWH who are virally suppressed [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells represent a minor subset of circulating lymphocytes and have been implicated in various pathological conditions, including autoimmune diseases, cancer, and viral infections [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although DP T-cell proportions are often elevated during chronic viral infections \u0026ndash; particularly in advanced HIV \u0026ndash; their functional role in immune modulation and inflammation remains unclear [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some studies suggest that DP T cells differentiate from CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the thymus into effector memory and central memory phenotypes in response to chronic inflammation, whereas others propose a regulatory role bridging innate and adaptive immunity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, there is a paucity of studies describing their involvement in SARS-CoV-2 infection, especially in the context of HIV-1 co-infection.\u003c/p\u003e\u003cp\u003eSevere COVID-19 is characterised by lymphopenia and increased co-expression of HLA-DR (human leukocyte antigen \u0026ndash; DR isotype) and CD38 on CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, which are indicative of heightened T-lymphocyte activation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, PLWH with high HIV-1 viral loads and low CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts also have high levels of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cells expressing HLA-DR and CD38 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although effective ART reduces immune activation, PLWH typically retain higher levels of HLA-DR\u003csup\u003e+\u003c/sup\u003e and CD38\u003csup\u003e+\u003c/sup\u003e T cells compared with HIV-uninfected people. CD26 (dipeptidyl peptidase 4, DPP4), a host target of anti-diabetic medication and a T-cell activation antigen, has been associated with increased COVID-19 severity in high-risk populations [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Immune activation associates with shedding of CD26 from the cell surface, which occurs to a greater extent with HIV-1 progressive infection with accompanying high HLA-DR and CD38 expression, while PLWH with better HIV-1 control have higher CD26 expression and reduced expression of HLA-DR and CD38 on their T-cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. There is a paucity of studies on SARS-CoV-2\u0026ndash;specific T-cell responses in PLWH who are na\u0026iuml;ve to COVID-19 vaccination, which is of particular relevance in sub-Saharan Africa where two-thirds of PLWH live and COVID-19 vaccine coverage is low [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we characterised SARS-CoV-2\u0026ndash;specific T-cell responses and activation profiles in PLWH and HIV-uninfected adults one month after being hospitalised for COVID-19\u0026ndash;related illness. Participants had been infected during three COVID-19 waves when the circulating strains were: ancestral SARS-CoV-2 (Wuhan-Hu-1), Beta variant (B.1.351), or Delta variant (B.1.617.2). Furthermore, we also evaluated cross-reactive T-cell responses against the Omicron variant (BA.4/BA.5), which harbours multiple spike mutations associated with reduced sensitivity to neutralising antibodies induced by infection with earlier circulating variants [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e\u003cp\u003eA total of 21 HIV-uninfected participants (male: n\u0026thinsp;=\u0026thinsp;10, female: n\u0026thinsp;=\u0026thinsp;11) and 17 PLWH (male: n\u0026thinsp;=\u0026thinsp;7, female: n\u0026thinsp;=\u0026thinsp;10) were recruited, with median ages of 54 years (IQR: 45\u0026ndash;56) and 50 years (IQR: 44\u0026ndash;56), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants were hospitalised with severe respiratory infections and presented with respiratory symptoms of any duration, with or without systemic features, including those with suspected tuberculosis or gastroenteritis. The majority (39.5%, 15/38) had been infected during the third COVID-19 wave, when the Delta variant (B.1.617.2) predominated, followed by 31.6% (12/38) during the first wave (ancestral Wuhan-Hu-1 SARS-CoV-2), and 28.9% (11/38) during the second wave (Beta variant, B.1.351). The distribution of infections across variant waves was comparable between PLWH and HIV-uninfected participants. The median duration of hospitalisation was 8 days (IQR: 6\u0026ndash;12) for HIV-uninfected participants and 10 days (IQR: 7\u0026ndash;12) for PLWH. PBMCs were collected from participants between 14\u0026ndash;50 days post-positive SARS-CoV-2 NAAT diagnosis, after hospital discharge. HIV-uninfected participants had earlier PBMC isolation times (34 days, IQR: 30\u0026ndash;38) compared to PLWH (39 days, IQR: 34\u0026ndash;45; p\u0026thinsp;=\u0026thinsp;0.039). Other clinical characteristics can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of SARS-CoV-2\u0026ndash;infected participants, stratified by HIV status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIV-uninfected Participants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipants living with HIV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber tested\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, no. (%):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (45\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (44\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19 wave (variant) recruitment period, no. (%)\u003csup\u003ea\u003c/sup\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st wave (Wuhan-Hu-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2nd wave (Beta)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3rd wave (Delta)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSARS-CoV-2 N-gene NAAT CT-value, median (IQR)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.3 (31.2\u0026ndash;38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.3 (27.4\u0026ndash;36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBMC isolation post-positive SARS-CoV-2 NAAT (days), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (30\u0026ndash;38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (34\u0026ndash;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital duration (days), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (6\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities, no. (%):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTuberculosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Emphysema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpilepsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovid-19-related symptoms at recruitment, no. (%):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCough\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShortness of breath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSore throat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFever (\u0026ge;\u0026thinsp;37.5\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiarrhoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyalgia/Arthralgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalaise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiminished taste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiminished smell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeadache\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLethargy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoss of appetite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsolute CD4\u003csup\u003e+\u003c/sup\u003e T-cell count (cells/\u0026micro;L), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (156\u0026ndash;610)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T-cell count ranges, no. (%):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;200 cells/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;200\u0026ndash;750 cells/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;750 cells/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot recorded*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV-1 viral loads, no. (%)\u003csup\u003ec\u003c/sup\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower than detectable limit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;1000 RNA copies/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot recorded*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART regimens, no. (%):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026thinsp;+\u0026thinsp;2 NRTIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINSTI\u0026thinsp;+\u0026thinsp;2 NRTIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNNRTI\u0026thinsp;+\u0026thinsp;2 NRTIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot recorded*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage T cells, median (IQR)\u003csup\u003ed\u003c/sup\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eT cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.4 (42.9\u0026ndash;70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.9 (14.1\u0026ndash;50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.4 (20.7\u0026ndash;36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.5 (43.1\u0026ndash;75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive) T cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 (0.42\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37 (0.19\u0026ndash;0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T-cell ratio, median (IQR):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.09 (0.94\u0026ndash;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48 (0.19\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eRecruitment periods during three COVID-19 waves in South Africa, individually driven by Wuhan-Hu-1 (ancestral) SARS-CoV-2, the Beta variant (B.1.351), and the Delta variant (B.1.617.2).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe cycle threshold (CT) values for the SARS-CoV-2 N-gene.\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eHIV-1 viral loads as determined by the Cobas\u0026reg; 6800/8800 or Cobas\u0026reg; AmpliPrep/Cobas\u0026reg; TaqMan\u0026reg; HIV-1 Tests (Roche Diagnostics International Ltd, Rotkreuz, Switzerland) with linear detection ranges of 50\u0026ndash;10\u003csup\u003e7\u003c/sup\u003e RNA copies/mL (lower limit of detection: 50 RNA copies/mL) and 20\u0026ndash;10\u003csup\u003e7\u003c/sup\u003e RNA copies/mL (lower limit of detection: 20 RNA copies/mL), respectively.\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eThe percentage CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells as determined by flow cytometry staining after acquiring 100,000 live CD3\u003csup\u003e+\u003c/sup\u003e cells. Refer to Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 for additional information.\u003c/p\u003e\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eStatistical analyses using the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e-test between two unpaired groups. The \u003cem\u003eP\u003c/em\u003e-value represents direct comparisons between HIV-uninfected participants and participants living with HIV (PLWH).\u003c/p\u003e\u003cp\u003e*There are no recent medical records available with the variable\u0026rsquo;s exact information.\u003c/p\u003e\u003cp\u003eDenotations: ART, antiretroviral treatment; COPD, chronic obstructive pulmonary disease; CT, cycle threshold value; HIV-1, human immunodeficiency virus type 1; INSTI, integrase strand transfer inhibitor (Dolutegravir); IQR, interquartile range; N/A, not applicable; NAAT, nucleic acid amplification test; N-gene, nucleocapsid protein gene; NNRTI, nonnucleoside reverse transcriptase inhibitor (Efavirenz); NRTI, nucleoside or nucleotide reverse transcriptase inhibitor (Tenofovir and Lamivudine/Emtricitabine); PBMC, peripheral blood mononuclear cells; PI, protease inhibitor (Lopinavir/Ritonavir); RNA, ribonucleic acid; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong PLWH, the median CD4\u003csup\u003e+\u003c/sup\u003e T-cell count was 400 cells/\u0026micro;L (IQR: 156\u0026ndash;610), and nearly half (47%, 8/17) of PLWH had undetectable HIV-1 viral loads (\u0026lt;\u0026thinsp;50 RNA copies/mL), while 24% (4/17) had HIV-1 viral loads\u0026thinsp;\u0026ge;\u0026thinsp;1000 RNA copies/mL. Medical records for five participants lacked recent absolute CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts and HIV-1 viral load data. The majority (88%, 15/17) of PLWH were receiving fixed-dose combination ART. Overall, the CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T-cell ratio, determined by the proportion of live CD3\u003csup\u003e+\u003c/sup\u003e T cells expressing CD4 or CD8 (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2), was lower in PLWH compared with HIV-uninfected participants (0.48 vs. 2.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was due to a higher proportion of CD3\u003csup\u003e+\u003c/sup\u003e T cells with a CD4\u003csup\u003e+\u003c/sup\u003e phenotype in HIV-uninfected participants compared with PLWH (64.4% vs. 29.9%; p\u0026thinsp;=\u0026thinsp;0.002), and a higher proportion of CD3\u003csup\u003e+\u003c/sup\u003e T cells with a CD8\u003csup\u003e+\u003c/sup\u003e phenotype in PLWH compared with HIV-uninfected participants (60.5% vs. 29.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells did not differ by HIV status nor sex (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eT-cell immunity to ancestral SARS-CoV-2\u003c/h3\u003e\n\u003cp\u003eSARS-CoV-2\u0026ndash;specific T-cell responses were characterised based on the ability of T-lymphocytes to produce IFN-γ, IL-2, or TNF-α following PBMC stimulation with peptide antigens spanning the FLS and N proteins of ancestral SARS-CoV-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PLWH and HIV-uninfected participants mounted comparable CD4\u003csup\u003e+\u003c/sup\u003e T-cell response magnitudes to FLS (1.35% [IQR: 1.20\u0026ndash;2.00%] vs. 1.73% [IQR: 1.19\u0026ndash;2.20%]; p\u0026thinsp;=\u0026thinsp;0.255) and N (1.95% [IQR: 1.52\u0026ndash;2.15%] vs. 2.11% [IQR: 1.61\u0026ndash;2.66%]; p\u0026thinsp;=\u0026thinsp;0.636) proteins when adjusted for age, sex, SARS-CoV-2 variant, hypertension, diabetes, obesity, and the number of days between SARS-CoV-2 diagnosis and PBMC isolation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). CD8\u003csup\u003e+\u003c/sup\u003e T-cell response