On the Application of Metagenomic Next-Generation Sequencing and Real-time PCR with Melting Curve Analysis in the Auxiliary Diagnosis of Mycobacterial Pulmonary Disease

On the Application of Metagenomic Next-Generation Sequencing and Real-time PCR with Melting Curve Analysis in the Auxiliary Diagnosis of Mycobacterial Pulmonary Disease | 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 On the Application of Metagenomic Next-Generation Sequencing and Real-time PCR with Melting Curve Analysis in the Auxiliary Diagnosis of Mycobacterial Pulmonary Disease Zhou Lv, Ziran Wang, Xinfei Chen, Ziyi Wang, Yujie Sun, Huiting Su, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8693419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective Rapid and accurate identification of Mycobacteria is of critical importance in clinical practice. With the advancement of molecular diagnostics, techniques such as metagenomic next-generation sequencing ( mNGS ) and real-time PCR with melting curve analysis ( MC-PCR ) are being increasingly employed for the diagnosis of mycobacterial infections. This study aimed to evaluate the diagnostic value of these two methods in the context of pulmonary mycobacterial disease. Methods Bronchoalveolar lavage fluid (BALF) samples from 86 patients suspected of pulmonary mycobacterial infection were analyzed using both mNGS and MC-PCR. The concordance between the results of these two methods was compared. Using a comprehensive clinical diagnosis as the reference standard, the sensitivity, specificity, and agreement of these two molecular techniques, alongside conventional detection methods, were evaluated. Results In the group suspected of Mycobacterium tuberculosis (TB) infection, mNGS and MC-PCR demonstrated substantial agreement (Kappa = 0.667). The sensitivities were 96.67% (29/30) and 83.33% (25/30), respectively. In the group suspected of non-tuberculous mycobacteria (NTM) infection, the two methods showed a high level of agreement (Kappa = 0.824), with sensitivities of 96.77% (30/31) and 93.55% (29/31), respectively. The concordance rate for NTM species identification between the two methods was 90.9% (30/33). Conclusion In the diagnosis of mycobacterial pulmonary infection and the identification of non-tuberculous mycobacteria (NTM) species, both mNGS and MC-PCR exhibited higher sensitivity and superior consistency compared to the other methods evaluated in this study. The combined application of these techniques with conventional detection methods may provide a novel and effective approach for the diagnosis of mycobacterial pulmonary infections. Mycobacterial pulmonary infection Non-tuberculous mycobacteria identification Metagenomic next-generation sequencing (mNGS) Real-time PCR with melting curve analysis (MC-PCR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mycobacterium tuberculosis (TB) is a pathogenic bacterium capable of causing tuberculosis and even death in humans. According to the World Health Organization's 2024 Global Tuberculosis Report, there were approximately 10.8 million new TB cases and about 1.25 million deaths worldwide in 2023, making it one of the top ten causes of death globally. The incidence and mortality rates are notably higher in developing countries ( 1 , 2 ). Non-tuberculous mycobacteria (NTM), which belong to the same genus as M. tuberculosis , currently comprise approximately 200 identified species. They are widely distributed in natural environments such as soil, water, and air. Although some NTM species are non-pathogenic to humans, certain strains can cause disease, particularly in the elderly or immunocompromised individuals ( 3 – 5 ). It has been reported that NTM infection rates are increasing in many regions and countries worldwide. NTM infections can lead to diseases affecting multiple body sites, making them an issue of growing clinical concern ( 6 – 8 ). The clinical presentation and radiological features of non-tuberculous mycobacteria (NTM) infection are highly similar to those of Mycobacterium tuberculosis (TB) infection, leading to frequent confusion in clinical practice. However, NTM generally exhibits higher intrinsic resistance to first-line anti-tuberculosis drugs, and the drug susceptibility profiles vary significantly among different NTM species. Misdiagnosis may result in patients receiving ineffective antimicrobial therapy, leading to delays in appropriate treatment, increased risk of adverse drug reactions, and a significant negative impact on treatment efficacy and long-term prognosis ( 3 , 4 , 9 , 10 ). Therefore, accurate early differentiation between TB and NTM infection, coupled with further identification of the specific NTM species, is of critical clinical importance for formulating targeted treatment strategies and improving patient outcomes ( 6 ). The main clinical methods for diagnosing Mycobacterium tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM) infections currently include acid-fast staining microscopy, mycobacterial culture, and molecular biological assays ( 11 ). Conventional acid-fast staining is simple and rapid but cannot differentiate MTBC from NTM, and its specificity and sensitivity are relatively low. Although mycobacterial culture remains the diagnostic gold standard, its prolonged turnaround time may lead to delays in treatment decisions. Molecular biological techniques, due to their significantly improved sensitivity (particularly in immunocompromised populations), efficient typing capability, and rapid detection cycle, have become pivotal in optimizing the tuberculosis diagnostic algorithm ( 12 – 14 ). Real-time PCR with melting curve analysis (MC-PCR) enables species discrimination by utilizing probes that specifically bind to target gene sequences (e.g., ITS regions) and differentiating species based on variations in melting temperature (Tm). This method allows for the identification of up to 19 mycobacterial