
==== Front
J Infect Dis
J Infect Dis
jid
The Journal of Infectious Diseases
0022-1899
1537-6613
Oxford University Press US

38412342
10.1093/infdis/jiae042
jiae042
Major Article
Bacteria
AcademicSubjects/MED00290
Uncovering the Bronchoalveolar Single-Cell Landscape of Patients With Pulmonary Tuberculosis With Human Immunodeficiency Virus Type 1 Coinfection
Xiao Guohui National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

Huang Waidong BGI Research, Shenzhen
College of Life Sciences, University of Chinese Academy of Sciences, Beijing

Zhong Yu BGI Research, Shenzhen

Ou Min National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

Ye Taosheng National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

Wang Zhifeng BGI Research, Shenzhen

Zou Xuanxuan BGI Research, Shenzhen
College of Life Sciences, University of Chinese Academy of Sciences, Beijing

Ding Feng National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

Yang Yuan BGI Research, Shenzhen

Zhang Zhe MGI Tech, Shenzhen

Liu Chuanyu BGI Research, Shenzhen
BGI Research, Hangzhou

Liu Aimei Department of Tuberculosis, Guangxi Chest Hospital, Liuzhou

Liu Longqi BGI Research, Shenzhen
BGI Research, Hangzhou

Lu Shuihua National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

Wu Liang BGI Research, Shenzhen
BGI Research, Chongqing, China

Zhang Guoliang National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen

G. X., W. H., Y. Z., and M. O. contributed equally to this work.

Correspondence: Guoliang Zhang, PhD, National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital, Southern University of Science and Technology, Bulan Road, Shenzhen 518112, China (szdsyy@aliyun.com); Liang Wu, PhD, BGI Research, Beishan Road, Shenzhen 518083, China (wuliang@genomics.cn).
Potential conflicts of interest. All authors: No reported conflicts.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.
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https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Coinfection of human immunodeficiency virus type 1 (HIV-1) is the most significant risk factor for tuberculosis (TB). The immune responses of the lung are essential to restrict the growth of Mycobacterium tuberculosis and avoid the emergence of the disease. Nevertheless, there is still limited knowledge about the local immune response in people with HIV-1–TB coinfection.

Methods

We employed single-cell RNA sequencing (scRNA-seq) on bronchoalveolar lavage fluid from 9 individuals with HIV-1–TB coinfection and 10 with pulmonary TB.

Results

A total of 19 058 cells were grouped into 4 major cell types: myeloid cells, T/natural killer (NK) cells, B cells, and epithelial cells. The myeloid cells and T/NK cells were further divided into 10 and 11 subsets, respectively. The proportions of dendritic cell subsets, CD4+ T cells, and NK cells were lower in the HIV-1–TB coinfection group compared to the TB group, while the frequency of CD8+ T cells was higher. Additionally, we identified numerous differentially expressed genes between the CD4+ and CD8+ T-cell subsets between the 2 groups.

Conclusions

HIV-1 infection not only affects the abundance of immune cells in the lungs but also alters their functions in patients with pulmonary TB.

We utilized single-cell RNA sequencing technology to provide a detailed analysis of immune cells in the lungs of patients with tuberculosis (TB) or with HIV–TB coinfection. Our findings revealed notable variations in immune response between these 2 disease types.

single-cell RNA sequencing
pulmonary TB
HIV-1
bronchoalveolar lavage fluid
local immunity
National Key Research and Development Plan 2021YFA1300902 National Natural Science Foundation of China 10.13039/501100001809 82170009 Guangdong Science Fund for Distinguished Young Scholars 440300231181537705092 State Key Laboratory of Respiratory Diseases Open Project SKLRD-OP-202324 Shenzhen Scientific and Technological Foundation 10.13039/501100019341 KCXFZ20211020163545004 RCJC20221008092726022 LCYX20220620105200001 Guangxi Scientific and Technological Foundation AA22096027 China National GeneBank Shenzhen Key Laboratory of Single-Cell Omics ZDSYS20190902093613831
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pmcIndividuals with human immunodeficiency virus (HIV) are more susceptible to developing active tuberculosis (TB) from either a new exposure to Mycobacterium tuberculosis (Mtb) or a latent Mtb infection than those who are HIV negative [1]. It is well known that CD4+ T cells are the primary target of HIV type 1 (HIV-1), and macrophages can also be infected by HIV-1 [2]. Both cell types are thought to be essential for the body’s defense against Mtb infection [3]. People infected with HIV-1 have a significantly lower absolute CD4+ T-cell count, which is strongly linked to an increased risk of TB [4]. The depletion of CD4+ T cells caused by HIV-1 infection has been linked to poor granuloma formation and a higher bacterial load [5]. HIV-1 infection has been reported to inhibit macrophage apoptosis in response to Mtb infection, potentially through HIV-1 Nef inhibition of tumor necrosis factor alpha (TNF-α) production [6, 7]. Since macrophage apoptosis is a key part of the innate immune response to inhibit intracellular Mtb growth, HIV-1 infection is thought to facilitate Mtb infection. Conversely, Mtb infection may result in increased HIV-1 viral loads in the blood and the site of coinfection, which can worsen HIV-1 disease progression [8–11].

