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August 28, 2024

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New findings on tuberculosis could change how we treat inflammatory disorders

by Katherine Fenz, Rockefeller University

Tuberculosis (TB) is a confounding scourge. It's the leading cause of death from infectious disease in the world, and yet it's estimated that those deaths represent perhaps 5% of infections with Mycobacterium tuberculosis (Mtb). Antibiotics can take credit for saving the lives of some of those with Mtb, but a chasm nevertheless persists between the prevalence of infection and the targeted severity of its impact. A growing body of evidence suggests genetic vulnerabilities to TB account for that gap.

Now researchers from The Rockefeller University have found another rare mutation that leaves its carriers much more likely to become ill with TB—but, curiously, not with other infectious diseases. This finding, recently published in Nature , may upend long held assumptions about the immune system .

It's long been known that an acquired deficiency of a pro-inflammatory cytokine called TNF is linked to an increased risk of developing TB. The current study, led by Rockefeller's Stéphanie Boisson-Dupuis and Jean-Laurent Casanova, revealed a genetic cause of TNF deficiency, as well as the underlying mechanism: a lack of TNF incapacitates a specific immune process in the lungs, leading to severe—but surprisingly targeted—illness.

The findings suggest that TNF, long considered a key galvanizer of the immune response, might actually play a much narrower role—a discovery with far-reaching clinical implications.

"The past 40 years of scientific literature have attributed a wide variety of pro-inflammatory functions to TNF," says Casanova, head of the St. Giles Laboratory of Human Genetics of Infectious Diseases. "But beyond protecting the lungs against TB, it may have a limited role in inflammation and immunity."

Casanova's lab has been studying the genetic causes of TB for more than two decades through field work in several countries and a wide network of collaborating physicians across the world. They maintain an ever-growing database of whole-exome sequences from a global pool of patients—more than 25,000 people to date. Of those, some 2,000 have had TB.

Over the years they've identified several rare genetic mutations that render some people vulnerable to TB . For example, mutations in a gene called CYBB can disable an immune mechanism called the respiratory burst, which produces chemicals called reactive oxygen species (ROS). Despite its pulmonary-sounding name, the respiratory burst takes place in immune cells throughout the body.

ROS help pathogen-consuming white blood cells called phagocytes (from the Greek for "eating") to destroy the invaders they've devoured. If ROS aren't produced, those pathogens can thrive unchecked, leading to debilitating complications. As a result, carriers of this CYBB mutation become vulnerable to not just TB but to a wide variety of infectious diseases.

For the current study, the team suspected that a similar inborn error of immunity may lay behind the severe, recurring TB infections experienced by two people in Colombia—a 28-year-old woman and her 32-year-old cousin—who had been repeatedly hospitalized with significant lung conditions. In each cycle, they initially responded well to anti-TB antibiotics, but within a year, they were sick again.

Puzzlingly, however, their long-term health records showed that their immune systems functioned normally, and that they were otherwise healthy.

A telling deficiency

To find out why they were particularly prone to getting TB, the researchers performed whole-exome sequencing on the two, as well as a genetic analysis of their respective parents and relatives.

The two were the only members of their extended family with a mutation in the TNF gene, which encodes for proteins linked to the regulation of a variety of biological processes. Short for " tumor necrosis factor ," increased TNF production is also associated with a variety of conditions, including septic shock, cancer, rheumatoid arthritis, and cachexia, which causes dangerous weight loss.

The protein is largely secreted by a type of phagocyte called a macrophage, which relies on the ROS molecules generated by the respiratory burst to finish off pathogens they've consumed.

In these two patients, the TNF gene failed to function, preventing the respiratory burst from occurring, and thus the creation of ROS molecules. As a result, the patients' alveolar macrophages, located in their lungs, were overrun with Mtb.

"We knew that the respiratory burst was important for protecting people against various types of mycobacteria, but now we know that TNF is actually regulating the process," says Boisson-Dupuis. "And when it's missing in alveolar macrophages, people will be susceptible to airborne TB."

She adds, "It's very surprising that the people we studied are adults who have never been sick with other infectious diseases, despite being repeatedly exposed to their microbes. They are apparently selectively at risk for TB."

Treatment potential

The discovery also solves a long-standing mystery about why TNF inhibitors, which are used to treat autoimmune and inflammatory diseases, raise the chances of contracting TB. Without TNF, a key part of the defense against it is defunct.

The findings may lead to a radical reassessment of TNF's role in immune function—and new treatment possibilities.

"TNF is required for immunity against Mtb, but it seems to be redundant for immunity against many other pathogens," Casanova says. "So the question is, what other pro-inflammatory cytokines are doing the jobs we thought TNF was doing? If we can discover that, we may be able to block these cytokines rather than TNF to treat diseases where inflammation plays a role."

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Diabetes as a risk factor for tuberculosis disease

Affiliations.

• 1 Institute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
• 2 Family and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
• 3 Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany.
• 4 Institute for Biometrics and Epidemiology, German Diabetes Center (Deutsches Diabetes-Zentrum/DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
• 5 Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
• 6 Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
• 7 Department of Nutrition and Food Safety, World Health Organization, Geneva, Switzerland.
• 8 Department of Noncommunicable Diseases, World Health Organization, Geneva, Switzerland.
• 9 Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
• PMID: 39177079
• PMCID: PMC11342417
• DOI: 10.1002/14651858.CD016013.pub2

Background: Tuberculosis (TB) is amongst the leading causes of death from an infectious disease, with an estimated 1.3 million deaths from TB in 2022. Approximately 25% of the global population is estimated to be infected with the TB bacterium, giving rise to 10.6 million episodes of TB disease in 2022. The prevalence of diabetes influences TB incidence and TB mortality. It is associated not only with an increased risk of TB disease but also death during TB treatment, TB relapse after treatment completion and multidrug-resistant TB. Since 2011, the World Health Organization (WHO) has recommended collaborative TB and diabetes activities as outlined in the Collaborative Framework for Care and Control of TB and Diabetes.

Objectives: To determine the prognostic value of diabetes mellitus (DM) in the general population of adults, adolescents and children for predicting tuberculosis disease.

Search methods: We searched the literature databases MEDLINE (via PubMed) and WHO Global Index Medicus, and the WHO International Clinical Trials Registry Platform (ICTRP) on 3 May 2023 (date of last search for all databases); we placed no restrictions on the language of publication.

Selection criteria: We included retrospective and prospective cohort studies, irrespective of publication status or language. The target population comprised adults, adolescents and children from diverse settings, encompassing outpatient and inpatient cohorts, with varying comorbidities and risk of exposure to tuberculosis.

Data collection and analysis: We used standard Cochrane methodology and the Quality In Prognosis Studies (QUIPS) tool. Prognostic factors assessed at enrolment/baseline included diabetes, as defined by the individual studies, encompassing patient-reported status, abstracted from medical records or claims data, or diagnosed by plasma glucose/glycosylated haemoglobin. The primary outcome was the incidence of tuberculosis disease. The secondary outcome was recurrent TB disease. We performed a random-effects meta-analysis for the adjusted hazard ratios, risk ratios, or odds ratios, employing the restricted maximum likelihood estimation. We rated the certainty of the evidence using the GRADE approach.

Main results: We included 48 cohort studies with over 61 million participants from the six WHO regions. However, the representation was variable as eight population-based studies were from South Korea and 19 from China, with overlapping study periods, and only one from the African region (Ethiopia). All studies included adults, and nine studies also included children and adolescents. Most studies diagnosed DM based on clinical records, including fasting blood glucose levels or glucose-lowering treatments. The studies did not distinguish between type 1 and type 2 DM; only one study focused on type 1 DM. Diagnosis and exclusion of TB were performed using culture or molecular WHO-recommended rapid diagnostic tests (mWRD) in only 12 studies, which could have biassed the effect estimate. The median follow-up time was five years (interquartile range 1.5 to 10, range 1 to 16.9), and the studies primarily reported an adjusted hazard ratio from a multivariable Cox-proportional hazard model. Hazard Ratios (HR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. We present 95% confidence intervals (CI) and prediction intervals, which show between-study heterogeneity represented in measuring the variability of effect sizes (i.e. the interval within which the effect size of a new study would fall considering the same population of studies included in the meta-analysis). DM may increase the risk of tuberculosis disease (HR 1.90, 95% CI 1.51 to 2.40; prediction interval 0.83 to 4.39; 10 studies; 11,713,023 participants). The certainty of the evidence is low, due to a moderate risk of bias across studies and inconsistency. Considering a risk without diabetes of 129 cases per 100,000 population, this represents 102 more (59 to 153 more) cases per 100,000. When stratified by follow-up time, the results are more consistent across < 10 years follow-up (HR 1.52, 95% CI 1.47 to 1.57; prediction interval 1.45 to 1.59; 7 studies; 10,380,872 participants). This results in a moderate certainty of the evidence due to a moderate risk of bias across studies. However, at 10 or more years of follow-up, the estimates yield a wider CI and a higher HR (HR 2.44, 95% CI 1.22 to 4.88; prediction interval 0.09 to 69.12; 3 studies; 1,332,151 participants). The certainty of the evidence is low due to the moderate risk of bias and inconsistency. Odds Ratio (OR) DM may increase the odds of tuberculosis disease (OR 1.61, 95% CI 1.27 to 2.04; prediction interval 0.96 to 2.70; 4 studies; 167,564 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is low due to a moderate risk of bias and inconsistency. Risk Ratio (RR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. DM probably increases the risk of tuberculosis disease (RR 1.60, 95% CI 1.42 to 1.80; prediction interval 1.38 to 1.85; 6 studies; 44,058,675 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is moderate due to a moderate risk of bias.

Authors' conclusions: Diabetes probably increases the risk of developing TB disease in the short term (< 10 years) and may also increase the risk in the long term (≥ 10 years). As glycaemic control and access to care may be potential effect modifiers of the association between diabetes and the risk of TB disease, the overall estimates should be interpreted with caution when applied locally. Policies targeted at reducing the burden of diabetes are needed to contribute to the aims of ending TB. Large population-based cohorts, including those derived from high-quality national registries of exposures (diabetes) and outcomes (TB disease), are needed to provide estimates with a high certainty of evidence of this risk across different settings and populations, including low- and middle-income countries from different WHO regions. Moreover, studies including children and adolescents and currently recommended methods for diagnosing TB would provide more up-to-date information relevant to practice and policy.

Funding: World Health Organization (203256442) REGISTRATION: PROSPERO registration: CRD42023408807.

Copyright © 2024 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.

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Conflict of interest statement

Juan Victor Ariel Franco: none known.

Brenda Bongaerts: none known.

Maria‐Inti Metzendorf: none known.

Agostina Risso: none known.

Yang Guo: none known.

Laura Peña Silva: none known.

Melanie Boeckmann: none known.

Sabrina Schlesinger: Novo Nordisk (Independent Contractor), Alpro Foundation (Grant/Contract).

Johanna AAG Damen: Universitair Medisch Centrum Utrecht (Employment).

Bernd Richter: consultant for the World Health Organization.

Anna Carlqvist: consultant for the World Health Organization.

Mathieu Bastard: employee of the World Health Organization.

Maria Nieves Garcia‐Casal: employee of the World Health Organization.

Bianca Hemmingsen: employee of the World Health Organization.

Farai Mavhunga: employee of the World Health Organization.

Jennifer Manne‐Goehler: consultant for the World Health Organization and National Institutes of Health (Grant/Contract).

Kerri Viney: employee of the World Health Organization.

Annabel Baddeley: employee of the World Health Organization.

Juan Victor Ariel Franco, Maria‐Inti Metzendorf, Brenda Bongaerts, and Bernd Richter are editors for the Cochrane Metabolic and Endocrine Disorders Group, but they were excluded from the editorial processing of this review.

The authors alone are responsible for the views expressed in this article, and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

Prisma flow diagram

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Green indicates a low risk of bias, yellow indicates moderate risk of bias and…

Contour‐enhanced funnel plot for the overall…

Contour‐enhanced funnel plot for the overall hazard ratio for the risk of tuberculosis due…

Subgroup analysis ‐ studies on adults…

Subgroup analysis ‐ studies on adults vs those also including children and adolescents (across…

Subgroup analysis by age. The blue…

Subgroup analysis by age. The blue squares represent the risk ratios of the individual…

Subgroup analysis by age ‐ other…

Subgroup analysis by age ‐ other studies. The blue squares represent the risk ratios…

Subgroup analysis by sex. The blue…

Subgroup analysis by sex. The blue squares represent the risk ratios of the individual…

Subgroup analysis of the risk of…

Subgroup analysis of the risk of TB due to diabetes by different indicators of…

Risk/odds of TB recurrence in studies…

Risk/odds of TB recurrence in studies using different effect measures. The blue squares represent…

• doi: 10.1002/14651858.CD016013
• World Health Organization. Global Tuberculosis Report 2023. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/globa... (accessed 12 April 2024).
• CDC. Core Curriculum on Tuberculosis: What the clinician should know. https://www.cdc.gov/tb/education/corecurr/index.htm (accessed 21 April 2023).
• Brett K, Dulong C, Severn M; Canadian Agency for Drugs and Technologies in Health. Prevention of Tuberculosis: A Review of Guidelines 2020. pubmed.ncbi.nlm.nih.gov/33048484/ (accessed prior to 3 August 2024). [PMID: ] - PubMed
• Hargreaves JR, Boccia D, Evans CA, Adato M, Petticrew M, Porter JD. The social determinants of tuberculosis: from evidence to action. American Journal of Public Health 2011;101(4):654-62. [DOI: 10.2105/AJPH.2010.199505] - DOI
• World Health Organization. Tuberculosis and vulnerable populations. https://www.who.int/europe/news-room/fact-sheets/item/tuberculosis-and-v... (accessed 7 July 2023).

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• Open access
• Published: 29 August 2024

Single-cell transcriptome sequencing reveals altered peripheral blood immune cells in patients with severe tuberculosis

• Li Wang 1 , 2   na1 ,
• Ya He 1 , 2   na1 ,
• Peng Wang 1 , 2   na1 ,
• Hai Lou 1 , 2 ,
• Haipeng Liu 4 &
• Wei Sha 1 , 2 , 3  

European Journal of Medical Research volume  29 , Article number:  434 ( 2024 ) Cite this article

Metrics details

Tuberculosis is a serious global health burden, resulting in millions of deaths each year. Several circulating cell subsets in the peripheral blood are known to modulate the host immune response to Mycobacterium tuberculosis ( Mtb ) infection in different ways. However, the characteristics and functions of these subsets to varying stages of tuberculosis infection have not been well elucidated. Peripheral blood immune cells (PBICs) were isolated from healthy donors (HD group), individuals with mild tuberculosis (MI group), and individuals with severe tuberculosis (SE group). CD4+ naive T cells and CD8+ T cells were decreased in the SE and MI groups, while CD14+ monocytes were increased in the SE group. Further analysis revealed increased activated CD4+ T cells, transitional CD8+ T cells, memory-like NK cells, and IGHG3 high TTN high FCRL5 high B cells were increased in all patients with tuberculosis (SE and MI group). In contrast, Th17 cells, cytotoxic NK cells, and cytotoxic CD4+ T cells were decreased. Moreover, the increase of CD14+CD16+ monocytes correlated with severe tuberculosis, and the GBP5 high RSAD2 high neutrophils were unique to patients with severe tuberculosis. Cellular communication analysis revealed that CD8+ T cells exhibited the highest incoming interaction strength in the SE group. The increased CD8+ T cell incoming interactions are associated with the MHC-I and LCK pathways, with HLA-(A-E)-CD8A, HLA-(A-E)-CD8B, and LCK-(CD8A+CD8B) being ligand–receptor pairs. Patients with tuberculosis, especially severe tuberculosis, have profound changes in peripheral blood immune cell profiles. CD8+ T cells showed the highest incoming interaction strength in patients with severe tuberculosis, with the main signals being MHC-I and LCK pathways.