species in a single assay, minimizing false-positive results due to cross-reactivity. The entire detection process requires only 4–6 hours, significantly faster than culture-based or sequencing-based methods, thereby facilitating rapid mycobacterial species identification. This approach helps mitigate the risk of empirical treatment failure and directly supports the optimization of therapeutic strategy selection. Among various molecular detection methods for pathogens, metagenomic next-generation sequencing (mNGS) has emerged as a pivotal technology in the field of infectious disease diagnostics due to its unique non-targeted and broad-spectrum detection capability. This technique enables the simultaneous detection of nucleic acid sequences from a wide range of potential pathogens in a single sample, including bacteria, fungi, viruses, and parasites. Consequently, mNGS demonstrates substantial diagnostic value in complex infection cases with atypical clinical presentations and challenging pathogen differential diagnoses ( 15 – 17 ).However, precisely due to its inherent high sensitivity, mNGS results are susceptible to potential contamination or {Citation}operational biases throughout the entire workflow—including specimen collection, nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. This may lead to false-positive or false-negative outcomes and increase the complexity of result interpretation ( 2 , 12 ). Therefore, in actual clinical practice, it is strongly recommended to integrate mNGS findings with results from other etiological detection methods (such as conventional culture, specific PCR, and serological tests) as well as clinical manifestations and imaging features. Such comprehensive analysis aids in forming a more reliable multi-dimensional diagnostic evidence chain ( 18 ). In this study, a cohort of lower respiratory tract samples from patients clinically suspected of mycobacterial infection was established to compare the diagnostic sensitivity, specificity, and agreement (Kappa value) between MC-PCR and mNGS in detecting Mycobacteria . Furthermore, based on a composite clinical diagnosis (incorporating imaging findings and treatment response), the positive and negative predictive values of both techniques as auxiliary diagnostic tools were analyzed. Materials and Methods Study Subjects This retrospective cross-sectional study analyzed 50 patients with clinically suspected mycobacterial pulmonary disease at Peking Union Medical College Hospital between September 1, 2024, and June 30, 2025. All patients underwent routine bronchoalveolar lavage fluid (BALF) testing with metagenomic next-generation sequencing (mNGS). For cases in which mycobacteria were definitively detected by mNGS, species identification was subsequently performed using real-time PCR with melting curve analysis (MC-PCR). Similarly, for cases with low-sequence reads or ambiguous mNGS results, MC-PCR was employed for verification. Additionally, 36 BALF samples in which mycobacteria had been detected by mNGS between January 1, 2024, and August 31, 2024, were retrospectively included and re-tested using MC-PCR (Xiamen Zhishan Technology). Samples that either yielded Mycobacterium tuberculosis (TB) sequences by mNGS or were clinically highly suggestive of TB infection (n = 42) were classified as the suspected TB infection group (TB group). Samples that yielded non-tuberculous mycobacteria (NTM) sequences by mNGS or were clinically highly suggestive of NTM infection (n = 44) were classified as the suspected NTM infection group (NTM group) (Fig. 1). (Figure 1) Methods mNGS Testing The process of mNGS testing is relatively complex and can be summarized into several key steps: host cell depletion, nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. First, a 1 ml sample aliquot was centrifuged, and the supernatant was discarded. A minimum of 100 µl of the resulting pellet was then subjected to chemical host cell depletion using a commercial depletion reagent. To enhance nucleic acid release from Mycobacteria due to their unique cell wall structure, an intensified cell disruption step was performed by increasing the vortexing intensity from M/S 6.0 to M/S 7.0 during the mechanical lysis procedure ( 8 , 19 , 20 ). Following cell disruption, DNA was extracted from the processed sample using a dedicated DNA extraction kit (TIANGEN Biotech (Beijing) Co., Ltd., China). Library preparation was subsequently carried out using the Nextera XT DNA Library Prep Kit (Illumina, San Diego, CA). The indexed DNA was amplified by PCR under the following cycling conditions: 72°C for 3 min and 98°C for 30 sec (1 cycle); 98°C for 15 sec, 60°C for 30 sec, and 72°C for 30 sec (17 cycles); followed by a final extension at 72°C for 5 min and a hold at 4°C. Dual-indexing was performed using IDT for Illumina DNA/RNA UD Indexes. Library size distribution was assessed on a Qsep 1 system, and concentration was quantified using the Qubit dsDNA HS Assay Kit on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Pooled libraries were then loaded onto an Illumina Nextseq CN500 sequencing platform for 75 single-end sequencing cycles, generating approximately 20 to 40 million reads per library. Raw sequencing data were processed using Trimmomatic to remove low-quality sequences, reads shorter than 40 bp, and adapter sequences, thereby yielding high-quality data. Taxonomic analysis was performed using the IDseq commercial bioinformatics pipeline (Vision Medicals). Reads mapping to the human genome and plasmid sequences were filtered out. The remaining sequences were classified by alignment against the Vision Medicals microbial database. Interpretation of mNGS reports was conducted by a multidisciplinary team comprising experts in bioinformatics, clinical laboratory diagnostics, and infectious diseases from the Pathogen Sequencing Laboratory at Peking Union Medical College Hospital. Real-time PCR with