Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis

doi:10.21203/rs.3.rs-4910396/v1

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Research Article

Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis

https://doi.org/10.21203/rs.3.rs-4910396/v1

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Journal Publication

published 09 Nov, 2024

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Backgrond

Pulmonary tuberculosis (PTB) exacerbation can lead to respiratory failure, multi-organ failure, and symptoms related to central nervous system diseases. The purpose of this study is to screen biomarkers and metabolic pathways that can predict the progression of pulmonary tuberculosis, and to verify the role of the metabolic enzyme xanthine oxidase in the progression of PTB.

Methods

To explore the biomarkers and mechanisms underlying the progression of PTB, plasma metabolomics sequencing was conducted on patients with severe PTB, non-severe PTB, and healthy individuals. Screening differential metabolites and metabolic pathways that can predict the progression of pulmonary tuberculosis, and verifying the function and mechanism of action of XO through experiments.

Results

The purine metabolism, sphingolipid metabolism, and amino acid metabolism between the three groups differ. In patients with severe PTB, the levels of xanthosine and hypoxanthine are increased, while the levels of D-tryptophan, dihydroceramide and uric acid are decreased. Inhibition of XO activity has been observed to reduce the expression levels of tumor necrosis factor (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), as well as to suppress the production of reactive oxygen species (ROS) and the activation of the NF-κB pathway, while also promoting the growth of MTB within cells.

Conclusion

D-tryptophan, xanthosine, and dihydroceramide can be utilized as biomarkers for progression of PTB, assisting in the evaluation of disease progression, and XO stands out as a potential therapeutic target for impeding the progression of PTB.

Pulmonary tuberculosis

Biomarkers

Xanthine oxidase

Reactive oxygen species

Disease progression

Pulmonary tuberculosis (PTB) is an infectious disease caused by Mycobacterium tuberculosis (MTB) infection that spreads through the respiratory tract, without treatment, the mortality rate of TB is very high (around 50%)^[1]. Due to delayed diagnosis, dialysis, HIV infection, or the presence of other immunosuppressive states, as well as multidrug-resistant diseases, the disease can progress to a more severe form and require treatment in the intensive care unit (ICU). Approximately 3.4% of hospitalized TB patients need to be admitted to the ICU, compared with other causes of severe pneumonia, patients who need to be admitted to the ICU have poor prognosis and high mortality (60% vs 25%)^{[2, 3]}. Therefore, it is crucial to identify the severity of TB and understand the progression mechanisms of the disease for timely treatment of TB.

Currently, most studies focused primarily on disease susceptibility, early diagnosis, and differential diagnosis, with less emphasis on the progression of PTB. Innate immune cells are the first line of defense against MTB, as their response plays a crucial role in determining the outcome of infection^[4]. Macrophages, a type of innate immune cell, are particularly important in this process. Macrophages not only possess inherent antimicrobial mechanisms to control TB infection but also play a key role in initiating and maintaining the inflammatory response against MTB^[5]. The functional status of host macrophages affect different outcomes after MTB infection, and the host's immune level is related to the severity of the disease^{[6, 7]}. These indicate innate immune response plays a crucial role in the progression of TB. However, the pathogenesis of TB progression has not been fully elucidated, especially the molecular functions and mechanisms of TB progression are relatively limited.