Despite great achievement in study of the immunopathology of TB or HIV-1–TB coinfection, the exact changes in immune-cell subsets and the involvement of unidentified cell types in the immune response to Mtb or HIV-1 remain largely unknown. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying the response of host immune cells to infection, including the characterization of novel cell subsets, cell heterogeneity, and virus–host interactions [12]. Owing to challenges in sample collection, most studies of host immunity against infection have mainly focused on systemic immune responses in peripheral blood rather than local immune responses in the lungs. Host immune response in the lungs is essential in containing the growth of Mtb and preventing the progression of the disease. A comprehensive analysis of the pulmonary immune system is critical for gaining a better understanding of the pathogenesis of HIV-1–TB coinfection.

By using scRNA-seq, we obtained a comprehensive view of the immunological response in bronchoalveolar lavage fluid (BALF) from people with HIV-1–TB coinfection and people with TB. Our research depicted a detailed transcriptomic profile of bronchoalveolar immune cells and highlighted the significantly different immune responses between individuals with HIV-1–TB and those with TB, which will help us to comprehend the protective and pathogenic immune responses of the diseases more effectively.

METHODS

Ethics Approval

All BALF samples were collected as part of routine diagnostics. This study was conducted with the approval of the Shenzhen Third People's Hospital Ethical Committee (Reference No. 2021–016), and informed consent was obtained from each participant. All the methods used in this study were in compliance with the applicable guidelines and regulations.

Human Subjects and Sample Collection

Bronchoscopy was performed on people with suspected TB to diagnose TB. To ensure the quality of single-cell sequencing, BALF samples were immediately processed after collection (see detailed processing in the Supplementary Methods). People with active TB were diagnosed based on clinical symptoms and radiological signs as described previously [13], and had positive Mtb results from culture or GeneXpert. HIV-1 infection was diagnosed based on HIV-1 RNA levels. Once the diagnosis results became available, only BALF samples from patients with confirmed TB or HIV-1–TB were further subjected to construct single-cell libraries and sequencing. Therefore, it should be note that the number of collected BALF samples was much larger than the number ultimately presented in the study. Finally, a total of 10 participants with active TB alone and 9 participants with HIV-1–TB coinfection were recruited for this study. Supplementary Table 1 contains the detailed characteristics of participants. The methods for scRNA-seq, data processing, and bioinformatic analysis are detailed in the Supplementary Methods.

Supplementary Material

jiae042_Supplementary_Data

Data Availability

The data that support the findings of this study have been deposited into the CNGB Sequence Archive [14] of the China National GeneBank DataBase [15] with accession number CNP0004445.

RESULTS

Single-Cell Atlas of BALF in TB and HIV-1–TB Coinfection

Ten people with pulmonary TB (6 male, 4 female) and 9 with HIV-1–TB coinfection (6 male, 3 female) were recruited for scRNA-seq (Figure 1). None of the participants had received anti-TB treatment or antiretroviral treatment before sampling. The mean age of participants in the HIV-1–TB and TB groups was 44 and 43.2 years, respectively. The clinical data of all participants can be found in Supplementary Table 1. Cells were isolated from the collected BALF samples and subjected to scRNA-seq analysis. After quality control, a total of 19 058 cells were included for downstream analysis (Supplementary Table 2). Four major cell types were identified based on the expression levels of canonical marker genes without any patient bias. These cell types included T/natural killer (NK) cells (CD3D+CD3E+NKG7+), B cells (CD79A+CD79B+MS4A1), myeloid cells (CD68+LYZ+), and epithelial cells (SCGB1A1+EPCAM+KRT17+) (Figure 2A and 2B, Supplementary Figure 1A and 1B). In addition to the canonical cell-type marker genes, we also identified numerous highly expressed gene markers specific to each cell type as shown in the heatmap (Figure 2C).

Figure 1. Overview of the study design and experimental workflow.

After analyzing the percentage of each cell type in total cells, we found that T/NK cells had the highest average of 55%, followed by myeloid cells with an average of 29%, and B cells with an average of 3.5% (Figure 2D). Additionally, the TB and HIV-1–TB groups had similar frequencies of T/NK cells and myeloid cells (Figure 2D). However, the frequency of B cells was significantly higher in the TB group than in the HIV-1–TB group (Figure 2D). It is worth mentioning that B cells were rare in participants with HIV-1–TB coinfection, accounting for only an average of 1.7% of total cells.