Melting Curve Analysis (MC-PCR) Real-time PCR with melting curve analysis (MC-PCR) detects target DNA by analyzing the fluorescence curve generated when a fluorescent dye binds to double-stranded DNA during the strand dissociation phase of real-time PCR ( 21 ). Samples were tested using a commercial Mycobacteria Identification Kit (Xiamen Zhishan Technology). This kit is capable of detecting Mycobacterium tuberculosis complex (MTBC) and 18 species of non-tuberculous mycobacteria (NTM), including: M. smegmatis, M. bovis, M. scrofulaceum, M. chelonae, M. simiae, M. lentiflavum, M. gordonae, M. kansasii, M. abscessus, M. fortuitum, M. marinum or M. ulcerans, M. terrae, M. nonchromogenicum, M. szulgai, M. malmoense, M. xenopi, M. intracellulare, and M. avium. First, 1–2 volumes of sputum processing solution II were added to 500 µL of BALF sample. The mixture was vortexed and incubated at room temperature for at least 10 minutes (the incubation time and the ratio of processing solution could be appropriately extended or increased depending on the sample condition). After complete liquefaction, 1 mL of the processed sample was transferred into the first well of an MTB-Maxi nucleic acid extraction strip. The strip was labeled with the sample ID and placed into an 824S nucleic acid extraction instrument. The “MTB-Maxi-Fast” extraction program was executed for nucleic acid isolation. Upon completion of extraction, 25 µL of the corresponding nucleic acid extract, along with negative and positive controls, was added to individual PCR reaction tubes pre-filled with lyophilized reagent powder. The tubes were tightly sealed with an 8-tube strip cap, vigorously vortexed for 20 seconds, briefly centrifuged to remove bubbles, and then loaded into an ABI 7500 PCR thermal cycler for amplification. The PCR cycling protocol was as follows: 50°C for 2 min and 95°C for 10 min (1 cycle); followed by 55 cycles of 95°C for 15 sec, 57°C for 20 sec, and 78°C for 20 sec; then 40°C for 30 min (1 cycle); and finally 95°C for 2 min and 40°C for 2 min (1 cycle). After amplification, the results were interpreted based on the generated melting curves. Statistical Analysis Graphical plotting and statistical analyses were performed using R version 4.2.3 and Microsoft Office Excel. Continuous variables were analyzed using the Student's t-test and are presented as mean ± standard deviation (SD) ( 13 ). Inter-group comparisons and diagnostic performance evaluations between detection methods were conducted using McNemar’s test, the chi-square test, or Fisher’s exact test, as appropriate. A p-value < 0.05 was considered statistically significant. The sensitivity and specificity of each detection method were calculated and reported with 95% confidence intervals (95% CI) ( 12 ). Agreement between different detection methods was assessed using Cohen’s Kappa coefficient. The correlation between mNGS read counts and results from MC-PCR as well as culture was evaluated using the Wilcoxon rank-sum test and Spearman’s correlation coefficient. Results Demographic Characteristics A total of 86 BALF samples from patients with clinically suspected mycobacterial pulmonary infection, all of which underwent mNGS testing, were analyzed in this study. The cohort included 41 males (47.7%) and 45 females (52.3%), with an age range of 15 to 89 years. Detailed demographic characteristics are presented in Table 1. Comparisons between the TB group and the NTM group across various clinical baseline factors (sex, age, history of previous TB or NTM infection, diabetes), clinical symptoms (cough, fever, fatigue, chest pain/tightness, weight loss), chest imaging findings (pulmonary shadowing, cavity, nodules, bullae, bronchiectasis, bronchial stenosis, pleural effusion), status of immunodeficiency or immunosuppression (reduced immunity, immunodeficiency, immunosuppression), and other relevant factors (prolonged exposure to dust or soil, history of bird contact, smoking) revealed no statistically significant differences (p > 0.05). Comparison between MC-PCR and mNGS Results Among the 42 samples in the TB group, 30 (71.43%) were positive for TB by both mNGS and MC-PCR, 5 (11.90%) were TB-positive by mNGS but negative by MC-PCR, and 7 (16.67%) were negative by both methods. In the NTM group (n = 44), 31 samples (70.45%) were positive for NTM by both mNGS and MC-PCR, 2 (4.55%) were NTM-positive by mNGS but negative by MC-PCR, 1 (2.27%) was NTM-positive by MC-PCR but negative by mNGS, and 10 (22.73%) were negative by both methods. (Figure 2) (Figure 2) Fisher’s exact test indicated a statistically significant association between mNGS and MC-PCR results in both groups (p < 0.01). Furthermore, the calculated Kappa values demonstrated substantial agreement between the two methods in the TB group (Kappa = 0.667) and almost perfect agreement in the NTM group (Kappa = 0.824). Comparison of MC-PCR, mNGS, and Other Laboratory Methods for Mycobacterial Detection The results of mNGS were compared with those of MC-PCR, culture, acid-fast staining (AFS), quantitative fluorescent PCR (qPCR), Xpert MTB/RIF, and T-Spot.TB (Fig. 3). The diagnostic performance of each method was further evaluated against the final clinical diagnosis (FCD) (Table 2). In the TB group, the sensitivities of culture (53.33%, 16/30), qPCR (56%, 14/25), and Xpert MTB/RIF (52.38%, 11/21) were similar and all higher than that of AFS (6.67%, 2/30). In contrast, mNGS (96.67%, 29/30), MC-PCR (83.33%, 25/30), and T-Spot.TB (87.5%, 14/16) exhibited significantly higher sensitivities than the other methods. A similar pattern was observed in the NTM group, where both mNGS (96.77%, 30/31) and MC-PCR (93.55%, 29/31) achieved sensitivities above 90%. Xpert MTB/RIF and T-Spot.TB were not included in the NTM group evaluation, as they are not intended for NTM detection. The specificity of AFS was 100% in both groups, although the comparison between the two groups showed