Introduction

Mycobacterium tuberculosis ( Mtb ) is the causative organism of tuberculosis, which infects about a quarter of the world’s population [ 1 , 2 , 3 ]. Meanwhile, it is the leading cause of death globally from a single infectious pathogen, accounting for approximately 1.5 million deaths annually. Currently, the global control of Mtb is not optimistic, and there are over 10 million new cases reported each year [ 4 ].

Most patients with primary tuberculosis are asymptomatic or have mild symptoms such as fever and night sweats. However, a minority of tuberculosis patients suffer from more severe symptoms such as caseous necrosis of the lungs and concomitant caseous liquefaction, which usually leads to a worsening of prognosis or death. Mortality rates for severe tuberculosis requiring Intensive Care Unit were reported to range from 15.5 to 65.9% [ 5 ]. Mtb invasion and anti- Mtb host immune responses interact to influence disease severity and clinical outcomes, creating barriers to the design of effective strategies for the prevention and control of Mtb infection. Therefore, understanding the host immune response in patients with severe tuberculosis is important for better designing prognostic and diagnostic indicators and appropriate therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for profiling immune cell responses and is leading the field of pathogen infections [ 5 , 6 , 7 ]. In the present study, scRNA-seq was conducted to obtain a high-resolution transcriptomic landscape of peripheral immune cells in peripheral blood immune cells (PBICs) from healthy donors, patients with mild tuberculosis, and patients with severe tuberculosis (Fig.  2 A). Our study reveals the core of peripheral blood immune alterations in tuberculosis patients, especially in severe tuberculosis patients, and provides potential new therapeutic targets.

Study design and participants

The present study recruited two normal donors and six newly diagnosed patients from January 2020 to March 2020 at Shanghai Pulmonary Hospital. All tuberculosis patients included in the study were primary susceptible tuberculosis patients with Xpert positive and rifampicin resistance negative. In addition, none of the patients had received effective anti-tuberculosis medication in the 3 months before hospital admission. Three of these patients were included in the SE group, with manifestations of cavitary tuberculosis or caseous pneumonia and involvement of more than 2 pulmonary lobes (Fig.  1 ). The other three patients were included in the MI group, with lesions confined to one pulmonary lobe and presenting as hypodense patchy shadows (Fig.  1 ). Other inclusion criteria were as follows: (1) patients with a clear diagnosis (positive sputum microscopy or sputum culture or PCR; (2) patients between 20 and 70 years of age; and (3) patients with normal immune function (as assessed by routine blood counts and lymphocyte subpopulation counts). Exclusion criteria were as follows: (1) patients with extrapulmonary Mtb infection; (2) patients with diabetes mellitus, hyperlipidemia, and other metabolic disorders; (3) HIV-positive patients; (4) patients with co-infections; (5) patients with co-infections with other severe lung diseases; (6) patients with other comorbidities; (7) patients with smoking history, and alcohol consumption > 100 mL/day; and (8) patients who were receiving anti-tuberculosis treatment at the time of enrolment.

Radiographs of chest CT of the six tuberculosis patients in the present study

Single-cell transcriptomes were generated from PBICs from 2 healthy donors and 6 patients with mild (n = 3) and severe (n = 3) active tuberculosis (Fig.  2 A). Thus, the 8 participants were classified into three groups: healthy donors (HD), mild tuberculosis (MI), and severe tuberculosis (SE). The demographic characteristics of study populations are included in Table S1.

Study design and overall results of single-cell transcriptional profiling of PBICs from 8 participants. A Schematic diagram illustrating the overall study design. 8 subjects, including 2 healthy donors, 6 mild tuberculosis patients, and 3 severe tuberculosis patients. B The t-SNE plot of single-cell profiles, with cells divided by 23 clusters. C t-SNE plots of single-cell profiles, with cells divided by cell annotation. D Total cell proportions of the 12 cell types in the three comparison groups. E t-SNE plot of single-cell profiles, with cells divided by 5 main cell types

The study was approved by the Ethics Committee of Tongji University affiliated Shanghai Pulmonary Hospital (No. K23-198), with the informed written consent of each participant. The study was conducted following the principles of the Declaration of Helsinki and the ethical and biosafety agreements of the institution.

Single-cell suspension preparation and scRNA-seq library construction

Peripheral blood was collected from the donor into a heparin anticoagulation tube. After adding 10 mL of 6% hydroxyethyl solution, the sample was inverted several times and kept at room temperature for 30 min. Subsequently, the supernatant was centrifuged continuously at 290× g for 5 min. The cells were washed twice and then dissolved with ACK (ammonium–chloride–potassium) lysis buffer (Thermo Fisher Scientific, Waltham, MA) for 5 min at room temperature to completely remove erythrocytes. The de-erythropoietic cells were then centrifuged at 300× g for 5 min at 4 °C, and the cell microspheres were resuspended in 1 mL of PBS (HyClone). Peripheral blood de-erythropoietic cell suspension was counted using a TC20 automated cell counter (Bio-Rad) to determine cell concentration and viability.

The concentration of the cell suspension was adjusted to 1 × 10 5  cells/mL with PBS. Then, the single-cell suspension was loaded onto a microfluidic chip, and a scRNA-seq library was constructed following the manufacturer's instructions (Singleron Biotechnologies, Nanjing, China). The scRNA-seq library was sequenced on an Illumina HiSeq × 10 instrument.

Single-cell data analysis

Quality control was first performed on the raw data generated by the sequencing machine. Cell barcodes, UMI, and mRNA sequences were extracted, and the cell barcode sequences were corrected. The mRNA sequence was then aligned with the human reference genome, and the corresponding gene and transcript tags were added to the sequence. UMI counts were determined based on the combination of cells and genes. The gene expression matrix was obtained, and subsequent cell-type clustering and identification of major cell types were carried out.

Cells expressing less than 200 or more than 6000 genes were removed. In addition, mitochondria-expressed genes higher than 20% were discarded. Finally, a total of 28,417 cells were retained, with an average of 3552 cells in each sample. Gene expression matrices of the remaining cells were normalized using a linear regression model (Seurat R package version 4.3.0.1). Principal component analysis (PCA) was performed, and dimensionality reduction was carried out using UMAP and t-SNE [ 22 ]. Following that, clusters were identified and annotated based on the composition of marker genes. The cells were clustered using the FindCluster function, and the FindMarkers function was used to identify marker genes within the cell clusters. Cell annotation was performed using the SingleR package (version 3.17), with the Human Primary Cell Atlas ( https://www.humancellatlas.org/ ) serving as the reference data. The online database CellMarker ( http://xteam.xbio.top/CellMarker/ ) was used to further identify the cells [ 8 ]. Finally, the annotated platelets were removed to obtain the final gene expression matrix of PBICs.

The FindMarkers tool was used to calculate the differentially expressed genes (DEGs) between each cluster or group. Pathway analyses were performed on 50 hallmark pathways using the Genome Variation Analysis (GSVA) package (version 1.26.0) [ 9 ].

Pseudotime trajectory analysis

To construct single-cell trajectories and identify gene expression changes among different cell subtypes, Monocle 3 (version 1.3.1) was applied to the cell subtypes of the PBICs.

Cell communication analysis

Cell communication analysis was performed using the R package CellChat (version 1.6.1) [ 35 ]. The CellChatDB human was used for analysis. All three groups of samples were normalized together, and then each group was extracted, analyzed, and compared in parallel.

Statistical analysis

Statistical analyses and visualizations are performed in R software (version 4.3).

Integrated analysis of scRNA-seq data

A total of 29,150 cells were isolated and sequenced from 8 participants (2 healthy donors, HD group; 3 patients with mild tuberculosis, MI group; 3 patients with severe tuberculosis, SE group). After removing duplicate cells, empty droplets, and low-quality cells, a total of 28,218 cells were filtered out for further analysis. Using the Seurat package for unsupervised cell clustering [ 10 ], 22 cell clusters were obtained (Fig.  2 B).

After clustering, CD14+ monocytes, CD16+ monocytes, CD4+ memory T cells, CD4+ naive T cells, CD8+ T cells, Gamma-delta T cells, neutrophils, NK cells, and B cells, were annotated based on the expression of the canonical marker genes and variable genes (Fig.  2 C, Table S2). Cell proportion analysis showed that CD4+ naive T cells and CD8+ T cells were decreased in the SE and MI groups (CD4+ naive T cells: SE 5.53%, MI 4.78%, HD 8.07%; CD8+ T cells: SE 7.12%, MI 5.98%, HD 11.10%). In addition, CD14+ monocytes were increased in the SE group (Fig.  2 D, SE 16.84%, MI 10.00%, HD 13.15%).

The 22 cell clusters were then divided into five main cell types, including neutrophils (CXCR2 high CXCL8 high FCGR3B high , cluster 0, 1, 2, 4, 8, and 21), T cells (CD3D high CD2 high IL32 high , cluster 5, 6, 9, 11, 16, and 18), monocytes (LYZ high VCAN high CD14 high , cluster 3, 14, 17, 19, and 22), B cells (CD79A high CD79B high MS4A1 high , cluster 10, 13, and 20), and NK cells (GNLY high NKG7 high , cluster 7) (Table S3, Fig.  2 E, S1). Subsequently, the five main cell types were re-clustered and analyzed for more detailed information.

The increase of CD14+CD16+ monocyte cells correlated with the severity of tuberculosis

Monocytes play an important role in the pathogenesis of tuberculosis [ 11 ]. Tuberculosis induces the accumulation of monocytes in the peripheral blood, and these monocytes express high levels of the inflammatory markers S100A12 [ 12 , 13 ]. Therefore, further analysis of monocytes in our scRNA-seq was performed to reveal the differences in monocyte subsets occurring among the three groups. All monocytes were further divided into 10 subsets, M0-9 (Fig.  3 A, C ).

Further analysis of monocytes from HD, MI, and SE groups. A Monocytes were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among monocyte subsets. C The t-SNE plot of monocyte profiles, with cells divided by group. D Feature plots of two well-known monocyte marker genes, CD14 and FCGR3A, and their corresponding expression levels. E Heatmap displaying marker genes for cluster M0–9. F Proportions of monocyte subsets in the three comparison groups. G Pseudotime trajectory analysis of monocyte subsets. The Arabic numerals represent the pseudotime. Larger Arabic numerals represent an increase in the pseudotime. The solid black lines represent the pseudotime trajectory

Based on the expression levels of the marker genes CD14 and FCGR3A (also known as CD16), three monocyte subpopulations were identified, i.e., classical (CD14+CD16−), non-classical (CD14 low CD16+), and intermediate (CD14+CD16+) monocytes [ 13 ]. As shown in Fig.  3 D, the classical CD14+CD16− cell subset is the predominantly abundant cell subset, comprising clusters M0, M1, M2, and M5. Notably, M0, M1, M2, and M5 represent inflammatory monocytes that express high levels of inflammation-related markers, such as S100A12 [ 14 , 15 , 16 ]. There are also differences among these three subsets. M0 and M2, especially M0, are the most classic monocytes with high expression of S100A9, S100A8, and S100A12 (Fig.  3 E). M1 was a subset that highly expressed LGALS2 (Figure S2). M5 highly expressed interferon-induced or regulated molecules such as ISG15 [ 17 ], IFI44L [ 18 ], and IFIT1 [ 19 ], suggesting that M5 is a subset regulated by interferon (Figure S2). CD14 low CD16+ monocytes, mainly clustered in M4, are characterized by high expression of FCGR3A (Figure S2). CD14+CD16+ monocytes, mainly clustered in M3, exhibit high expression of HLA molecules such as HLA-DRA, HLA-DPB1, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-DQB1, HLA-DMA, and HLA-DQA1 (Figure S2).

The cell proportion of CD14+CD16+ monocytes is reported to increase in inflammatory diseases [ 20 ]. And another scRNA-seq study demonstrated a similar phenomenon in patients with tuberculosis [ 13 ]. Consistently, the proportion of M3 monocytes was observed to be higher in the SE group (12.72%) compared to the HD group (9.60%) and the MI group (8.95%) (Fig.  3 F). These results further suggest that the increase of CD14+CD16+ intermediate monocyte cells correlated with the severity of tuberculosis. Pathway analysis showed that M3 had up-regulated WNT-β-catenin signaling, Notch signaling, and MYC-Target signaling (Fig.  3 B).

In addition, the M0 cell subpopulation proportion was highest in the SE group (26.48%). Pathway analysis showed that M0 showed down-regulated interferon alpha response and interferon-gamma response, suggesting relatively insensitive to interferon. In addition, M0 showed up-regulated glycolysis and down-regulated fatty acid metabolism.

Pseudotime trajectory analysis further highlighted the heterogeneity and connectivity of cellular differentiation among monocyte subsets. As shown in Fig.  3 G, there are three main cellular trajectories in the prominent monocytes (M0–6): M2 to M0, M2 to M1 to M0, and M2 to M1 to M3 to M4. Genes that contribute most to the trajectory alignment were LYZ, S100A8, S100A9, VCAN, etc. The expression of these genes decreases with trajectory, implying that the inflammatory responsiveness of cells decreases with trajectory.

Tuberculosis patients exhibited increased activated CD4+ T cells and transitional CD8+ T cells and decreased Th17 cells and cytotoxic CD4+ T cells

T cells play a key role in controlling the infection of Mtb in patients with tuberculosis [ 21 ]. The present scRNA-seq analysis detected 5235 T cells in the three groups, which could be subdivided into 10 subsets (Fig.  4 A, C ). Five distinct CD4+ T cell subsets were identified: T0, T1, T3, T6, and T8 (Fig.  4 A, D ). Both T0 and T6 expressed high levels of the activated CD4+ T cell marker CCR6 [ 22 ], as well as functional markers such as LTB, AQP3, GPR183, and LDHB [ 23 ] (Figure S3). T1 expresses high levels of CCR7, indicating that it is a naive CD4+ T cell subset (Fig.  4 E) [ 24 ]. T3 expresses high levels of KLRB1 (CD161), indicating that it is a subset of T helper 17 (Th17) cells. T8 might be a cytotoxic CD4+ T cell subset that expresses high levels of SYNE1 [ 25 ]. Four distinct CD8+ T cell subsets were identified as well, including naive CD8+ T cells (T4, CCR7 high ) [ 26 ], cytotoxic CD8+ T cells (T2, GZMH high NKG7 high FGFBP2 high ), transitional CD8+ T cells (T5, GZMK high ) [ 27 ], and memory CD8+ T cells (T7, FCGR3B high ) [ 25 ].

Further analysis of T cells from the HD, MI, and SE groups. A T cells were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among T cell subsets. C The t-SNE plot of T cell profiles, with cells divided by group. D Feature plots of two well-known T cell marker genes, CD4 and CD8, and their corresponding expression levels. E Heatmap displaying marker genes for cluster T0–9. F Proportions of T cell subsets in the three comparison groups. G Pseudotime trajectory analysis of T cell subsets. The Arabic numerals represent the pseudotime. Larger Arabic numerals represent an increase in the pseudotime. The solid black lines represent the pseudotime trajectory

Pseudotime trajectory analysis showed that there are two cellular trajectories in the prominent monocytes: T2 to T8 and T2 to T5 to T3 to T6 to T0 to T1 (Fig.  3 G). Genes that contribute most to the trajectory alignment were CCL5, CST7, FGFBP2, GZMA, GZMH, NKG7, etc. The expression of these genes decreases with trajectory, indicating that the cellular cytotoxicity decreases with trajectory.

The proportion of activated CD4+ T cells (T0) and transitional CD8+ T cells (T5) were increased in the MI and SE groups compared to the HD group (Fig.  4 F, T0: HD14.80%, MI 23.80%, SE 24.32%; T5: HD 6.95%, MI 10.16%, SE 9.11%). The cellular proportion of Th17 cells (T3) and cytotoxic CD4+ T cells (T8) was significantly decreased in the MI and SE groups (T3: HD 17.01%, MI 9.96%, SE 8.67%; T8: HD 10.82%, MI 1.55%, SE 1.93%). GSEA revealed that T0 exhibited up-regulated KRAS signaling and down-regulated HEDGEHOG signaling (Fig.  3 B). T5 displayed up-regulated pathways including peroxisome, reactive oxygen species, MYC-targets, and E2F-targets. T3 displayed down-regulated NOTCH signaling, while T8 showed down-regulation of peroxisome, reactive oxygen species, adipogenesis, fatty acid metabolism, oxidative phosphorylation, and MYC-targets. Overall, tuberculosis patients (MI and SE groups) exhibited an increase in activated CD4+ T cells and transitional CD8+ T cells and a decrease in Th17 cells and cytotoxic CD4+ T cells, and these trends appeared to be independent of disease severity.