magnitudes were similarly comparable among PLWH and HIV-uninfected participants for the FLS (1.10% [IQR: 0.89\u0026ndash;1.55%] vs. 1.16% [IQR: 0.75\u0026ndash;1.80%]; p\u0026thinsp;=\u0026thinsp;0.729) and N (1.19% [IQR: 0.85\u0026ndash;1.57%] vs. 1.12% [IQR: 0.87\u0026ndash;1.46%]; p\u0026thinsp;=\u0026thinsp;0.522) proteins. All participants mounted detectable FLS- and N-specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses, except for one PLWH who lacked a detectable CD4\u003csup\u003e+\u003c/sup\u003e T-cell response. Fewer than 75% of participants mounted detectable DP T-cell responses to the FLS and N proteins of ancestral SARS-CoV-2. However, DP T-cell response magnitudes were comparable among PLWH and HIV-uninfected participants for both the FLS (0.66% [IQR: 0\u0026ndash;1.21%] vs. 0.14% [IQR: 0\u0026ndash;1.03%]; p\u0026thinsp;=\u0026thinsp;0.733) and N (0.40% [IQR: 0\u0026ndash;0.94%] vs. 1.53% [IQR: 0\u0026ndash;2.37%]; p\u0026thinsp;=\u0026thinsp;0.114) proteins. Additional comparisons of FLS-specific responses with N-specific responses within the three different T-cell subsets are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVarious combinations of T-lymphocyte cytokine production were investigated to assess potential differences in T-cell responses between participants living with and without HIV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec; Supplementary Fig. S3). SARS-CoV-2\u0026ndash;specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells among PLWH and HIV-uninfected participants were predominantly IL-2 monofunctional, with fewer cells producing IFN-γ or TNF-α. The highest proportion of polyfunctional T cells co-produced IL-2 and TNF-α. The overall FLS-specific polyfunctional cytokine production profiles were similar between participants living with and without HIV for CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e and DP T cells. A similar pattern was observed for N-specific polyfunctional cytokine production profiles of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells. However, DP T cells exhibited an increased N-specific polyfunctional cytokine production profile in PLWH compared to HIV-uninfected participants (p\u0026thinsp;=\u0026thinsp;0.039). Furthermore, within-group analyses revealed that FLS- and N-specific CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses were predominantly lower than CD4\u003csup\u003e+\u003c/sup\u003e T-cell responses among HIV-uninfected participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas in PLWH, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses were comparable (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Fig. S4).\u003c/p\u003e\u003cp\u003eA positive correlation was observed between FLS- and N-specific response magnitudes in both CD4\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.640; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD8\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.622; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) T cells, irrespective of HIV status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). While this correlation was present in both groups, it was strongest among PLWH for CD4\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.914; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD8\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.789; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) T cells. HIV-uninfected participants exhibited a moderate correlation between CD4\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.512; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) T cells and a weaker correlation in CD8\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.427; p\u0026thinsp;=\u0026thinsp;0.069) T cells. No significant correlation was observed for DP T cells (r\u0026thinsp;=\u0026thinsp;0.205; p\u0026thinsp;=\u0026thinsp;0.229). Since T-lymphocytes were predominantly monofunctional, we further assessed the correlation between FLS- and N-specific response magnitudes among single-cytokine-producing cells (i.e. cells that only produce IFN-γ, IL-2, or TNF-α; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Positive correlations were observed among single-cytokine-producing CD4\u003csup\u003e+\u003c/sup\u003e T cells for IFN-γ (r\u0026thinsp;=\u0026thinsp;0.722; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL-2 (r\u0026thinsp;=\u0026thinsp;0.641; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TNF-α (r\u0026thinsp;=\u0026thinsp;0.593; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar trends were observed in CD8\u003csup\u003e+\u003c/sup\u003e T cells for IFN-γ (r\u0026thinsp;=\u0026thinsp;0.674; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL-2 (r\u0026thinsp;=\u0026thinsp;0.471; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and TNF-α (r\u0026thinsp;=\u0026thinsp;0.505; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). While most correlations were maintained when stratifying by HIV status, the correlation between FLS- and N-specific TNF-α production was not sustained in HIV-uninfected participants for CD4\u003csup\u003e+\u003c/sup\u003e nor CD8\u003csup\u003e+\u003c/sup\u003e T cells. DP T cells exhibited a weak overall correlation for IFN-γ production between FLS- and N-specific responses (r\u0026thinsp;=\u0026thinsp;0.390; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); this correlation was lost when stratifying by HIV status.\u003c/p\u003e\n\u003ch3\u003eMarkers of T-cell activation\u003c/h3\u003e\n\u003cp\u003eT-cell activation was evaluated by assessing the extracellular expression of CD26 (DPP4) on CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and DP T cells in relation to the HLA-DR and CD38 activation markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Fig. S5). PLWH and HIV-uninfected participants expressed comparable levels of CD26 on their CD4\u003csup\u003e+\u003c/sup\u003e (73.4% [IQR: 57.3\u0026ndash;82.8%] vs. 81.5% [IQR: 75.6\u0026ndash;89.3%]; p\u0026thinsp;=\u0026thinsp;0.155), CD8\u003csup\u003e+\u003c/sup\u003e (21.2% [IQR: 5.5\u0026ndash;36.6%] vs. 27.9% [IQR: 20.7\u0026ndash;42.3%]; p\u0026thinsp;=\u0026thinsp;0.982), and DP (58.2% [IQR: 26.5\u0026ndash;75.2%] vs. 55.9% [IQR: 32.0\u0026ndash;72.7%]; p\u0026thinsp;=\u0026thinsp;0.759) T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among HIV-uninfected participants, the proportion of CD4\u003csup\u003e+\u003c/sup\u003e T cells expressing CD26 was higher than CD8\u003csup\u003e+\u003c/sup\u003e (81.5% vs. 27.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and DP (55.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) T cells. Similar results were observed in PLWH (73.4% CD4\u003csup\u003e+\u003c/sup\u003e T cells vs. 21.2% CD8\u003csup\u003e+\u003c/sup\u003e T cells; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and vs. 58.2% DP T cells; p\u0026thinsp;=\u0026thinsp;0.049). CD26-expressing CD8\u003csup\u003e+\u003c/sup\u003e T cells were, however, consistently lower than DP T cells among HIV-uninfected participants and PLWH. Overall, the proportions of CD26-expressing CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and DP T cells did not differ by HIV status in adjusted analysis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, among females living with HIV, CD4\u003csup\u003e+\u003c/sup\u003e T cells exhibited decreased CD26 expression (57.4% [IQR: 41.0\u0026ndash;73.4%]) compared with their male counterparts (83.4% [IQR: 79.5\u0026ndash;88.1%]; p\u0026thinsp;=\u0026thinsp;0.003) and HIV-uninfected females (84.5% [IQR: 76.2\u0026ndash;89.4%]; p\u0026thinsp;=\u0026thinsp;0.004; Supplementary Fig. S5a).