no statistical significance (p > 0.05). (Figure 3) Although mNGS results only provide qualitative detection of pathogenic microorganisms, we ranked the sequence read counts from high to low in both the TB and NTM groups and performed correlation analyses with the positivity/negativity results from MC-PCR and culture (Fig. 4). The differences in sequence read counts between positive and negative results from MC-PCR and culture were statistically significant in both groups (p < 0.05). (Figure 4) However, in our study, mNGS results were inconsistent with the clinical diagnosis in six cases from the TB group and four cases from the NTM group.A review of medical records revealed that in the TB group, there were five false-positive cases. These were attributed to clinical judgment based on chest imaging features inconsistent with TB infection. One false-negative case was identified, in which the original bioinformatics results showed three reads mapping to Mycobacterium tuberculosis. These were likely unreported due to low matching confidence, and all other test results were negative, leading to a negative report. However, given clinical suspicion of TB infection, empirical anti-tuberculosis therapy was initiated. In the NTM group, there were three false-positive cases. One case with 114 reads mapping to Mycobacterium abscessus was not considered clinically significant due to the patient’s severe underlying conditions (lung cancer and multidrug-resistant bacterial infection), combined with imaging findings. The other two cases, with 7 and 3 reads mapping to Mycobacterium intracellulare, respectively, were also excluded from NTM infection based on inconsistent chest imaging features. Additionally, one false-negative case was noted. The sample, tested on January 25, 2025, initially showed one read mapping to Mycobacterium intracellulare in the original bioinformatics analysis. As other tests were negative, this was considered part of the “background microbiota” and not reported. However, a positive mycobacterial culture result was obtained on February 7, 2025. Clinical suspicion of mycobacterial infection led to GM-CSF nebulization therapy, with subsequent improvement in respiratory symptoms after three months. Furthermore, one false-positive result was observed with MC-PCR. Review of the original melting curve indicated the detection of a mycobacterial species not covered by the 19-target panel. Verification was not possible due to insufficient residual sample. NTM Species Identification Results Among the 33 NTM-positive cases detected by mNGS, Mycobacterium intracellulare was identified in 19 (57.6%), Mycobacterium abscessus in 6 (18.2%), Mycobacterium xenopi in 4 (12.1%), Mycobacterium avium in 2 (6.1%), Mycobacterium kansasii in 2 (6.1%), and a mycobacterial species not covered by the panel in 1 (3.0%). One case showed co-infection with Mycobacterium intracellulare and Mycobacterium kansasii. Compared with the results from MC-PCR (Fig. 5), species identification was consistent in 30 cases (90.9%). In two cases (6.1%), MC-PCR was negative, while mNGS detected Mycobacterium avium (4 reads) and Mycobacterium intracellulare (3 reads), respectively. One case (3.0%) was positive by both methods but with discordant species identification: mNGS detected Mycobacterium intracellulare (206 reads), whereas MC-PCR identified Mycobacterium avium and Mycobacterium malmoense. The distribution of NTM species identified in this study was largely consistent with the epidemiological profile reported in China, predominantly featuring Mycobacterium intracellulare, Mycobacterium abscessus, and Mycobacterium xenopi (22). (Figure 5) Discussion This study included a total of 86 patients clinically suspected of mycobacterial pulmonary infection, comprising 42 cases in the TB group and 44 cases in the NTM group. Analysis of clinical characteristics and chest imaging findings revealed no statistically significant differences between the two groups (p > 0.05), indicating that clinical symptoms and imaging features alone offer limited utility in distinguishing TB from NTM infection ( 23 ). T-Spot.TB is widely used in clinical practice as an indicator of tuberculosis infection. In this study, T-Spot.TB demonstrated relatively high sensitivity (87.50%) but relatively low specificity (50.00%). Moreover, it cannot differentiate between active tuberculosis and latent tuberculosis infection ( 24 ). In the early stages of diagnosis, particularly when patients present with mild symptoms, conventional methods face challenges in accurately discriminating pulmonary tuberculosis from community-acquired pneumonia caused by other pathogens ( 12 , 19 , 25 ). With the rapid development of molecular detection technologies, metagenomic next-generation sequencing (mNGS) has attracted considerable attention in etiological diagnosis in recent years due to its short turnaround time and high accuracy ( 26 , 27 ). A positive high-throughput sequencing result is considered clinically meaningful only when at least one additional detection method confirms the identification of the same species ( 28 ). In this study, the results obtained by mNGS and MC-PCR were compared, showing an agreement rate exceeding 85% in both the TB and NTM groups. Both methods were further compared with traditional microbiological assays and other molecular detection techniques. In terms of diagnostic performance, mNGS, T-Spot.TB, and MC-PCR all demonstrated sensitivities exceeding 80% for TB detection. However, the specificity of T-Spot.TB (50.00%) was lower than that of MC-PCR (60.00%), and T-Spot.TB is not applicable for NTM detection. In the NTM group, both mNGS (96.77%) and MC-PCR (93.55%) exhibited significantly higher sensitivity than other methods. Furthermore, given the relative difficulty and prolonged time required for mycobacterial culture, both mNGS and MC-PCR enable species identification within a much shorter