Ten Myeloid Cell Types Identified in BALF Samples Using scRNA-seq

The myeloid cells were reclassified into 10 subsets based on their distinctive gene expression profiles (Figure 3A). Among these clusters, 6 were identified as macrophages due to their high expression of the macrophage-associated gene CD68 (Figure 3B and 3C, Supplementary Figure 2A and 2B). These macrophages were defined as FABP4+ MΦ subset, FCN1+ MΦ subset, SCGB3A1+ MΦ subset, SPP1+ MΦ subset, FTH1P10+ MΦ subset, and SELENOP+ MΦ subset based on the high expression characteristics of gene markers (Figure 3B, Supplementary Figure 2A and 2B). These highly expressed gene signatures are highly correlated with macrophage function. For instance, the FABP4+ MΦ population expressed high levels of FABP4 and MARCO, along with APOC1 and CD163, indicating its resemblance to M2-like macrophages (Figure 3B). The FCN1+ MΦ subset was marked by high expression of FCN1, CD300E, TIMP1, and APOBEC3A (Figure 3B) and also expressed high levels of chemokines such as CXCL10 and CXCL11 (Figure 3C). The SCGB3A1+ MΦ population expressed high levels of secretoglobin SCGB3A1, SCGB1A1, and LCN2 (Figure 3B). The SPP1+ MΦ population had a high expression of SPP1, along with high expression of genes encoding proteases including LGMN, TMIGD3, and HAMP (Figure 3B). Notably, the SPP1+ MΦ subset also showed high expression of CD163, indicating an anti-inflammatory phenotype (Figure 3B). The FTH1P10+ MΦ population showed similarities to the FABP4+ MΦ subset, as they both exhibited high expression of FABP4 and MARCO (Figure 3B and 3C). Additionally, the FTH1P10+ MΦ population showed high expression of FTLP3, CD52, and FTH1P8 (Figure 3B). The SELENOP+ MΦ population was specifically enriched for high expression of SELENOP, SLC40A1, and C1QC (Figure 3B).

Figure 2. Outline of cell composition in bronchoalveolar lavage fluid (BALF) from participants with human immunodeficiency virus type 1 (HIV-1)–tuberculosis (TB) coinfection and participants with TB by single-cell RNA sequencing. A, Uniform manifold approximation and projection (UMAP) of 19 058 single cells from participants with HIV-1–TB coinfection (n = 9) or TB (n = 10) are color-coded by cell type, group, and sample ID, respectively. The number of cells is indicated in each cluster. B, Dot plots show the expression levels of marker genes in each cell type. The blue to red color indicates low to high expression levels and the size of the dots indicates the percentage of cells expressing the gene. C, Heatmaps show the differentially expressed genes in various cell clusters of BALF. The gene names and their corresponding cell types are listed to the left and top, respectively. D, Fraction of each cell type in each sample and proportions of cell types between the HIV-1–TB and TB groups. The Wilcoxon test was used to analyze the differences between 2 groups. *P < .05; ns, not significant.

Figure 3. Subclustering of myeloid cells in bronchoalveolar lavage fluid (BALF) from human immunodeficiency virus type 1 (HIV-1–TB) coinfection and tuberculosis (TB) groups. A, Uniform manifold approximation and projection (UMAP) of myeloid cells from HIV-1–TB coinfection and TB patients are color-coded by cell clusters, cell types, groups, and sample ID, respectively. The number of cells is indicated in each cluster. B, Dot plots show the expression levels of marker genes in each cell subset. The blue to red indicates low to high expression levels and the size of the dots indicates the percentage of cells expressing the gene. C, Heatmaps show the differentially expressed genes in myeloid cell subsets of BALF. The gene names and their corresponding cell types are listed to the left and top, respectively. D, Fraction of each cell type in each sample and proportions of myeloid cell subsets between the 2 groups. The Wilcoxon test was used to analyze the differences between 2 groups. Significance levels are indicated as *P < .05, **P < .01,; ns, not significant.

Tissue-resident alveolar macrophages and monocyte-derived interstitial macrophages play distinct roles in relation to Mtb infection. Alveolar macrophages tend to support Mtb growth, while interstitial macrophages tend to restrict Mtb growth [16]. To determine the preferences of macrophage subsets for infiltration or tissue residence, we utilized the AddModuleScore function in Seurat with reported gene sets to assess the origin of different macrophage subsets [17]. According to the score, the FABP4+ MΦ subset, SCGB3A1+ MΦ subset, SPP1+ MΦ subset, and FTH1P10+ MΦ subset may originate from alveolar macrophages, while the FCN1+ MΦ subset and FTH1P10+ MΦ subset may originate from the periphery (Supplementary Figure 2C).

Two subsets of dendritic cells (DCs), CLEC10A+ DCs and LAMP3+ DCs, were identified based on canonical marker genes. The CLEC10A+ DC population was characterized by elevated expression of CLEC10A, PLAC8, and LGAL32 (Figure 3B). The LAMP3+ DC was characterized by high expression of CD86, LAMP3, FSCN1, and IDO1 (Figure 3B). Furthermore, we identified a neutrophil cell population with high expression of S100A8, BCL2A1, and FCGR3B and a mast cell subset with high levels of TPSAB1, TPSB2, MS4A2, and KIT (Figure 3B). Moreover, we present a heatmap that shows many other gene markers specific to each myeloid cell subpopulation that may be involved in the functional diversification of myeloid cell subpopulations (Figure 3C).

