Exploring Differences in Alveolar Microbiome Between Pulmonary Tuberculosis Patients with Different Treatment Outcomes: A Metagenomic Study from China

Exploring Differences in Alveolar Microbiome Between Pulmonary Tuberculosis Patients with Different Treatment Outcomes: A Metagenomic Study from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Differences in Alveolar Microbiome Between Pulmonary Tuberculosis Patients with Different Treatment Outcomes: A Metagenomic Study from China Chaochao Qiu, Zhiruo Lin, Xiaoqing Lin, Yanhong Mei, Yueying Zhou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9000417/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study aimed to investigate differences in the composition and functional characteristics of alveolar microbiota in patients with pulmonary tuberculosis (PTB) exhibiting differential therapeutic responses. Thirty-two patients with drug-sensitive PTB who had completed standard anti-tuberculosis therapy were enrolled and classified into good-response (n = 16) and poor-response (n = 16) groups. Bronchoalveolar lavage fluid (BALF) samples were collected and analysed using metagenomic sequencing to characterize microbial community and functional pathways. No significant differences were observed in α-diversity between the two groups; however, β-diversity analysis demonstrated marked differences in microbial community structure (ANOSIM, R = 0.381, P < 0.001). The good efficacy group was characterized by enrichment of Prevotella, Staphylococcus, and oral commensal bacteria including Fusobacterium and Rothia, together with significantly increased pathways related to peptidoglycan biosynthesis, glutathione metabolism, energy production, and DNA repair. In contrast, the poor efficacy groups was characterised by enrichment of Microbacterium and activation of functional pathways associated with biofilm formation, urea cycle–mediated ammonia production, and apoptosis. These findings suggest that both the taxonomic composition and functional activity of the pulmonary microbiome are closely associated with anti-tuberculosis treatment outcomes. Microbial communities in patients with favourable responses may support recovery through immune modulation, antioxidative capacity, and metabolic competition, whereas microbiota enriched in poor responders may contribute to treatment failure via biofilm formation and production of immunosuppressive metabolites such as ammonia. Tuberculosis Bronchoalveolar lavage fluid Therapeutic efficacy Microecology Metagenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB), transmitted primarily through the respiratory route. According to the World Health Organization[1], the number of newly confirmed tuberculosis cases worldwide reached 8.2 million in 2023. Tuberculosis has re-emerged as the world's leading infectious disease, posing a critical global health challenge. Treatment outcomes in tuberculosis vary widely in clinical practice. Even with adherence to standard anti-tuberculosis regimens, a substantial proportion of patients experience treatment failure, relapse, or persistent mycobacterial positivity [2]. With the advent of metagenomic next-generation sequencing (mNGS), the role of respiratory microecology in tuberculosis pathogenesis and treatment response has gained increasing attention [3]. Emerging evidence indicates that the microbial composition of bronchoalveolar lavage fluid (BALF) can influence the clearance of MTB by modulating host immune responses, including macrophage polarisation and T-cell–mediated immunity [4]. To further elucidate the relationship between pulmonary microecology and treatment outcomes in tuberculosis, we performed metagenomic sequencing of BALF samples from patients with drug-sensitive pulmonary tuberculosis who exhibited divergent therapeutic responses. We systematically investigated differences in microbial community composition, gene functions, and metabolic pathways, with the aim of identifying microbial and functional signatures associated with treatment efficacy and potential targets for early intervention and personalised therapy. Materials and Methods Study Subjects Bronchoalveolar lavage fluid (BALF) samples were collected from patients at Wenzhou Sixth People’s Hospital beginning in January 2024. By May 2025, a total of 542 BALF samples had been obtained. After applying the predefined inclusion and exclusion criteria, 32 samples were eligible for analysis. The sample selection process is summarised in Figure 1. Based on treatment response, patients were classified into a good efficacy group (Group A) and a poor efficacy groups (Group B), with 16 patients in each group. All patients provided written informed consent to participate in the study (For any participant under the age of 16, written informed consent was obtained from their parent(s) or legal guardian prior to participation). Inclusion criteria were as follows: (1) age 14–75 years; (2) confirmed pulmonary tuberculosis with positive sputum or lavage fluid mycobacterial culture and drug sensitivity testing indicating sensitivity to first-line anti-tuberculosis drugs; and (3) regular anti-tuberculosis treatment for ≥2 months. Exclusion criteria were as follows: (1) use of corticosteroids, immunosuppressants, or immunoenhancing drugs within the past 6 months; (2) patients with serious diseases of the heart, liver, kidney, or spleen; (3) patients with AIDS, mental illness, epilepsy, or immune system diseases; (4) diagnosis of other viral, bacterial, fungal, or other infections; (5) diabetic patients with poor glycemic control; (6) patients with limited understanding or inability to cooperate with treatment and examination; (7) patients unable to tolerate bronchoscopy; and (8) pregnant or lactating women. Definitions Good treatment efficacy was defined as follows: respiratory symptoms significantly reduced or resolved following treatment; chest X-ray or computed tomography re-examination showing absorption of infiltrative lesions with cavity closure or reduction; and sputum smear or culture conversion to negative. Poor treatment efficacy was defined according to World Health Organization (WHO) guidelines for tuberculosis diagnosis and treatment as initial treatment failure. Patients with initial pulmonary tuberculosis treatment (no prior anti-tuberculosis treatment or treatment ≤1 month) who, after completing standard 6-month short-course chemotherapy (2HRZE/4HR), presented any of the following conditions: (1) bacteriological non-conversion, defined as sputum smear or culture still positive at the end of the intensive treatment phase (end of second month) or persistent positive bacteria after completing the full treatment course (5–6 months); (2) bacteriological recurrence, defined as MTB detection following sputum bacteria conversion during treatment; or (3) clinical or imaging deterioration, defined as symptoms (cough, fever, hemoptysis, etc.) not improved or worsened, or chest imaging showing enlargement of existing lesions or appearance of new lesions. Specimen Collection All eligible participants provided written informed consent and underwent fiberoptic bronchoscopy. Bronchoalveolar lavage fluid (15 mL) was collected from the lesion site and immediately stored at −80°C until metagenomic sequencing was performed. Sequencing Data Processing All 32 lavage fluid samples passed quality inspection. DNA samples were fragmented by Covaris ultrasonication (350 bp) for library construction, then sequenced using PE150 on the Illumina Nova Xplus platform. Quality control of raw data was performed using fastp (v0.23.2): (1) paired reads containing adapters were removed; (2) low-quality reads (Q≤5 bases accounting for >50%) were removed; and (3) reads with N content >10% were removed. Bowtie2 software (--end-to-end --sensitive -I 200 -X 400) was used to filter host sequences to obtain valid data (Clean Data) for subsequent analysis. All sequencing was performed by Hangzhou Guhe Technology Co. Ltd. Research content A total of 32 patients with initial treatment of pulmonary tuberculosis were enrolled in this study. According to WHO response criteria, patients were divided into good efficacy and poor efficacy groups, with 16 cases in each group. MetaPhlAn3 (mpa_v30 database) and HUMAnN3 (UniRef90 database) were used for species and functional annotation, respectively. Compare the differences in species composition and functional pathways between the two groups. Statistical Analysis Statistical analyses were performed as follows. For baseline characteristics, continuous variables were compared using Student’s t test for normally distributed data or the Mann–Whitney U test for non-normally distributed data, while categorical variables were compared using the χ² test. For microbiome analyses, α-diversity indices (Shannon and Simpson) and differences in species abundance were assessed using the Wilcoxon rank-sum test. β-diversity was calculated based on Bray–Curtis dissimilarity and visualised using non-metric multidimensional scaling (NMDS), with stress values reported in the Supplementary Materials; between-group differences were evaluated using analysis of similarities (ANOSIM). Differential species analysis was conducted using the MetaGenomeSeq package. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant. Results General Characteristics The baseline characteristics of the 32 enrolled patients are shown in Table 1. Table 1. Baseline characteristics of patients in both treatment response groups Characteristics Good Efficacy (Group A) Poor Efficacy (Group B) t/χ² value P-value Age (years, mean ± SD) 47.2±18.1 40.5±21.5 0.963 0.343 Sex 0.139 0.710 Male, n (%) 11 (68.8%) 10 (62.5%) Female, n (%) 5 (31.2 percent) 6 (37.5 percent) Comorbidities 1.166 0.280 None, n (%) 11 (68.8%) 8 (50.0%) Present, n (%) 5 (31.2 percent) 8 (50.0%) Hypertension 4 (25.0%) 3 (18.9%) Diabetes mellitus 1 (6.2%) 2 (12.5%) Associated silicosis 0 (0%) 1 (6.2%) Malignancy 0 (0%) 1 (6.2%) COPD 0 (0%) 1 (6.2%) Note: All P-values >0.05, indicating no significant differences between groups. SD, standard deviation; COPD, chronic obstructive pulmonary disease. Analysis of Species Composition and Functional Differences Species Composition Analysis Genus-level relative abundance analysis identified the ten most abundant taxa in each group, with all remaining genera classified as “Others” (Figure 2A). In the good efficacy group (Group A), Prevotella , Staphylococcus , and Citrobacter were predominant, whereas the poor efficacy groups (Group B) was characterised by a marked enrichment of Microbacterium , which accounted for 42.6% of the total microbial abundance. Hierarchical clustering based on the Bray–Curtis dissimilarity matrix was used to evaluate similarities in microbial community structure across samples. The resulting dendrogram, integrated with species-level relative abundance profiles, revealed clear clustering patterns between the two groups (Figure 2B). Evaluation of alpha diversity at the genus level revealed no significant differences between groups, with Simpson index values