timeframe. This facilitates rapid diagnosis and targeted treatment of tuberculosis and NTM infections in clinical practice. Overall, the combined use of mNGS and MC-PCR demonstrates superior feasibility in the diagnosis of mycobacterial pulmonary disease. By comparing the sequence read counts from mNGS with the positivity rates of MC-PCR and culture, we observed a significant correlation between the number of mycobacterial reads detected by mNGS and the positivity rates of both MC-PCR and culture (p < 0.01). The number of sequences detected by mNGS can be influenced by multiple factors, including sample quality, contamination, experimental procedures, host DNA ratio, and bioinformatics analysis. Relevant consensus guidelines also indicate that read counts should only be interpreted qualitatively ( 28 , 29 ). Based on the findings of this study, we hypothesize that under standardized and rigorously controlled mNGS experimental conditions, a higher number of detected mycobacterial reads is associated with an increased likelihood of a positive MC-PCR result and a higher probability of successful mycobacterial culture. Although mNGS demonstrates excellent performance in detecting pathogenic microorganisms, it has certain limitations. In this study, a total of eight false-positive and two false-negative results were observed with mNGS. During the bioinformatics analysis phase of mNGS, factors such as patient symptoms, host DNA ratio, and background microbiota are considered. However, for high-priority pathogens like TB—a highly pathogenic microorganism—or NTM species not typically present in the background microbiota, even a single read may be reported as positive ( 28 ). Consequently, when issues such as sample contamination, data analysis ambiguities, atypical clinical presentations, or prior antibiotic use leading to negative results by other methods arise, distinguishing true positives from false positives or negatives based solely on mNGS can be challenging ( 19 , 29 ). The relatively high cost of mNGS testing may impose a financial burden on economically disadvantaged patients, which also limits the possibility of repeated testing for result verification ( 30 ). In contrast, MC-PCR is more cost-effective. Using MC-PCR to verify mNGS results can help reduce false positives without substantially increasing overall testing costs. Furthermore, in this study, the concordance rate for NTM species identification between mNGS and MC-PCR reached 90.9%. When mNGS detects NTM sequences but conventional culture yields false-negative results due to factors such as insufficient incubation time or low microbial load, MC-PCR can serve as an alternative method to support and confirm the species identification of NTM. Despite the valuable findings obtained in this study, several limitations should be acknowledged. First, some cases were from outpatient settings or were transferred to other hospitals for further treatment. Patient compliance and subsequent therapeutic outcomes could not be fully evaluated, and the possibility that the final clinical diagnosis may have been influenced by the mNGS results cannot be excluded. Second, the MC-PCR assay used (Xiamen Zhishan Technology) can only detect MTB and 18 specific NTM species. NTM strains outside this panel cannot be identified to the species level. Finally, although both mNGS and MC-PCR demonstrated high sensitivity and substantial agreement in detecting mycobacterial pulmonary disease, neither method can distinguish between true pulmonary NTM infection and colonization. Clinical judgment based on the overall presentation remains essential for final interpretation. Conclusion As an emerging detection technology, mNGS offers unique advantages among various mycobacterial diagnostic methods and provides a basis for the early diagnosis and treatment of tuberculosis and NTM infections. Our study validated the application of mNGS and real-time PCR with melting curve analysis (MC-PCR) in the auxiliary diagnosis of mycobacterial pulmonary disease, demonstrating the feasibility of both approaches. We hope that by optimizing these methodologies, new diagnostic strategies can be developed for pulmonary tuberculosis and NTM infections, thereby contributing to global tuberculosis control efforts and to the detection and management of non-tuberculous mycobacterial diseases. Abbreviations mNGS metagenomics next-generation sequencing MC-PCR real-time PCR with melting curve analysis PCR Polymerase Chain Reaction BALF Bronchoalveolar lavage fluid TB Mycobacterium tuberculosis MTBC Mycobacterium tuberculosis complex NTM non-tuberculous mycobacteria FCD final clinical diagnosis M. smegmatis Mycobacterium smegmatis M. bovis Mycobacterium bovis M. scrofulaceum Mycobacterium scrofulaceum M. chelonae Mycobacterium chelonae M. simiae Mycobacterium simiae M. lentiflavum Mycobacterium lentiflavum M. gordonae Mycobacterium gordonae M. kansasii Mycobacterium kansasii M. abscessus Mycobacterium abscessus M. fortuitum Mycobacterium fortuitum M. marinum Mycobacterium marinum M. ulcerans Mycobacterium ulcerans M. terrae Mycobacterium terrae M. nonchromogenicum Mycobacterium nonchromogenicum M. szulgai Mycobacterium szulgai M. malmoense Mycobacterium malmoense M. xenopi Mycobacterium xenopi M. intracellulare Mycobacterium intracellulare M. avium Mycobacterium avium Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences (Approval No. : I-24PJ2561).The studies were conducted in accordance with local legislation and institutional requirements. Written informed consent was obtained from the patients for publication of this article and any accompanying images. Ethics Statement Not applicable Consent for publication Written informed consent was obtained from the patient for publication of the details of their medical case and any accompanying images. Declaration of Competing Interest We confirm that this manuscript has not been published