Depletion of DCs in the HIV-1–TB Coinfection Group Versus the TB Group

Among the myeloid cells, macrophages comprised the majority (80%), followed by DCs (13%), neutrophil cells (5.6%), and mast cells (1.4%) (Figure 3D). Of the 10 subsets, the FCN1+ MΦ subset had the highest average proportion of 36%, followed by FABP4+ MΦ with an average of 18% and SCGB3A1+ MΦ with an average of 14% (Figure 3D). However, there were no significant differences in the frequencies of any MΦ subsets between the 2 groups, both in total myeloid cells and in total immune cells (Figure 3D, Supplementary Figure 3A). Similarly, neutrophils and mast cells also showed similar frequencies between the 2 groups, both in total myeloid cells and in total immune cells (Figure 3D, Supplementary Figure 3A). However, the HIV-1–TB group had lower frequencies of CLEC10A+ DC and LAMP3+ DC subsets in total myeloid cells than the TB group (Figure 3D). Furthermore, the HIV-1–TB group also had a significantly lower frequency of CLEC10A+ DCs in total immune cells (Supplementary Figure 3A). These results suggest that HIV-1 infection may have depleted DCs in patients with TB.

Analysis of the Transcriptomic Alterations of Myeloid Cells Between 2 Groups

We identified hundreds of genes that were significantly expressed in each macrophage subset between the 2 groups (Supplementary Tables 3–14), indicating that HIV-1 infection has a significant effect on macrophage functions. The heatmap displays the top 15 differentially expressed genes (DEGs) in each macrophage subset between the 2 groups (Figure 4A and Supplementary Figure 3B). Among them, CD163 was significantly upregulated in FABP4+ MΦ, FCN1+ MΦ, and SELENOP+ MΦ subsets in the HIV-1–TB group, suggesting that these macrophage subsets tend to exhibit M2-like anti-inflammatory phenotype in people with HIV-1–TB coinfection (Figure 4A). Gene Ontology (GO) term enrichment analysis revealed that the upregulated DEGs in each macrophage subset in the HIV-1–TB group were mainly associated with cellular cation homeostasis, virus replication, host defense to virus, and type I interferon responses (Figure 4B). On the other hand, the upregulated DEGs in each macrophage subset in the TB group were mainly related to major histocompatibility complex (MHC) protein complex assembly, peptide antigen assembly with MHC protein complex, and humoral immune response (Figure 4C).

Figure 4. Analysis of the transcriptomic alterations of each macrophage subset between 2 groups. A, Heatmaps show the top 15 differentially expressed genes (DEGs) of macrophage subsets between 2 groups. The gene names and their corresponding groups are listed to the left and top, respectively. Bubble plots show the top 20 Gene Ontology biological process enrichment terms for upregulated DEGs in the human immunodeficiency virus type 1 (HIV-1)–tuberculosis (TB) group (B) and TB group (C). Abbreviations: BP, biological process; DC, dendritic cell; GO, Gene Ontology; HIV-1, human immunodeficiency virus type 1; MHC, major histocompatibility complex; TB, tuberculosis.

Eleven T/NK Cell Subsets Identified in BALF Samples Using scRNA-seq

The total T/NK cells were reclustered into 11 subsets (Figure 5A). Among these subsets, 10 expressed high levels of CD3D, a canonical T-cell gene marker (Figure 5B and 5C, Supplementary Figure 4A and 4B). The remaining cluster was identified as NK cells based on its high expression of typical NK cell genes such as KLRC1 and KLRD1 (Figure 5B and 5C). These NK cells were also characterized with high levels of GNLY, XCL1, and XCL2 (Figure 5B and 5C). Three CD4 T-cell subsets were defined as naive CD4 T cells, Th17 cells, and Treg cells based on canonical cell gene markers (Figure 5B and 5C, Supplementary Figure 4A and 4B). Naive CD4 T cells were characterized by high expression of IL7R, TCF7, CCR7, and KLF2; Th17 cells by high expression of IL26, CTSH, and CCR6; and Treg cells by high expression of FOXP3, IL2RA, and CTLA4 (Figure 5B and 5C). Four CD8 T-cell subsets were identified based on the high expression of CD8A and CD8B, which were all effector cells due to their NKG7, GZMB, and GZMH expression (Figure 5B and 5C). The 4 CD8 effector T subsets were defined as GNLY+ CD8 T subset, CCL4+ CD8 T subset, CCL5+ CD8 T subset, and IFIT3+ CD8 T subset. The GNLY+ CD8 T subset highly expressed cytotoxic genes including GNLY, FCGR3A, and PRF1, indicating its cytotoxic state (Figure 5B and 5C). The CCL4+ CD8 T subset was characterized by high levels of chemokines including CCL4, CCL3, EGR2, and IFNG, suggesting it played a proinflammatory role (Figure 5B and 5C). The CCL5+ subset was marked by high expression levels of cytotoxic signals, including CCL5 and GZMH (Figure 5B and 5C). The IFIT3+ subset was characterized by high levels of interferon inducible genes such as IFIT3, IFIT1, and RSAD2, suggesting a robust type I interferon response (Figure 5B and 5C). A subpopulation characterized by the high expression of genes encoding heat shock proteins including HSPA1A, HSPA1B, and HSPH1, but not CD4 and CD8, was classified as the HSPA1A+ T-cell subset (Figure 5B and 5C). Additionally, a subset with low expression of CD4 and CD8, but highly expressed MKI67, TOP2A, and RRM2, was identified as the cycling T subset (Figure 5B and 5C).