GBP5 high RSAD2 high neutrophil subset was unique to patients with severe tuberculosis

Neutrophils are a major component of the innate immune system and, as the most numerous cell type of circulating leukocytes, are the host's first line of defense against invading bacteria and other pathogens [ 28 ]. Therefore, further analyses were performed on neutrophils. Neutrophils were divided into 14 cell subsets: N0–N13 (Fig.  5 A, C ). The top 10 marker genes of N0–N13 subsets are shown with heatmaps in Fig.  5 D. As shown in Fig.  5 E, there were no significant differences in the cellular proportions in the N0–N6 and N8 subsets among the three groups. However, compared to the HD and MI groups, the N7 (SLPI high PTGS2 high LST1 high ) subset was significantly decreased in the SE group, and the N10 (GBP5 high RSAD2 high ) subset was only present in the SE group. In addition, the MI and SE groups had additional N11, N12, and N13 subsets compared to the HD group. GSVA analysis revealed that N7 exhibited up-regulated oxidative phosphorylation and down-regulated glycolysis (Fig.  4 B). N10 displayed up-regulated IL2-STAT5 signaling and MYC-targets and down-regulated NOTCH signaling and inflammatory response.

Further analysis of neutrophils from the HD, MI, and SE groups. A Neutrophils were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among neutrophil subsets. C The t-SNE plot of neutrophil profiles, with cells divided by group. D Heatmap displaying marker genes for cluster N0–13. E Proportions of neutrophil subsets in the three comparison groups

Cytotoxic NK cells are decreased while memory-like NK cells are increased in tuberculosis patients

NK cells can kill Mtb -infected cells directly or through antibody-dependent cellular cytotoxicity [ 29 ]. Sub-cluster analysis revealed five distinct NK cell clusters: NK0–4 (Fig.  6 A, B ). NK0 expressed high levels of GZMB and GNLY, indicative of high cytotoxic activity, while NK1 selectively expressed high levels of KLRC2, indicating the presence of a memory-like NK cell subset (Fig.  6 E, S5). The proportion of the NK0 subset decreased, while the proportion of the NK1 subset increased in the MI and SE groups compared to the HD group (NK0: HD 61.75%, MI 49.6%, SE 56.84%; NK1: HD 20.37%, MI 28.90%, SE 24.47%, Fig.  6 I), which suggests that cytotoxic NK cell subset is decreased in all tuberculosis patients, and memory-like NK cell subset was increased. GSVA analysis (Fig.  6 G) showed that NK0 displayed up-regulated glycolysis, while NK1 displayed up-regulated fatty acid metabolism. In addition, NK1 displayed up-regulated reactive oxygen species, MTORC1 signaling, the P53 pathway, and peroxisome.

Further analysis of NK and B cells from HD, MI, and SE groups. Figure  5 Further analysis of neutrophils from HD, MI, and SE groups. A NK cells were highlighted and extracted for further clustering. B The t-SNE plot of NK cell profiles, with cells divided by group. C B cells were highlighted and extracted for further clustering. D The t-SNE plot of B cell profiles, with cells divided by group. E Heatmap displaying marker genes for cluster NK0–4. F Heatmap displaying marker genes for cluster B0–4. G Differences in GSVA pathway analysis among NK cell subsets. H Differences in GSVA pathway analysis among B cell subsets. I Proportions of NK cell subsets in the three comparison groups. J Proportions of B cell subsets in the three comparison groups

IGHG3 high TTN high FCRL5 high B cell subset was increased in all tuberculosis patients

Recently, the dynamics of B cell memory responses have been characterized at different stages of the clinical spectrum of Mtb infection, suggesting that B cells play an important role in human tuberculosis [ 30 ]. Five different B cell subsets, B0-4, were identified in the present study, each representing a distinct stage of B cell development (Fig.  6 C, D ). B0 expressed high levels of TCL1A, CD79A, CD79B, and MS4A1 but lacked CD27 expression, indicating that B0 is a subset of follicular B cells [ 13 , 31 , 32 ]. B1 expressed high levels of MS4A1, CD79A, and CRIP1, and low levels of TCL1A (Fig.  6 F, S6), indicating a mature B cell subset [ 32 ]. B2 is a cell subset that exhibits a relatively high expression of IGHG3, TTN, and FCRL5. B4 expresses high levels of CD38 and low levels of CD19, indicating that it is probably an abnormal plasma cell subset [ 33 ]. As observed in t-SNE plots and cell proportion plots (Fig.  6 J), the B2 subset was significantly increased in the MI group (10.88%) and the SE group (6.54%) compared to the HD group (1.75%). GSVA analysis (Fig.  6 H) showed that B2 displayed up-regulated pathways including MYC-targets, WNT-beta-catenin, and interferon alpha response.

CD8+ T cells from patients with severe tuberculosis exhibit the highest outgoing interaction strengths

During Mtb infection, numerous crucial interactions take place between Mtb and immune cells, as well as among different immune cells [ 34 ]. Understanding global communication among PBICs requires an accurate representation of intercellular signaling links. Therefore, an effective system-level analysis has been applied to quantitatively infer and analyze intercellular communication networks [ 35 ]. To demonstrate cellular communication among different cell subsets, all PBICs were reclassified as neutrophils, CD14+ monocytes, CD16+ monocytes, CD8+ T cells, CD4+  naive T cells, CD4+ memory T cells, B cells, NK cells, and plasmas. The numbers of significant interactions among different cell types in the HD, MI, and SE groups are shown in Fig.  7 A, B , respectively.

Cellular communication analysis. A Interactions strength between cell populations in the HD group. B Interactions strength between cell populations in the MI group. C Interactions strength between cell populations in the SE group. The width of the connecting line is directly related to the strength of the interaction. The wider the line, the stronger the interaction. D Incoming and outgoing interaction strength of all pathways in the HD group. E Incoming and outgoing interaction strength of all pathways in the MI group. F Incoming and outgoing interaction strength of all pathways in the SE group. The horizontal coordinates represent the outgoing interaction strength. The vertical coordinates represent the incoming interaction strength. The size of the circle represents the number of pathways. G The inferred incoming communication patterns of target cells are visualized by an alluvial plot. The thickness of the flow indicates the contribution of the cell group or signaling pathway to each latent pattern. The height of each pattern is proportional to the number of its associated cell groups or signaling pathways. H The incoming communication patterns of target cells in the SE group. The size of the dots represents the contribution of the pattern to intercellular communication. I Ligand–receptor pairs of MHC-I pathway targeting CD8+ T cells in the SE group. J Ligand–receptor pairs of LCK pathway targeting CD8+ T cells in the SE group. The horizontal coordinates represent the direction of the ligand–receptor pairs. The vertical coordinates represent ligand–receptor pairs of the MHC-I ( I ) and LCK pathway ( J ). The size of the dot represents the p-value. Colors represent communication probabilities

Notably, there was the highest incoming interaction strength exhibited by CD8+ T cells in the SE group (Fig.  7 D–F), which was significantly higher than that of CD14+ monocytes and FCGR3A+ monocytes. The latter two cell types exhibited the highest and second-highest incoming interaction strengths in both the HD and MI groups. These results indicated that CD8+ T cells receive more cellular interactions in severe tuberculosis.

Further analysis of incoming communication patterns of receiver cells showed that CD8+ T cells in the SE group received incoming communications through pattern 2 (Fig.  7 G). And pattern 2 is comprised of MHC-I, ANNEXIN, CD45, LCK, and CD86 pathways. Since there was no cellular communication received from the ANNEXIN, CD45, and CD86 pathways by CD8+ T cells in the SE group, the increase in CD8+ T cell incoming interactions was associated with MHC-I and LCK pathways (Fig.  7 H).

We further analyzed the ligand–receptor pairs of MHC-I and LCK pathways targeting CD8+ T cells in the SE group. HLA-(A-E)-CD8A, HLA-(A-E)-CD8B, and LCK-(CD8A+CD8B) are ligand–receptor pairs that targeting CD8+ T cells in the SE group (Fig.  7 I, J). These results suggest that the above ligand–receptor pairs that target CD8+ T cells are associated with severe tuberculosis.

Our understanding of the mechanisms by which humans coordinate immune responses to pathogens is limited due to a lack of information about the immune landscape in peripheral blood [ 36 ]. With the assistance of scRNA-seq, the peripheral blood immune profiles of MI and SE patients were mapped, revealing differences in peripheral blood immune cell subpopulations and their functions between MI and SE patients as well as between healthy donors. In addition, cellular communication analyses revealed peripheral blood immune cell interactions, pathways, and receptor–ligand pairs.

Monocytes/macrophages play an essential role in the control of Mtb infection. Paradoxically, macrophages also serve as the natural habitat of Mtb [ 37 ]. This phenomenon has aroused tremendous interest among scientists. Huang et al. [ 38 ] studied alveolar macrophage (AM) and interstitial macrophage (IM) profiles using fluorescent Mtb reporter strains. According to their finding, IMs have glycolytic activity, while AMs are dedicated to fatty acid oxidation. IMs exhibit nutrient limitation and control of bacterial growth, while AMs represent more nutrient-permissive environments. In vitro macrophage infection, treatment with the glycolysis inhibitor 2-deoxyglucose increased bacterial growth, whereas the fatty acid oxidation inhibitor etomoxir inhibited bacterial growth. Consistently, Mtb survival in monocytes is also mainly fueled by fatty acids and cholesterol [ 11 ]. The above strongly suggests that macrophages/monocytes in preference for glycolytic metabolism are more efficient in clearing Mtb , whereas fatty acid oxidative metabolism is the opposite. These findings are consistent with our results that the most classic M0 subset showed up-regulated glycolysis and down-regulated fatty acid metabolism. In addition, our results also show that M0 has a higher proportion in SE.

Neutrophils, the most abundant innate immune cells, are strongly heterogeneous [ 39 , 40 , 41 ]. Neutrophils migrate from the circulating blood to infected tissues in response to inflammatory stimuli and protect the host by phagocytizing, killing, and digesting bacteria [ 42 ]. Differentiation and maturation of neutrophils give rise to different neutrophil subpopulations that might be functionally preprogrammed differently. The discrete microenvironment can alter neutrophil function and behavior. In addition, the rapid aging of neutrophils, short lifespan, and mechanically induced cellular responses as they enter and exit capillaries contribute to neutrophil heterogeneity. Some neutrophil subpopulations overlap and lead to controversy regarding neutrophil function. As a result, scRNA-seq studies of neutrophils are difficult, leading to the majority of scRNA-seq studies only investigating peripheral blood mononuclear cells (PBMCs). Currently, the full heterogeneity and differentiation status of neutrophils is still not fully determined [ 7 ]. To demonstrate the complete immune landscape of the peripheral blood of MI and SE patients, neutrophils were not discarded in the present study. Our findings revealed, for the first time, that a SLPI high PTGS2 high LST1 high subset was significantly reduced in SE patients, and a GBP5 high RSAD2 high subset was only present in SE patients. It has implications for subsequent research as well as treatment.

Human NK cells are the effector cells of innate immunity and have a potential role in immunosurveillance against Mtb infection. According to our data, cytotoxic NK cells and cytotoxic CD4+ T cells are decreased in all tuberculosis patients. This might be related to the lack of protective immune response in patients with tuberculosis infection. It is consistent with the findings of Kathamuthu [ 43 ] et al. that the number of T cells and NK cells expressing cytotoxic markers decreased at the site of Mtb infection, which might reflect the lack of protective immune response. B cells mediate adaptive immune activation and participate in host defense against Mtb , however, fewer studies have been conducted on B cells. Based on scRNA-seq, it is revealed that a B cell subset with relatively high expression of IGHG3, TTN, and FCRL5 was increased in tuberculosis patients. It has been reported that overexpression of FCRL5 by B cells is associated with low antibody titers following HCV infection [ 44 ]. Therefore, it seems that the B-cell-mediated adaptive immune response has been hampered in tuberculosis patients.

CD8+ T cells have been proven to play a direct role in the response to Mtb infection and coordinate many different functions in the overall host immune response. CD8+ T cells can recognize Mtb-specific antigens presented by classical (HLA-A, -B, -C) and non-classical MHC molecules (HLA-E) [ 45 ]. CD8+ T cells can lyse Mtb-i nfected macrophages and kill the intracellular bacilli in an antigen-specific fashion. Our findings revealed that CD8+ T cells from SE patients received more MHC cell signaling, which might be associated with enhanced killing of infected macrophages and Mtb . Lymphocyte-specific protein tyrosine kinase (LCK) is a member of the Src family of protein tyrosine kinases (PTKs), encoding a key signaling protein in the selection and maturation of developing T-cells. Studies using LCK knock-out mice or LCK deficient T-cell lines have shown that LCK regulates the initiation of TCR signaling, T-cell development, and T-cell homeostasis [ 46 ]. Enhanced LCK signaling promotes the activation of CD8+ T cells [ 47 ]. Therefore, enhancement of the LCK pathway targeting CD8+ T cells in SE patients is also related to the activation of CD8+ T cells.

Patients with tuberculosis, especially those with severe tuberculosis, have the highest intensity of CD8+ T-cell interactions, which gives us a hint that CD8+ T cells play a critical role in tuberculosis, and that intervention in CD8+ T cells might be the key to the treatment of tuberculosis patients. Meanwhile, the MHC-I and LCK pathways are the main signals involved in the significantly increased CD8+ T cell interactions, suggesting that these are valuable targets for therapeutic intervention in tuberculosis.

The present study has several limitations and further optimization and expansion of the tuberculosis scRNA-seq dataset. Firstly, our scRNA-seq dataset consists of relatively small samples with gender bias, and inter-individual differences might harm the results. Secondly, this study is only an exploratory experiment based on unbiased and unlabeled scRNA-seq, and a further combination of other techniques, such as multiparameter flow cytometry analyses, is required to provide valid results to draw more reliable conclusions. Thirdly, the resolution of the study data is still insufficient for cell populations with lower frequencies. Some cell clusters may be composed of more than one phenotype of cells. Additional single-cell analysis tools such as surface protein labeling will improve our ability to detect and identify these important cell populations in future analyses.

The present study mapped the peripheral blood immune profiles of healthy donors and patients with mild tuberculosis and severe tuberculosis, revealing that patients with tuberculosis, especially severe tuberculosis, have profound changes in peripheral blood immune cell profiles. CD8+ T cells showed the highest incoming interaction strength in patients with severe tuberculosis, with the main signals being MHC-I and LCK pathways.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

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This study was supported by the funds from: (1) National Key R&D Program of China (2023YFC2307300). (2) Shanghai Shenkang Hospital Development Center Emerging Frontier Technology Joint Research Project (SHDC12022108). (3) Shanghai 2020 “science and technology innovation action plan” technological innovation fund: Clinical Study on New Short-Course treatment regimens and Host-Directed Therapy for MDR-TB (Grant ID: 20Z11900500). (4) Shanghai Clinical Research Center for Infectious Disease (tuberculosis) (Grant ID: 19MC1910800). (5) Shanghai’s 2023 “Science and Technology Innovation Action Plan”, special general project of medical innovation research (Grant No.23Y11900300).

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Li Wang, Ya He, and Peng Wang contributed equally to this work and should be considered co-first authors.

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Clinic and Research Center of Tuberculosis, School of Medicine, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China

Li Wang, Ya He, Peng Wang, Hai Lou & Wei Sha

Department of Tuberculosis, School of Medicine, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China

Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

Haipeng Liu

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WL and HY wrote the main manuscript text. PW contributes to data curation. HL contributes to the investigation. All authors read and approved the final manuscript. SW and HPL contributes to conceptualization. All authors reviewed the manuscript.