else where and is not under simultaneous consideration by any other journal. Furthermore, all authors have agreed to submit this manu script and declare no potential conflicts of interest. Funding This study was supported by the Key Laboratory of Molecular Diagnostics and Individualized Therapy of Huangshi City (Project No.: FZ2026007). Author Contribution **Zhou Lv:** Data curation, Visualization, Writing – original draft. **Ziran Wang:** Writing – review & editing. **Xinfei Chen:** Initial experiments, Bioinformatics analysis. **Ziyi Wang:** Sample collection, Initial experiments. **Yujie Sun:** Sample collection, Initial experiments. **Huiting Su:** Initial experiments, Data curation. **Jiayu Guo:** Initial experiments, Data curation. **Minya Lu:** Initial experiments, Data curation. **Chenglin Yang:** Sample collection, Initial experiments. **Wan Huang:** Sample collection, Initial experiments. **Lina Guo:** Methodology, Resources. **Juan Du:** Guidance, Supervision, Review. **Qiwen Yang:** Supervision, Resources. Acknowledgments We are thankful to all the study participants and their families. We also gratefully acknowledge the generous financial support provided by the Joint Fund of the Key Laboratory of Molecular Diagnostics and Individualized Therapy of Huangshi City. 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Miao Qing Y, Yumeng P, Jue B, Rong W, Qingqing L. Etiological diagnostic value of metagenomic next-generation sequencing in non-tuberculous mycobacteria infection. Chin J Clin Med. 2020;27(4):559–62. Gaston DC. Clinical metagenomics for infectious diseases: progress toward operational value. Simner PJ, editor. J Clin Microbiol. 2023;61(2):e01267-22. Dai X, Xu K, Tong Y, Li J, Dai L, Shi J, et al. Application of targeted next-generation sequencing in bronchoalveolar lavage fluid for the detection of pathogens in pulmonary infections. Infect Drug Resist. 2025;18:511–22. Holzheimer M, Buter J, Minnaard AJ. Chemical synthesis of cell wall constituents of mycobacterium tuberculosis . Chem Rev. 2021;121(15):9554–643. Khosravi AD, Meghdadi H, Hashemzadeh M, Alami A, Tabandeh MR. Application of a new designed high resolution melting analysis for mycobacterial species identification. BMC Microbiol. 2024;24(1):205. Wang X, Li H, Jiang G, Zhao L, Ma Y, Javid B, et al. Prevalence and drug resistance of nontuberculous mycobacteria, northern China, 2008–2011. Emerg Infect Dis. 2014;20(7):1252–3. Li Nna, Gao L, lu, Liu M, Zhang W, min, Zhang X ke, Chen L et al. Analysis of non-tuberculous mycobacteria types in high tuberculosis endemic areas. J Health Popul Nutr. 2025;44(1):54. Zellweger JP, Sotgiu G, Block M, Dore S, Altet N, Blunschi R, et al. Risk assessment of tuberculosis in contacts by IFN-γ release assays. A tuberculosis network european trials group study. Am J Respir Crit Care Med. 2015;191(10):1176–84. Cudahy P, Shenoi SV. Diagnostics for pulmonary tuberculosis. Postgrad Med J. 2016;92(1086):187–93. Hong R, Lin S, Zhang S, Yi Y, Li L, Yang H, et al. Pathogen spectrum and microbiome in lower respiratory tract of patients with different pulmonary diseases based on metagenomic next-generation sequencing. Front Cell Infect Microbiol. 2024;14:1320831. Wang S, Xing L. Metagenomic next-generation sequencing assistance in identifying non-tuberculous mycobacterial infections. Front Cell Infect Microbiol. 2023;13:1253020. Expert Group on Consensus for High throughput Sequencing. Expert consensus on the application of highthroughput sequencing technology in the diagnosis of mycobacterial diseases. Chin J Infect Dis. 2023;41(3):175–82. Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol: Mech Dis. 2019;14(1):319–38. Xiang ZB, Leng EL, Cao WF, Liu SM, Zhou YL, Luo CQ, et al. A systematic review and meta-analysis of the diagnostic accuracy of metagenomic next-generation sequencing for diagnosing tuberculous meningitis. Front Immunol. 2023;14:1223675. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Editor invited by journal 05 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 04 Feb, 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-8693419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598164553,"identity":"950f33c4-795b-4283-8764-5f0f0afef282","order_by":0,"name":"Zhou Lv","email":"","orcid":"","institution":"Huangshi Aikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Lv","suffix":""},{"id":598164554,"identity":"2f46854b-603e-42f8-8065-c1132f3e856e","order_by":1,"name":"Ziran Wang","email":"","orcid":"","institution":"Peking Union Medical College 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08:03:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1763592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8693419/v1/41f99a00-6c8a-4231-86a9-451ea79659c7.pdf"},{"id":103837973,"identity":"dbcba919-462a-4643-8526-b32bbd75509a","added_by":"auto","created_at":"2026-03-03 14:20:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14748,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8693419/v1/22abbbbc2c64f4ff673436b5.docx"},{"id":103837970,"identity":"94857ed2-d76c-4e4c-8bfd-66defb85649c","added_by":"auto","created_at":"2026-03-03 14:20:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14081,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8693419/v1/b90b26ac11fd3df37c97c11c.docx"},{"id":104779199,"identity":"cc45ab6b-8f11-4291-8263-5cbc2a3cb44a","added_by":"auto","created_at":"2026-03-17 07:36:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12149,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8693419/v1/b8e0957c366be1806b845303.