Figure 5. Subclustering of T/natural killer (NK) cells in bronchoalveolar lavage fluid (BALF) from participants with human immunodeficiency virus type 1 (HIV-1)–tuberculosis (TB) coinfection or TB. A, Uniform manifold approximation and projection (UMAP) of T/NK cells from participants with HIV-1–TB coinfection and those with TB were color-coded by cell cluster, cell type, group, and sample ID, respectively. The number of cells are indicated in each cluster. B, Dot plots show the expression levels of marker genes in each cell subset. Blue to red indicates low to high expression levels and the size of the dots indicates the percentage of cells expressing the gene. C, Heatmaps show the differentially expressed genes in various T/NK subsets of BALF. The gene names and their corresponding cell types are listed to the left and top, respectively. D, Fraction of T/NK subsets in each sample and proportions of T/NK subsets between the 2 groups. The Wilcoxon test was used to analyze the differences between 2 groups. Significance levels are indicated as *P < .05, **P < .01, ***P < .001; ns, not significant.

CD4+ T Cells Depleted and CD8+ T Cells Expanded in Patients With HIV-1–TB Coinfection Compared to Those With TB

The frequency of CD4 T cells was much lower than that of CD8 T cells in the HIV-1–TB group, which is consistent with the clinical test results showing a lower frequency of CD4 T cells compared to CD8 T cells in the participants’ blood (Figure 5D, Supplementary Table 1). In both total T/NK cells and total immune cells, the frequencies of all 3 CD4 T-cell subsets (naive CD4 T cells, Th17 cells, and Treg cells) were significantly lower in the HIV-1–TB group compared to the TB group (Figure 5D, Supplementary Figure 5A). In contrast, the proportions of all 4 CD8 T-cell subsets (GNLY+ CD8 T cells, CCL4+ CD8 T cells, CCL5+ CD8 T cells, and IFIT3+ CD8 T cells) were significantly higher in the HIV-1–TB group compared to the TB group (Figure 5D). Additionally, the proportions of the IFIT3+ CD8 T-cell and GNLY+ CD8 T-cell subsets in immune cells were also significantly higher in the HIV-1–TB group than in the TB group (Supplementary Figure 5B). However, the frequency of CCL4+ CD8 T-cell subsets in total immune cells was not significantly different between the 2 groups (Supplementary Figure 5A). Notably, the frequency of NK cells in both total T/NK cells and total immune cells was significantly lower in the HIV-1–TB group compared to the TB group (Figure 5D, Supplementary Figure 5B). The remaining T-cell subsets (cycling T, unknown T, and HSPA1A+ T subsets) had similar frequencies in both total T/NK cells and immune cells in the 2 groups (Figure 5D, Supplementary Figure 5A). These findings suggest that HIV-1 infection depletes CD4 T and NK cells while expanding CD8 T cells in the lungs of TB patients.

Analysis of the Transcriptomic Differences in NK, CD4, and CD8 T-Cell Subsets Between the HIV-1–TB and TB Groups

Within each analyzed T-cell subset, hundreds of genes were found to be significantly expressed between the 2 groups (Supplementary Tables 15–30). It is noteworthy that the number of upregulated DEGs in participants with HIV-1–TB coinfection patients was much greater than that in TB patients for each analyzed subset (Supplementary Tables 15–30). The heatmap displayed the top 15 upregulated or downregulated DEGs in each analyzed T-cell subset between the 2 groups (Figure 6A, Supplementary Figure 5B). All analyzed subsets showed an elevated expression of cytotoxic genes, such as GNLY, GZMA/B/H/K, and NKG7, indicating a higher level of cytotoxicity in the HIV-1–TB group compared to the TB group (Figure 6A, Supplementary Figure 5B). In individuals with HIV-1–TB coinfection, both CD4 and CD8 T cells showed higher expression of IFN-inducible genes such as IFI27, IFI44, IFI44L, and ISG15 (Figure 6A, Supplementary Figure 5B), suggesting greater antiviral activity in these cells compared to those in TB patients. GO term enrichment analysis revealed that the upregulated DEGs in each CD4 and CD8 T-cell subsets in the HIV-1–TB group were mainly associated with virus replication, host responses to virus, leukocyte-mediated cytotoxicity, and cell killing (Figure 5B). In contrast, the upregulated DEGs in CD4 T-cell subsets were mainly enriched in cytoplasmic translation, T-cell differentiation, T-cell activation, leukocyte cell–cell adhesion, and leukocyte differentiation, while the DEGs in each CD8 T-cell and NK cell subset had distinct enrichment of GO terms (Figure 6C). These findings suggest that HIV-1 infection not only affects the frequency of CD4 and CD8 T cells, but also influences their functions.