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe proportion of activated but non-proliferating CD4\u003csup\u003e+\u003c/sup\u003e T cells (CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e) was similar between PLWH and HIV-uninfected participants (3.35% [IQR: 2.32\u0026ndash;12.3%] vs. 2.81% [IQR: 1.64\u0026ndash;5.44%]; p\u0026thinsp;=\u0026thinsp;0.536; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In contrast, PLWH had lower proportions of CD8\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e T cells compared with HIV-uninfected participants (12.9% [IQR: 7.78\u0026ndash;17.4%] vs. 15.0% [IQR: 8.32\u0026ndash;18.6%]; p\u0026thinsp;=\u0026thinsp;0.013). The proportion of highly activated and proliferating T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e), as well as the proportion of proliferating but less activated T cells (HLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e) was also similar between participants living with and without HIV. CD8\u003csup\u003e+\u003c/sup\u003e T cells consistently exhibited higher proportions of both HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e and HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e phenotypes than CD4\u003csup\u003e+\u003c/sup\u003e T cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), irrespective of HIV status. However, the proportion of CD8\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e T cells was consistently lower than that of CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e T cells in both PLWH and HIV-uninfected participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAn inverse relationship was observed between CD26 expression and both CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e (r=-0.588; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e T cells (r=-0.596; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These negative correlations remained consistent when stratified by HIV status. In contrast, CD26 expression did not correlate with CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e T cells, nor with any of the three activation states assessed in CD8\u003csup\u003e+\u003c/sup\u003e and DP T cells. Furthermore, in both PLWH and HIV-uninfected participants, there were no significant associations between T-cell response magnitudes and activation states (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Stimulation with FLS- and N-specific peptides had no measurable effect on CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell activation states (Supplementary Fig. S5b-d).\u003c/p\u003e\n\u003ch3\u003eT-cell cross-reactivity to Omicron (BA.4/BA.5)\u003c/h3\u003e\n\u003cp\u003eParticipants were enrolled during earlier waves of the COVID-19 pandemic, prior to the emergence of the Omicron variant, and were therefore presumed unexposed to Omicron. To assess T-cell cross-reactivity, we evaluated whether participants\u0026rsquo; T-lymphocytes could recognise and respond to spike mutations of the Omicron variant (BA.4/BA.5) compared to the ancestral virus, using partial spike-specific peptide antigens (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PLWH and HIV-uninfected participants mounted comparable Omicron-specific CD4\u003csup\u003e+\u003c/sup\u003e (1.85% [IQR: 1.35\u0026ndash;2.04%] vs. 1.74% [IQR: 1.27\u0026ndash;2.37%]; p\u0026thinsp;=\u0026thinsp;0.884), CD8\u003csup\u003e+\u003c/sup\u003e (1.08% [IQR: 0.57\u0026ndash;1.68%] vs. 1.10% [IQR: 0.93\u0026ndash;1.41%]; p\u0026thinsp;=\u0026thinsp;0.678), and DP (0.47% [IQR: 0\u0026ndash;2.66%] vs. 0.89% [IQR: 0.27\u0026ndash;1.71%]; p\u0026thinsp;=\u0026thinsp;0.373) T-cell response magnitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Ancestral-specific response magnitudes were similarly comparable among PLWH and HIV-uninfected participants for CD4\u003csup\u003e+\u003c/sup\u003e (1.50% [IQR: 1.27\u0026ndash;2.30%] vs. 1.90% [IQR: 1.24\u0026ndash;2.47%]; p\u0026thinsp;=\u0026thinsp;0.613), CD8\u003csup\u003e+\u003c/sup\u003e (1.31% [IQR: 0.77\u0026ndash;1.73%] vs. 1.14% [IQR: 0.79\u0026ndash;1.45%]; p\u0026thinsp;=\u0026thinsp;0.594), and DP (0.55% [IQR: 0\u0026ndash;1.48%] vs. 0.96% [IQR: 0.41\u0026ndash;2.20%]; p\u0026thinsp;=\u0026thinsp;0.809) T cells. Additional comparisons showed no differences in T-cell reactivity between ancestral and Omicron spike peptides in either HIV-uninfected participants or PLWH and are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe median fold change in spike-specific T-cell responses to Omicron relative to the ancestral virus was comparable between HIV-uninfected participants and PLWH for both CD4\u003csup\u003e+\u003c/sup\u003e (0.96 [IQR: 0.79\u0026ndash;1.05] vs. 0.99 [IQR: 0.78\u0026ndash;1.07]; p\u0026thinsp;=\u0026thinsp;0.894) and CD8\u003csup\u003e+\u003c/sup\u003e (1.01 [IQR: 0.94\u0026ndash;1.18] vs. 0.86 [IQR: 0.82\u0026ndash;1.10]; p\u0026thinsp;=\u0026thinsp;0.316) T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Median fold changes in both groups remained close to 1.0 and within the 0.5\u0026ndash;1.5 range. However, one HIV-uninfected participant exhibited an elevated CD4\u003csup\u003e+\u003c/sup\u003e T-cell response fold change of 2.52, suggesting a more than 2.5-fold increase in reactivity to Omicron compared to the ancestral virus. Polyfunctional profiling further demonstrated that CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells were predominantly monofunctional, with no significant differences in cytokine production between PLWH and HIV-uninfected participants, irrespective of ancestral- or Omicron-specific peptides (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Furthermore, correlation analysis revealed a positive relationship between partial spike responses to ancestral SARS-CoV-2 and the Omicron variant in both CD4\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.856; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD8\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.888; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee), which remained consistent upon stratification by HIV status. In contrast, DP T cells demonstrated only a weak correlation (r\u0026thinsp;=\u0026thinsp;0.396; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which persisted among HIV-uninfected participants (r\u0026thinsp;=\u0026thinsp;0.463; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not in PLWH (r\u0026thinsp;=\u0026thinsp;0.272; p\u0026thinsp;=\u0026thinsp;0.341).