of P=0.669 and Shannon index values of P=0.959 (Wilcoxon rank-sum test), as illustrated in Figures 2C and 2D. β-diversity analysis, however, revealed significant differences in microbial community structure between the two groups. Analysis of similarities (ANOSIM) demonstrated substantial community dissimilarity (R=0.381, P<0.001), as shown in Figure 2E. Non-metric multidimensional scaling (NMDS) analysis achieved acceptable model fit with a stress value of 0.174 (Figure 2F). MetaGenomeSeq analysis was employed to identify differentially abundant taxa between groups; a volcano plot illustrating differential species abundance between groups is shown in Figure 2G. Statistical analysis revealed that Fusobacterium (logFC=6.51, FDR=0.013) and Solobacterium (logFC=5.29, FDR=0.013) were significantly enriched in Group A. Conversely, Microbacterium (FDR<0.001) and Bradyrhizobium (FDR=0.001) showed significant enrichment in Group B. Detailed statistical parameters for all differentially abundant species are presented in Table 2. Table 2. Differentially abundant bacterial genera between treatment response groups Genus Log 2 FC (Good vs Poor) SE Adjusted P-value (FDR) Biological Significance Fusobacterium 6.51 1.811 0.013 Oral pathogen Rothia 5.53 1.752 0.043 Oral commensal bacterium Capnocytophaga 5.42 1.738 0.043 Opportunistic pathogen Solobacterium 5.29 1.462 0.013 Oral anaerobe Tannerella 4.85 1.479 0.034 Periodontal pathogen Bradyrhizobium -3.16 0.729 0.001 Soil nitrogen-fixing bacterium Microbacterium -2.79 0.554 < 0.001 Environmental actinomycete Log 2 FC, log₂ fold change; SE, standard error; FDR, false discovery rate. Positive values indicate enrichment in good efficacy group; negative values indicate enrichment in poor efficacy group. Functional Difference Analysis and MetaCyc Pathway Analysis The MetaCyc database[5], which documents experimentally validated metabolic pathways across all domains of life, was employed to assess functional differences between treatment response groups. This comprehensive resource encompasses primary and secondary metabolic pathways along wit h their associated metabolites, reactions, enzymes, and genes. Analysis of relative pathway abundance identified the ten most prevalent metabolic pathways, with the distribution presented in Figure 3A. The most abundant pathways included peptidoglycan maturation (PWY0-1586), S-adenosine-L-methionine rescue (PWY-6151), and selenoamino acid biosynthesis (PWY-6936). Alpha diversity analysis revealed statistically significant differences between groups for both the Simpson index (P=0.002) and Shannon index (P<0.001), as shown in Figures 3B and 3C. β-diversity assessment similarly demonstrated significant differences in functional profiles between groups, with ANOSIM analysis yielding R=0.356 and P<0.001 (Figure 3D). Non-metric multidimensional scaling analysis achieved excellent separation between groups with a stress value <0.001 (Figure 3E). Differential pathway analysis identified metabolic functions with significant enrichment patterns between groups; a volcano plot showing the differences between the two groups is presented in Figure 3F. Statistical parameters for differentially enriched pathways are detailed in Table 3. Group A showed significant enrichment in L-cysteine biosynthesis VI (PW7-19, FDR=0.002) and diphosphate sulfate formation (PW7-7357, FDR=0.002). In contrast, Group B demonstrated significant enrichment in the urea cycle pathway (PW7-4984, FDR=0.002). These distinct metabolic profiles suggest fundamental differences in microbial community function associated with treatment response outcomes. Table 3. Differentially enriched MetaCyc metabolic pathways between treatment response groups Pathway ID Log 2 Fold Change (Group A vs Group B) Standard Error Adjusted P-value (FDR) Potential Biological Relevance PW7-4984 -2.327 0.537 0.002 Urea cycle PW7-19 1.792 0.422 0.002 L-cysteine biosynthesis VI (sulfur transfer from methionine) PW7-7357 1.725 0.387 0.002 Thiamine diphosphate formation from pyrithiamine and oxythiamine (yeast) PW7-5981 1.457 0.434 0.049 CDP-diacylglycerol biosynthesis III CITRULBIO-PWY -1.176 0.353 0.049 L-citrulline biosynthesis KEGG Pathway Analysis The Kyoto Encyclopedia of Genes and Genomes (KEGG) database[6] was utilized for functional pathway analysis. In this database, genes with similar functions are grouped together with their corresponding enzymes to form metabolic pathways. Analysis of KEGG pathway relative abundance identified the ten most prevalent pathways in the samples, with the distribution shown in Figure 4A. The main pathways involved translation (ribosome, ko03010), amino acid metabolism (ko00290, ko00970, ko01230), carbon metabolism (ko00660, ko00710), and bacterial environmental adaptation (ko02030). Comparison of alpha diversity indices between the two groups revealed statistically significant differences for both the Simpson index (P<0.001) and Shannon index (P<0.001), as shown in Figures 4B and 4C. β-diversity analysis similarly demonstrated significant differences between groups, with ANOSIM analysis yielding R=0.4459 and P<0.001 (Figure 4D). Non-metric multidimensional scaling analysis achieved excellent data fit with a stress value <0.001 (Figure 4E). Further differential analysis identified pathways with significant differences between groups; a volcano plot showing the differences between the two groups is presented in Figure 4F. Pathways with statistically significant differences were selected, and their logFC values are detailed in Table 4. Group A showed relative enrichment in renal cell carcinoma (ko05211, FDR=0.041), lipopolysaccharide biosynthesis (ko00540, FDR=0.009), nucleotide excision repair (ko03420, FDR=0.002), lipoic acid metabolism (ko00785, FDR=0.002), and prodigiosin biosynthesis (ko00333, FDR=0.041). In contrast, Group B demonstrated relative enrichment in apoptosis (ko04215, FDR=0.046) and small cell lung cancer (ko05222, FDR=0.046) pathways. Table 4. KEGG pathway analysis of significant differences between the two groups Pathway ID Log 2 Fold Change (Group A vs Group B) Standard Error Adjusted P-value (FDR) Biological Relevance Ko00540 2.101 0.545 0.009 Lipopolysaccharide biosynthesis Ko04215 -1.925 0.596 0.046 Apoptosis - multiple species Ko05222 -1.915 0.599 0.046 Small cell lung cancer Ko03420 1.664 0.370 0.002 Nucleotide excision repair Ko00785 1.632 0.373 0.002 Lipoic acid metabolism Ko00333 1.267 0.371 0.041 Prodigiosin biosynthesis Gene Ontology Analysis Gene Ontology (GO), established by the Gene Ontology Consortium[7], provides a standardized framework for describing gene and protein functions across biological systems. Analysis of GO term relative abundance identified the ten most prevalent functional categories within our dataset. The distribution presented in Figure 5A revealed predominant expression of integral membrane component functions (GO:0016021), ATP binding activity (GO:0005524), and cytoplasmic localization (GO:0005737). Comparison of alpha diversity indices between treatment response groups demonstrated statistically significant differences for both the Simpson index (P<0.001) and Shannon index (P<0.001), as illustrated in Figures 5B and 5C. β-diversity analysis similarly revealed significant functional differences between groups, with ANOSIM analysis yielding R=0.637 and P<0.001 (Figure 5D). Non-metric multidimensional scaling (NMDS) analysis achieved acceptable model fit with a stress value of 0.051, indicating robust separation between groups (Figure 5E). Differential functional analysis identified GO terms with significant enrichment patterns between groups; a volcano plot showing the differences between the two groups is presented in Figure 5F. Statistical parameters for differentially enriched functions are detailed in Table 5. Group A demonstrated significant enrichment in thiaminase activity (GO:0050334), CDP-glycerol glycerophosphotransferase activity (GO:0047355), and coenzyme A metabolic processes (GO:0015936), all with FDR values below 0.001. In contrast, Group B showed significant enrichment in mannose-6-phosphate isomerase activity (GO:0103011), synaptic functions (GO:0045202), methylenetetrahydrofolate cyclohydrolase activity (GO:0030412), L-fucose dehydratase activity (GO:0050023), and host cell nucleus-related functions (GO:0042025), with FDR values below 0.05. Table 5. Differentially enriched Gene Ontology terms between treatment response groups GO Term ID Log 2 Fold Change (Group A vs Group B) Standard Error Adjusted P-value (FDR) Functional Category GO:0103011 -4.181 0.910 < 0.001 Mannose-6-phosphate isomerase activity GO:0045202 -3.883 0.780 < 0.001 Synapses GO:0030412 -3.832 1.232 0.036 Methylenetetrahydrofolate cyclohydrolase activity GO:0050023 -3.805 0.978 0.006 L-fucose dehydratase activity GO:0042025 -3.755 1.248 0.041 Host cell nucleus GO:0050334 2.868 0.482 < 0.001 Thiaminase activity GO:0047355 2.415 0.645 < 0.001 CDP-glycerol glycerophosphotransferase activity GO:0015936 2.411 0.539 < 0.001 Coenzyme A metabolic process Discussion This study analyzed the pulmonary alveolar microbiota of patients with pulmonary tuberculosis who responded differently to treatment, revealing specific microbial compositions and functional characteristics related to clinical efficacy. Our findings demonstrate that while the primary genus-level species composition appears similar between patient groups, with no significant differences in alpha diversity indices (Simpson index P=0.669, Shannon index P=0.959), β-diversity analysis reveals significant differences in microbial community structure (ANOSIM analysis, R=0.381, P<0.001). Patients with a good treatment response had a pulmonary alveolar microbiota characterized by the enrichment of Prevotella, Staphylococcus, and oral symbiotic bacteria including Fusobacterium and Ralstonia, and significant enhancement of related functional pathways such as peptidoglycan biosynthesis, glutathione metabolism, energy production, and DNA repair. In contrast, patients with a poor treatment response showed enrichment of Microbacterium and activation of related pathways such as biofilm formation, urea cycle-mediated ammonia production, and apoptosis. Previous research indicates that Prevotella and Staphylococcus can attenuate tuberculosis-related pulmonary inflammatory damage through regulation of Th17/Treg cell balance[8], with their elevated respiratory tract abundance significantly associated with favorable patient prognosis[9].Differential analysis revealed that Group A demonstrated significant enrichment of oral commensal bacteria, including Fusobacterium and Rothia , compared with Group B. Supporting literature suggests that oral microbiota may enhance clinical efficacy through immunomodulation (particularly via the IL-17 pathway) or competitive inhibition of Mycobacterium tuberculosis colonization[10]. Conversely, the poor efficacy group (Group B) displayed abnormal Microbacterium proliferation alongside persistent Mycobacterium tuberculosis . Microbacterium showed significant enrichment in Group B (LogFC=−2.79, FDR<0.001). Existing research demonstrates that such environmental actinomycetes may contribute to treatment failure through biofilm-mediated antibiotic resistance[11]. Xiao et al[12] discovered that Microbacterium forms biofilms through extracellular polysaccharide (EPS) secretion, physically impeding anti-tuberculosis drug penetration. These findings suggest that BALF Microbacterium enrichment may serve as a potential microbial marker for poor tuberculosis prognosis. From a functional perspective, analysis of biological function genes and metabolic pathways revealed significant differences in BALF microecological function between patient groups (P<0.05 for both alpha and β-diversity comparisons). MetaCyc database analysis of the ten most abundant pathways demonstrated higher overall metabolic functional activity in Group A's microbial community compared with Group B. Key metabolic pathways including peptidoglycan maturation (PWY0-1586) and S-adenosyl-L-methionine (SAMe) salvage (PWY-6151) showed significant enrichment. Peptidoglycan represents a core bacterial cell wall component, and elevated peptidoglycan maturation pathway abundance indicates that Group A's lung microbial community maintains an active growth and division state[13]. The high expression of the SAMe salvage pathway warrants particular attention. SAMe serves as a critical methyl donor in biological systems, participating in nucleic acid, protein, and phospholipid methylation modifications. Additionally, it functions as a key precursor molecule for glutathione (GSH) synthesis, with its homeostasis being crucial for maintaining cellular redox balance[14]. Through the analysis of differences, group A specifically demonstrated enrichment in L-cysteine biosynthesis VI (reverse transsulfuration pathway, PWY-19), which provides the rate-limiting substrate for GSH synthesis. GSH represents one of the most important intracellular antioxidants, playing a crucial role in neutralizing excessive reactive oxygen species (ROS) generated by host immune cells during anti-infection responses and protecting cells from oxidative damage[15]. Group A's microbial community may indirectly alleviate excessive inflammatory damage and create a more favorable microenvironment for tissue repair through regulation of local redox balance, potentially representing an important ecological foundation for improved therapeutic outcomes in these patients.Furthermore, Group A showed relative enrichment in the thiamine diphosphate (TPP) formation pathway (PWY-7357). In contrast, Group B demonstrated relative enrichment in the urea cycle pathway (PWY-4984), typically associated with amino acid degradation and ammonia production. While international studies directly exploring the urea cycle in lung microecology remain limited, research from hepatic disease provides compelling mechanistic analogies. In hepatic encephalopathy, ammonia produced by intestinal microorganisms demonstrably inhibits macrophage phagocytosis and disrupts normal T cell immune responses[16]. We hypothesize that increased ammonia concentration in the lung microecological environment may suppress local antibacterial immunity and promote Mycobacterium tuberculosis persistence, contributing to poor clinical efficacy. KEGG pathway analysis revealed that both groups' core microbial community functions concentrated on fundamental life activities, primarily involving ribosomal processes (ko03010), multiple amino acid metabolism pathways (ko00290, ko00970, ko01230), carbon metabolism (ko00660, ko00710), and bacterial environmental adaptation (ko02030). Differential analysis demonstrated that Group A showed significant enrichment in lipopolysaccharide biosynthesis (ko00540) and nucleotide excision repair (NER, ko03420) pathways. While lipopolysaccharide (LPS) traditionally functions as a potent immune activator as a major Gram-negative bacterial cell wall component, recent studies indicate that LPS derived from certain commensal bacteria (such as Prevotella and Staphylococcus ) can function as weak TLR4 signaling agonists or antagonists, alleviating excessive inflammatory damage through immune response regulation[17]. This finding corroborates the enrichment of Prevotella and Staphylococcus observed in Group A's species analysis. Additionally, NER pathway enrichment in Group A indicates exposure of its microbial community to high oxidative stress environments. NER pathway upregulation suggests that Group A bacteria maintain genomic stability and community functional activity through enhanced DNA maintenance capabilities to resist host immunity-generated oxidative stress. This finding aligns with Group A's enrichment in energy metabolism and antioxidation-related pathways, including cysteine and glutathione synthesis, observed in the MetaCyc analysis. Gene Ontology functional enrichment analysis revealed that the most abundant functions in both groups involved basic life processes including membrane structure (GO:0016021), ATP binding (GO:0005524), and cytoplasmic components (GO:0005737). This indicates that despite significant differences in community structure (β-diversity, R=0.637, P<0.001), core functions remain focused on maintaining basic cellular structure and energy metabolism, aligning with fundamental requirements for microbial colonization and survival in the host[12]. Differential analysis identified significant enrichment of coenzyme and vitamin metabolism-related pathways in Group A, particularly thiaminase activity (GO:0050334) and coenzyme A metabolic processes (GO:0015936). Notably, Mycobacterium tuberculosis is a thiamine auxotroph[18]. These results suggest that elevated thiamine metabolic activity in Group A bacteria may indirectly inhibit Mycobacterium tuberculosis metabolism and adaptability through competitive nutrient consumption, potentially representing one mechanism underlying this group's improved efficacy. This finding aligns with TPP pathway enrichment in the MetaCyc analysis, suggesting that Group A's microenvironment maintains vigorous energy metabolism and nutritional competition capacity. Additionally, coenzyme A serves as a core coenzyme in fatty acid metabolism, and Mycobacterium tuberculosis can utilize host lipids as carbon and energy sources for persistent infection[19]. Group B demonstrated significant enrichment in mannose-6-phosphate isomerase activity (GO:0103011) and L-fucose dehydratase activity (GO:0050023), both involved in bacterial capsule and extracellular polysaccharide biosynthesis. This finding corresponds with Microbacterium enrichment in Group B, suggesting the presence of highly structured biofilm microecology in these patients' lungs. Biofilm formation represents an important mechanism of bacterial antibiotic resistance and treatment failure. The bacterial toxin–antitoxin (TA) system plays a central regulatory role in biofilm formation and stability, inducing bacterial dormancy and coordinating group behavior to cope with antibiotic pressure[20]. We hypothesize that under drug stress, Group B lung microbes may activate TA systems and other stress mechanisms to upregulate EPS synthesis pathways, forming physical and physiological barriers that protect commensal bacteria while providing shelter for Mycobacterium tuberculosis , hindering drug penetration and immune clearance, ultimately leading to poor efficacy. The primary strength of this study lies in its pioneering use of BALF metagenomics to systematically reveal lung microecological differences in tuberculosis patients with differential treatment responses. Given the relative paucity of international research in this area, our study not only confirms that oral commensal bacteria (such as Fusobacterium and Rothia ) may associate with favorable prognosis through competitive inhibition and immunomodulation but also identifies Microbacterium as a potential poor prognosis microbe whose enrichment may relate to biofilm formation and antibiotic resistance. The research reveals differences in key pathways including energy metabolism and oxidative stress balance at the functional level, demonstrating that nutritional competition and microenvironmental regulation represent possible mechanisms affecting tuberculosis treatment efficacy. However, this study has several limitations. The small sample size may affect statistical power and limit control for confounding factors. As an observational study, our results suggest associations rather than causality, and the proposed mechanistic hypotheses require subsequent experimental validation. Future research should expand sample sizes and combine multi-omics technologies with experimental verification to further elucidate causal mechanisms. Conclusions Through metagenomic analysis, we have systematically revealed for the first time significant differences in species structure and functional characteristics of bronchoalveolar lavage fluid microbiomes between pulmonary tuberculosis patients with good versus poor treatment responses. The good efficacy group was characterized by Prevotella , Staphylococcus , and various oral commensal bacteria (including Fusobacterium and Rothia ). This group's microbial community exhibited higher metabolic activity, with enrichment in peptidoglycan synthesis, antioxidant synthesis (particularly glutathione), and energy metabolism pathways. These communities may promote therapeutic efficacy through mechanisms including immune regulation, nutritional competition, and maintenance of redox balance. Conversely, Microbacterium showed significant enrichment in the poor efficacy group. Functional analysis revealed activation of pathways related to biofilm formation, extracellular polysaccharide synthesis, and urea cycling, suggesting that lungs of patients with poor efficacy may harbor biofilm-based antibiotic-resistant microenvironments that impede drug penetration and impair local immune clearance. In summary, lung microbiome composition and functional characteristics may represent key factors influencing anti-tuberculosis treatment efficacy. Certain commensal bacteria, particularly those of oral origin, may play protective roles, while environmental microbe enrichment such as Microbacterium may indicate poor prognosis. These findings provide potential microbial markers and novel intervention targets for personalized tuberculosis treatment. Abbreviations BALF: Bronchoalveolar lavage fluid DNA: Deoxyribonucleic Acid mNGS: Metagenomic Next-generation sequencing MTB: Mycobacterium tuberculosis WHO: World Health Organization NMDS: Non-metric Multidimensional Scaling KEGG: Kyoto Encyclopedia of Genes and Genomes GO: Gene Ontology EPS: extracellular polysaccharide Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Wenzhou Central Hospital (Approval No: K2021-01-003). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All patients provided written informed consent to participate in the study (For any participant under the age of 16, written informed consent was obtained from their parent(s) or legal guardian prior to participation). Consent for publication Not applicable. Clinical trial number Not applicable. Availability of data and materials The raw sequencing data generated in this study have been deposited in the Science Data Bank (ScienceDB) and are accessible via DOI: 10.57760/sciencedb.30999. These data will be publicly available upon publication of this article. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the Wenzhou Science and Technology Major Innovation Project (No. ZY2020019). Authors' contributions Z-RL: Data collection and analysis; manuscript drafting. J-CS and Y-HM: Clinical implementation. C-CQ, and Y-YZ: Bronchoscopy procedures. N-P: Data collection and chart preparation. X-QL: Statistical analysis. X-GJ: Study design conceptualisation; critical review of the manuscript; and major revisions. All authors approved the final version of the manuscript. Acknowledgements We thank the Department of Infectious Diseases, Wenzhou Sixth People's Hospital, for providing BALF samples and clinical data. We also appreciate Dr. Gao (ShuLan Hospital Affiliated with Zhejiang Shuren University, Hangzhou, China) for her guidance in metagenomic sequencing analysis. References World Health Organization (2024). Global Tuberculosis Report. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024 Huang T, Chen Q, Wu HG, Tang SJ (ed) (2025). Interpretation of the World Health Organization consolidated guidelines on tuberculosis Module 4: treatment and care. Chin J Tubere Respir Dis, 48(8):708-718. doi:10.3760/cma.j.cn112147-20250512-00259. Sun W, Zheng L, Kang L, Chen C, Wang L (2024). Comparative analysis of metagenomic and targeted next-generation sequencing for pathogens diagnosis in bronchoalveolar lavage fluid specimens. Front Cell Infect Microbiol, (14):2235-2988. doi:10.3389/fcimb.2024.1451440. 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Heliyon, 9(11):2405-8440. doi:10.1016/j.heliyon.2023.e22124. Jason L, Anup M, Gholamali R, Jonathan C, Joshua O (2017). Strains, functions and dynamics in the expanded Human Microbiome Project. Nature, 550(7674)：1476-4687. doi：10.1038/nature23889. Sivaranjani N, Alan S, Michael S, Matthew F (2018). The Microbiome and Tuberculosis: Early Evidence for Cross Talk. mBio，9(5)：2150-7511. doi:10.1128/mBio.01420-18. Sarangi A, Singh SP, Das BS, Rajput S, Fatima S (2024). Mycobacterial biofilms: A therapeutic target against bacterial persistence and generation of antibiotic resistance. Heliyon, 10(11):2405-8440. doi:10.1016/j.heliyon.2024.e32003. Xiaohong X, Wanyan D, Minqiang L, Jianping X (2014). Mycobacterium biofilms: factors involved in development, dispersal, and therapeutic strategies against biofilm-relevant pathogens, Crit Rev Eukaryot Gene Expr, 24(3):269-279. doi:10.1615/critreveukaryotgeneexpr.2014010545. Schleifer KH, Kandler O (1972). Peptidoglycan types of bacterial cell walls and their taxonomic implications. Bacteriol Rev, 36(4):0005-3678. doi:10.1128/br.36.4.407-477.1972. Shelly C, José M (2012). S-adenosylmethionine in liver health, injury, and cancer. Physiol Rev, 92(4):1522-1210. doi:10.1152/physrev.00047.2011. Jay HF, Hongqiao Z, Alessandra R (2009). Glutathione: overview of its protective roles, measurement, and biosynthesis. Mol Aspects Med, 30(1-2):1-12. doi:10.1016/j.mam.2008.08.006. Debbie L, Gavin AK, Vanessa S, Stephen J, Nathan A (2008). Ammonia impairs neutrophil phagocytic function in liver disease. Hepatology, 48(4):1202-1212. doi:10.1002/hep.22474. Dickson RP, Erb-Downward JR, Freeman CM, McCloskey L, Falkowski NR (2017), Bacterial Topography of the Healthy Human Lower Respiratory Tract. mBio, 8(1):2150-7511. doi:10.1128/mBio.02287-16. Berney M, Berney-Meyer L (2017). Mycobacterium tuberculosis in the Face of Host-Imposed Nutrient Limitation. Microbiol Spectr, 5(3):2165-0497. doi:10.1128/microbiolspec.TBTB2-0030-2016. Singh V, Jamwal S, Jain R, Verma P, Gokhale R (2012). Mycobacterium tuberculosis -Driven Targeted Recalibration of Macrophage Lipid Homeostasis Promotes the Foamy Phenotype. Cell Host Microbe, 12(5):669-681. doi:10.1016/j.chom.2012.09.012. Jurėnas D, Fraikin N, Goormaghtigh F, Van Melderen L (2022). Biology and evolution of bacterial toxin-antitoxin systems. Nat Rev Microbiol, 20(6):335-350. doi:10.1038/s41579-021-00661-1. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9000417","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607205884,"identity":"a5ca2706-6c69-46ef-a120-ebcb9bbf317e","order_by":0,"name":"Chaochao Qiu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chaochao","middleName":"","lastName":"Qiu","suffix":""},{"id":607205885,"identity":"79c22d87-6cff-44b6-90c1-5c6940bb4144","order_by":1,"name":"Zhiruo Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhiruo","middleName":"","lastName":"Lin","suffix":""},{"id":607205886,"identity":"89faf78e-9bc7-4cb2-9705-b0d9b099d190","order_by":2,"name":"Xiaoqing Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Lin","suffix":""},{"id":607205887,"identity":"1fd2d7a8-9d7b-4527-b425-7df91651c43c","order_by":3,"name":"Yanhong Mei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"Mei","suffix":""},{"id":607205889,"identity":"bd67dbef-3788-4c89-9d40-7a858270515a","order_by":4,"name":"Yueying Zhou","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yueying","middleName":"","lastName":"Zhou","suffix":""},{"id":607205890,"identity":"f9078238-bba2-4653-be71-4314c0076418","order_by":5,"name":"Ning Pan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Pan","suffix":""},{"id":607205891,"identity":"b65f326b-7daf-40d9-ac5a-462692fbc5a2","order_by":6,"name":"Jichan Shi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jichan","middleName":"","lastName":"Shi","suffix":""},{"id":607205892,"identity":"e92f5a10-ecc9-432d-a616-729b838d9245","order_by":7,"name":"Xiangao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACZijFBiI/QDgGxGthnEGUFhTtPMRoMTjO/Ozhlxobdj72s4df27ZtS2xgb94mwVBzB6cWyWY2c2OZY2nMbDx5ada5bbcTG3iOlUkwHHuGUws/M4OZtATbYaBfcsyMc7cBtUjkmEkwNhzGqYWNmf2btMQ/oBb+N2bGliAt8m/wa+Fn5jGT/NgG1CKRY/yYEWwLD34tks08ZdKMfUC/SLwxY+z9d9u4jSet2CLhGG4tBuePb5P88c0mWb4/x/jDjzO3ZfvZD2+88aEGtxYQAEVHMshfEmDfgYgEvBqAkf6DgcEOpPUDAYWjYBSMglEwQgEA0pRMnr49AtoAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Xiangao","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-03-01 09:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9000417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9000417/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104995968,"identity":"01d65e08-fc44-4d4b-ab1d-17c8649a1a22","added_by":"auto","created_at":"2026-03-19 16:11:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":535971,"visible":true,"origin":"","legend":"\u003cp\u003eScreening process of research subjects\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/80c43bc9675f8b82b88ca048.jpg"},{"id":104996049,"identity":"29aff022-7d1f-4cfe-a7db-93e66c9c97e4","added_by":"auto","created_at":"2026-03-19 16:11:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304685,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of bronchoalveolar lavage fluid microecological species composition between treatment response groups. (A) Distribution of the ten most abundant bacterial genera in both groups; (B) Hierarchical clustering dendrogram of species composition between sample groups; (C) Simpson alpha diversity index analysis between groups (P = 0.669); (D) Shannon alpha diversity index analysis between groups (P = 0.959); (E) β-diversity analysis between groups using analysis of similarities (ANOSIM; R = 0.381, P \u0026lt; 0.001); (F) Non-metric multidimensional scaling (NMDS) ordination of samples (Stress = 0.174); (G) Volcano plot illustrating differential species abundance between groups.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/f806a5f2667fa789b2ba0c91.jpg"},{"id":104996155,"identity":"40b50519-68dd-4bcc-9592-7aa35dad4bc7","added_by":"auto","created_at":"2026-03-19 16:11:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247392,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of MetaCyc metabolic pathways in bronchoalveolar lavage fluid microecological function between treatment response groups. (A) Distribution of the ten most abundant MetaCyc pathways in both groups; (B) Simpson alpha diversity index of MetaCyc pathways between groups (P = 0.002); (C) Shannon alpha diversity index of MetaCyc pathways between groups (P \u0026lt; 0.001); (D) β-diversity analysis of MetaCyc pathways using ANOSIM (R = 0.356, P \u0026lt; 0.001); (E) Non-metric multidimensional scaling (NMDS) ordination of MetaCyc pathway profiles between groups (Stress \u0026lt; 0.001); (F) Volcano plot shows the difference between the two groups.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/047488ae475581353b5d2eb4.jpg"},{"id":104996198,"identity":"cd294923-7849-42e1-97c5-fd193bf00f1a","added_by":"auto","created_at":"2026-03-19 16:11:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278845,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of KEGG pathways in bronchoalveolar lavage fluid microecological function between treatment response groups. (A) Distribution of the ten most abundant KEGG pathways in both sample groups; (B) Simpson alpha diversity index of KEGG pathways between groups (P \u0026lt; 0.001); (C) Shannon alpha diversity index of KEGG pathways between groups (P \u0026lt; 0.001); (D) β-diversity analysis of KEGG pathways using ANOSIM (R = 0.4459, P \u0026lt; 0.001); (E) Non-metric multidimensional scaling (NMDS) ordination of KEGG pathway profiles between groups (Stress \u0026lt; 0.001); (F) Volcano plot shows the difference between the two groups.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/c19671dc996adc0f8278f535.jpg"},{"id":104996153,"identity":"34cd272a-38fb-458d-a5f8-d47408fa3f93","added_by":"auto","created_at":"2026-03-19 16:11:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":338564,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of Gene Ontology functional categories in bronchoalveolar lavage fluid microecological function between treatment response groups. (A) Distribution of the ten most abundant GO terms in both sample groups; (B) Simpson alpha diversity index of GO functions between groups (P \u0026lt; 0.001); (C) Shannon alpha diversity index of GO functions between groups (P \u0026lt; 0.001); (D) β-diversity analysis of GO functions using ANOSIM (R = 0.637, P \u0026lt; 0.001); (E) Non-metric multidimensional scaling (NMDS) ordination of GO functional profiles between groups (Stress = 0.051); (F) Volcano plot shows the difference between the two groups.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/4a9224d58e52066c0c69c4a6.jpg"},{"id":105035869,"identity":"43649e13-7595-40bd-8e2f-b948f7074c9b","added_by":"auto","created_at":"2026-03-20 07:26:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2665286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9000417/v1/dba560e9-3532-4697-98c8-ffcd96ed13ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Differences in Alveolar Microbiome Between Pulmonary Tuberculosis Patients with Different Treatment Outcomes: A Metagenomic Study from China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis is a chronic infectious disease caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB), transmitted primarily through the respiratory route. According to the World Health Organization[1], the number of newly confirmed tuberculosis cases worldwide reached 8.2 million in 2023. Tuberculosis has re-emerged as the world\u0026apos;s leading infectious disease, posing a critical global health challenge.