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"On the Application of Metagenomic Next-Generation Sequencing and Real-time PCR with Melting Curve Analysis in the Auxiliary Diagnosis of Mycobacterial Pulmonary Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (TB) is a pathogenic bacterium capable of causing tuberculosis and even death in humans. According to the World Health Organization's 2024 Global Tuberculosis Report, there were approximately 10.8\u0026nbsp;million new TB cases and about 1.25\u0026nbsp;million deaths worldwide in 2023, making it one of the top ten causes of death globally. The incidence and mortality rates are notably higher in developing countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Non-tuberculous mycobacteria (NTM), which belong to the same genus as \u003cem\u003eM. tuberculosis\u003c/em\u003e, currently comprise approximately 200 identified species. They are widely distributed in natural environments such as soil, water, and air. Although some NTM species are non-pathogenic to humans, certain strains can cause disease, particularly in the elderly or immunocompromised individuals (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It has been reported that NTM infection rates are increasing in many regions and countries worldwide. NTM infections can lead to diseases affecting multiple body sites, making them an issue of growing clinical concern (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe clinical presentation and radiological features of non-tuberculous mycobacteria (NTM) infection are highly similar to those of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (TB) infection, leading to frequent confusion in clinical practice. However, NTM generally exhibits higher intrinsic resistance to first-line anti-tuberculosis drugs, and the drug susceptibility profiles vary significantly among different NTM species. Misdiagnosis may result in patients receiving ineffective antimicrobial therapy, leading to delays in appropriate treatment, increased risk of adverse drug reactions, and a significant negative impact on treatment efficacy and long-term prognosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, accurate early differentiation between TB and NTM infection, coupled with further identification of the specific NTM species, is of critical clinical importance for formulating targeted treatment strategies and improving patient outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main clinical methods for diagnosing \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC) and non-tuberculous mycobacteria (NTM) infections currently include acid-fast staining microscopy, mycobacterial culture, and molecular biological assays (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Conventional acid-fast staining is simple and rapid but cannot differentiate MTBC from NTM, and its specificity and sensitivity are relatively low. Although mycobacterial culture remains the diagnostic gold standard, its prolonged turnaround time may lead to delays in treatment decisions. Molecular biological techniques, due to their significantly improved sensitivity (particularly in immunocompromised populations), efficient typing capability, and rapid detection cycle, have become pivotal in optimizing the tuberculosis diagnostic algorithm (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReal-time PCR with melting curve analysis (MC-PCR) enables species discrimination by utilizing probes that specifically bind to target gene sequences (e.g., ITS regions) and differentiating species based on variations in melting temperature (Tm). This method allows for the identification of up to 19 mycobacterial species in a single assay, minimizing false-positive results due to cross-reactivity. The entire detection process requires only 4\u0026ndash;6 hours, significantly faster than culture-based or sequencing-based methods, thereby facilitating rapid mycobacterial species identification. This approach helps mitigate the risk of empirical treatment failure and directly supports the optimization of therapeutic strategy selection.\u003c/p\u003e \u003cp\u003eAmong various molecular detection methods for pathogens, metagenomic next-generation sequencing (mNGS) has emerged as a pivotal technology in the field of infectious disease diagnostics due to its unique non-targeted and broad-spectrum detection capability. This technique enables the simultaneous detection of nucleic acid sequences from a wide range of potential pathogens in a single sample, including bacteria, fungi, viruses, and parasites. Consequently, mNGS demonstrates substantial diagnostic value in complex infection cases with atypical clinical presentations and challenging pathogen differential diagnoses (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).However, precisely due to its inherent high sensitivity, mNGS results are susceptible to potential contamination or {Citation}operational biases throughout the entire workflow\u0026mdash;including specimen collection, nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. This may lead to false-positive or false-negative outcomes and increase the complexity of result interpretation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, in actual clinical practice, it is strongly recommended to integrate mNGS findings with results from other etiological detection methods (such as conventional culture, specific PCR, and serological tests) as well as clinical manifestations and imaging features. Such comprehensive analysis aids in forming a more reliable multi-dimensional diagnostic evidence chain (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, a cohort of lower respiratory tract samples from patients clinically suspected of mycobacterial infection was established to compare the diagnostic sensitivity, specificity, and agreement (Kappa value) between MC-PCR and mNGS in detecting \u003cem\u003eMycobacteria\u003c/em\u003e. Furthermore, based on a composite clinical diagnosis (incorporating imaging findings and treatment response), the positive and negative predictive values of both techniques as auxiliary diagnostic tools were analyzed.