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that HIV-1 infection did not substantially alter SARS-CoV-2\u0026ndash;specific T-cell responses and activation profiles, which were largely comparable between ART-treated PLWH and HIV-uninfected black African adults in South Africa, all of whom were na\u0026iuml;ve to COVID-19 vaccination and had been hospitalised for COVID-19\u0026ndash;related illness. We observed that CD8\u003csup\u003e+\u003c/sup\u003e T cells exhibited greater activation (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e and HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e) than CD4\u003csup\u003e+\u003c/sup\u003e T cells, while CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e proportions exceeded those of their CD8\u003csup\u003e+\u003c/sup\u003e counterparts. Elevated CD26 (DPP4) expression was predominantly observed among CD4\u003csup\u003e+\u003c/sup\u003e T cells and inversely correlated with CD4\u003csup\u003e+\u003c/sup\u003e T-cell activation (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e and HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e). Furthermore, we demonstrated that SARS-CoV-2\u0026ndash;specific T cells in HIV-uninfected people and PLWH effectively cross-recognised the Omicron variant (BA.4/BA.5) and to a similar degree, suggesting preserved T-cell immunity following infection with earlier variants.\u003c/p\u003e\u003cp\u003eComparable incidence of SARS-CoV-2 infection has been reported among PLWH and HIV-uninfected people; however, caution is warranted when interpreting the independent effects of HIV infection on COVID-19 outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While effective ART improves clinical outcomes, immune reconstitution remains highly variable and is influenced by confounding factors such as age, sex, comorbidities, and socio-economic status [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although HIV-uninfected participants and PLWH in our study exhibited minimal variability in their clinical and demographic characteristics \u0026ndash; except for their T-cell counts and PBMC isolation days post-positive diagnosis \u0026ndash; we adjusted the analyses for confounders (i.e. age, sex, SARS-CoV-2 variant, comorbidities, and PBMC isolation days post-positive diagnosis). This relatively homogenous setting strengthens the internal validity of our findings by reducing confounding factors that could have influenced the observed T-cell responses.\u003c/p\u003e\u003cp\u003eThe CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T-cell ratio serves as a key biomarker for assessing immune competence, often providing a more accurate indication of immune dysfunction than absolute CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts and HIV-1 viral loads [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In our study, PLWH exhibited a CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T-cell ratio lower than 1.0, a threshold generally associated with adverse clinical outcomes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This imbalance is a consequence of chronic HIV-1\u0026ndash;induced immune activation, which drives persistent CD8\u003csup\u003e+\u003c/sup\u003e T-cell expansion and CD4\u003csup\u003e+\u003c/sup\u003e T-cell depletion, even in participants receiving ART. However, immune reconstitution remains incomplete in up to 30% of ART-treated PLWH [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which may impair the development of functional SARS-CoV-2\u0026ndash;specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. SARS-CoV-2\u0026ndash;specific T-cell responses typically peak between 14\u0026ndash;30 days post-diagnosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To maximise sample size, we extended this window to include participants with PBMC samples collected one month following hospital discharge (between 14\u0026ndash;50 days post-positive diagnosis), aligning with a previous study by our group investigating T-cell immunity in pregnant and postpartum women living with and without HIV [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the lower CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T-cell ratio, our findings demonstrate that most participants mounted strong SARS-CoV-2\u0026ndash;specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses against the FLS and N proteins of ancestral SARS-CoV-2, which were comparable between PLWH and HIV-uninfected participants. Only one PLWH lacked detectable CD4\u003csup\u003e+\u003c/sup\u003e T-cell responses to both proteins. The magnitude, functionality, and correlation of FLS- and N-specific T-cell responses were largely comparable between HIV-uninfected participants and PLWH, aligning with previous studies showing that ART-treated PLWH with suppressed viraemia maintain SARS-CoV-2\u0026ndash;specific T-cell immunity comparable to those of HIV-uninfected people [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. N-specific T-cell responses consistently exceeded FLS-specific responses, suggesting that SARS-CoV-2 infection leading to severe disease may preferentially drive more robust and durable N-specific immunity. In line with previous research, we also observed that CD4\u003csup\u003e+\u003c/sup\u003e T-cell responses were mostly higher than CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses in HIV-uninfected participants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, PLWH exhibited comparable magnitudes of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses, diverging from our previous findings among pregnant women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], possibly reflecting the combined effects of COVID-19 severity and chronic HIV-associated immune activation on T-cell responses.\u003c/p\u003e\u003cp\u003e In our study, both HIV-uninfected participants and PLWH exhibited comparable DP T-cell proportions, as well as similar response magnitudes and functionality profiles. Furthermore, while moderate to strong correlations were observed between FLS- and N-specific CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses, these associations were absent \u0026ndash; or even inversely related \u0026ndash; when assessing single-cytokine production by DP T cells. This suggests that DP T cells may be subject to distinct regulatory mechanisms during SARS-CoV-2 infection. Given their emerging role in chronic immune activation and regulation, further studies are needed to delineate whether DP T cells contribute to protective immunity or are indicative of immune dysfunction in the context of SARS-CoV-2 and HIV co-infection.\u003c/p\u003e\u003cp\u003eCD26 (DPP4) is a multifunctional glycoprotein expressed on various cell types, including T cells, and has been suggested as a potential biomarker for severe COVID-19, particularly in elderly individuals and those with high-risk comorbidities, such as diabetes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While its role in T-cell immunity during SARS-CoV-2 infection in PLWH remains poorly characterised, CD26 expression has been linked to immune regulation and HIV control [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CD26 is highly expressed on CD4\u003csup\u003e+\u003c/sup\u003e T cells compared to CD8\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, CD4\u003csup\u003e+\u003c/sup\u003e T cells exhibited higher CD26 expression compared to CD8\u003csup\u003e+\u003c/sup\u003e and to DP T cells, with no differences observed between HIV-uninfected participants and PLWH. Furthermore, we identified a strong inverse correlation between CD26 expression and both the CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e T-cell activation phenotypes, irrespective of HIV status. Since chronic immune activation is a hallmark of HIV infection and can contribute to immune dysfunction, further investigation is needed to determine whether CD26 may play a protective role in modulating SARS-CoV-2\u0026ndash;specific T-cell responses in PLWH.