\u003c/p\u003e\n\u003cp\u003eTreatment outcomes in tuberculosis vary widely in clinical practice. Even with adherence to standard anti-tuberculosis regimens, a substantial proportion of patients experience treatment failure, relapse, or persistent mycobacterial positivity [2]. With the advent of metagenomic next-generation sequencing (mNGS), the role of respiratory microecology in tuberculosis pathogenesis and treatment response has gained increasing attention [3]. Emerging evidence indicates that the microbial composition of bronchoalveolar lavage fluid (BALF) can influence the clearance of MTB by modulating host immune responses, including macrophage polarisation and T-cell\u0026ndash;mediated immunity [4].\u003c/p\u003e\n\u003cp\u003eTo further elucidate the relationship between pulmonary microecology and treatment outcomes in tuberculosis, we performed metagenomic sequencing of BALF samples from patients with drug-sensitive pulmonary tuberculosis who exhibited divergent therapeutic responses. We systematically investigated differences in microbial community composition, gene functions, and metabolic pathways, with the aim of identifying microbial and functional signatures associated with treatment efficacy and potential targets for early intervention and personalised therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBronchoalveolar lavage fluid (BALF) samples were collected from patients at Wenzhou Sixth People\u0026rsquo;s Hospital beginning in January 2024. By May 2025, a total of 542 BALF samples had been obtained. After applying the predefined inclusion and exclusion criteria, 32 samples were eligible for analysis. The sample selection process is summarised in Figure 1. Based on treatment response, patients were classified into a good efficacy group (Group A) and a poor efficacy groups (Group B), with 16 patients in each group. All patients provided written informed consent to participate in the study (For any participant under the age of 16, written informed consent was obtained from their parent(s) or legal guardian prior to participation).\u003c/p\u003e\n\u003cp\u003eInclusion criteria were as follows: (1) age 14\u0026ndash;75 years; (2) confirmed pulmonary tuberculosis with positive sputum or lavage fluid mycobacterial culture and drug sensitivity testing indicating sensitivity to first-line anti-tuberculosis drugs; and (3) regular anti-tuberculosis treatment for \u0026ge;2 months.\u003c/p\u003e\n\u003cp\u003eExclusion criteria were as follows: (1) use of corticosteroids, immunosuppressants, or immunoenhancing drugs within the past 6 months; (2) patients with serious diseases of the heart, liver, kidney, or spleen; (3) patients with AIDS, mental illness, epilepsy, or immune system diseases; (4) diagnosis of other viral, bacterial, fungal, or other infections; (5) diabetic patients with poor glycemic control; (6) patients with limited understanding or inability to cooperate with treatment and examination; (7) patients unable to tolerate bronchoscopy; and (8) pregnant or lactating women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGood treatment efficacy was defined as follows: respiratory symptoms significantly reduced or resolved following treatment; chest X-ray or computed tomography re-examination showing absorption of infiltrative lesions with cavity closure or reduction; and sputum smear or culture conversion to negative.\u003c/p\u003e\n\u003cp\u003ePoor treatment efficacy was defined according to World Health Organization (WHO) guidelines for tuberculosis diagnosis and treatment as initial treatment failure. Patients with initial pulmonary tuberculosis treatment (no prior anti-tuberculosis treatment or treatment \u0026le;1 month) who, after completing standard 6-month short-course chemotherapy (2HRZE/4HR), presented any of the following conditions: (1) bacteriological non-conversion, defined as sputum smear or culture still positive at the end of the intensive treatment phase (end of second month) or persistent positive bacteria after completing the full treatment course (5\u0026ndash;6 months); (2) bacteriological recurrence, defined as MTB detection following sputum bacteria conversion during treatment; or (3) clinical or imaging deterioration, defined as symptoms (cough, fever, hemoptysis, etc.) not improved or worsened, or chest imaging showing enlargement of existing lesions or appearance of new lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll eligible participants provided written informed consent and underwent fiberoptic bronchoscopy. Bronchoalveolar lavage fluid (15 mL) was collected from the lesion site and immediately stored at \u0026minus;80\u0026deg;C until metagenomic sequencing was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing Data Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 32 lavage fluid samples passed quality inspection. DNA samples were fragmented by Covaris ultrasonication (350 bp) for library construction, then sequenced using PE150 on the Illumina Nova Xplus platform. Quality control of raw data was performed using fastp (v0.23.2): (1) paired reads containing adapters were removed; (2) low-quality reads (Q\u0026le;5 bases accounting for \u0026gt;50%) were removed; and (3) reads with N content \u0026gt;10% were removed. Bowtie2 software (--end-to-end --sensitive -I 200 -X 400) was used to filter host sequences to obtain valid data (Clean Data) for subsequent analysis. All sequencing was performed by Hangzhou Guhe Technology Co. Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 32 patients with initial treatment of pulmonary tuberculosis were enrolled in this study. According to WHO response criteria, patients were divided into good efficacy and poor efficacy groups, with 16 cases in each group. MetaPhlAn3 (mpa_v30 database) and HUMAnN3 (UniRef90 database) were used for species and functional annotation, respectively. Compare the differences in species composition and functional pathways between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed as follows. For baseline characteristics, continuous variables were compared using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test for normally distributed data or the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test for non-normally distributed data, while categorical variables were compared using the \u0026chi;\u0026sup2; test. For microbiome analyses, \u0026alpha;-diversity indices (Shannon and Simpson) and differences in species abundance were assessed using the Wilcoxon rank-sum test. \u0026beta;-diversity was calculated based on Bray\u0026ndash;Curtis dissimilarity and visualised using non-metric multidimensional scaling (NMDS), with stress values reported in the Supplementary Materials; between-group differences were evaluated using analysis of similarities (ANOSIM). Differential species analysis was conducted using the MetaGenomeSeq package. All statistical tests were two-sided, and a \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the 32 enrolled patients are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of patients in both treatment response groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood Efficacy (Group A)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor Efficacy (Group B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et/\u0026chi;\u0026sup2; value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eAge (years, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e47.2\u0026plusmn;18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e40.5\u0026plusmn;21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e11 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e10 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e5 (31.2 percent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e6 (37.5 percent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eNone, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e11 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e8 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003ePresent, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e5 (31.2 percent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e8 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e3 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e1 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e2 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eAssociated silicosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e1 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e1 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0638%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5177%;\"\u003e\n \u003cp\u003e1 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: All P-values \u0026gt;0.05, indicating no significant differences between groups. SD, standard deviation; COPD, chronic obstructive pulmonary disease.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Species Composition and Functional Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecies Composition Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenus-level relative abundance analysis identified the ten most abundant taxa in each group, with all remaining genera classified as \u0026ldquo;Others\u0026rdquo; (Figure 2A). In the\u0026nbsp;good efficacy group\u0026nbsp;(Group A), \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, and \u003cem\u003eCitrobacter\u003c/em\u003e were predominant, whereas the\u0026nbsp;poor efficacy groups\u0026nbsp;(Group B) was characterised by a marked enrichment of \u003cem\u003eMicrobacterium\u003c/em\u003e, which accounted for 42.6% of the total microbial abundance.\u003c/p\u003e\n\u003cp\u003eHierarchical clustering based on the Bray\u0026ndash;Curtis dissimilarity matrix was used to evaluate similarities in microbial community structure across samples. The resulting dendrogram, integrated with species-level relative abundance profiles, revealed clear clustering patterns between the two groups (Figure 2B).