The discovery of the role of metabolic pathways in immune cell function has made immunometabolism a focus of research. However, the complexity of metabolites and their enzymatic reactions poses challenges for study. Metabolic processes such as glycolysis, the Krebs cycle, and fatty acid metabolism have highly specific effects on macrophages, and intervening in these pathways can alter cell function in specific ways^[8]. Accumulation of metabolic products such as citrate can lead to increased itaconate levels, which have antimicrobial and anti-inflammatory properties and can restrict intracellular MTB growth^[9]. Reactive oxygen species (ROS) generated during metabolism have potential inflammatory and metabolic regulatory properties, which are fundamental for macrophages to eliminate invading microorganisms^[10]. ROS plays an important role in combating various intracellular pathogens, such as signal transduction, differentiation, and gene expression^[11]. ROS is catalyzed and synthesized by oxidases associated with organelles, such as mitochondria, peroxisomes, and the endoplasmic reticulum, including NADPH oxidase, xanthine oxidase (XO), the electron transport chain, and monoamine oxidase^[12]. Therefore, exploring the mechanisms by which these metabolites promote host defense will help elucidate the molecular functions and mechanisms contributing to the progression of PTB. This study aims to identify metabolic biomarkers for severe PTB and to preliminarily investigate the mechanisms underlying the progression of PTB.

Ethics statement.

This study was conducted in accordance with the Helsinki Declaration and has been approved by the Ethics Committee of Beijing Chest Hospital, Capital Medical University (Ethics No: scientific research 2023 (44)). All subjects signed written informed consent forms.

Subjects and sample collection

Patients diagnosed with PTB at Beijing Chest Hospital, Capital Medical University, from June 2023 to February 2024 were divided into severe and non-severe groups. The inclusion of the severe PTB group requires meeting any one of the following four criteria: (1) Resting oxygen saturation ≤ 93% when breathing air; (2) Oxygenation index (arterial oxygen partial pressure/oxygen concentration) ≤ 300 mmHg, or arterial oxygen partial pressure ≤ 60 mmHg when breathing air at rest; (3) Chest imaging shows TB lesions in both lungs accumulating over more than half of the lungs; (4) If a patient meets one of the above inclusion criteria and experiences shock or organ failure, ICU monitoring and treatment are required. Patients who do not meet the criteria for severe patients at the same time are classified as non-severe patients. According to the criteria, 30 cases each of severe tuberculosis (STB), non-severe tuberculosis (NSTB) patients, and healthy controls (HC) were included, with HC from Changping Physical Examination Center in Beijing. Subjects were matched for age and gender. Subjects had fasting blood samples collected in EDTA anticoagulant tubes on the first day of hospitalization, centrifuged at 4,500 rpm for 10min, and the plasma stored at -80°C.

UHPLC-MS/MS analysis

100 µl plasma was thoroughly mixed with 400 µl of cold methanol acetonitrile (v/v, 1:1) via vortexing. And then the mixture were processed with sonication for 1 h in ice baths. The mixture was then incubated at -20°C for 1 h, and centrifuged at 4°C for 20 min with a speed of 14,000g. The supernatants were then harvested and dried under vacuum LC-MS analysis. Metabolomics profiling was analyzed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2 LC-30AD, Shimadzu, Japan) coupled with Q-Exactive Plus (Thermo Scientific, San Jose, USA). For liquid chromatography (LC) separation, samples were analyzed using a ACQUITY UPLC® HSS T3 column (2.1×100 mm, 1.8 µm) (Waters, Milford, MA, USA). The flow rate was 0.3 ml/min and the mobile phase contained: 0.1% FA in water (A) and 100% acetonitrile (B). The gradient was 0% buffer B for 2 min and was linearly increase to 48% in 4 min, and then up to 100% in 4 min and maintained for 2 min, and then decreased to 0% buffer B in 0.1 min, with 3 min re-equilibration period employed. The electrospray ionization (ESI) with positive-mode and negative mode were applied for MS data acquisition separately. The raw MS data were processed using MS-DIAL for peak alignment, retention time correction and peak area extraction. The metabolites were identified by accuracy mass (mass tolerance < 10 ppm) and MS/MS data (mass tolerance < 0.02 Da) which were matched with HMDB, massbank and other public databases and our self-built metabolite standard library. In the extracted-ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept.