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Wang, L., He, Y., Wang, P. et al. Single-cell transcriptome sequencing reveals altered peripheral blood immune cells in patients with severe tuberculosis. Eur J Med Res 29 , 434 (2024). https://doi.org/10.1186/s40001-024-01991-5

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Association of mutations in Mycobacterium tuberculosis complex (MTBC) respiration chain genes with hyper-transmission

• Yameng Li 1 , 2   na1 ,
• Yifan Li 3   na1 ,
• Yao Liu 1 ,
• Xianglong Kong 4 ,
• Ningning Tao 1 ,
• Yawei Hou 5 ,
• Tingting Wang 2 ,
• Qilin Han 6 ,
• Yuzhen Zhang 6 ,
• Fei Long 3 &
• Huaichen Li 1 , 2  

BMC Genomics volume  25 , Article number:  810 ( 2024 ) Cite this article

Metrics details

The respiratory chain plays a key role in the growth of Mycobacterium tuberculosis complex (MTBC). However, the exact regulatory mechanisms of this system still need to be elucidated, and only a few studies have investigated the impact of genetic mutations within the respiratory chain on MTBC transmission. This study aims to explore the impact of respiratory chain gene mutations on the global spread of MTBC.

A total of 13,402 isolates of MTBC were included in this study. The majority of the isolates ( n  = 6,382, 47.62%) belonged to lineage 4, followed by lineage 2 ( n  = 5,123, 38.23%). Our findings revealed significant associations between Single Nucleotide Polymorphisms (SNPs) of specific genes and transmission clusters. These SNPs include Rv0087 ( hycE , G178T), Rv1307 ( atpH , C650T), Rv2195 ( qcrA , G181C), Rv2196 ( qcrB , G1250T), Rv3145 ( nuoA , C35T), Rv3149 ( nuoE , G121C), Rv3150 ( nuoF , G700A), Rv3151 ( nuoG , A1810G), Rv3152 ( nuoH , G493A), and Rv3157 ( nuoM , A1243G). Furthermore, our results showed that the SNPs of atpH C73G, atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T were positively correlated with cross-country transmission clades and cross-regional transmission clades.

Conclusions

Our study uncovered an association between mutations in respiratory chain genes and the transmission of MTBC. This important finding provides new insights for future research and will help to further explore new mechanisms of MTBC pathogenicity. By uncovering this association, we gain a more complete understanding of the processes by which MTBC increases virulence and spread, providing potential targets and strategies for preventing and treating tuberculosis.

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Tuberculosis (TB) is a highly contagious disease caused by Mycobacterium tuberculosis complex (MTBC), which is one of the two main human-adapted TB bacteria. The MTBC consists of animal-adapted and human-adapted strains. Among the human-adapted MTBC are the Mycobacterium tuberculosis sensu stricto comprising Lineages (L) 1–4, 7 and 8 and Mycobacterium africanum comprising L5 and L6, L9 and L10) predominantly found in Africa [ 1 ]. In our study, we specifically focused on the phylogeographical distribution of Mycobacterium tuberculosis sensu stricto L 2 and L4. Despite the availability of preventive and treatment measures, TB remains a significant global public health concern [ 2 ]. According to the World Health Organization (WHO), approximately one-fourth of the world’s population is infected with MTBC [ 2 ]. Therefore, it is crucial to understand the factors that influence the transmission of MTBC in order to develop effective strategies for TB control.

The respiratory chain is a critical process within bacterial cells that transfers electrons from substrates to terminal acceptors through redox reactions, generating energy. The respiratory chain of MTBC, the causative agent of tuberculosis, mainly consists of three complexes: NADH dehydrogenase complex I, cytochrome c oxidase complex IV, and respiratory chain enzyme III [ 3 , 4 , 5 ]. These complexes work together to facilitate electron transfer and proton pumping, ultimately resulting in the creation of a chemical gradient and intracellular ATP synthesis. NADH dehydrogenase complex I receives electrons from NADH, a product of cellular respiration, and passes them to the next complex in the chain [ 3 , 6 ]. Cytochrome c oxidase complex IV acts as the terminal acceptor of electrons, combining them with oxygen to complete the final step of the respiratory chain, the reduction of oxygen to water. Respiratory chain enzyme III plays a critical role in electron transfer between complexes I and IV while simultaneously facilitating proton pumping across the membrane [ 4 , 5 ]. The overall process of electron transfer and proton pumping leads to the establishment of an electrochemical gradient across the bacterial membrane. This gradient is essential for ATP synthesis as it drives the rotation of ATP synthase, an enzyme responsible for converting ADP into ATP. ATP, the primary energy currency of cells, provides the necessary energy for various cellular activities and metabolic processes [ 7 , 8 , 9 ]. Understanding the complexity of the respiratory chain in MTBC is crucial for unraveling the pathogenesis of tuberculosis and identifying potential therapeutic targets. Disrupting the acquisition and utilization mechanisms of the bacterial respiratory chain shows promise in inhibiting the growth and survival of MTBC, providing opportunities for developing effective anti-tuberculosis strategies. It is important to note that further research is needed to determine the specific regulatory mechanisms of respiratory chain-related genes in the dissemination of MTBC. While various gene groups may impact TB transmission, our focus on respiratory chain genes stems from their central role in cellular energy production. By investigating the influence of mutations in these genes on the transmission dynamics of MTBC, we aim to gain insights into the pathogenesis of tuberculosis and identify potential intervention targets.

Whole-genome sequencing (WGS) is increasingly being employed to explore the transmission dynamics of the MTBC [ 10 ]. In this study, we utilized WGS to examine the impact of respiratory chain gene mutations on the global transmission of the MTBC focusing on large clusters and clades as indicators of transmission.

Sample collection

During the period from 2011 to 2018, a total of 1,550 cases of MTBC infection with positive culture were documented in two medical institutions in China: the Shandong Public Health Clinical Research Center (SPHCC) and the Weifang Respiratory Disease Hospital (WRDH). It is important to note that this study excluded cases where MTBC culture was positive but had already been evaluated and treated previously.

DNA extraction and sequencing

We extracted genomic DNA from a total of 1,447 isolates of MTBC using the Cetyltrimethylammonium Bromide (CTAB) method. After an accidental loss of two isolates, genome sequencing was performed on the remaining 1,445 isolates using the Illumina HiSeq 4000 system. Subsequently, the generated sequence data was deposited in the National Center for Biotechnology Information (NCBI) and assigned the BioProject PRJNA1002108 accession. In addition, we included 11,957 MTBC genomes sourced from published literature from a diverse range of geographic locations worldwide [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. These genomes came from 52 countries and 18 regions of the world. For precise mapping of our genomes to the of standard H37Rv reference genome, we utilized the BWA-MEM algorithm (version 0.7.17-r1188). Our analysis was specifically focused on samples with a coverage rate equal to or exceeding 98% and a minimum depth of at least 20% [ 20 ]. In summary, this study involved the analysis of a total of 13,402 genomes. Please refer to Additional file 1: Tables S1 - S2 for the specific sample numbers.

Single nucleotide polymorphism (SNP) analysis

We conducted variant calling using Samclip (version 0.4.0) and SAMtools (version 1.15), followed by additional filtering steps to refine the identified variants. To further filter the variants, we employed Free Bayes (version 1.3.2) and Bcftools (version 1.15.1). In order to ensure the accuracy of our analysis, we excluded SNPs located within repeat regions, including polymorphic GC-rich sequences in PE/PPE genes, direct repeat SNPs, and repeat bases identified using Tandem Repeat Finder (version 4.09) and RepeatMask (version 4.1.2-P1) [ 21 , 22 ]. Additionally, SNP annotation was performed using SnpEff v 4.1 l, and the output was obtained utilizing the Python programming language [ 23 ].

Phylogenetic analysis

According to Coll et al [ 24 ], the isolates in this study were classified into different lineages (Additional file 1: Tables S1 - S2 ). To construct the maximum likelihood phylogenetic tree, we utilized the IQ-TREE software package (version 1.6.12). The JC nucleotide substitution model and gamma model of rate heterogeneity were used, with 100 bootstrap replicates included for statistical support [ 25 ]. The resulting tree was rooted on Mycobacterium canettii CIPT140010059 as an outlier. The resulting phylogenetic tree was visualized using iTOL ( https://itol.embl.de/ ) for better representation and interpretation.

Propagation analysis

We utilized cluster and clade analysis to investigate the influence of mutations in respiratory chain genes on the transmission dynamics of MTBC [ 26 ]. The selection of SNP thresholds for defining clusters and clades was based on factors such as the study context, geographic location, and the genetic diversity of the isolates analyzed. From previous research, it was suggested that a difference of less than 5 SNPs indicated recent transmission, while a difference of more than 12 SNPs generally did not represent recent transmission networks [ 27 ]. However, we took into consideration various factors that could affect the number of observed SNP differences between isolates. These factors included time lapse, acquisition of drug resistance, lineage characteristics, mixed infections, laboratory contamination, and variations in the molecular clock of the tuberculosis genome [ 27 ]. Given the wide range of locations and time periods (1991–2019) represented by the collected MTBC strains, we selected a threshold of 12 SNPs to define clusters representing recent transmission events and a threshold of 25 SNPs to capture broader transmission patterns within clades [ 28 ]. To further categorize these transmission clades, we adopted a widely used classification system. Clades were classified into three groups based on their size: large (above the 75th percentiles), medium (between the 25th and 75th percentiles), and small (below the 25th percentiles) [ 29 ]. Specifically, clades comprising two strains were classified as “small clades,” those with 3 to 14 strains as “medium clades,” and those with 15 or more strains as “large clades.”

To conduct a comprehensive analysis of the global distribution patterns and transmission dynamics of MTBC isolates, we classified them into two categories: cross-country and within-country clades. The cross-country clade is defined as a clade containing isolates from two or more different countries. Furthermore, based on geographical regions defined by the United Nations standard regions (UN M.49), the MTBC isolates were classified into cross-regional and within-regional clades. Cross-regional clades included isolates from multiple regions. The cross-regional clade is defined as a clade containing isolates from two or more different regions.

Acquisition of respiratory chain genes

Our analysis commenced by retrieving all genes associated with the MTBC from the NCBI database, resulting in a comprehensive dataset comprising 4015 genes. We specifically focused on the H37Rv strain and meticulously filtered the gene list based on their respective organism names, resulting in a refined set of 4009 genes. Subsequently, we directed our attention towards refining the gene selection process with a particular emphasis on identifying genes that are linked to the respiratory chain. This involved evaluating their functional descriptions and characteristic annotations, which successfully led to the identification of 29 genes directly implicated in the respiratory chain. To identify mutations within this set of respiratory chain genes, we employed a detailed pipeline utilizing snippy (v4.6.0). The pipeline encompassed BWA-MEM for sequence alignment, Samtools for sorting and indexing, FreeBayes for variant calling, Bcftools for quality filtering, and SnpEff for SNP annotation in genes associated with the respiratory chain. (Additional file 1: Table S3 ).

Modeling and statistical analysis

To ensure statistical reliability, we excluded mutations that were observed fewer than ten times from the analysis. The data were presented as percentages. For statistical analysis, we employed generalized linear mixed models (GLMM) in the R statistical language (version 4.2.3). Additionally, Python 3.7.4 with the Scikit-learn library (Python Software Foundation, USA; Packt Publishing, UK) was used to implement random forest and gradient boosting decision tree algorithms for further analysis. The dataset was randomly divided into a training set and a test set at a ratio of 7:3. Spearman’s rank correlation analysis was conducted using R version 4.2.3 to assess the impact of mutations in respiratory chain genes on clade size. Confounding factors such as lineage and geographical location were taken into account during all analyses. All statistical analyses were performed using SPSS version 26.0. Two-tailed tests were employed, and statistical significance was defined as a P -value below 0.05.

Sample description

A total of 13,402 isolates were included in the analysis, with the majority belonging to lineage 4 ( n  = 6,382, 47.62%) and lineage 2 ( n  = 5,123, 38.23%). A smaller proportion of isolates belonged to lineage 3 ( n  = 969, 7.23%) and lineage 1 ( n  = 851, 6.35%). Employing a threshold of 12 SNPs for clustering, a total of 6,021 isolates formed clusters, resulting in a clustering rate of 0.45. This finding revealed that approximately 45% of the isolates share closely related genetic profiles, indicating potential recent transmission events. Among the lineage 2 isolates, 2,131 isolates clustered together, yielding a clustering rate of 0.42. For lineage 4, 3,445 isolates clustered together, resulting in a clustering rate of 0.54. By utilizing a threshold of 25 SNPs for clade definition, 8,299 isolates formed clades, resulting in a clade rate of 0.62. The MTBC isolates were further categorized into 2,218 clades, varying in size from 2 to 224 isolates per clade. Within the cross-country clades, a total of 189 clades were identified, encompassing 739 isolates. These clades spanned two or three countries. Similarly, within the cross-regional clades, a total of 167 clades were observed, encompassing 685 isolates. These clades spanned two or three regions. For further details see in Table  1 . The phylogenetic tree of MTBC isolates was constructed as depicted in Fig.  1 and Additional file 2: Figs. S1 - S3 .

A phylogenetic tree is depicted for the Mycobacterium tuberculosis isolates, with the outer circle indicating mutation sites of the respiratory chain genes. ( A ) Phylogenetic tree for the Mycobacterium tuberculosis isolates of lineage4.1. ( B ) Phylogenetic tree for the Mycobacterium tuberculosis isolates of lineage4.3

Relationship between respiratory chain gene mutations and transmission clusters

We conducted a comparison between clustered isolates and non-clustered isolates and explored the relationship between 166 SNPs and clustered isolates. In the analysis of the generalized linear mixed model (GLMM), we removed variables with multicollinearity, resulting in the exploration of only 148 SNPs. The subsequent GLMM analysis uncovered that 34 of these SNPs displayed statistically significant clustering ( P  < 0.05) (Additional file 1: Table S4 ). Among these SNPs, there were 14 nonsynonymous SNPs, a stop SNP, and 12 synonymous SNPs that demonstrated a positive correlation with transmission clusters of MTBC isolates. Notable SNPs included Rv0087 ( hycE , G178T), Rv0392c ( ndhA , A1028G), Rv1307 ( atpH , C650T), Rv1622c ( cydB , G583C), Rv2195 ( qcrA , G181C), Rv2196 ( qcrB , G1250T), Rv3145 ( nuoA , C35T), Rv3149 ( nuoE , G121C), Rv3150 ( nuoF , G700A), Rv3151 ( nuoG , C668T, A1810G), Rv3152 (nuoH, G493A), and Rv3157 ( nuoM , A1243G).

To build up tree decision models, we adopted random forest and gradient boosting decision tree (Table  2 , Additional file 1: Table S5 , and Fig.  2 ). These models identified hycE G178T, atpH C650T, qcrA G181C, qcrB G1250T, nuoA C35T, nuoE G121C, nuoF G700A, nuoG A1810G, nuoH G493A, and nuoM A1243G as most influential factors. However, ndhA A1028G, cydB G583C, and nuoG C668T did not significantly contribute to the gradient boosting decision tree model. Overall, our results suggested a positive correlation between hycE G178T, atpH C650T, qcrA G181C, qcrB G1250T, nuoA C35T, nuoE G121C, nuoF G700A, nuoG A1810G, nuoH G493A, nuoM A1243G, and transmission clusters of MTBC isolates.