\u003c/p\u003e\n\u003cp\u003eEvaluation of alpha diversity at the genus level revealed no significant differences between groups, with Simpson index values of P=0.669 and Shannon index values of P=0.959 (Wilcoxon rank-sum test), as illustrated in Figures 2C and 2D. \u0026beta;-diversity analysis, however, revealed significant differences in microbial community structure between the two groups. Analysis of similarities (ANOSIM) demonstrated substantial community dissimilarity (R=0.381, P\u0026lt;0.001), as shown in Figure 2E. Non-metric multidimensional scaling (NMDS) analysis achieved acceptable model fit with a stress value of 0.174 (Figure 2F). MetaGenomeSeq analysis was employed to identify differentially abundant taxa between groups; a volcano plot illustrating differential species abundance between groups is shown in Figure 2G. Statistical analysis revealed that \u003cem\u003eFusobacterium\u003c/em\u003e (logFC=6.51, FDR=0.013) and \u003cem\u003eSolobacterium\u003c/em\u003e (logFC=5.29, FDR=0.013) were significantly enriched in Group A. Conversely, \u003cem\u003eMicrobacterium\u003c/em\u003e (FDR\u0026lt;0.001) and \u003cem\u003eBradyrhizobium\u003c/em\u003e (FDR=0.001) showed significant enrichment in Group B. Detailed statistical parameters for all differentially abundant species are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Differentially abundant bacterial genera between treatment response groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog\u003csub\u003e2\u003c/sub\u003eFC (Good vs Poor)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P-value (FDR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eFusobacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e1.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eOral pathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eRothia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e1.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eOral commensal bacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eCapnocytophaga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e1.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eOpportunistic pathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eSolobacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e1.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eOral anaerobe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eTannerella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003ePeriodontal pathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eBradyrhizobium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eSoil nitrogen-fixing bacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.1426%;\"\u003e\n \u003cp\u003e\u003cem\u003eMicrobacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5174%;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.7239%;\"\u003e\n \u003cp\u003eEnvironmental actinomycete\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eLog\u003c/em\u003e\u003cem\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003cem\u003eFC, log₂ fold change; SE, standard error; FDR, false discovery rate. Positive values indicate enrichment in good efficacy group; negative values indicate enrichment in poor efficacy group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Difference Analysis and MetaCyc Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MetaCyc database[5], which documents experimentally validated metabolic pathways across all domains of life, was employed to assess functional differences between treatment response groups. This comprehensive resource encompasses primary and secondary metabolic pathways along wit\u003c/p\u003e\n\u003cp\u003eh their associated metabolites, reactions, enzymes, and genes. Analysis of relative pathway abundance identified the ten most prevalent metabolic pathways, with the distribution presented in Figure 3A. The most abundant pathways included peptidoglycan maturation (PWY0-1586), S-adenosine-L-methionine rescue (PWY-6151), and selenoamino acid biosynthesis (PWY-6936). Alpha diversity analysis revealed statistically significant differences between groups for both the Simpson index (P=0.002) and Shannon index (P\u0026lt;0.001), as shown in Figures 3B and 3C. \u0026beta;-diversity assessment similarly demonstrated significant differences in functional profiles between groups, with ANOSIM analysis yielding R=0.356 and P\u0026lt;0.001 (Figure 3D). Non-metric multidimensional scaling analysis achieved excellent separation between groups with a stress value \u0026lt;0.001 (Figure 3E). Differential pathway analysis identified metabolic functions with significant enrichment patterns between groups; a volcano plot showing the differences between the two groups is presented in Figure 3F. Statistical parameters for differentially enriched pathways are detailed in Table 3. Group A showed significant enrichment in L-cysteine biosynthesis VI (PW7-19, FDR=0.002) and diphosphate sulfate formation (PW7-7357, FDR=0.002). In contrast, Group B demonstrated significant enrichment in the urea cycle pathway (PW7-4984, FDR=0.002). These distinct metabolic profiles suggest fundamental differences in microbial community function associated with treatment response outcomes.\u003c/p\u003e\n\u003cp\u003eTable 3. Differentially enriched MetaCyc metabolic pathways between treatment response groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold Change (Group A vs Group B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P-value (FDR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential Biological Relevance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003ePW7-4984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e-2.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003eUrea cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003ePW7-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003eL-cysteine biosynthesis VI (sulfur transfer from methionine)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003ePW7-7357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003eThiamine diphosphate formation from pyrithiamine and oxythiamine (yeast)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003ePW7-5981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e1.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003eCDP-diacylglycerol biosynthesis III\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2482%;\"\u003e\n \u003cp\u003eCITRULBIO-PWY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2624%;\"\u003e\n \u003cp\u003e-1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7163%;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7305%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.0426%;\"\u003e\n \u003cp\u003eL-citrulline biosynthesis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kyoto Encyclopedia of Genes and Genomes (KEGG) database[6]\u0026nbsp;was utilized for functional pathway analysis. In this database, genes with similar functions are grouped together with their corresponding enzymes to form metabolic pathways. Analysis of KEGG pathway relative abundance identified the ten most prevalent pathways in the samples, with the distribution shown in Figure 4A. The main pathways involved translation (ribosome, ko03010), amino acid metabolism (ko00290, ko00970, ko01230), carbon metabolism (ko00660, ko00710), and bacterial environmental adaptation (ko02030). Comparison of alpha diversity indices between the two groups revealed statistically significant differences for both the Simpson index (P\u0026lt;0.001) and Shannon index (P\u0026lt;0.001), as shown in Figures 4B and 4C. \u0026beta;-diversity analysis similarly demonstrated significant differences between groups, with ANOSIM analysis yielding R=0.4459 and P\u0026lt;0.001 (Figure 4D). Non-metric multidimensional scaling analysis achieved excellent data fit with a stress value \u0026lt;0.001 (Figure 4E). Further differential analysis identified pathways with significant differences between groups; a volcano plot showing the differences between the two groups is presented in Figure 4F. Pathways with statistically significant differences were selected, and their logFC values are detailed in Table 4. Group A showed relative enrichment in renal cell carcinoma (ko05211, FDR=0.041), lipopolysaccharide biosynthesis (ko00540, FDR=0.009), nucleotide excision repair (ko03420, FDR=0.002), lipoic acid metabolism (ko00785, FDR=0.002), and prodigiosin biosynthesis (ko00333, FDR=0.041). In contrast, Group B demonstrated relative enrichment in apoptosis (ko04215, FDR=0.046) and small cell lung cancer (ko05222, FDR=0.046) pathways.\u003c/p\u003e\n\u003cp\u003eTable 4. KEGG pathway analysis of significant differences between the two groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold Change (Group A vs Group B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P-value (FDR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological Relevance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo00540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e2.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eLipopolysaccharide biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo04215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e-1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eApoptosis - multiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo05222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e-1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eSmall cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo03420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e1.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eNucleotide excision repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo00785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e1.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eLipoic acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.233%;\"\u003e\n \u003cp\u003eKo00333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e1.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1577%;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3835%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9462%;\"\u003e\n \u003cp\u003eProdigiosin biosynthesis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO), established by the Gene Ontology Consortium[7], provides a standardized framework for describing gene and protein functions across biological systems. Analysis of GO term relative abundance identified the ten most prevalent functional categories within our dataset. The distribution presented in Figure 5A revealed predominant expression of integral membrane component functions (GO:0016021), ATP binding activity (GO:0005524), and cytoplasmic localization (GO:0005737). Comparison of alpha diversity indices between treatment response groups demonstrated statistically significant differences for both the Simpson index (P\u0026lt;0.001) and Shannon index (P\u0026lt;0.001), as illustrated in Figures 5B and 5C. \u0026beta;-diversity analysis similarly revealed significant functional differences between groups, with ANOSIM analysis yielding R=0.637 and P\u0026lt;0.001 (Figure 5D). Non-metric multidimensional scaling (NMDS) analysis achieved acceptable model fit with a stress value of 0.051, indicating robust separation between groups (Figure 5E). Differential functional analysis identified GO terms with significant enrichment patterns between groups; a volcano plot showing the differences between the two groups is presented in Figure 5F. Statistical parameters for differentially enriched functions are detailed in Table 5.