Isolation of peripheral blood mononuclear cells

Blood samples were collected from 30 patients with STB and 30 patients with NSTB, and peripheral blood mononuclear cells (PBMCs) were isolated for subsequent RNA extraction and RT-qPCR. PBMCs were isolated and stored at -80℃ using the instructions provided in the Peripheral Blood Lymphocyte Separation Fluid (TBD, Tianjin, China, #LTS1977) manual. Separate PBMCs from 10ml whole blood and 1 ml of serum-free cell freezing solution (CELLSAVING, Suzhou, China, #C854497) was added to the isolated PBMCs before storage. Concurrently, record the patient's uric acid results.

Cell counting kit-8 assay (CCK-8)

THP-1 cells were seeded in a 96-well plate at a density of 5 × 10⁴ cells/well and induced by 0.1 µg/ml PMA(Sigma, USA, #P8139) for 12 h to differentiate into macrophages. Different concentrations (0, 0.05, 0.1, 0.2, 0.4, 1, 2 mM) of allopurinol (AP, Sigma, USA, #A8003) solution were added to the cells. The AP stock solution was prepared by adding 10 mg of AP powder to 7.347 ml of DMSO (Sangon Biotech, Shanghai, China, #A503039-0250). After 24 h of cell stimulation, 10 µL of CCK-8 solution (Beyotime, Shanghai, China, #C0048S) was added to each well and incubated in the dark for 4 h. The OD value at 450 nm was measured using an enzyme-linked immunosorbent assay (ELISA) reader (Bio Tek, USA).

Macrophages infection and colony-forming unit counting

THP- 1 originated from the American Type Culture Collection (ATCC) and was grown in Roswell Park Memorial Institute (RPMI) medium (Gibco, Carlsbad, CA, USA, #8117072) with 10% fetal calf serum (Gibco, #2045512CP) at 37°C in 5% CO2.The cells were seeded in a 12-well plate at a cell density of 5×10⁵ cells/ml. THP-1 cells were induced with 0.1 µg/ml PMA. The cells were then washed and incubated in fresh culture medium without PMA for an additional 24 h.The MTB H37Rv strain, sourced from ATCC in Manassas, VA, USA, was cultivated in Middlebrook 7H9 broth (BD, USA, #371301) supplemented with 10% OADC (oleic acid-albumin-dextrose-catalase) and 0.05% Tween-80 (Sigma, USA, #P1754) for a period of 2 to 3 weeks. Subsequently, the culture was transferred to a neutral Roche solid medium (BASO, Guangdong, China, #BA7005D-2) for an additional 3 to 4 weeks. Bacterial colonies were harvested from the solid medium using a sterile loop and placed into a grinding bottle. To ensure a uniform dispersion, a homogeneous bacterial suspension was prepared using an ultrasonic disperser. This suspension was then diluted with 1×PBS (Solarbio, Beijing, China, #P1003) to achieve an optical density (OD) at 600 nm of 0.3, which corresponds to a concentration of 1 x 10⁷ colony-forming units (CFUs) per milliliter of MTB. Macrophage cells were infected with 2.5 x 10⁶ CFUs of MTB to achieve a multiplicity of infection (MOI) of 10 for 2 h at 37°C. Afterward, remove the culture medium for ELISA, and the cells were washed three times with 1×PBS to eliminate non-internalized bacteria, followed by incubation with fresh medium. The experiment was conducted in two groups: a control group and an experimental group. The control group was treated with DMSO, while the experimental group was treated with AP. Samples were collected at 0, 4, 8, and 24 h, with the 0 h point representing the uninfected state. To enumerate the intracellular bacteria, cells were lysed by adding 500 µl of 0.05% sodium dodecyl sulfate (SDS) to each well for 10 min. The lysed mixture was then serially diluted with 1×PBS, and the diluted samples were spread on Middlebrook 7H10 agar (BD, USA, #220959) supplemented with 10% OADC. The plates were incubated at 37°C for 3 to 4 weeks, after which the colonies were counted.