\u003c/p\u003e\u003cp\u003eHLA-DR is a major histocompatibility complex (MHC) class II cell surface receptor involved in the immune system\u0026rsquo;s ability to recognise foreign molecules and is crucial for presenting antigens to CD4\u003csup\u003e+\u003c/sup\u003e T-cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. CD38 is a type II transmembrane glycoprotein and its expression on CD8\u003csup\u003e+\u003c/sup\u003e T cells correlates with HIV-1 viral loads and disease progression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Both HLA-DR and CD38 are associated with immune activation and have been shown to be elevated in PLWH indicating chronic immune activation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. During SARS-CoV-2 infection, these markers are further heightened, particularly in severe cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In general, we found no differences in CD4\u003csup\u003e+\u003c/sup\u003e or CD8\u003csup\u003e+\u003c/sup\u003e T-cell activation phenotypes (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e, HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, and HLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e) between HIV-uninfected participants and PLWH, suggesting that immune activation was driven by SARS-CoV-2 infection in both groups rather than being solely attributable to HIV infection. This is particularly relevant given that PLWH typically exhibit elevated activation markers despite effective ART. The only exception was a decreased proportion of CD8\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e T cells in PLWH compared with HIV-uninfected participants.\u003c/p\u003e\u003cp\u003eWe additionally observed that CD8\u003csup\u003e+\u003c/sup\u003e T cells were more activated than CD4\u003csup\u003e+\u003c/sup\u003e T cells, characterised by increased proportions of activated but non-proliferating (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e) phenotypes, as well as highly activated and proliferating (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e) phenotypes, consistent with other studies reporting heightened T-cell dysfunction in severe COVID-19 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast, CD4\u003csup\u003e+\u003c/sup\u003e T cells exhibited increased proportions of proliferating, but less activated (HLA-DR\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e) phenotypes compared with CD8\u003csup\u003e+\u003c/sup\u003e T cells. Activation markers did not correlate with SARS-CoV-2\u0026ndash;specific T-cell response magnitudes to the FLS and N proteins of the ancestral virus, nor did it change following antigen stimulation. This may be due to the kinetics of activation marker modulation, as significant alterations are typically observed 24\u0026ndash;72 hours post-stimulation rather than within the 16-hour stimulation period used in this study [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Further longitudinal investigations are warranted to better characterise the dynamics of T-cell activation over time, particularly in the context of HIV and SARS-CoV-2 co-infection.\u003c/p\u003e\u003cp\u003eLastly, we demonstrated that CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and DP T-cell responses not only cross-react with Omicron but also exhibit comparable magnitudes and functionality profiles to those induced by ancestral SARS-CoV-2, irrespective of HIV status or the specific SARS-CoV-2 variant that caused infection. These findings align with previous studies showing that SARS-CoV-2\u0026ndash;specific T-cell immunity remains largely preserved across diverse populations, despite spike-specific mutations in Beta, Delta, and Omicron variants that can facilitate immune evasion by disrupting MHC class I and II epitope presentation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While our analysis only focussed on partial spike protein regions containing Omicron-specific mutations and their corresponding ancestral counterparts, a more comprehensive characterisation of T-cell responses targeting other immunodominant epitopes across the entire viral proteome would have provided a broader assessment of T-cell immunity and its overall preservation across SARS-CoV-2 variants.\u003c/p\u003e\u003cp\u003eOur study had several limitations. First, the availability of PBMC samples was restricted to a single time point, limiting the power of our analyses and preventing us from assessing the kinetics of SARS-CoV-2\u0026ndash;specific T-cell responses over time. The absence of longitudinal samples for these participants precluded analyses of long-term immunity, including memory responses and the potential interplay between SARS-CoV-2 and HIV during immune recovery. Second, we lacked recent medical records for some PLWH, preventing confirmation of absolute CD4\u003csup\u003e+\u003c/sup\u003e T-cell counts and HIV-1 viral loads at the time of enrolment, which may introduce variability in immune response interpretations. Third, we did not include a non-hospitalised control group, which could have provided a comparative baseline to better contextualise T-cell responses and activation profiles observed in our cohort. A fixed threshold of 0.02% was applied to define positive T-cell responders, chosen to balance sensitivity in the absence of standardised guidelines. Although not empirically derived or externally validated, this approach ensured analytical consistency. Finally, our manuscript only focuses on T-cell immunity. While our flow cytometry panel included extracellular markers to assess SARS-CoV-2\u0026ndash;specific innate immune responses from monocytes (i.e. CD14 and CD16) and natural killer cells (i.e. CD16 and CD56), these findings will be reported separately.\u003c/p\u003e\u003cp\u003eIn conclusion, our study provides evidence that SARS-CoV-2\u0026ndash;specific T-cell immunity is comparable among unvaccinated, hospitalised black African adults living with and without HIV. Despite chronic immune activation associated with HIV, we observed comparable SARS-CoV-2\u0026ndash;specific T-cell responses in PLWH on ART, further strengthening the understanding of cell-mediated immunity\u0026rsquo;s ability to contribute to protection against severe COVID-19. These findings are particularly important in high HIV-burdened settings, where disruptions in vaccine access, immunological heterogeneity, and the ongoing risk of emerging SARS-CoV-2 variants pose substantial public health challenges. Future research should focus on the durability and long-term quality of T-cell immunity to inform immunisation strategies and pandemic preparedness efforts in vulnerable populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eStudy participants\u003c/h2\u003e\u003cp\u003e Hospitalised adults (\u0026ge;\u0026thinsp;18 years) admitted with severe respiratory infections at Chris Hani Baragwanath Academic Hospital (CHBAH) and Bheki Mlangeni District Hospital (BMDH) in Johannesburg, South Africa, were invited to participate in this study. CHBAH and BMDH are located in Soweto, a densely populated, low-middle\u0026ndash;income urban settlement (or \u0026lsquo;township\u0026rsquo;) with a predominantly black African population and diverse socio-economic backgrounds [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. SARS-CoV-2 infection was screened using a nucleic acid amplification test (NAAT), as previously described [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Participants with confirmed SARS-CoV-2 infection were requested to return to the study clinic for a follow-up visit approximately one month after hospital discharge, at which time venous blood was collected, peripheral blood mononuclear cells (PBMCs) were isolated and stored at the Vaccines and Infectious Diseases Analytics (VIDA) Research Unit located at CHBAH.