\u003c/p\u003e\n\u003cp\u003eGroup A demonstrated significant enrichment in thiaminase activity (GO:0050334), CDP-glycerol glycerophosphotransferase activity (GO:0047355), and coenzyme A metabolic processes (GO:0015936), all with FDR values below 0.001. In contrast, Group B showed significant enrichment in mannose-6-phosphate isomerase activity (GO:0103011), synaptic functions (GO:0045202), methylenetetrahydrofolate cyclohydrolase activity (GO:0030412), L-fucose dehydratase activity (GO:0050023), and host cell nucleus-related functions (GO:0042025), with FDR values below 0.05.\u003c/p\u003e\n\u003cp\u003eTable 5. Differentially enriched Gene Ontology terms between treatment response groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO Term ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold Change (Group A vs Group B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P-value (FDR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0103011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e-4.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eMannose-6-phosphate isomerase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0045202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e-3.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eSynapses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0030412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e-3.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e1.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eMethylenetetrahydrofolate cyclohydrolase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0050023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e-3.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eL-fucose dehydratase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0042025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e-3.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eHost cell nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0050334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e2.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eThiaminase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0047355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e2.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eCDP-glycerol glycerophosphotransferase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGO:0015936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9032%;\"\u003e\n \u003cp\u003e2.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5448%;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7706%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.5018%;\"\u003e\n \u003cp\u003eCoenzyme A metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the pulmonary alveolar microbiota of patients with pulmonary tuberculosis who responded differently to treatment, revealing specific microbial compositions and functional characteristics related to clinical efficacy. Our findings demonstrate that while the primary genus-level species composition appears similar between patient groups, with no significant differences in alpha diversity indices (Simpson index P=0.669, Shannon index P=0.959), \u0026beta;-diversity analysis reveals significant differences in microbial community structure (ANOSIM analysis, R=0.381, P\u0026lt;0.001). \u0026nbsp;Patients with a\u0026nbsp;\u0026nbsp;good treatment response had a pulmonary alveolar microbiota characterized by the enrichment of Prevotella, Staphylococcus, and oral symbiotic bacteria including Fusobacterium and Ralstonia, and significant enhancement of related functional pathways such as peptidoglycan biosynthesis, glutathione metabolism, energy production, and DNA repair. In contrast, patients with a poor treatment response showed enrichment of Microbacterium and activation of related pathways such as biofilm formation, urea cycle-mediated ammonia production, and apoptosis.\u003c/p\u003e\n\u003cp\u003ePrevious research indicates that \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e can attenuate tuberculosis-related pulmonary inflammatory damage through regulation of Th17/Treg cell balance[8], with their elevated respiratory tract abundance significantly associated with favorable patient prognosis[9].Differential analysis revealed that Group A demonstrated significant enrichment of oral commensal bacteria, including \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eRothia\u003c/em\u003e, compared with Group B. Supporting literature suggests that oral microbiota may enhance clinical efficacy through immunomodulation (particularly via the IL-17 pathway) or competitive inhibition of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e colonization[10]. Conversely, the poor efficacy group (Group B) displayed abnormal \u003cem\u003eMicrobacterium\u003c/em\u003e proliferation alongside persistent \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e. \u003cem\u003eMicrobacterium\u003c/em\u003e showed significant enrichment in Group B (LogFC=\u0026minus;2.79, FDR\u0026lt;0.001). Existing research demonstrates that such environmental actinomycetes may contribute to treatment failure through biofilm-mediated antibiotic resistance[11]. Xiao et al[12]\u0026nbsp;discovered that \u003cem\u003eMicrobacterium\u003c/em\u003e forms biofilms through extracellular polysaccharide (EPS) secretion, physically impeding anti-tuberculosis drug penetration. These findings suggest that BALF \u003cem\u003eMicrobacterium\u003c/em\u003e enrichment may serve as a potential microbial marker for poor tuberculosis prognosis.\u003c/p\u003e\n\u003cp\u003eFrom a functional perspective, analysis of biological function genes and metabolic pathways revealed significant differences in BALF microecological function between patient groups (P\u0026lt;0.05 for both alpha and \u0026beta;-diversity comparisons).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetaCyc database analysis of the ten most abundant pathways demonstrated higher overall metabolic functional activity in Group A\u0026apos;s microbial community compared with Group B. Key metabolic pathways including peptidoglycan maturation (PWY0-1586) and S-adenosyl-L-methionine (SAMe) salvage (PWY-6151) showed significant enrichment. Peptidoglycan represents a core bacterial cell wall component, and elevated peptidoglycan maturation pathway abundance indicates that Group A\u0026apos;s lung microbial community maintains an active growth and division state[13].\u003c/p\u003e\n\u003cp\u003eThe high expression of the SAMe salvage pathway warrants particular attention. SAMe serves as a critical methyl donor in biological systems, participating in nucleic acid, protein, and phospholipid methylation modifications. Additionally, it functions as a key precursor molecule for glutathione (GSH) synthesis, with its homeostasis being crucial for maintaining cellular redox balance[14]. Through the analysis of differences, group A specifically demonstrated enrichment in L-cysteine biosynthesis VI (reverse transsulfuration pathway, PWY-19), which provides the rate-limiting substrate for GSH synthesis. GSH represents one of the most important intracellular antioxidants, playing a crucial role in neutralizing excessive reactive oxygen species (ROS) generated by host immune cells during anti-infection responses and protecting cells from oxidative damage[15]. Group A\u0026apos;s microbial community may indirectly alleviate excessive inflammatory damage and create a more favorable microenvironment for tissue repair through regulation of local redox balance, potentially representing an important ecological foundation for improved therapeutic outcomes in these patients.Furthermore, Group A showed relative enrichment in the thiamine diphosphate (TPP) formation pathway (PWY-7357). In contrast, Group B demonstrated relative enrichment in the urea cycle pathway (PWY-4984), typically associated with amino acid degradation and ammonia production. While international studies directly exploring the urea cycle in lung microecology remain limited, research from hepatic disease provides compelling mechanistic analogies. In hepatic encephalopathy, ammonia produced by intestinal microorganisms demonstrably inhibits macrophage phagocytosis and disrupts normal T cell immune responses[16]. We hypothesize that increased ammonia concentration in the lung microecological environment may suppress local antibacterial immunity and promote \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e persistence, contributing to poor clinical efficacy. KEGG pathway analysis revealed that both groups\u0026apos; core microbial community functions concentrated on fundamental life activities, primarily involving ribosomal processes (ko03010), multiple amino acid metabolism pathways (ko00290, ko00970, ko01230), carbon metabolism (ko00660, ko00710), and bacterial environmental adaptation (ko02030). Differential analysis demonstrated that Group A showed significant enrichment in lipopolysaccharide biosynthesis (ko00540) and nucleotide excision repair (NER, ko03420) pathways. While lipopolysaccharide (LPS) traditionally functions as a potent immune activator as a major Gram-negative bacterial cell wall component, recent studies indicate that LPS derived from certain commensal bacteria (such as \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e) can function as weak TLR4 signaling agonists or antagonists, alleviating excessive inflammatory damage through immune response regulation[17]. This finding corroborates the enrichment of \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e observed in Group A\u0026apos;s species analysis. Additionally, NER pathway enrichment in Group A indicates exposure of its microbial community to high oxidative stress environments. NER pathway upregulation suggests that Group A bacteria maintain genomic stability and community functional activity through enhanced DNA maintenance capabilities to resist host immunity-generated oxidative stress. This finding aligns with Group A\u0026apos;s enrichment in energy metabolism and antioxidation-related pathways, including cysteine and glutathione synthesis, observed in the MetaCyc analysis. Gene Ontology functional enrichment analysis revealed that the most abundant functions in both groups involved basic life processes including membrane structure (GO:0016021), ATP binding (GO:0005524), and cytoplasmic components (GO:0005737). This indicates that despite significant differences in community structure (\u0026beta;-diversity, R=0.637, P\u0026lt;0.001), core functions remain focused on maintaining basic cellular structure and energy metabolism, aligning with fundamental requirements for microbial colonization and survival in the host[12]. Differential analysis identified significant enrichment of coenzyme and vitamin metabolism-related pathways in Group A, particularly thiaminase activity (GO:0050334) and coenzyme A metabolic processes (GO:0015936). Notably, \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e is a thiamine auxotroph[18]. These results suggest that elevated thiamine metabolic activity in Group A bacteria may indirectly inhibit \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e metabolism and adaptability