ROS detection

ROS detection was performed according to the instructions of the Reactive Oxygen Species Assay Kit (Yeasen, Shanghai, China, #50101ES01). The culture medium containing the bacterial suspension was removed from the wells, and the wells were washed with serum-free RPMI 1640 medium. The appropriate working solution of DCFH-DA (1:1000) was added to the wells, and the plate was incubated at 37℃ in the dark for 30 min. The wells were then washed 1–2 times with serum-free culture medium, and the fluorescence intensity was measured at an excitation wavelength of 488 nm and an emission wavelength of 525 nm using a multi-function microplate reader (Bio Tek, USA).

RT-qPCR

Extract total RNA from macrophage samples collected at various infectious time points and PBMCs of subjects. Reverse transcription was performed using the Hifar II 1st Strand cDNA Synthesis Kit (Yesen, Shanghai, China, #11119ES) according to the manufacturer's instructions. Gene expression was measured using the QuantiNova SYBR PCR Mix Kit (QIAGEN, Shanghai, China, #208252) on a 7500 real-time fluorescence quantitative PCR system. GADPH was used as the internal reference, and the relative expression levels of the genes were normalized to GADPH using the 2^−ΔΔCt method. The gene names and primers required for the experiment are listed in Table S1.

ELISA

Cell supernatants collected at different time points before and after infection and plasma were used to measure the concentrations of TNF-α (NEOBIOSCIENCE, Guangdong, China, #EHC103a.96), IL-1β (BBI, Shanghai, China, #D711068-0096), and IL-6 (BBI, Shanghai, China, #D7113918-0096) using ELISA kits according to the manufacturer's protocol.

Western blot

RIPA lysis buffer (Beyotime, Shanghai, China) containing protease and phosphatase inhibitors (Solarbio, Shanghai, China, #P1260) was added to the wells. The BCA protein assay kit (Beyotime, Shanghai, China, #P0012) was used to quantify the protein concentration. The protein samples were loaded onto a 12% SDS-PAGE gel, transferred to a PVDF membrane (Millipore, Shanghai, China, IPVH00010), and blocked with 5% BSA at room temperature for 1.5 h. The membrane was then incubated overnight at 4°C with antibodies against NF-κB p65 (Cell Signalling Technology, Shanghai, China, #9609), phospho-NF-κB p65 (Cell Signalling Technology, Shanghai, China, #3033), and β-actin (Cell Signalling Technology, Shanghai, China, #3700). After washing with TBST, the membrane was incubated with HRP-conjugated anti-rabbit IgG (Cell Signalling Technology, Shanghai, China, #7074) or anti-mouse IgG (Cell Signalling Technology, Shanghai, China, #7076) secondary antibodies at room temperature for 1 h. The specific membranes were visualized using a chemiluminescence imaging system (GelView 6000Plus, Guangzhou, China).

Data Processing and Statistical Analysis

R (version:4.0.3) and R packages were used for all multivariate data analyses and modeling. Data were mean-centered using Pareto scaling. Models were built on principal component analysis (PCA), orthogonal partial least-square discriminant analysis (PLS-DA) and partial least-square discriminant analysis (OPLS-DA). OPLS-DA allowed the determination of discriminating metabolites using the variable importance on projection (VIP). Parameter R2Y (the model's interpretability towards the categorical variable Y) and Q2 (the model's predictability) are used to evaluate the effectiveness of the model. Finally, by randomly changing the order of the categorical variable Y multiple times, different random Q2 values are obtained to further test the effectiveness of the model. Metabolites with VIP values greater than 1.0 and P values less than 0.05 were considered statistically significant. To identify the perturbed biological pathways, the differential metabolite data were performed KEGG pathway analysis using KEGG database (http://www.kegg.jp). KEGG enrichment analyses were carried out with the Fisher’s exact test, and FDR correction for multiple testing was performed. Enriched KEGG pathways were nominally statistically significant at the P < 0.05 level.Statistical analysis of the experimental results was performed using GraphPad Prism 8 software. Unpaired t-test or one-way ANOVA was used for intergroup comparisons. Chi-square test and Kruskal-Wallis H test were used for non-parametric analysis of three or more groups. A P-value less than 0.05 was considered statistically significant, and the significance levels were denoted by asterisks (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001).