Figure 6. Analysis of the transcriptomic alterations of CD4 and CD8 T-cell subsets between 2 groups. A, Heatmaps show the top 15 differentially expressed genes (DEGs) of each T-cell subset between 2 groups. The gene names and their corresponding groups are listed to the left and top, respectively. Bubble plots show the top 20 Gene Ontology biological process enrichment terms for upregulated DEGs in the human immunodeficiency virus type 1 (HIV-1)–tuberculosis (TB) coinfection group (B) and TB group (C). Abbreviations: BP, biological process; GO, Gene Ontology; HIV-1, human immunodeficiency virus type 1; NK, natural killer; TB, tuberculosis.

DISCUSSION

There are limited information on the local cellular immune responses in the lungs of individuals coinfected with HIV-1–TB, due to the challenges in obtaining lung samples. To gain insight into the immune responses in lungs of individuals with HIV-1–TB coinfection, as well as those with TB only, we collected BALF samples from 10 patients with active TB and 9 with HIV-1–TB coinfection (Figure 1). ScRNA-seq was conducted on BALF samples to explore the transcriptomic landscape of immune cells.

We identified 6 distinct macrophage subsets in BALF samples, each with its own transcriptomic profile, indicating that these subsets may serve different purposes in response to microbial infections in lungs (Figure 3A–C). The majority of myeloid cell subsets displayed no significant difference in frequency between the HIV-1–TB coinfection group and the TB group (Figure 3D). However, we did find a number of DEGs in macrophage subsets between the 2 groups (Figure 4A and Supplementary Figure 3B). Macrophages in HIV-1–TB patients exhibited a stronger antiviral activity, while macrophages in TB patients demonstrated a more prominent antimicrobial humoral response. DCs were the only myeloid cell subset to display different frequencies between people with HIV-1–TB coinfection and those with TB (Figure 3D). This decrease in DCs in people with HIV-1 may increase their susceptibility to TB.

A drastic decrease in CD4+ T cells is a characteristic of progressive HIV-1 infection [18]. It has been suggested that Th17 cells are essential for the immune system to protect against tuberculosis, as they are involved in the induction of Th1 cells to activate macrophages, the recruitment of these cells to the sites of infection, and the formation of granuloma [19–21]. A decrease in the frequency of BALF Th17 cells in HIV-1–TB–coinfected patients has been observed, which is consistent with other studies that have demonstrated a depletion of Th17 cells in the gut of HIV-positive individuals [22]. In people with HIV-1–TB coinfection, the decrease in Th17 cell subcluster frequency can be damaging to Mtb infection but may help to reduce inflammation related to lung injury. During the initial stages of tuberculosis, Mtb causes an increase in Treg cells in the lungs, which delays the start of adaptive immunity [23, 24]. During chronic infection, Treg cells can be beneficial to the lungs by moderating inflammation [25]. We determined that the frequencies of Tregs in the lungs of participants with HIV-1–TB coinfection were dramatically lower in comparison to those with TB, which is contrary to other studies, which highlighted that HIV infection leads to an enlargement of Treg frequency in total T cells in peripheral blood in both acute and chronic phases of the infection [26–29]. The remarkable depletion of Tregs may indicate a severe imbalance of anti-inflammatory and proinflammatory response in people with HIV-1–TB coinfection. Expansion of CD8 T cells can be observed in both peripheral blood and BALF in people with HIV-1 [30–33]. Animal models have demonstrated that CD8 T cells are capable of providing host protection against Mtb [34]. We discovered 4 CD8 effector T-cell subsets with high expression of cytotoxic genes in BALF samples, and these populations were significantly more frequent in the HIV-1–TB group compared to the TB group (Figure 5D, Figure 6A, Supplementary Figure 5B). The expansion of CD8 T cells in lungs may contribute to contain Mtb in people with HIV-1–TB coinfection.

Numerous studies have provided evidence that HIV-1 not only depletes the number of CD4+ T cells, but also weakens the T cells’ capacity to fight Mtb [35–38]. In this study, we observed numerous DEGs in both CD4 and CD8 T-cell subsets between the HIV-1–TB and TB groups (Figure 6A, Supplementary Figure 5B). In individuals with HIV-1–TB coinfection, CD4 and CD8 T cells exhibited enhanced antiviral activity, as evidenced by their higher expression of interferon-inducible genes like IFI27, IFI44, IFI44L, and ISG15, compared to TB patients (Figure 6A, Supplementary Figure 5B). These findings highlight that HIV-1 infection not only impacts the number of T cells in the lungs of people with TB, but also their functionality.