through competitive nutrient consumption, potentially representing one mechanism underlying this group\u0026apos;s improved efficacy. This finding aligns with TPP pathway enrichment in the MetaCyc analysis, suggesting that Group A\u0026apos;s microenvironment maintains vigorous energy metabolism and nutritional competition capacity. Additionally, coenzyme A serves as a core coenzyme in fatty acid metabolism, and \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e can utilize host lipids as carbon and energy sources for persistent infection[19]. Group B demonstrated significant enrichment in mannose-6-phosphate isomerase activity (GO:0103011) and L-fucose dehydratase activity (GO:0050023), both involved in bacterial capsule and extracellular polysaccharide biosynthesis. This finding corresponds with \u003cem\u003eMicrobacterium\u003c/em\u003e enrichment in Group B, suggesting the presence of highly structured biofilm microecology in these patients\u0026apos; lungs. Biofilm formation represents an important mechanism of bacterial antibiotic resistance and treatment failure. The bacterial toxin\u0026ndash;antitoxin (TA) system plays a central regulatory role in biofilm formation and stability, inducing bacterial dormancy and coordinating group behavior to cope with antibiotic pressure[20]. We hypothesize that under drug stress, Group B lung microbes may activate TA systems and other stress mechanisms to upregulate EPS synthesis pathways, forming physical and physiological barriers that protect commensal bacteria while providing shelter for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, hindering drug penetration and immune clearance, ultimately leading to poor efficacy.\u003c/p\u003e\n\u003cp\u003eThe primary strength of this study lies in its pioneering use of BALF metagenomics to systematically reveal lung microecological differences in tuberculosis patients with differential treatment responses. Given the relative paucity of international research in this area, our study not only confirms that oral commensal bacteria (such as \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eRothia\u003c/em\u003e) may associate with favorable prognosis through competitive inhibition and immunomodulation but also identifies \u003cem\u003eMicrobacterium\u003c/em\u003e as a potential poor prognosis microbe whose enrichment may relate to biofilm formation and antibiotic resistance. The research reveals differences in key pathways including energy metabolism and oxidative stress balance at the functional level, demonstrating that nutritional competition and microenvironmental regulation represent possible mechanisms affecting tuberculosis treatment efficacy.\u003c/p\u003e\n\u003cp\u003eHowever, this study has several limitations. The small sample size may affect statistical power and limit control for confounding factors. As an observational study, our results suggest associations rather than causality, and the proposed mechanistic hypotheses require subsequent experimental validation. Future research should expand sample sizes and combine multi-omics technologies with experimental verification to further elucidate causal mechanisms.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough metagenomic analysis, we have systematically revealed for the first time significant differences in species structure and functional characteristics of bronchoalveolar lavage fluid microbiomes between pulmonary tuberculosis patients with good versus poor treatment responses. The good efficacy group was characterized by \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, and various oral commensal bacteria (including \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eRothia\u003c/em\u003e). This group\u0026apos;s microbial community exhibited higher metabolic activity, with enrichment in peptidoglycan synthesis, antioxidant synthesis (particularly glutathione), and energy metabolism pathways. These communities may promote therapeutic efficacy through mechanisms including immune regulation, nutritional competition, and maintenance of redox balance.\u003c/p\u003e\n\u003cp\u003eConversely, \u003cem\u003eMicrobacterium\u003c/em\u003e showed significant enrichment in the poor efficacy group. Functional analysis revealed activation of pathways related to biofilm formation, extracellular polysaccharide synthesis, and urea cycling, suggesting that lungs of patients with poor efficacy may harbor biofilm-based antibiotic-resistant microenvironments that impede drug penetration and impair local immune clearance.\u003c/p\u003e\n\u003cp\u003eIn summary, lung microbiome composition and functional characteristics may represent key factors influencing anti-tuberculosis treatment efficacy. Certain commensal bacteria, particularly those of oral origin, may play protective roles, while environmental microbe enrichment such as \u003cem\u003eMicrobacterium\u003c/em\u003e may indicate poor prognosis. These findings provide potential microbial markers and novel intervention targets for personalized tuberculosis treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eBALF: Bronchoalveolar lavage fluid\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDNA: Deoxyribonucleic\u0026nbsp;Acid\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003emNGS: Metagenomic Next-generation sequencing\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMTB: Mycobacterium tuberculosis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWHO: World Health Organization\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNMDS: Non-metric Multidimensional Scaling\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGO: Gene Ontology\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEPS: extracellular polysaccharide\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Wenzhou Central Hospital (Approval No: K2021-01-003). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All patients provided written informed consent to participate in the study (For any participant under the age of 16, written informed consent was obtained from their parent(s) or legal guardian prior to participation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data generated in this study have been deposited in the Science Data Bank (ScienceDB) and are accessible via DOI: 10.57760/sciencedb.30999. These data will be publicly available upon publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Wenzhou Science and Technology Major Innovation Project (No. ZY2020019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ-RL: Data collection and analysis; manuscript drafting. J-CS and Y-HM: Clinical implementation. C-CQ, and Y-YZ: Bronchoscopy procedures. N-P: Data collection and chart preparation. X-QL: Statistical analysis. X-GJ: Study design conceptualisation; critical review of the manuscript; and major revisions. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Department of Infectious Diseases, Wenzhou Sixth People\u0026apos;s Hospital, for providing BALF samples and clinical data. We also appreciate Dr. Gao (ShuLan Hospital Affiliated with Zhejiang Shuren University, Hangzhou, China) for her guidance in metagenomic sequencing analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization (2024). Global Tuberculosis Report. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024\u003c/li\u003e\n\u003cli\u003eHuang T, Chen Q, Wu HG, Tang SJ (ed) (2025). Interpretation of the World Health Organization consolidated guidelines on tuberculosis Module 4: treatment and care. 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Nat Rev Microbiol, 20(6):335-350. doi:10.1038/s41579-021-00661-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Bronchoalveolar lavage fluid, Therapeutic efficacy, Microecology, Metagenomics","lastPublishedDoi":"10.21203/rs.3.rs-9000417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9000417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study aimed to investigate differences in the composition and functional characteristics of alveolar microbiota in patients with pulmonary tuberculosis (PTB) exhibiting differential therapeutic responses. Thirty-two patients with drug-sensitive PTB who had completed standard anti-tuberculosis therapy were enrolled and classified into good-response (n = 16) and poor-response (n = 16) groups. Bronchoalveolar lavage fluid (BALF) samples were collected and analysed using metagenomic sequencing to characterize microbial community and functional pathways. No significant differences were observed in α-diversity between the two groups; however, β-diversity analysis demonstrated marked differences in microbial community structure (ANOSIM, R = 0.381, P \u003c 0.001). The good efficacy group was characterized by enrichment of Prevotella, Staphylococcus, and oral commensal bacteria including Fusobacterium and Rothia, together with significantly increased pathways related to peptidoglycan biosynthesis, glutathione metabolism, energy production, and DNA repair. In contrast, the poor efficacy groups was characterised by enrichment of Microbacterium and activation of functional pathways associated with biofilm formation, urea cycle–mediated ammonia production, and apoptosis. These findings suggest that both the taxonomic composition and functional activity of the pulmonary microbiome are closely associated with anti-tuberculosis treatment outcomes. Microbial communities in patients with favourable responses may support recovery through immune modulation, antioxidative capacity, and metabolic competition, whereas microbiota enriched in poor responders may contribute to treatment failure via biofilm formation and production of immunosuppressive metabolites such as ammonia.","manuscriptTitle":"Exploring Differences in Alveolar Microbiome Between Pulmonary Tuberculosis Patients with Different Treatment Outcomes: A Metagenomic Study from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:10:08","doi":"10.21203/rs.3.rs-9000417/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T16:35:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T02:21:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T06:53:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8348821277989881641155058011055411540","date":"2026-03-24T01:11:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139564112853566931577334535796739279662","date":"2026-03-23T17:59:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T16:00:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T15:51:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T18:46:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T16:18:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-03-12T08:49:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4369cd7-6961-4a47-a1d0-98ad31fc4612","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T18:39:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:10:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9000417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9000417","identity":"rs-9000417","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}