STB group exhibits changes in multiple metabolic pathways

After performing multivariate pattern recognition analyses using PCA and OPLS-DA, it was discovered that the three groups exhibited distinct metabolic characteristics. The OPLS-DA analysis scores indicated that the model could clearly distinguish STB, NSTB, and HC (Fig. 1A). Permutation test was further conducted to evaluate the stability of the model. The results showed that the model was not overfitting and had good predictive performance (Fig. 1B, parameters R2X (cum) = 0.261, R2Y (cum) = 0.987, Q2 (cum) = 0.917). Using the HMDB to classify and statistically analyze the differential metabolites among the three groups revealed that the differential metabolites mainly include lipids and lipid-like molecules, organic acids and their derivatives, steroids and steroid derivatives, as well as purine nucleosides. Further KEGG pathway analysis of the differential metabolites revealed that top 30 enriched pathways were ABC transporters, central carbon metabolism in cancer, purine metabolism, biosynthesis of amino acids, and protein digestion and absorption (Fig. 1C). Enrichment analysis of the primary pathways in the HMDB sub-library SMPDB also showed that the enriched metabolic pathways were mainly urea cycle, sphingolipid metabolism, aspartate metabolism, purine metabolism, thiamine metabolism, and alanine metabolism, among others (Fig. 1D).

D-tryptophan, xanthoside, and dihydroceramide can serve as biomarkers for the progression of PTB

In this study, a total of 1628 differential metabolites were identified, including 569 differential metabolites between the NSTB group and the HC group (Fig. 2A), 607 differential metabolites between the STB group and the HC group (Fig. 2B), and 452 differential metabolites between the STB group and the NSTB group (Fig. 2C). Based on the criteria of P < 0.05, VIP > 1, and pathway analysis results, it was found that the levels of xanthosine (Fig. 2D) and hypoxanthine (Fig. 2E) in purine metabolism increased, while the levels of D-tryptophan (Fig. 2F) in amino acid metabolism and dihydroceramide (Fig. 2G) in sphingolipid metabolism decreased. We used receiver operating characteristic (ROC) curve to evaluate the sensitivity and specificity of differential metabolites, and found that the areas under the ROC curves of D-tryptophan, xanthoside and dihydroceramide were 0.864 (Fig. 2H), 0.838 (Fig. 2I) and 0.833 (Fig. 2J), respectively, which had good prediction performance.

STB group's levels of XO, uric acid, and cytokines TNF-α, IL-1β and IL-6 are decreased

Metabolomics data indicate that compared to the HC group, the levels of xanthoside and hypoxanthine in the STB group and NSTB group were elevated (P < 0.05), with no difference in uric acid levels in the STB group (P > 0.05), and elevated uric acid levels in the NSTB group (P < 0.05). Compared with the NSTB group, the uric acid levels in the STB group were reduced (P < 0.001). Our study found that the levels of XO and uric acid in STB group patients were lower than those in the NSTB group (Fig. 3A, 3B). Compared with the NSTB patient group, the levels of plasma cytokines TNF-α (Fig. 3C), IL-1(Fig. 3D) and IL-6 (Fig. 3E) in the STB patient group are reduced.

Inhibiting XO activity inhibits the expression of TNF-α, IL-1β and IL-6 related to inflammation

The CCK-8 results indicate (Fig. 4A), that the concentration of AP at 0.05 mM and 0.1 mM has little effect on the cells. During the experiment, it was found that the concentration of AP at 0.05 mM had a poor inhibitory effect on XO, hence the experiment used a concentration of AP at 0.1 mM. Collecting total RNA from infected cells at different time points for RT-qPCR detection revealed that the gene expression of inflammatory cytokines TNF-α (Fig. 4B), IL-1β (Fig. 4C), and IL-6 (Fig. 4D) decreased after the inhibition of XO activity. Simultaneously, collecting supernatants at different time points for ELISA detection showed that the concentrations of TNF-α (Fig. 4E), IL-1β (Fig. 4F), and IL-6 (Fig. 4G) decreased after the inhibition of XO activity, leading to a downregulation of the inflammatory response in macrophages.