\u003c/p\u003e\u003cp\u003eEnrolment occurred during three periods: 1 April to 31 October 2020; 1 November 2020 to 30 April 2021; and 1 May to 30 November 2021, corresponding to South Africa\u0026rsquo;s first, second, and third COVID-19 waves, driven by the ancestral (Wuhan-Hu-1) SARS-CoV-2, Beta variant (B.1.351), and Delta variant (B.1.617.2), respectively. The infecting SARS-CoV-2 variant was inferred based on the timing of infection and not confirmed by viral genome sequencing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Clinical data were collected and managed electronically as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. All participants provided written informed consent to participate and for the anonymised publication of their results.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003ePeripheral blood mononuclear cell processing\u003c/h3\u003e\n\u003cp\u003ePBMCs were isolated, cryopreserved, and thawed as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, PBMCs with a viability of \u0026ge;\u0026thinsp;70% were rested for 4 h at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e in R10 medium before antigen stimulation. All PBMCs were isolated after hospital discharge.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSARS-CoV-2 peptide antigens\u003c/h2\u003e\u003cp\u003ePeptide pools (Miltenyi Biotec, Bergisch Gladbach, Germany) used for the \u003cem\u003ein vitro\u003c/em\u003e stimulation of SARS-CoV-2\u0026ndash;specific T cells were prepared as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Briefly, PepTivator\u0026reg; peptide pools Prot_S1 and Prot_S were combined to represent the near full-length spike (FLS) glycoprotein of ancestral SARS-CoV-2. Prot_N was included to represent the complete nucleocapsid (N) protein of ancestral SARS-CoV-2, and Prot_S B.1.1.529/BA.5 was used to represent the Omicron variant. A corresponding ancestral reference pool was included as a control for the variant pool.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell stimulation and immunofluorescent staining\u003c/h2\u003e\u003cp\u003eCell stimulation and immunofluorescent staining procedures were performed as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], with minor modifications. Briefly, additional extracellular markers were included: CD14 APC (1:10, clone M5E2; BD Pharmingen\u0026trade;, BD Biosciences, San Jose, California, USA), CD16 APC-Cy7 (1:67, clone 3G8; BD Pharmingen\u0026trade;), CD26 PE (1:100, clone BA5b; BioLegend, San Diego, California, USA), CD38 BV421 (1:40, clone S17015A; BioLegend), CD56 BV650 (1:100, clone NCAM16.2; BD Horizon\u0026trade;), and HLA-DR FITC (1:20, clone L243; BD Biosciences). Furthermore, fixed and permeabilized cells were stained with CD3 PerCP (1:40, clone SK7; BD Biosciences).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry\u003c/h2\u003e\u003cp\u003eFlow cytometry was performed as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], with data acquired on a 4-laser BD LSRFortessa\u0026trade; X-20 flow cytometer (BD Biosciences) and analysed using FlowJo\u0026trade; software (v.10.10.0; FlowJo LLC., Ashland, Oregon, USA). In addition to standard gating [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], this study incorporated gating for CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T cells, and assessment of activation marker expression (CD26, CD38, and HLA-DR). A representative gating strategy is provided in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Functionality data are presented as percentages after background subtraction from unstimulated controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], with a few additions. Briefly, a fixed threshold of 0.02% was applied to define positive CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell responses through the individual or combined production of any of the cytokines (IFN-γ, IL-2, or TNF-α) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Participants with T-cell responses below the 0.02% threshold were classified as non-responders and were assigned a value of 0.015% in log-scaled figures solely for visualisation purposes. T-cell responses and activation statuses (CD26, HLA-DR, and CD38) were compared between PLWH and HIV-uninfected participants using multivariate log-linear regression, adjusted for age, sex, SARS-CoV-2 variant, hypertension, diabetes, obesity, and number of days between SARS-CoV-2 NAAT diagnosis and PBMC isolation.\u003c/p\u003e\u003cp\u003eThe Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e-test and the Wilcoxon signed-rank test were used for comparisons between unpaired and paired groups, respectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Pearson\u0026rsquo;s chi-squared test was used for comparing proportions. Spearman\u0026rsquo;s rank correlation coefficient (two-sided; α\u0026thinsp;=\u0026thinsp;0.05) was used to describe the linear relationship between two continuous variables (e.g. total FLS and N responses) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Polyfunctional T-cell response differences were determined by a permutation test with 10,000 iterations using SPICE (v.6.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://niaid.github.io/spice/\u003c/span\u003e\u003cspan address=\"https://niaid.github.io/spice/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Vaccine Research Center, NIAID, NIH, Bethesda, Maryland, USA) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. All other statistical analyses and graphical representations were performed using STATA\u0026reg; (v.18.5; StataCorp LLC, College Station, Texas, USA) and GraphPad Prism\u0026reg; (v.10.4.1; GraphPad Software Inc., San Diego, California, USA), respectively. \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eAuthors S.A.M., M.C.N. and G.K. report receiving grant support, paid to their institution, from the Bill \u0026amp; Melinda Gates Foundation. No competing interests were declared for the remaining authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics declaration\u003c/h2\u003e\u003cp\u003e This is a sub-study of a parent study, entitled \u0026ldquo;Sentinel, hospital-based surveillance for the investigation of SARS-Coronavirus-2 and other respiratory pathogens\u0026rdquo; which was performed in line with the principles of the Declaration of Helsinki. The parent study was approved by the Human Research Ethics Committee of the University of the Witwatersrand (Reference number: 200313), as well as this sub-study (Reference number: M220285).