Conduct ROC curve analysis to evaluate the performance of models for the relationship between mutations in respiratory chain genes and transmission clusters. ( A ) ROC analysis showing the performance of the random forest model. ( B ) ROC analysis showing the performance of the gradient boosting decision tree

Relationship between respiratory chain gene mutations and transmission clusters of lineages

In comparison to non-clustered isolates, we conducted an analysis to examine the relationship between 37 SNPs and clustered isolates specifically belonging to lineage 2. In the analysis of the GLMM, we removed variables with multicollinearity, resulting in the exploration of only 34 SNPs. The subsequent GLMM revealed that eight SNPs exhibited statistical significance for clustering ( P  < 0.05) (Table  3 ). Among these, there were four nonsynonymous SNPs and three synonymous SNPs that displayed a positive correlation with clustering. These significant SNPs included Rv2196 ( qcrB , G1250T), Rv3150 ( nuoF , G66C), Rv3151 ( nuoG , A1422G), and Rv3152 ( nuoH , G493A). Two prediction models were established using random forest and gradient boosting decision tree algorithms (Additional file 1: Table S6 , Table S7 , and Additional file 2: Fig. S4 ). Our findings demonstrated that qcrB G1250T, nuoF G66C, nuoG A1422G, and nuoH G493A significantly contributed to both the random forest and gradient boosting decision tree models. Overall, our results indicated a positive correlation between qcrB G1250T, nuoF G66C, nuoG A1422G, nuoH G493A, and transmission clusters of lineage 2.

In comparison to non-clustered isolates, we conducted an analysis on the relationship between 77 SNPs and clustered isolates specifically belonging to lineage 4. In the analysis of the GLMM, we removed variables with multicollinearity, resulting in the exploration of only 73 SNPs. The subsequent GLMM revealed that 18 SNPs were found to be statistically significant for clustering ( P  < 0.05) (Table  4 ). Among these, there were eight nonsynonymous SNPs, a stop SNP, and eight synonymous SNPs that displayed a positive correlation with clustering. These significant SNPs included Rv0087 ( hycE , G178T), Rv1307 ( atpH , C650T), Rv1622c ( cydB , G583C), Rv2195 ( qcrA , G181C), Rv3145 ( nuoA , C35T), Rv3146 ( nuoB , G490C), Rv3149 ( nuoE , G121C), Rv3150 ( nuoF , A694G), Rv3151 ( nuoG , A1810G). Two prediction models were established using random forest and gradient boosting decision tree algorithms (Additional file 1: Table S8 , Table S9 , and Additional file 2: Fig. S5 ). We found hycE G178T, atpH C650T, qcrA G181C, nuoA C35T, nuoB G490C, nuoE G121C, nuoF A694G, and nuoG A1810G contributed significantly to both the random forest and gradient boosting decision tree models. However, the cydB G583C did not contribute significantly to the gradient boosting decision tree model. Our findings indicated a positive correlation between specific SNPs, including hycE G178T, atpH C650T, qcrA G181C, nuoA C35T, nuoB G490C, nuoE G121C, nuoF A694G, nuoG A1810G, and transmission clusters of lineage 4.

Relationship between respiratory chain gene mutations and cross-country transmission

A total of 114 SNPs in respiratory chain genes were analyzed to assess their relationship with cross-country transmission clades. In the analysis of the GLMM, we removed variables with multicollinearity, resulting in the exploration of only 91 SNPs. The subsequent GLMM revealed the statistical significance of 13 SNPs in relation to cross-country transmission clades ( P  < 0.05) (Additional file 1: Table S10 ). Among these, there were seven nonsynonymous SNPs and four synonymous SNPs that displayed a positive correlation with transmission clades. These significant SNPs included Rv1307 ( atpH , C73G, A1264G), Rv1308 ( atpA , G271C), Rv2195 (q crA , G181C), Rv3154 ( nuoJ , G115A), Rv3157 ( nuoM , G772A), Rv3158 ( nuoN , G1084T).

Two prediction models were established using random forest and gradient boosting decision tree algorithms (Additional file 1: Table S11 , Table S12 , and Additional file 2: Fig. S6 ). The SNPs of atpH (C73G, A1264G), atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T exhibited significant contributions in both the random forest and gradient boosting decision tree models. Our findings demonstrated a positive correlation between specific SNPs, including atpH (C73G, A1264G), atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T, and the presence of cross-country transmission clades.

Relationship between respiratory chain gene mutations and cross-regional transmission

A total of 114 SNPs in respiratory chain genes were analyzed. In the analysis of the GLMM, we removed variables with multicollinearity, resulting in the exploration of only 91 SNPs. The subsequent GLMM showed that 14 SNPs were found to be statistically significant for cross-regional transmission clades ( P  < 0.05) (Additional file 1: Table S13 ). Among these, there were seven nonsynonymous SNPs and five synonymous SNPs that displayed a positive correlation with cross-regional transmission clades. These SNPs included Rv1307 ( atpH , C73G), Rv1308 ( atpA , G271C), Rv2195 ( qcrA , G181C), Rv3150 ( nuoF , C56T), Rv3154 ( nuoJ , G115A), Rv3157 ( nuoM , G772A), Rv3158 ( nuoN , G1084T). Two prediction models, random forest and gradient boosting decision tree, were established (Additional file 1: Table S14 , Table S15 , and Additional file 2: Fig. S7 ). The results indicated significant contributions of specific SNPs, including atpH C73G, atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T, in both the random forest and gradient boosting decision tree models. However, the SNP of nuoF C56T did not contribute significantly to the gradient boosting decision tree model. The presence of cross-regional transmission clades showed a positive association with specific SNPs, including atpH C73G, atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T, as indicated by our findings.

Relationship between respiratory chain gene mutations and clade size

A total of 114 SNPs in respiratory chain genes were analyzed. The results demonstrated that 75 SNPs were significantly associated with clade size ( P  < 0.05). Among these, there were seven nonsynonymous SNPs, two stop SNPs, and 12 synonymous SNPs that displayed a positive correlation with clade size. These included Rv0087 ( hycE , G178T), Rv0392c ( ndhA , C1024T), Rv1307 ( atpH , A428G), Rv1623c ( cydA , T31C), Rv2195 ( qcrA , G181C), Rv2196 ( qcrB , G1250T, C1376A), Rv3151 ( nuoG , A1810G), Rv3154 (nuoJ , G115A). For further details, please refer to Fig.  3 and Additional file 1: Table S16 .

Correlation analysis of respiratory chain gene mutations and clade size. Larger and red points indicate stronger correlations, while smaller and orange points represent weaker correlations. Positive correlations are shown in shades of red, while negative correlations are depicted in shades of orange

The transmission factors of pathogens have always posed a challenging issue to comprehend, and the transmission factors of MTBC are equally intricate. Up until now, there has been limited research on the impact of genetic mutations in the respiratory chain on the transmission of MTBC. However, we have demonstrated the potential influence of genetic mutations in the respiratory chain on the transmission of MTBC. Our findings provided evidence for the widespread and significant role of respiratory chain gene mutations in the transmission of MTBC. Our findings suggested a significant association between respiratory chain gene mutations and the transmission of MTBC.

In this study, we made an in-depth study on the respiratory chain of MTBC and made important progress, revealing its key role in the occurrence and development of tuberculosis. Our results showed that a large number of mutations in respiratory chain genes pose a significant risk to the spread of MTBC. The atpH gene plays a significant role in MTBC. It is part of the respiratory chain complex ATP synthase, responsible for catalyzing the conversion of ADP and phosphate into ATP within the cell [ 30 ]. In our study, we identified specific mutations in the atpH gene that showed associations with transmission clusters of MTBC. The mutation Ala217Val in atpH was found to be correlated with lineage 4 transmission clusters, while the mutation Leu25Val in atpH was observed in cross-country and cross-regional transmission clades of MTBC. Additionally, the mutation Ile422Val in atpH was linked to cross-country transmission clades. CydA is a subunit of the respiratory chain complex, cytochrome bd oxidase, and it participates in the oxygen metabolism process of bacteria [ 31 ]. Our research revealed that a mutation Ile314Ile in the cydA gene was associated with transmission clusters of MTBC, lineage 4 transmission clusters, cross-country transmission clades, and cross-regional transmission clades. Although this mutation is synonymous, it may still impact the functionality of the oxidase, thereby influencing the oxygen metabolism and physiological adaptation of MTBC [ 32 ]. QcrA plays a crucial role in MTBC. It is a subunit of the respiratory chain complex, quinol-cytochrome c reductase, and participates in the electron transfer process of bacteria [ 33 ]. This enzyme plays a key role in oxidizing quinol to cytochrome c within the respiratory chain. Our findings indicated that a mutation Glu61Gln in the qcrA gene was associated with transmission clusters of MTBC, lineage 4 transmission clusters, cross-country transmission clades, and cross-regional transmission clades. The mutation in the qcrA gene may potentially impact the functionality of quinol-cytochrome c reductase, thereby disrupting the energy metabolism and survival capability of MTBC. The protein encoded by the nuoJ gene is a subunit of NADH dehydrogenase complex I, which participates in the respiratory chain and ATP synthesis. However, limited research has been conducted on this gene. Our study found that the mutation Val165Ile of the nuoJ gene was associated with transmission clusters of MTBC, lineage 4 transmission clusters, cross-country transmission clades, and cross-regional transmission clades.

In addition, we have observed that the nuoG gene is not only associated with missense mutations but also with synonymous mutations in the transmission of MTBC. The nuoG gene corresponds to a subunit of NADH dehydrogenase complex I, which plays a crucial role in the respiratory chain as an oxidative phosphorylation enzyme [ 34 ]. Specifically, the mutation Thr604Ala of the nuoG gene was linked to the transmission clusters, lineage 4 transmission clusters, and clade size of MTBC, while the mutation Ile474Met was associated with lineage 2 transmission clusters. Our findings align with the results reported by Velmurugan et al., who discovered that the absence of nuoG in MTBC eliminates its ability to inhibit macrophage apoptosis and significantly reduces its virulence in mice [ 6 ]. Nevertheless, further research is required to elucidate the specific functionality and regulatory mechanisms of the nuoG gene in MTBC. Furthermore, our findings corroborate that both synonymous and non-synonymous mutations could influence the transmission of MTBC. This implied that synonymous mutations in respiratory chain genes were not universally benign, supporting prior research by Xukang Shen, which posited that synonymous mutations in yeast genes tended to be predominantly potent non-neutral mutations [ 9 ].

This study acknowledges and discusses the limitations that may have influenced our findings. One limitation relates to non-clustered isolates, where the absence of evidence does not necessarily indicate evidence of absence. This could be attributed to potential sampling bias, as it is practically impossible to exhaustively sample all isolates within a given population. Therefore, the limited diagnostic contribution of the identified mutations could be partially explained by this sampling bias. Additionally, our study focused on specific variables and may not encompass all possible factors influencing the observed outcomes. Other unmeasured or unknown factors might contribute to the limitations of our study. Furthermore, the generalizability of our findings may be constrained by the specific characteristics of the study population or sample size. Including all isolates from the same cluster or clade carries the risk of overrepresenting certain genotypes or lineages, potentially introducing bias into the statistical results. This sampling bias may impact the generalizability of our findings beyond the studied population. The selection of isolates based on available data and the specific inclusion/exclusion criteria employed may have introduced biases regarding strain distribution and characteristics. It is crucial to interpret the results within the context of these limitations and potential bias risks. They underscore the need for future research involving larger and more diverse sample sizes, representative sampling strategies, and the consideration of additional confounding factors. Addressing these limitations will enhance the reliability and generalizability of our findings. Another limitation is the lack of comprehensive assessment of the impact of mutations on the function of respiratory chain enzymes. While we identified and analyzed the presence and frequency of mutations in respiratory chain genes, further experimental verification is necessary to determine the functional consequences of these mutations on respiratory chain enzyme activity. Future studies should focus on conducting detailed biochemical and biophysical characterizations to evaluate the effect of nonsynonymous substitutions on enzyme function. Moreover, our study did not present specific hypotheses exploring the potential implications of respiratory chain enzyme dysfunction on infectivity and disease risk, despite their known roles in cellular metabolism and pathogenesis. Future research should develop hypotheses based on existing knowledge to elucidate the mechanisms by which respiratory chain dysfunction could impact infectivity and disease risk. Additionally, the evaluation of synonymous mutations and their significance was relatively limited in this study. The biological significance of synonymous mutations may be underestimated, requiring further investigation to accurately interpret their impact. Furthermore, the ratio of nonsynonymous to synonymous substitutions, commonly used as an indicator of selective pressure, was not specifically analyzed due to the specific research objectives and limitations of our study design. These limitations highlight areas for future research to build upon our findings. By conducting functional assays, developing hypotheses, and evaluating the impact of both synonymous and nonsynonymous variants on enzyme function and infectivity, future studies can provide a more comprehensive understanding of the role of respiratory chain mutations in infection transmission dynamics.

We fully acknowledge the limitations of not performing dN/dS ratio analysis. Firstly, the reason we did not perform a dN/dS ratio analysis primarily relates to method selection and data availability. dN/dS ratio analysis requires a large amount of high-quality whole-genome sequence data to ensure accuracy. However, due to resource and time constraints, our available dataset may not be sufficient to support such an analysis. Additionally, dN/dS ratio analysis requires a deep understanding of gene annotation and function, but our current knowledge of MTBC gene function remains quite limited. Secondly, although the dN/dS ratio is a commonly used parameter to measure natural selection pressure, it cannot capture all the complex factors influencing the dynamics of infection transmission. For instance, environmental pressures, host immune responses, and other factors can have significant impacts during the infection process, but these factors are not directly reflected in a dN/dS ratio analysis. However, this does not mean that we have completely ignored the role of natural selection. We have attempted to assess the potential impact of natural selection in other ways, such as by analyzing the frequency and distribution of gene mutations, and through detailed discussion of the biological effects of known mutations. Furthermore, we emphasize that future research, including dN/dS ratio analysis, is necessary for a more comprehensive understanding of the role of natural selection in infection transmission.

The findings of this study suggested that mutations in genes associated with the respiratory chain could potentially elevate the risk of MTBC transmission, underscoring the importance of conducting additional research to explore the impact of these mutations on the control and dissemination of MTBC. These results offered significant insights that could inform the development of therapeutic interventions for tuberculosis.

Data availability

The whole genome sequences have been submitted to the NCBI under the accession number PRJNA1002108. Additionally, we have uploaded the code to the GitHub repository, which can be accessed at https://github.com/shenmemingziheshi/Statistical-code.git .

Abbreviations

• Mycobacterium tuberculosis complex

Whole-genome sequencing

Shandong Public Health Clinical Research Center

Weifang Respiratory Disease Hospital

Cetyltrimethylammonium Bromide

Quality control

Single nucleotide polymorphism

Single nucleotide polymorphisms

National Center for Biotechnology Information

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Acknowledgements

We thank Shandong Public Health Clinical Research Center and Weifang Respiratory Disease Hospital for providing us with the clinical sample data. Additionally, we extend our thanks to all the authors who have shared their sequence datasets on NCBI.

This research was supported by the Natural Science Foundation of Shandong Provincial. (No. ZR2020KH013; No. ZR2021MH006; No. ZR2022QH259), the Department of Science & Technology of Shandong Province (CN) (No. 2007GG30002033; No. 2017GSF218052), and the Jinan Science and Technology Bureau (CN) (No. 201704100).

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Yameng Li and Yifan Li contributed equally to this work.

Authors and Affiliations

Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China

Yameng Li, Yao Liu, Ningning Tao & Huaichen Li

Clinical Department of Integrated Traditional Chinese and Western Medicine , The First Clinical Medical College of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250014, China

Yameng Li, Tingting Wang & Huaichen Li

Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Shandong First Medical University (Affiliated Hospital of Shandong Academy of Medical Sciences), Jinan, Shandong, 250031, China

Yifan Li & Fei Long

Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250011, China

Xianglong Kong

Institute of Chinese Medical Literature and Culture of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China

Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, China

Qilin Han & Yuzhen Zhang

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Contributions

HCL, FL, YML, and YFL participated in the study design. FL, YL, HCL, YML, XLK, NNT, and YFL performed data collection and statistical analyses. YL, TTW, YZZ, and YWH helped draft the manuscript. YWH, QLH, and YZZ overviewed and supervised the project. All authors read and approved the final manuscript.

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Correspondence to Fei Long or Huaichen Li .

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Ethics approval and consent to participate.

This study complies with the Declaration of Helsinki, and was approved by the Ethics Committee of Shandong Provincial Hospital, affiliated with Shandong University (SPH), the Ethics Weifang Respiratory Disease Hospital (WRDH) and the Ethics Committee of Shandong Provincial Chest Hospital (SPCH), which waived informed patient consent because all patient records and information were anonymized and deidentified before the analysis.