Inhibiting XO suppresses the generation of ROS and NF-κB pathway, while simultaneously promoting the growth of MTB inside cells

Upon MTB infection, the expression of XO in macrophages increased (Fig. 5A). After infection for 24 h, the production of ROS increased, and inhibiting XO activity could reduce the level of ROS in macrophages (Fig. 5B). After 24 h of infection, the expression of NF-κB p65 and phospho-NF-κB p65 proteins increased. Inhibiting the activity of XO suppressed the activation of the NF-κB p65 and phospho-NF-κB p65 protein pathways, suggesting that XO activity affects the NF-κB pathway (Fig. 5C). At the same time, inhibiting XO activity promoted the survival of intracellular bacteria (Fig. 5D).

Conventional diagnostic tests for TB have serious limitations in terms of speed, sensitivity and specificity. Sputum smear have a low sensitivity, and the culture cycle of MTB is relatively long, leading to delayed diagnosis. Although the sensitivity and specificity of cultures and GeneXpert are very high in smear-positive cases, their diagnostic accuracy reduces in patients with smear-negative disease, people living with HIV (PLWH), children, and patients with extrapulmonary tuberculosis (EPTB)^[13]. In addition, culture results are slow to generate, and GeneXpert MTB/RIF is costly and requires a significant amount of infrastructure for implementation, which limits its widespread use^[14]. Biomarkers with diagnostic and prognostic value have been widely explored to help diagnose and assess the disease status and risk of progression^{[15, 16]} .

Our study found that the expression levels of D-tryptophan and xanthoside were elevated, while the level of dihydroceramide decreases. D-tryptophan is a natural amino acid and the stereoisomer of L-tryptophan, which can be converted into the corresponding α-keto acid by D-amino acid oxidase, activating the aryl hydrocarbon receptor and playing different roles in the body^[17]. D-tryptophan can regulate microbial tryptophan metabolism, inhibit intestinal pathogens, and prevent colitis^[18]. Studies have found that D-tryptophan were metabolized in the brain and periphery and converted into metabolites in the kynurenine pathway ^[19], and the kynurenine pathway is significantly activated in TB patients, which can predict the progression of TB^[20]. Xanthosine is produced by the deamination of guanosine, and xanthosine reduces gluconeogenesis and increases glycogen by activating the AMPK and AKT pathways in CC1 hepatocytes and STZ-induced diabetic rats^{[21, 22]}. Sphingolipids are a class of lipids with important signaling functions. Dihydroceramide is a precursor of ceramide in the de novo synthesis of sphingolipids and is involved in a variety of important physiological and pathological processes, including responses related to autophagy, apoptosis, and cell cycle arrest^[23–25], Therefore, xanthosine glycosides and dihydroneuramins may also be involved in the progression of PTB.

A substantial amount of data indicated that TB is related to amino acid metabolism and sphingolipid metabolism^[26–29], but the relationship between TB and purine metabolism is not yet clear. Uric acid is the end product of purine metabolism,the level of uric acid may be an indicator of increased XO activity^[30]. The XO gene is located on the p22 band of chromosome 2, and it includes four CCAAT/enhancer binding sites, three IL-6 response elements, one NF-κB site, and response units for TNF-α, interferon-γ, and interleukin-1, which are controlled by a variety of factors, including hormones, growth factors, and inflammatory cytokines^{[31, 32]}. XO is an enzyme that induces oxidative stress and is a source of host inflammation, which can catalyze the production of uric acid as well as the production of ROS^{[30, 33]}. ROS can be delivered to the site of infection in various forms and have a strong antibacterial effect against Gram-positive and Gram-negative bacteria, viruses, and fungi, making them potential new antimicrobial agents^[34]. ROS plays a crucial role in the immune response to TB, and can control the replication of MTB within human macrophages^[35]. The study suggested that mice with defects in the NADPH oxidase within macrophages will have severe lung lesions after being vaccinated with BCG^[36]. Our experimental results indicated a decrease in XO expression in STB patients. XO, as one of the sources of ROS, may be involved in the progression of PTB through ROS.