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003cp\u003eThe authors acknowledge that the opinions, findings, and conclusions expressed in this manuscript are that of the authors alone, and that the National Research Foundation of South Africa accepts no liability whatsoever in this regard.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the Bill \u0026amp; Melinda Gates Foundation [grant number INV-016202] and the South African Medical Research Council [grant number SHIP NCD 96756]. Author W.C.M. received grants from the Poliomyelitis Research Foundation [grant number 22/82] and the National Research Foundation of South Africa [grant number PMDS2205067384] in support of this study\u0026rsquo;s research. Author C.T.T. is funded in part through the South African Chairs Initiative of the Department of Science and Innovation/National Research Foundation of South Africa [grant number 84177].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.C.M. prepared this manuscript. M.C.N., N.S., and F.L. enrolled participants, as well as collected clinical data and samples. W.C.M. and members of the Wits VIDA COVID team processed PBMC samples. W.C.M. generated data by performing all other associated experiments. W.C.M. and A.I. analysed the data by performing statistical analyses. W.C.M., G.K., A.I., N.S., F.L., S.S., C.T.T., S.A.M., and M.C.N. interpreted results. All authors had full access to this study\u0026rsquo;s data and had final responsibility for the decision to submit for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Centre for Vaccines and Immunology, as well as the Centre for HIV and STIs at the National Institute for Communicable Diseases, a division of the National Health Laboratory Services (NHLS), for the use of their laboratory facilities and flow cytometry equipment. We also thank all study participants, their clinicians, and collaborating members of the Wits VIDA COVID team.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSource data for main figures (Fig. 1-3) and for the supplementary figures (Fig. S2-S6) are provided in Supplementary Information 2. Anonymised participant-level data and raw data generated during the study will be made available upon reasonable request directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRussell, C. D., Lone, N. I. \u0026amp; Baillie, J. K. Comorbidities, multimorbidity and COVID-19. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 334\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-022-02156-9\u003c/span\u003e\u003cspan address=\"10.1038/s41591-022-02156-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertagnolio, S. et al. 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SPICE: exploration and analysis of post-cytometric complex multivariate datasets. \u003cem\u003eCytometry A\u003c/em\u003e. \u003cb\u003e79\u003c/b\u003e, 167\u0026ndash;174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.21015\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.21015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV and COVID-19, Omicron cross-reactivity, SARS-CoV-2 T-cell immunity, T-cell activation (HLA-DR, CD38, and CD26)","lastPublishedDoi":"10.21203/rs.3.rs-7588105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7588105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHIV-associated immune dysfunction may impact SARS-CoV-2\u0026ndash;specific T-cell responses, yet data in COVID-19\u0026ndash;unvaccinated people living with HIV (PLWH) remain limited. We evaluated virus-specific T-cell responses one month after COVID-19\u0026ndash;related hospitalisation in antiretroviral-treated PLWH and HIV-uninfected adults recovering from ancestral (Wuhan-Hu-1), Beta (B.1.351), or Delta (B.1.617.2) variant infection. Flow cytometry assessed the magnitude, polyfunctionality, and activation (HLA-DR, CD38, and CD26) of CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (double positive, DP) T-cell subsets, as well as cross-reactivity to Omicron (BA.4/BA.5). Seventeen PLWH and 21 HIV-uninfected black African adults were enrolled. SARS-CoV-2\u0026ndash;specific CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and DP T-cell response magnitudes, responder frequencies, and cytokine production profiles (IFN-γ, IL-2, and TNF-α) were comparable between groups. Spike- and nucleocapsid-specific responses correlated strongly in PLWH (CD4\u003csup\u003e+\u003c/sup\u003e: r\u0026thinsp;=\u0026thinsp;0.914, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CD8\u003csup\u003e+\u003c/sup\u003e: r\u0026thinsp;=\u0026thinsp;0.789; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas correlations were weaker in HIV-uninfected participants (CD4\u003csup\u003e+\u003c/sup\u003e: r\u0026thinsp;=\u0026thinsp;0.512, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; CD8\u003csup\u003e+\u003c/sup\u003e: r\u0026thinsp;=\u0026thinsp;0.427; p\u0026thinsp;=\u0026thinsp;0.069). CD26 expression and most activation phenotypes (HLA-DR/CD38 subsets) did not differ by HIV status, though PLWH had fewer CD8\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e-\u003c/sup\u003e T cells (adjusted p\u0026thinsp;=\u0026thinsp;0.013). Both groups demonstrated cross-recognition of Omicron, irrespective of the infecting SARS-CoV-2 variant. Our results demonstrate comparable SARS-CoV-2\u0026ndash;specific T-cell responses and activation profiles between PLWH on antiretroviral therapy and HIV-uninfected adults, with preserved cross-reactive T-cell responses to Omicron.\u003c/p\u003e","manuscriptTitle":"T-cell responses to ancestral SARS-CoV-2 and Omicron in unvaccinated hospitalised adults living with and without HIV in South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:43:41","doi":"10.21203/rs.3.rs-7588105/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T13:29:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7172387074616362946568042222606615523","date":"2026-04-07T09:36:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31702799256650998804542745450793667442","date":"2026-02-06T12:22:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213737284982560479214637605415863300462","date":"2025-12-01T13:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-13T13:48:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T05:15:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T07:45:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-22T07:38:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74b414bc-f1ba-4597-ba43-5194190ca65b","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58456907,"name":"Health sciences/Diseases"},{"id":58456908,"name":"Biological sciences/Immunology"},{"id":58456909,"name":"Health sciences/Medical research"},{"id":58456910,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-11-26T06:43:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:43:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7588105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7588105","identity":"rs-7588105","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}