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Electronic supplementary material

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Supplementary Material 1: Table S1 Information of 1445 isolates of Mycobacterium tuberculosis.

Supplementary material 2: table s2 information of 11957 isolates of mycobacterium tuberculosis., supplementary material 3: table s3 information of respiratory chain gene mutations., supplementary material 4: table s4 generalized linear mixed model analysis on clustered and non-clustered isolates., supplementary material 5: table s5 important scores of each feature of various models in cluster., 12864_2024_10726_moesm6_esm.xlsx.

Supplementary Material 6: Table S6 The performance of various models for discriminating clustered isolates from non-clustered isolates in lineage2 cohort.

Supplementary Material 7: Table S7 Important scores of each feature in various models within the lineage 2 clusters.

12864_2024_10726_moesm8_esm.docx.

Supplementary Material 8: Table S8 The performance of various models for discriminating clustered isolates from non-clustered isolates in lineage4 cohort.

Supplementary Material 9: Table S9 Important scores of each feature in various models within the lineage 4 clusters.

Supplementary material 10: table s10 generalized linear mixed model analysis on cross-country transmission clades., 12864_2024_10726_moesm11_esm.xlsx.

Supplementary Material 11: Table S11 The performance of various models for discriminating cross-country from non-cross-country transmission clades.

12864_2024_10726_MOESM12_ESM.docx

Supplementary Material 12: Table S12 Important scores of each feature in various models within the cross-country transmission clades.

Supplementary Material 13: Table S13 Generalized linear mixed model analysis on cross-regional transmission clades.

12864_2024_10726_moesm14_esm.xlsx.

Supplementary Material 14: Table S14 The performance of various models for discriminating cross-regional from non-cross-regional transmission clades.

12864_2024_10726_MOESM15_ESM.xlsx

Supplementary Material 15: Table S15 Important scores of each feature in various models within the cross-regional transmission clades.

Supplementary Material 16: Table S16 Correlation analysis between respiratory chain gene mutations and clade size.

12864_2024_10726_moesm17_esm.docx.

Supplementary Material 17: Fig. S1 Phylogenetic tree for the Mycobacterium tuberculosis isolates of lineage4.2. Fig. S2 Phylogenetic tree for the Mycobacterium tuberculosis isolates of lineage4.4. Fig.S3 Phylogenetic tree for the Mycobacterium tuberculosis isolates of lineage4.8. Fig. S4 ROC curve analysis was conducted to evaluate the performance of models for cluster analysis within lineage 2. (A) ROC analysis showing the performance of the random forest model. (B) ROC analysis showing the performance of the gradient boosting decision tree. Fig. S5 ROC curve analysis was conducted to evaluate the performance of models for cluster analysis within lineage 4. (A) ROC analysis showing the performance of the random forest model. (B) ROC analysis showing the performance of the gradient boosting decision tree. Fig. S6 The ROC curve analysis was conducted to evaluate the performance of models for cross-country transmission clades analysis. (A) ROC analysis showing the performance of the random forest model. (B) ROC analysis showing the performance of the gradient boosting decision tree. Fig. S7 The ROC curve analysis was conducted to evaluate the performance of models for cross-regional transmission clades analysis. (A) ROC analysis showing the performance of the random forest model. (B) ROC analysis showing the performance of the gradient boosting decision tree.

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Li, Y., Li, Y., Liu, Y. et al. Association of mutations in Mycobacterium tuberculosis complex (MTBC) respiration chain genes with hyper-transmission. BMC Genomics 25 , 810 (2024). https://doi.org/10.1186/s12864-024-10726-z

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Facilitators and barriers to initiating and completing tuberculosis preventive treatment among children and adolescents living with HIV in Uganda: a qualitative study of adolescents, caretakers and health workers

• Pauline Mary Amuge 1 ,
• Denis Ndekezi 2 ,
• Moses Mugerwa 1 ,
• Dickson Bbuye 1 ,
• Diana Antonia Rutebarika 3 ,
• Lubega Kizza 4 ,
• Christine Namugwanya 1 ,
• Angella Baita 1 ,
• Peter James Elyanu 1 ,
• Patricia Nahirya Ntege 1 ,
• Dithan Kiragga 1 ,
• Carol Birungi 4 ,
• Adeodata Rukyalekere Kekitiinwa 1 ,
• Agnes Kiragga 5 ,
• Moorine Peninah Sekadde 6 ,
• Nicole-Austin Salazar 7 ,
• Anna Maria Mandalakas 8 &
• Philippa Musoke 9  

AIDS Research and Therapy volume  21 , Article number:  59 ( 2024 ) Cite this article

Metrics details

Introduction

People living with HIV (PLHIV) have a 20-fold risk of tuberculosis (TB) disease compared to HIV-negative people. In 2021, the uptake of TB preventive treatment among the children and adolescents living with HIV at the Baylor-Uganda HIV clinic was 45%, which was below the national target of 90%. Minimal evidence documents the enablers and barriers to TB preventive treatment (TPT) initiation and completion among children and adolescents living with HIV(CALHIV). We explored the facilitators and barriers to TPT initiation and completion among CALHIV among adolescents aged 10-19years and caretakers of children below 18years.

We conducted a qualitative study from February 2022 to March 2023, at three paediatric and adolescent HIV treatment centers in Uganda. In-depth interviews were conducted at TPT initiation and after completion for purposively selected health workers, adolescents aged 10–19 years living with HIV, and caretakers of children aged below 18years.

The desire to avoid TB disease, previous TB treatment, encouragement from family members, and ministry of health policies emerged as key facilitators for the children and adolescents to initiate TPT. Barriers to TPT initiation included; TB and HIV-related stigma, busy carer and adolescent work schedules, reduced social support from parents and family, history of drug side effects, high pill burden and fatigue, and perception of not being ill. TPT completion was enabled by combined TPT and ART refill visits, delivery of ART and TPT within the community, and continuous education and counseling from health workers. Reported barriers to TPT completion included TB and HIV-related stigma, long waiting time. Non-disclosure of HIV status by caretakers to CALHIV and fear of side effects was cited by health workers as a barrier to starting TPT. Facilitators of TPT initiation and completion reported by healthcare workers included patient and caretaker health education, counselling about benefits of TPT and risk of TB disease, having same appointment for TPT and ART refill to reduce patient waiting time, adolescent-friendly services, and appointment reminder phone calls.

The facilitators and barriers of TPT initiation and completion among CALHIV span from individual, to health system and structural factors. Health education about benefits of TPT and risk of TB, social support, adolescent-friendly services, and joint appointments for TPT and ART refill are major facilitators of TPT initiation and completion among CALHIV in Uganda.

Globally, 10.6 million people fell ill with tuberculosis (TB) in 2022, of which 12% were children below 15 years of age, and 23% reported in Africa [ 1 ]. People living with HIV (PLHIV) accounted for a disproportionate 6.7% of the TB cases and TB-HIV co-infection rates greater than 50% persist in numerous countries [ 1 ]. Out of the 1.6 million TB related deaths that occurred in 2021, 187,000 were among PLHIV, with 11% among children living with HIV [ 1 ].

Following TB exposure, PLHIV have a 20-fold increased life-time risk of progressing to TB disease, and up to 15% annual risk of TB disease, compared to the general population [ 2 ]. There is evidence that TB preventive treatment (TPT) in combination with anti-retroviral therapy (ART), reduces the risk of TB disease by up to 90% [ 3 , 4 ]. During the period 2018–2021, 10.6 million PLHIV received TPT globally, which was more than the targeted 6 million PLHIV. Nevertheless, there is minimal global data reporting TPT completion rates.

Uganda is one of the 30 countries categorized as high TB and TB/HIV burden by the World Health Organization (WHO) [ 1 ], with 74,799 TB patients reported in 2022, of which 32% were HIV-co-infected, and 12% were children below 15years of age [ 1 ]. Following three nation-wide TPT uptake campaigns led by the Ugandan ministry of health, 88.8% of the eligible PLHIV received TPT [ 5 ]. In Ugandan public health facilities, only 17% PLHIV initiated TPT out of the 93% who were eligible for TPT, with only 58% completing the full TPT course [ 6 ]. Some of the documented challenges contributing to such gaps in TPT uptake among PLHIV include; hesitancy of health workers to prescribe TPT for fear of promoting drug resistance, interrupted TPT supply, patients’ fear of additional pill burden and side-effects [ 6 ]. Non-completion of TPT was also associated with ART non-adherence, ART regime switch, and patient representation among adult PLHIV in rural Uganda [ 7 ]. Effective implementation of TPT, through addressing identified barriers and enhancing the facilitators of TPT [ 8 ], is key in reducing the burden of TB disease among PLHIV and bridging the TPT uptake and completion gaps [ 9 , 10 , 11 ]. However, there is limited data on TPT completion especially among PLHIV who are concurrently on ART. Therefore, it is important to understand the multi-faceted barriers and facilitators of initiating and completing TPT among the PLHIV. These may be related to the different healthcare system components such as; the clients or community, health policies, leadership and governance, drugs and logistics management, clinical information systems, service delivery, health workforce and financing [ 12 ]. Individual factors reported to facilitate TPT uptake and delivery among PLHIV in Tanzania include; alignment of ART and TPT visits, and TPT-related education and counseling. In South Africa, individual facilitators of TPT completion among PLHIV included; knowledge about TB and TPT, acceptance of one’s HIV status, having social support in the community and at the health facility, and desire for health preservation [ 13 ]. Individual barriers to TPT uptake and delivery included; perceived or previous experience of side effects, HIV stigma, pill burden, negative cultural and religious values, misunderstanding of TPT’s preventive role, financial burden of transport to the clinic and lost wages, and ineffective communication with the health workers [ 13 , 14 , 15 ].

Health care worker facilitators of TPT initiation among PLHIV include; comprehensive and collective planning, and supervision, presence of guidelines, TB-HIV training, positive attitude and being knowledgeable about TPT, known benefit of TPT, and effective health worker communication [ 8 , 13 , 16 ]. Health care worker and health system barriers to TPT delivery and uptake include; fear for isoniazid resistance due to interrupted drug supply, poor knowledge and attitude, misunderstanding about timing of TPT initiation, shortage of skilled health workers, variable TB screening practices and responsibilities, drug shortage [ 10 ], and contradicting guidelines from TB programs and HIV care programs [ 14 , 17 , 18 , 19 ]. In South Africa, lack of fidelity to national TPT guidelines was a barrier among health workers to initiation of TPT for PLHIV [ 20 ]. Absence of parental risk perception was reported as a barrier to TPT uptake among children in TB endemic areas [ 21 ]. Most of the documented facilitators and barriers to TPT initiation and completion are among adults, with limited reports for children, adolescents and their care takers.

Therefore, we conducted a qualitative study to explore the perceived and experienced barriers and facilitators to TPT initiation and completion among Ugandan children and adolescents living with HIV (CALHIV).

Theoretical orientation

A growing body of literature illustrates that health outcomes are progressively influenced by the environments within which individuals thrive and less by individual behaviors [ 22 ]. We therefore adopted the social ecological model (SEM) as a theoretical framework for analysis (see Fig.  1 below). The social-ecological model (SEM) of health promotion by McLeroy and colleagues states that health behaviour and promotion are interrelated and occur around multiple levels in the individual, interpersonal, institutional, community, and policy levels [ 23 ] This multifaceted perspective is important to understand and explicate barriers and facilitators of TPT initiation and completion among children and adolescents living with HIV, caregivers, and health care workers. The first level refers to individual factors that facilitate or inhibit a person’s choices, including personal stigma, limited knowledge about the prevention treatment, financial constraints and drug characteristics. The second level is interpersonal or network influences. An individual’s relationship with their closest caretakers, and family members influences their uptake and completion of preventative treatments. The third level is community perspectives, as children, caregivers and health care workers are influenced by community-held mass awareness campaigns community drug delivery services and community misconception about prevention treatments. The fourth level refers to health system (institutional) influences, including busy, unapproachable health care workers, limited access to the right treatment and the long waits. The final level refers to structural influences including the accessibility of the information and services related to TB.

Illustration of the SEM framework showing the interrelations at various levels

Study design and data collection methods

This qualitative study was part of a prospective cohort study conducted from February 2022 to March 2023; where CALHIV and their care takers were offered to choose either facility-based or community-based initiation and delivery of TPT. This was part of the differentiated TPT delivery among CALHIV in Uganda (COMBAT TB study).

Study setting

The study was conducted at three high-volume paediatric and adolescent HIV treatment clinics; Baylor College of Medicine Children’s Foundation-Uganda (Baylor-Uganda) center of excellence (COE) HIV clinic located in Mulago Hospital Kampala, Joint Clinical Research Center (JCRC) located in Lubowa, and the Makerere Joint AIDS Program (MJAP) ISS Clinic located on Mulago Hill in Kampala. The Baylor-Uganda clinic located about 4 km from the Kampala city center, provides comprehensive HIV care services for more than 4000 CALHIV out of more than 8000 PLHIV in care at the clinic. The JCRC Lubowa HIV clinic located in Wakiso district, 11 km from Kampala, and it provides comprehensive HIV care services to 1300 CALHIV out of 15,000 PLHIV in care. The MJAP ISS clinic located on Mulago Hill in Kampala, provides comprehensive HIV care services to 612 adolescents out of over 17,000 PLHIV in care. The three clinics run from Monday to Friday as one-stop-centers for care and research on HIV, TB and other HIV-related conditions. The HIV and TB care is provided by multi-disciplinary teams which include counselors, community health workers, peer educators, nurses, pharmacy staff, doctors and laboratory staff. The clients receive HIV prevention services, ART, TB preventive treatment and TB treatment. There is also screening and treatment of other opportunistic infections and non-communicable conditions like mental health issues, hypertension, and diabetes. The services are provided at the health facilities or within the community, based on the national HIV and TB treatment and prevention guidelines.

The CALHIV were screened for TB using the WHO-recommended TB symptom screening tool at every clinic visit. Individuals with TB symptoms completed a clinical evaluation, and TB diagnostic tests, such as Xpert MTB/RIF ultra, urine TB lipoarabinomannan (TB-LAM) for those with CD4 count < 200cells/ul, and chest X-ray. Patients diagnosed with TB then start TB treatment.

Individuals who were assessed as not having TB were considered eligible for TPT, such as; PLHIV above one year of age with no evidence of TB disease, PLHIV who are close contacts of TB patients, and PLHIV who have recently completed a full course of TB treatment. The ministry of health supplied the study sites with TPT drugs; initially isoniazid taken daily for six (6) months, and later rolled-out once weekly isoniazid and rifapentine for three months. The TPT is dispensed with pyridoxine, to prevent peripheral neuropathy, a common side-effect of isoniazid. Individuals who developed mild or moderate side effects, were usually advised to continue with the TPT while the side-effects were managed. If any individuals developed severe side effects, the TPT was withheld to first manage the side effects.

Individuals who initiated TPT within the differentiated delivery approach, had follow-up done via phone calls at two weeks and four weeks after TPT initiation. Follow-up was done at 3months after TPT initiation, and thereafter every three-months at the clinic or within the community to identify and manage side-effects, screen for TB symptoms, and assess adherence to the TPT and ART.

TB screening and diagnostic tests were done for participants with TB symptoms after starting TPT. Participants diagnosed with TB disease before completion of their full TPT course had their TPT stopped and TB treatment started. Adolescents living with HIV were eligible for the study if they were aged 10–19 years, initiating TPT, and completed or did not complete the full dose of TPT. Care takers were eligible for the study if their children aged < 18years living with HIV were initiating TPT, completed or did not complete the full dose of TPT and were willing to provide written informed consent. Health care workers were eligible if they were actively involved in providing TPT and willing to provide written informed consent.

Purposive sampling was done to select eligible health workers, adolescents aged 10-19years and parents or care takers of children who were eligible to start TPT.

During selection of adolescents and care takers, selection was done to try and achieve representation from; the three clinics, with almost equal numbers of; males and females, and age categories (10-14years, 15-19years), TPT completion status (completed, did not complete, missed doses or lost to follow-up), facility-based or community-based delivery models, and ART status (initiating ART or ART-experienced).