Oxidative stress caused by the production of ROS plays an important role in the inflammatory process^[37]. The activation of NF-κB is highly dependent on oxidative stress^[33]. Multiple studies have shown that ROS can activate the NF-κB pathway^[38–40]. The activation of NF-κB pathway leads to macrophage activation and the production of pro-inflammatory cytokines TNF-α, IL-6 and IL-1β, which are mainly induced by the NF-κB signaling pathways^[41]. Our study results indicated that the inhibition of XO activity mainly affects the production of TNF-α, IL-1β, and IL-6 in MTB-infected macrophages and the activation of the NF-κB signaling pathway, which is consistent with previous research findings. A decrease in TNF-α levels can lead to the progression of TB, while excessively high levels can disrupt the cell death process and induce excessive inflammation^[42]. IL-6 expression reduction can impact the protective immune response, leading to increased susceptibility of macrophages to MTB^{[43, 44]}. Patients with severe PTB have lower expression levels of IL-1β in their PBMC^[45]. Therefore, TNF-α, IL-6, and IL-1β are all involved in the progression of TB. XO mediates the production of ROS, thereby promoting the activation of the NF-κB pathway, and the production of pro-inflammatory cytokines can participate in the the role of resisting MTB.

Meanwhile, there are still some flaws in our study. Currently, it is not yet clear whether the NF-κB pathway is activated through ROS, and there has not been further verification in patients with STB. Further case collection and relevant experiments are still required.

Our study findings revealed that in patients with STB, the expression of D-tryptophan, xanthosine, and dihydroceramide is elevated, which could serve as biomarkers for the progression and worsening of TB, and XO is involved in the progression of PTB and represents a potential therapeutic target for halting the progression of PTB.

Obtaining an informed consent form

First, it is necessary to determine who has the right to give informed consent, and: the signature on the informed consent form should be completed by the consenter who has the ability to read and write. Before collecting blood samples, a conversation should be held in the hospital department's consultation room, where the purpose, procedures, potential risks, etc., are described in detail. Sufficient time should be given to read the informed consent form, and any questions about its content should be answered patiently. The signed informed consent form should be in two copies, one of which should be kept by the subject, and the other should be kept by the researcher.

Acknowledgments

We would like to thank all the participants who participated in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

'The data that support the findings of this study are openly available in MetaboLights database at https://ngdc.cncb.ac.cn/gsub/, reference number is HRA006609.

Funding

This work was supported by the National Key Research and Development Program of China (2022YFC2304803).

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No competing interests reported.

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Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis

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Journal Publication

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Abstract

Figures

Introduction

Materials and methods

Subjects and sample collection

UHPLC-MS/MS analysis

Isolation of peripheral blood mononuclear cells

Cell counting kit-8 assay (CCK-8)

Macrophages infection and colony-forming unit counting

ROS detection

RT-qPCR

ELISA

Western blot

Data Processing and Statistical Analysis

Results

STB group exhibits changes in multiple metabolic pathways

D-tryptophan, xanthoside, and dihydroceramide can serve as biomarkers for the progression of PTB

STB group's levels of XO, uric acid, and cytokines TNF-α, IL-1β and IL-6 are decreased

Inhibiting XO activity inhibits the expression of TNF-α, IL-1β and IL-6 related to inflammation

Discussion

Conclusion

Obtaining an informed consent form

Declarations

References

Additional Declarations

Supplementary Files

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