Supplementary Data

Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

Notes

Author contributions. All authors were involved in the design, implementation, and composition of the manuscript. All authors have read and agreed to the final version of the article.

Acknowledgments. We are profoundly grateful for the nurses and doctors who aided in sample collection.

Financial support. This work was supported by the National Key Research and Development Plan (No. 2021YFA1300902); the National Natural Science Foundation of China (No. 82170009); the Guangdong Science Fund for Distinguished Young Scholars (No. 0620220214); the State Key Laboratory of Respiratory Diseases Open Project (No. SKLRD-OP-202324); the Shenzhen Scientific and Technological Foundation (No. KCXFZ20211020163545004, RCJC20221008092726022, LCYX20220620105200001); the Guangxi Scientific and Technological Foundation (No. AA22096027); and China National GeneBank and the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831). Funding to pay the Open Access publication charges for this article was provided by the National Natural Science Foundation of China (No. 82170009).
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References

1 Bock N , ReichmanLB. Tuberculosis and HIV/AIDS: epidemiological and clinical aspects (world perspective). Semin Respir Crit Care Med 2004; 25 :337–44.16088474
2 Pathak S , Wentzel-LarsenT, AsjoB. Effects of in vitro HIV-1 infection on mycobacterial growth in peripheral blood monocyte-derived macrophages. Infect Immun 2010; 78 :4022.20624908
3 Nunes-Alves C , BootyMG, CarpenterSM, JayaramanP, RothchildAC, BeharSM. In search of a new paradigm for protective immunity to TB. Nat Rev Microbiol 2014; 12 :289–99.24590243
4 Sonnenberg P , GlynnJR, FieldingK, MurrayJ, Godfrey-FaussettP, ShearerS. How soon after infection with HIV does the risk of tuberculosis start to increase? A retrospective cohort study in South African gold miners. J Infect Dis 2005; 191 :150–8.15609223
5 Diedrich CR , O’HernJ, WilkinsonRJ. HIV-1 and the Mycobacterium tuberculosis granuloma: a systematic review and meta-analysis. Tuberculosis (Edinb) 2016; 98 :62–76.27156620
6 Patel NR , ZhuJ, TachadoSD, et al HIV impairs TNF-α mediated macrophage apoptotic response to Mycobacterium tuberculosis. J Immunol 2007; 179 :6973–80.17982088
7 Kumawat K , PathakSK, SpetzAL, KunduM, BasuJ. Exogenous Nef is an inhibitor of Mycobacterium tuberculosis–induced tumor necrosis factor-α production and macrophage apoptosis. J Biol Chem 2010; 285 :12629–37.20068037
8 Goletti D , WeissmanD, JacksonRS, et al Effect of Mycobacterium tuberculosis on HIV replication. Role of immune activation. J Immunol 1996; 157 :1271–8.8757635
9 Lawn SD , PisellTL, HirschCS, WuM, ButeraST, ToossiZ. Anatomically compartmentalized human immunodeficiency virus replication in HLA-DR+ cells and CD14+ macrophages at the site of pleural tuberculosis coinfection. J Infect Dis 2001; 184 :1127–33.11598835
10 Mancino G , PlacidoR, BachS, et al Infection of human monocytes with Mycobacterium tuberculosis enhances human immunodeficiency virus type 1 replication and transmission to T cells. J Infect Dis 1997; 175 :1531–5.9180201
11 Badri M , EhrlichR, WoodR, PulerwitzT, MaartensG. Association between tuberculosis and HIV disease progression in a high tuberculosis prevalence area. Int J Tuberc Lung Dis 2001; 5 :225–32.11326821
12 Papalexi E , SatijaR. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 2017; 18 :35–45.28787399
13 Diagnostic standards and classification of tuberculosis in adults and children. Am J Respir Crit Care Med 2000; 161 (4 Pt 1 ):1376–95.10764337
14 Guo X , ChenF, GaoF, et al CNSA: a data repository for archiving omics data. Database (Oxford) 2020; 2020 :baaa055.32705130
15 Chen FZ , YouLJ, YangF, et al CNGBdb: China National GeneBank database. Yi Chuan 2020; 42 :799–809.32952115
16 Huang L , NazarovaEV, TanS, LiuY, RussellDG. Growth of Mycobacterium tuberculosis in vivo segregates with host macrophage metabolism and ontogeny. J Exp Med 2018; 215 :1135–52.29500179
17 Pisu D , HuangL, GrenierJK, RussellDG. Dual RNA-seq of Mtb-infected macrophages in vivo reveals ontologically distinct host-pathogen interactions. Cell Rep 2020; 30 :335–50.e4.31940480