The health care workers in this study were involved in screening the children and adolescents for TB, assessing TPT eligibility, prescribing TPT, monitoring individuals on TPT, and providing TB-HIV counseling and guidance according to the national TB and leprosy control guidelines (24). Among the health workers, efforts were made to select equal numbers of males and females, and fair representation by different cadres (nurses, clinical officers, doctors, pharmacists).

Data collection procedure

A semi-structured interview guide was used for each category to obtain in-depth descriptions and valuable insights about the barriers and facilitators to TPT initiation and completion from the three categories of participants.

During the TPT initiation visits, qualitative in-depth interviews (IDIs) were conducted face to-face by an experienced male social scientist (DN), using the piloted interview guide for the data collection process. Interviews lasted between 30 and 45 min. Field notes were also made after each data collection session. Participants were recruited through purposive sampling with the help of the study nurse (CN) at three HIV clinics between June 2022 and August 2023. The IDIs were carried out with the CALHIV, Caretakers/parents and health workers. All the IDIs were held in a conducive place that was safe, neutral and with minimal distractions for the participants and the researcher. This place was either suggested by the interviewee or preset by the interviewer at the participating HIV clinics. Data collection was conducted in a language preferred by the participant, either English or Luganda. The interviewer (DN) took time at the outset of the discussions to develop a rapport with participants, acknowledging the sensitivity of the topic and creating a safe space for them to share their thoughts and experiences. Participants were fully informed about the purpose and objectives of the study, and they provided their informed consent to participate, indicating their understanding and agreement with the research goals and procedures. Approximately four months into the TPT study, participants were approached to participate in the second phase of IDIs for TPT completion.

Sample size

During TPT initiation, thirty (30) IDIs were carried out with the caretakers/parents and children ( N  = 30; 10 health workers, 10 CALHIV, and 10 Caretakers/parents). After TPT completion, interviews were conducted with 10 care takers, and 10 CALHIV. Participants were purposively sampled to represent those CALHIV who completed and those who did not complete or defaulted their TPT dose. The interview guide explored both the facilitators and the barriers for the TPT initiation and completion.

Data management and analysis

In-depth interviews were audio recorded, transcribed verbatim, and then translated into English for a hybrid approach of inductive and deductive thematic analysis [ 22 ] by two researchers (DN and PMA) experienced in qualitative methodology. The initial deductive coding was based on the five levels of the Social Ecological Model (SEM) in Fig.  1 above, and inductive coding was used to explore other themes that were not covered by the SEM. Three transcripts were initially selected and read through for familiarization and coded manually by DN. To ensure coding consistency, the developed codes were shared with the study principal investigator PMA to facilitate collaborative thematic analyses throughout [ 23 ]. All transcripts were imported into NVivo 14 and coded using the refined codebook by DN and PMA. The transcripts were not returned to the participants. The data was organized into pre-defined key themes outlined by the levels of the SEM. A framework approach using SEM was used for data analysis [ 25 ]. Themes and sub-themes were continually reviewed and refined to capture emerging new codes. Quotes were captured to highlight thematic areas and increase our understanding of the context. The methods and results were aligned to the consolidated criteria for reporting qualitative research (CORE-Q) [ 26 ].

A total of 50 IDIs were conducted for the selected participants (health workers ( N  = 10), adolescents ( N  = 10), care takers ( n  = 10) until saturation of content was achieved. Table  1 below summarises the demographic characteristics of the study participants.

Facilitators to initiation and completion of TPT among adolescents and children

From the IDIs, we found the following facilitators at individual level. Participants perceiving themselves as being at risk of contracting TB was a key facilitator to initiate and complete TPT. In addition, some care takers highlighted that the TPT will also help the child to have a good life without TB, but if she acquires TB and yet is already HIV positive, the child may be severally affected.

“Apart from the fact that it will help me to prevent TB, it will help me not to get TB and am assured that I will not get TB because TB is very risky, inconvenient and I will protect others because I know I am at a very high risk. So by taking the drugs, at least I know am protecting someone in case I get it, am protecting a family member, a sibling, a sister”. Male Adolescent 15 years.

Further analysis revealed that care takers and participants who were once diagnosed with TB and recovered narrated their agony and the experience of treating TB which they noted that they would not want to experience again. The experience they had with TB disease compelled them to initiate and complete their TPT dose.

“Another reason why I accepted my child to start on TPT is because my child has ever suffered from TB, and given that now we have the drugs for preventing it, I had no reason to resist it. I was afraid the child might acquire it again”. Female carer of 10-year-old adolescent.

The desire to remain free from TB emerged as a facilitator to initiating and completing TPT. The TPT was perceived as a breakthrough strategy to prevent acquisition of TB.

“Since I had an experience of a person with TB that I told you about, I didn’t want to wait until he is affected as it did to the other one I saw. So that forced me to ensure that the dose is completed”. Female caretaker of 14-year-old adolescent.

At the interpersonal level, support, care and encouragement from family, supervision from the caretakers also emerged as important facilitators to initiate and complete TPT. The participants remarked that receiving care and support (reminders) from immediate family encouraged them to complete their treatment.

“Like at home, there is my mother who always reminds me to take my drugs. That helped me to always take my drugs in time”. Female Adolescent, 18 years.

Community level facilitators included guidance and counseling, comprehensive information, mass awareness and sensitization about TPT. Participants mentioned that receiving adequate information and sensitization was helpful for their decision to initiate TPT. Participants reported that they received information from the health workers on how the child should take the medicine and how the treatment works to prevent the disease, something that encouraged most of them to start their children on treatment.

“The encouragement I got from doctors helped me to give treatment to my child for TB treatment which also made it easy for me to start him on TPT. I believe by the time the dose is completed the child will be okay. Doctors also sensitized us about the possible side effects of the drugs and they follow up with phone calls”.  Female care taker for a 7-year old child.

It emerged that information about the TPT made available by the health workers, with opportunities to discuss the treatment with the doctors, and making it known in the community, enabled the care givers to allow TPT to be given to their children and adolescents.

“When people are aware, it makes the services easy to access. Many people talk about other things on TVs and radios but they don’t take about TB. We have to tell people TB is real and a killer disease. You can also inform them in case someone sees the symptoms they should be screened for TB”. Medical doctor 01.

At the institutional and organizational level, participants preferred to have convenient services as a facilitator for the initiation and completion of the treatment. This was in terms of having TPT appointments scheduled on the same days of ART refill so that they can have all the drugs on the same appointment as this will reduce the time spent at the clinic and cost of repeat visits.

“The other issue is integrating those TPT refills with their usual clinic visits and community services so that they can readily receive the drugs at times without even wasting much time and transport to come to the clinic”. Medical doctor 02.

Among the healthcare providers, it emerged that many young people preferred to have the drugs taken to them so that they don’t have any excuses of not coming to the clinic for treatment.

“Also initiating TPT delivery models that reduce the transport costs and avoid missing clinical appointments and doses. Also to make sure their drugs are delivered before they are out of stock”. Nursing officer 01.

Besides the convenient services, health workers recognized mechanisms of following up the patients initiated on TPT or reminding them when to take their treatment as facilitator for the completion of TPT.

“We need to make mechanisms of follow ups when you put someone on TPT, you have to check on them to see how they are doing sometimes when you tell them to take the drug on Sunday it means they will even shift the ARVs to the same date”. Epidemiologist 01.

Health workers also cited frequent and friendly communication with children and caretakers in terms of the health talks at the clinic, calling the patients through the mobile phones and receive their feedback.

“Another thing is when you relate with children they bring out their challenges where you share and help them out. Smoothly they can cooperate and complete the six months’ TB preventive treatment". Study counsellor 01. “With the care takers, it is just a matter of explaining to them. It will not be hard for them if they have understood the importance of TPT and even the challenges will be less. The information should be explained in a way which is understood.” TB community linkage facilitator 01.

At the structural level, what emerged was having national policies and good performance indicators at the health facilities that are developed to create demand for the TPT among CALHIV has a great advantage and facilitates TPT uptake.

“Demand creation, tasking health workers. We have our weekly performance review and TPT is among the many indicators we track. Ministry of health asks us how many people are on TPT which helps the health worker to improve on performance and this will facilitate the uptake of TPT”. Medical officer 01.

Regular auditing and identifying the challenges and weaknesses at the facilitate level in relation to the prescription of the treatment emerged as a key facilitator for the uptake of TPT among CALHIV.

“We have reached that level where we appreciate if you find your health workers are not performing well, sit down as a unit and ask yourself on the weaknesses. If you planned to start 56 participants on TPT this week what happened, open the file and do file audits. You will discover interesting things other than patients missed to come or ask the pharmacist why were you not prescribing the drugs when there was even an alert”. Epidemiologist 02.

The following themes emerged as barriers to TPT initiation and completion at patient-level, structural, community and interpersonal levels.

We found the following individual-level barriers to TPT initiation and completion. One of the emerging barriers to initiate or complete their TPT was the stigma associated with taking TB or HIV drugs. The fear of being seen taking many pills on a daily basis was cited as affecting their emotional well-being and mental health.

“Stigma will always be there and I think it’s a reason why so many kids out there fear. Personally before, I didn’t have any problem taking my medicine. So when the stigma started I stopped taking medicine, I stopped caring, it really caused me a lot of mental damage and trauma”. Male Adolescent 18 years.

Where there is limited privacy, taking the treatment would be difficult. Participants also mentioned that they would fail to come for their HIV clinic appointments, for fear of being identified as HIV patients or TB patients.

“…the main challenge is the stigma of HIV which is a leading factor in the community. Some of them fail to come for their appointments because of stigma. They don’t want to be identified as HIV or TB-positive”. Medical officer 03.

The fear of drug-related side effects was reported as a key barrier to starting TPT. Participants expressed their fear of taking TPT treatment for fear of side effects based on their past experiences with different drugs. At TPT completion, experience of side-effects like dizziness and nausea emerged as barriers to TPT completion.

“It would make me feel nausea or feel like vomiting, headache and dizziness. Me I decided not to take them anymore… I even didn’t tell anyone”. Male adolescent, 12 years old.

High pill burden coupled with poor drug adherence also emerged as key barriers reported by the participants, especially if the child was also on ART regimens.

“Another issue is about the pill burden because these are people who are already on ARVs and then they are added more pills for TB so it becomes a lot for them”. Nursing officer 3. “The biggest barrier is adherence because it’s still a challenge to even those that are HIV negative. There are clients who are not used to taking treatment and if the treatment is for six months there will be a challenge of commitment to take the drugs every day.” Medical officer 03.

Among the caretakers, it emerged that pill fatigue created by taking tablets when a person is not sick with TB, caused many adolescents to miss their doses and some did not complete, even though they reported taking the drugs when it is not true.

“Some children fear taking drugs and time comes when the child is tired and no longer wants to take the medicine. … the child can pretend to be taking the medicine when it is not true because the child got tired of taking the drugs”. Female Caretaker of 8-year-old child. “That the medicine was a lot, and the child got tired of it, so she didn’t complete. “Sometimes she could say, “it is just for prevention, I will not take it”. The fact that the child didn’t have TB, she could not care at all”. Female caretaker of 15 years adolescent.

Caretakers expressed the discomfort of children taking pills with a bad smell, big size, unpleasant color and poorly packaged. Participants said that a pill with no smell, small size and attractive packaging would be easier to swallow.

“One, the smell of the medication might not be really good to the child, the pill size can be too big, you even see and say ooh! Female caretaker to 13-year-old adolescent.

It emerged that some adolescents and their caretakers are “ engaged in demanding jobs that may not allow time to collect their medication or they may forget to take it ”. Community Health linkage officer 01.

Forgetting to take the additional drugs also emerged as hinderance to complete the TPT.

“…when you work a lot and do not get time, because you are not used to it like ARVs, the busy schedule can also cause you from not taking the drugs. Male adolescent-18 years. “She is so forgetful. You always have to ask her whether she has taken the medicine. If you are not around, I just know she has not taken and that’s why she didn’t complete”. Female caretaker to a 16year-old adolescent.

At the interpersonal level, the change of caretakers and lack of support mainly from parents also emerged as key barriers to the completion of TPT.

“Some of them like children depend on their caretakers and sometimes we experience changes of the caretakers”. Nursing officer 04.

Among female caregivers, denial or restrictions by the husbands to come to the clinic for refills, also emerged as a barrier for TPT completion among their children

“For those that are married, their husbands don’t allow them to come to the clinic since it was not on the program”. Female caretaker 14 years child.

Financial constraints and lack of food contributed to delay in TPT initiation and failure to complete the treatment. Caretakers expressed concerns that certain medications require a specific diet to be effective, but they struggled to provide the necessary nutritional support, particularly for their school-aged children, which in turn impacted their ability to adhere to treatment regimens, as highlighted by one adolescent’s experience

“Ok the major challenge I faced at school is sometimes I don’t take medicine because I have not eaten. I know the medicine is very strong and I know it will affect my stomach. It will affect me so if am to take it on an empty stomach it wouldn’t be possible. So sometimes I just don’t take it because I know it will cause me effects”. Female Adolescent 18 years.

Failure of the caretakers to disclose HIV status to the children was cited as a barrier of children to initiate and take TPT treatment. One health worker noted that most mothers at home have never disclosed the reason why their children take these drugs daily, and when the husband is around they cannot take their drugs.

“There is also no disclosure especially to the children. So you find when the child doesn’t take the drugs because they do not understand why they are taking the drugs”. Medical doctor 04.

This has also been a challenge to trace TB contacts in families where the patient has never disclosed to the family members and as a result, children in these families miss the opportunity to take the TPT treatment.

“Disclosure is the problem when families have not yet disclosed, and someone comes down with TB. It is difficult to conduct contact tracing, for example on what ground are you asking the family about TB. So it is hard”. Epidemiologist 02.

At the community level, misconception about TPT and Community stigma associated to TB were some of the barriers identified. Further analysis revealed that some adolescents are so inquisitive about drugs and the intended benefit of taking the drugs. However, many are confused with the different sources of information about the benefits of the drugs. In addition, they did not understand how it could work to prevent infection. For example, there was a misconception about the dangers of taking medication when you are well. Some perceived that the government would introduce these treatments as a gateway to reducing their life span.

“Adolescents are very inquisitive. They keep questioning depending on the different sources of information they receive. So some of the questions are like, “don’t you think these are the drugs that stimulate our TB?” Most of them have those questions and I don’t know whether it’s propaganda now they keep saying “the government or the health facilities are trying to make us fall sick quickly and we even google some of these drugs kill the cells that could have protected our bodies”. This affects their TPT drug adherence”. Medical officer 02.

Participants also reported that there was stigma related to TB disease at health facilities and in the communities where patients reside. The situation worsens especially for adolescents in schools where students fail to take their medication until their next appointment because of the stigma from their fellow peers.

“Students may stigmatize you, which at times makes you not to take the drugs or hide it from them that you are not taking the drugs”. Female adolescent 18 years. “Yes, because they disturb you, they say that one is a TB patient, and they talk a lot. This caused me to miss the refill days”. Female adolescent 14 years.

At the institution level, the long waiting-time at the clinic emerged as a barrier to completing TPT. Participants revealed that they preferred quick access to services without having to spend long hours in queues waiting to receive the treatment.

“It’s just embarrassing, it’s just too much. The long waiting really makes me feel like opting out. That’s the truth I can tell you”. Female care takers to a 13-year-old adolescent. “I come early and leave late. That issue made it hard for me. Sometimes I tell her to go by herself but then I remember that she will not give in her complaints. Sometimes we missed coming”. Female caretaker to a 12-year-old adolescent.

Participants were concerned about the attitude of health workers when they are seeking services. This was viewed as a major barrier because they thought if the health workers are rude to the clients, they might not find it conducive to collect their treatment. This was echoed by some health workers who shared the experience that when patients are mistreated, they fail to come back until they are followed up.

“You may find when the person has failed to come on a clinic visit because he was mistreated by a nurse and has not been listened to. Then the person concludes by saying I will not come back”. When it comes to the next appointment, they don’t come back”. Medical officer 05.

Health care workers forgetting to prescribe the drugs at refill visits emerged as one of the barriers to TPT completion.

“Also to the prescribers, someone might have taken TPT like for three months and when they report back, the prescriber forgets to give the refill to add up the six months. So, a patient ends up missing the three months and restart the treatment again”. Medical officer 01.

Health care workers also commented that health facilities may lack essential medicines, and clients are advised to buy from private pharmacies which hinders completion.

At the structural level, participants reported that if the clinic was not within easy reach, they found it a problem to pick their drug refills. This required them to travel long distances with costly transport.

“Transport also affects us, there is a time when you have to come and get treatment but when you don’t have money and that’s why some people fail to come”. Female care giver to 12-year-old adolescent.

This qualitative study explored the perceived, and experienced facilitators, and barriers to TPT initiation and completion among children and adolescents living with HIV, as reported by the Ugandan health workers, adolescents, and care takers of children.

Parental support and supervision, perceived risk of TB disease, and previous experiences of TB treatment were reported by adolescents and care takers of children as the major facilitators of TPT initiation and completion. Similar to a Kenyan study by S. Ngugi et al. [ 15 ], this study found that provision of adequate information about TPT benefits and dosing by health workers, family and community support, and experience of treating children with TB were highlighted by care takers as facilitators that enabled their children to initiate and complete TPT. Social support is very key in determining TPT initiation and completion among CALHIV, calling for integration of psychosocial support in TPT programs.

Facilitators of TPT initiation and completion highlight the need to provide adolescent friendly services and integrated TB and HIV services to facilitate initiation and completion of TPT among adolescents living with HIV [ 8 ]. Adolescent friendly services should be accessible, acceptable, appropriate and delivered in safe and respectful environment by supportive healthcare providers (27, 28). These include promotive, preventive, curative, and referral health services (28).

The barriers to TPT initiation and completion reported by adolescents included; TB or HIV-related stigma, busy work schedules of the adolescents and care takers, reduced social support from parents and family, previous experience of side effects from other drugs, pill burden and fatigue when that are not sick, financial constraints to travel to the clinic, and lack of food to take with the medicines. The roll-out of shorter TPT regimens is very timely [ 9 ], and will most likely address concerns of pill burden and fatigue among CALHIV who are already receiving daily ART.

Although care takers identified barriers to TPT initiation and completion that were similar to those reported by the adolescents, care takers additionally reported barriers such as; pill size, burden and odour, misconception and misinformation about the benefits and duration of the TPT, long distances to the health facilities, and rude health workers. It is important to provide regular adherence support from TPT initiation to facilitate completion, and therefore the efficacious benefits of TPT.

In contrast to the study by Teklay G et al. [ 18 ], health workers did not report fear of creating isoniazid resistance as a barrier to TPT initiation among CALHIV. Barriers cited by health workers included; TB and HIV-related stigma, undisclosed HIV status to the CALHIV, misconceptions that TPT puts their life at risk, fear of side effects, missed opportunities due to forgetting by health workers, poor attitude of health workers towards the adolescents, long waiting hours, change of care takers, and lack of parental or social support. These are closely related to the contextual barriers reported by Nyarubamba R. F et al. in Tanzania [ 14 ], and Lai J et al. in Ethiopia [ 16 ]. Drug stock outs in some facilities were reported as barriers, similar to a study among health workers in Ethiopia [ 18 ].

Limitations

The purposively selected sample is not widely representative of the CALHIV and their care takers in high TB burden countries. Therefore, transferability of these results in other settings may vary based on; the social-ecological models used to assess patient perceptions, TB disease burden, patient/family education and support initiatives within the healthcare system. There were limited numbers of participants who did not complete TPT, limiting the depth of lived experiences about barriers to TPT completion among CALHIV. This study did not explore the perspectives of policy makers in TB care, as these are also important to guide concerted efforts to improve TPT uptake and completion among CALHIV. There was no quantitative data for triangulation with the qualitative results.

The in-depth interviews were conducted at TPT initiation and after TPT completion. This minimised recall bias. This enabled deeper understanding of both perceived and experienced facilitators and barriers to TPT initiation and completion among CALHIV.

The facilitators and barriers of TPT initiation and completion among CALHIV are diverse, spanning from individual factors to healthcare system and structural factors. Educating patients about the benefits of TPT and the need to reduce the risk of TB, facilitates TPT initiation and completion among CALHIV. Availability of social support, adolescent-friendly services, and integration of TPT refills into ART refill visits are also major facilitators of TPT initiation and completion among CALHIV.

TB and HIV-related stigma, high pill burden of TPT in addition to ART, non-disclosure of HIV status of the children and adolescents, lack of parental support, transport difficulties, and misconceptions about TPT side effects, were the major barriers to initiation and completion among these CALHIV. Therefore, it is important to implement patient-centered TB and TPT services for CALHIV and their caretakers, so as to improve TPT initiation and completion, ultimately, reducing TB burden in this high-risk population.

Recommendations

Provision of clear information about TPT and TB, psychosocial and adherence support, adolescent-friendly TB-HIV services, and integration of TPT delivery into ART delivery models, are promising strategies to improve the uptake and completion of TPT among children and adolescents living with HIV in high TB-HIV burden settings like Uganda. TPT completion is likely where services are offered within a family-centered approaches to enhance psychosocial support for adherence. We recommend integrating TPT delivery into existing ART delivery approaches, at health facility and community level, to enhance uptake and completion of TPT among CALHIV.

Data availability

The data that support the findings of this study are available on request from the corresponding author Dr Pauline Mary Amuge (PMA) [email protected], and the institutional representative [email protected] This is to ensure that the data is shared within the provisions of the protocol approved by the Makerere University School of Medicine research and ethics committee, as it was aimed to accomplish specified study objectives.

Abbreviations

Assisted Partner Notification

Anti-retroviral therapy

Anti-retroviral drugs

Children and Adolescents Living with HIV

Severe Acute Respiratory Syndrome due to Corona Virus-19

Differentiated Service Delivery

Differentiated Service Delivery Models

Human Immune-deficiency Virus

3months course of Isoniazid and Rifapentine

3months course of Isoniazid and Rifampicin

Integrated community case management

Isoniazid (isonicotinylhydrazide)

Isoniazid Preventive Therapy

Interrupted time series

Latent Tuberculosis Infection

Ministry of Health

National Drug Authority

National Tuberculosis and Leprosy control Program

Bacteriologically Confirmed Pulmonary Tuberculosis

Clinically Diagnosed Pulmonary Tuberculosis

People Living with HIV

Pulmonary Tuberculosis

• Tuberculosis

Tuberculosis Preventive Treatment

Village Health Team

World Health Organisation

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Acknowledgements

Baylor College of Medicine Children’s Foundation-Uganda: Henry Balwa, Susan Tukamuhebwa, Rachel Namuddu Kikabi, Florence Namuli, Kizito David, Wasswa George, Rogers Nizeyimana, Geofrey Musoba, Alex Tekakwo, Brenda Nakabuye, David Mpagi. Joint Clinical Research Center (JCRC) Lubowa: Flavia Nakato, Joan Nangiya, Henry Mugerwa, Drollah Ssebagala. Makerere Joint AIDS Program (MJAP) Mulago ISS Clinic Kampala Uganda: Douglas Musimbago, Fred Semitala.

This work was supported by the Collaborative Initiative for Paediatric HIV Education and Research (CIPHER) grant programme at the International AIDS Society (IAS), through the CIPHER Research grant awarded to PA for the period 1st November 2021 to 31st October 2023, for a project titled “Differentiated delivery of tuberculosis preventive treatment (TPT) within existing health facility and community HIV care models to improve TPT uptake and completion among children and adolescents living with HIV in Uganda following the COVID-19 pandemic.”

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Pauline Mary Amuge, Moses Mugerwa, Dickson Bbuye, Christine Namugwanya, Angella Baita, Peter James Elyanu, Patricia Nahirya Ntege, Dithan Kiragga & Adeodata Rukyalekere Kekitiinwa

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Diana Antonia Rutebarika

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PMA conceived the original concept. The funding was secured by PMA, PJE, PNN, ARK, AK, AMM, PM. The study was designed by PMA, PJE, MSP, AG, NAS, AMM, PM. Data was curated by PMA, DN, AB, DB, MM, CB, LK and CN. The data was analysed by DN and PMA. The project was co-ordinated by PMA, DN, MM, DB, DAR, and CB. The project technical advisors and mentors were; PJE, AK, ARK, AMM, NAS, MSP, AMM, PM. The original manuscript draft and responses to all author comments were written by PMA and DN. All authors reviewed and edited the original manuscript draft before submission. PMA and DN addressed all comments, and revised the manuscript. All authors reviewed and approved the final manuscript for publication.

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Written informed consent was obtained before data collection from participants aged ≥ 18 years, and parents/carers of children under 18years. Written informed assent was obtained from children aged 8years to under 18 years. All data were stored on encrypted computers. Filed notes and signed participant-informed consent forms were kept in a locked drawer at the study site. Participants’ names were not recorded anywhere during data collection. Each participant was given a unique identifying number to ensure confidentiality. The research teams did not include any identifying information that could have harmful consequences for the participants. Ethical approval was granted by the Makerere University school of medicine Research and Ethics Committee (17th June 2020, REF 2020 − 127), and the Uganda National Council for Science and Technology (12th November 2020; HS768ES).

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Amuge, P.M., Ndekezi, D., Mugerwa, M. et al. Facilitators and barriers to initiating and completing tuberculosis preventive treatment among children and adolescents living with HIV in Uganda: a qualitative study of adolescents, caretakers and health workers. AIDS Res Ther 21 , 59 (2024). https://doi.org/10.1186/s12981-024-00643-2

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DOI : https://doi.org/10.1186/s12981-024-00643-2

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Tuberculosis: A Global Health Problem

Tuberculosis (TB) is an ancient disease that has affected mankind for more than 4,000 years ( 1 ). It is a chronic disease caused by the bacillus Mycobacterium tuberculosis and spreads from person to person through air. TB usually affects the lungs but it can also affect other parts of the body, such as brain, intestines, kidneys, or the spine. Symptoms of TB depend on where in the body the TB bacteria are growing. In the cases of pulmonary TB, it may cause symptoms, such as chronic cough, pain in the chest, haemoptysis, weakness or fatigue, weight loss, fever, and night-sweats.

TB remains a leading cause of morbidity and mortality in developing countries, including Bangladesh. With the discovery of chemotherapy in the 1940s and adoption of the standardized short course in the 1980s, it was believed that TB would decline globally. Although a declining trend was observed in most developed countries, this was not evident in many developing countries ( 2 ). In developing countries, about 7% of all deaths are attributed to TB which is the most common cause of death from a single source of infection among adults ( 3 ). It is the first infectious disease declared by the World Health Organization (WHO) as a global health emergency ( 4 ). In 2007, it was estimated globally that there were 9.27 million incident cases of TB, 13.7 million prevalent cases, 1.32 million deaths from TB in HIV-negative and 0.45 million deaths in HIV-positive persons ( 5 ). Asia and Africa alone constitute 86% of all cases ( 5 ). Bangladesh ranked the 6th highest for the burden of TB among 22 high-burden countries in 2007, with 353,000 new cases, 70,000 deaths, and an incidence of 223/100,000 people per year ( 5 ).

Implementation of directly-observed therapy short course (DOTS) has been a ‘breakthrough’ in the control of tuberculosis. In many countries, it has become the cornerstone in the treatment of tuberculosis. The number of countries and the coverage of DOTS within the countries have increased over the years ( 5 ). Over the last 15 years, about 35 million people have been cured, and eight million deaths have been averted with the adoption of DOTS ( 6 ). Implementation of DOTS was started in 1993 in Bangladesh, and it gradually covered the whole country ( 7 ).

Men are more commonly affected than women. The case notifications in most countries are higher in males than in females. There were 1.4 million smear-positive TB cases in men and 775,000 in women in 2004 ( 8 ). The ratio of female to male TB cases notified globally is 0.47:0.67 ( 9 ). The reasons for these gender differences are not clear. These may be due to differences in the prevalence of infection, rate of progression from infection to disease, under-reporting of female cases, or the differences in access to services.

The association between poverty and TB is well-recognized, and the highest rates of TB were found in the poorest section of the community ( 10 ). TB occurs more frequently among low-income people living in overcrowded areas and persons with little schooling ( 11 ). Poverty may result in poor nutrition which may be associated with alterations in immune function. On the other hand, poverty resulting in overcrowded living conditions, poor ventilation, and poor hygiene-habits is likely to increase the risk of transmission of TB ( 12 ).

Various surveys have been conducted to understand the knowledge, attitudes, and practices regarding tuberculosis ( 13 – 14 ). One survey in India reported that most (93%) people had heard of TB but only 20.5% of the people demonstrated sufficient knowledge of TB ( 13 ). This issue of the Journal includes an article by Rundi who explored healthcare-seeking behaviour with regard to TB among the people of Sabah in East Malaysia and the impact of TB on patients and their families ( 15 ). The author used qualitative methods and interviewed patients with TB and their relatives. It was found that most (96%) respondents did not know the cause of TB. TB also affected life-styles of the people. The author emphasized the need to understand the reasons for misconceptions about TB and to address it through health education.

Better understanding of the prevalence of drug resistance against tuberculosis is one of the key elements in the control of TB. Drug resistance, in combination with other factors, results in increased morbidity and mortality due to tuberculosis. Drug-resistant strains of TB is rapidly emerging worldwide ( 16 ). The WHO reported alarming rise of not only multidrug-resistant (MDR) TB but also of XDR TB (extreme drug-resistant TB) globally. Both treatment and management of such cases are well beyond the capacity of any developing country. Globally, there were about 0.5 million cases of MDR TB. In Bangladesh, the MDR rate is 3.5% among new cases and 20% among previously-treated cases ( 5 ). The death rate in MDR cases is high (50–60%) and is often associated with a short span of disease (4–16 weeks) ( 17 ). Several factors have been identified for the development of MDR cases. These include non-adherence to therapy, lack of direct observed treatment, limited or interrupted drug supplies, poor quality of drugs, widespread availability of anti-TB drugs without prescription, poor medical management, and poorly-managed national control programmes ( 18 – 20 ). Continuation of the existing MDR surveillance is important to effectively plan for the treatment of MDR cases and implementation of the DOTS-Plus strategy. It requires rapid, concerted action and close collaboration among government, non-government and private organizations to control MDR tuberculosis ( 21 ).

The diagnosis of TB among children is difficult. Moreover, young children cannot produce sputum. Estimates indicate that children constitute about 10% of all new cases in high-burden areas ( 8 ). Clinical signs and symptoms and scoring system have been used for the diagnosis of TB among children ( 22 ). Various diagnostic techniques have been used for improving the diagnosis among children. These include culture, serodiagnosis, and nucleic acid amplification ( 23 ).

Many countries use BCG vaccine as part of their TB-control programme. The protective efficacy of BCG viccine against all forms of TB is about 50% but it was more in serious forms of infection (64% in cases of tuberculosis meningitis and 78% in disseminated infection) ( 24 ). Several new vaccines against TB are being developed. These vaccines are now being field-tested in different countries in different phases ( 25 ).

There are several challenges which need to be addressed for effective control of TB, particularly in developing countries. These include the development of an effective surveillance system, accelerated identification of cases, expansion of DOTS to hard-to-reach areas, strengthening of DOTS in urban settings, ensuring adequate staff and laboratory facilities, involvement of private practitioners, treatment facilities for MDR cases, identification of TB among children and extra-pulmonary cases, and effective coordination among healthcare providers ( 5 , 26 – 27 ). Moreover, the prevalence of TB is influenced by HIV, and effective control measures are needed for both the diseases.

Further research is warranted to improve diagnostics, develop new drugs and vaccines, simple and effective regimen for simultaneous treatment of TB and HIV, ways to improve programme effectiveness, and better understanding of the relationship between TB and chronic diseases, e.g. diabetes and smoking, and identify social and behavioural factors which limit the detection of cases ( 8 , 28 ).