18 Esmail H , RiouC, BruynED, et al The immune response to Mycobacterium tuberculosis in HIV-1-coinfected persons. Annu Rev Immunol 2018; 36 :603–38.29490165
19 Monin L , GriffithsKL, SlightS, LinY, Rangel-MorenoJ, KhaderS. Immune requirements for protective Th17 recall responses to Mycobacterium tuberculosis challenge. Mucosal Immunol 2015; 8 :1099–109.25627812
20 Gopal R , MoninL, SlightS, et al Unexpected role for IL-17 in protective immunity against hypervirulent Mycobacterium tuberculosis HN878 infection. PLoS Pathog 2014; 10 :e1004099.24831696
21 Khader SA , BellGK, PearlJE, et al IL-23 and IL-17 in the establishment of protective pulmonary CD4+ T cell responses after vaccination and during Mycobacterium tuberculosis challenge. Nat Immunol 2007; 8 :369–77.17351619
22 Brenchley JM , PaiardiniM, KnoxKS, et al Differential Th17 CD4 T-cell depletion in pathogenic and nonpathogenic lentiviral infections. Blood 2008; 112 :2826–35.18664624
23 Shafiani S , Tucker-HeardG, KariyoneA, TakatsuK, UrdahlKB. Pathogen-specific regulatory T cells delay the arrival of effector T cells in the lung during early tuberculosis. J Exp Med 2010; 207 :1409–20.20547826
24 Larson RP , ShafianiS, UrdahlKB. Foxp3(+) regulatory T cells in tuberculosis. Adv Exp Med Biol 2013; 783 :165–80.23468109
25 McBride A , KonowichJ, SalgameP. Host defense and recruitment of foxp3+ T regulatory cells to the lungs in chronic Mycobacterium tuberculosis infection requires Toll-like receptor 2. Plos Pathog 2013; 9 :e1003397.23785280
26 Moreno-Fernandez ME , PresicceP, ChougnetCA. Homeostasis and function of regulatory T cells in HIV/SIV infection. J Virol 2012; 86 :10262–9.22811537
27 Presicce P , OrsbornK, KingE, PrattJ, FichtenbaumCJ, ChougnetCA. Frequency of circulating regulatory T cells increases during chronic HIV infection and is largely controlled by highly active antiretroviral therapy. PLoS One 2011; 6 :e28118.22162758
28 Bi X , SuzukiY, GatanagaH, OkaS. High frequency and proliferation of CD4+FOXP3+ Treg in HIV-1-infected patients with low CD4 counts. Eur J Immunol 2009; 39 :301–9.19089812
29 Weiss L , Donkova-PetriniV, CaccavelliL, BalboM, CarbonneilC, LevyY. Human immunodeficiency virus-driven expansion of CD4+CD25+ regulatory T cells, which suppress HIV-specific CD4 T-cell responses in HIV-infected patients. Blood 2004; 104 :3249–56.15271794
30 Bastidas S , GrawF, SmithMZ, KusterH, GünthardHF, OxeniusA. CD8+ T cells are activated in an antigen-independent manner in HIV-infected individuals. J Immunol 2014; 192 :1732–44.24446519
31 Giorgi JV , DetelsR. T-cell subset alterations in HIV-infected homosexual men: NIAID Multicenter AIDS Cohort Study. Clin Immunol Immunopathol 1989; 52 :10–8.2656013
32 Salazar-Gonzalez JF , MoodyDJ, GiorgiJV, Martinez-MazaO, MitsuyasuRT, FaheyJL. Reduced ecto-5′-nucleotidase activity and enhanced OKT10 and HLA-DR expression on CD8 (T suppressor/cytotoxic) lymphocytes in the acquired immune deficiency syndrome: evidence of CD8 cell immaturity. J Immunol 1985; 135 :1778–85.2991373
33 Muema DM , MthembuMM, SchiffA, et al Contrasting inflammatory signatures in peripheral blood and bronchoalveolar cells reveal compartment-specific effects of HIV infection. Front Immunol 2020; 11 :864.32508817
34 Woodworth JS , BeharSM. Mycobacterium tuberculosis–specific CD8+ T cells and their role in immunity. Crit Rev Immunol 2006; 26 :317–52.17073557
35 Amelio P , PortevinD, HellaJ, et al 1 HIV infection functionally impairs Mycobacterium tuberculosis–specific CD4 and CD8 T- 2 cell responses. J Virol 2018; 93 :e01728–18.
36 Kalsdorf B , ScribaTJ, WoodK, et al HIV-1 infection impairs the bronchoalveolar T-cell response to mycobacteria. Am J Resp Crit Care 2009; 180 :1262–70.
37 Day CL , AbrahamsDA, HarrisLD, et al HIV-1 infection is associated with depletion and functional impairment of Mycobacterium tuberculosis–specific CD4 T cells in individuals with latent tuberculosis infection. J Immunol 2017; 199 :2069–80.28760884
38 Jambo KC , BandaDH, AfranL, et al Asymptomatic HIV-infected individuals on antiretroviral therapy exhibit impaired lung CD4(+) T-cell responses to mycobacteria. Am J Respir Crit Care Med 2014; 190 :938–47.25225948