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eThis retrospective cross-sectional study analyzed 50 patients with clinically suspected mycobacterial pulmonary disease at Peking Union Medical College Hospital between September 1, 2024, and June 30, 2025. All patients underwent routine bronchoalveolar lavage fluid (BALF) testing with metagenomic next-generation sequencing (mNGS). For cases in which mycobacteria were definitively detected by mNGS, species identification was subsequently performed using real-time PCR with melting curve analysis (MC-PCR). Similarly, for cases with low-sequence reads or ambiguous mNGS results, MC-PCR was employed for verification. Additionally, 36 BALF samples in which mycobacteria had been detected by mNGS between January 1, 2024, and August 31, 2024, were retrospectively included and re-tested using MC-PCR (Xiamen Zhishan Technology). Samples that either yielded Mycobacterium tuberculosis (TB) sequences by mNGS or were clinically highly suggestive of TB infection (n\u0026thinsp;=\u0026thinsp;42) were classified as the suspected TB infection group (TB group). Samples that yielded non-tuberculous mycobacteria (NTM) sequences by mNGS or were clinically highly suggestive of NTM infection (n\u0026thinsp;=\u0026thinsp;44) were classified as the suspected NTM infection group (NTM group) (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Figure 1)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003emNGS Testing\u003c/h2\u003e \u003cp\u003eThe process of mNGS testing is relatively complex and can be summarized into several key steps: host cell depletion, nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. First, a 1 ml sample aliquot was centrifuged, and the supernatant was discarded. A minimum of 100 \u0026micro;l of the resulting pellet was then subjected to chemical host cell depletion using a commercial depletion reagent. To enhance nucleic acid release from \u003cem\u003eMycobacteria\u003c/em\u003e due to their unique cell wall structure, an intensified cell disruption step was performed by increasing the vortexing intensity from M/S 6.0 to M/S 7.0 during the mechanical lysis procedure (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Following cell disruption, DNA was extracted from the processed sample using a dedicated DNA extraction kit (TIANGEN Biotech (Beijing) Co., Ltd., China). Library preparation was subsequently carried out using the Nextera XT DNA Library Prep Kit (Illumina, San Diego, CA). The indexed DNA was amplified by PCR under the following cycling conditions: 72\u0026deg;C for 3 min and 98\u0026deg;C for 30 sec (1 cycle); 98\u0026deg;C for 15 sec, 60\u0026deg;C for 30 sec, and 72\u0026deg;C for 30 sec (17 cycles); followed by a final extension at 72\u0026deg;C for 5 min and a hold at 4\u0026deg;C. Dual-indexing was performed using IDT for Illumina DNA/RNA UD Indexes. Library size distribution was assessed on a Qsep 1 system, and concentration was quantified using the Qubit dsDNA HS Assay Kit on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Pooled libraries were then loaded onto an Illumina Nextseq CN500 sequencing platform for 75 single-end sequencing cycles, generating approximately 20 to 40\u0026nbsp;million reads per library. Raw sequencing data were processed using Trimmomatic to remove low-quality sequences, reads shorter than 40 bp, and adapter sequences, thereby yielding high-quality data. Taxonomic analysis was performed using the IDseq commercial bioinformatics pipeline (Vision Medicals). Reads mapping to the human genome and plasmid sequences were filtered out. The remaining sequences were classified by alignment against the Vision Medicals microbial database. Interpretation of mNGS reports was conducted by a multidisciplinary team comprising experts in bioinformatics, clinical laboratory diagnostics, and infectious diseases from the Pathogen Sequencing Laboratory at Peking Union Medical College Hospital.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReal-time PCR with Melting Curve Analysis (MC-PCR)\u003c/h3\u003e\n\u003cp\u003eReal-time PCR with melting curve analysis (MC-PCR) detects target DNA by analyzing the fluorescence curve generated when a fluorescent dye binds to double-stranded DNA during the strand dissociation phase of real-time PCR (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Samples were tested using a commercial Mycobacteria Identification Kit (Xiamen Zhishan Technology). This kit is capable of detecting Mycobacterium tuberculosis complex (MTBC) and 18 species of non-tuberculous mycobacteria (NTM), including: M. smegmatis, M. bovis, M. scrofulaceum, M. chelonae, M. simiae, M. lentiflavum, M. gordonae, M. kansasii, M. abscessus, M. fortuitum, M. marinum or M. ulcerans, M. terrae, M. nonchromogenicum, M. szulgai, M. malmoense, M. xenopi, M. intracellulare, and M. avium. First, 1\u0026ndash;2 volumes of sputum processing solution II were added to 500 \u0026micro;L of BALF sample. The mixture was vortexed and incubated at room temperature for at least 10 minutes (the incubation time and the ratio of processing solution could be appropriately extended or increased depending on the sample condition). After complete liquefaction, 1 mL of the processed sample was transferred into the first well of an MTB-Maxi nucleic acid extraction strip. The strip was labeled with the sample ID and placed into an 824S nucleic acid extraction instrument. The \u0026ldquo;MTB-Maxi-Fast\u0026rdquo; extraction program was executed for nucleic acid isolation. Upon completion of extraction, 25 \u0026micro;L of the corresponding nucleic acid extract, along with negative and positive controls, was added to individual PCR reaction tubes pre-filled with lyophilized reagent powder. The tubes were tightly sealed with an 8-tube strip cap, vigorously vortexed for 20 seconds, briefly centrifuged to remove bubbles, and then loaded into an ABI 7500 PCR thermal cycler for amplification. The PCR cycling protocol was as follows: 50\u0026deg;C for 2 min and 95\u0026deg;C for 10 min (1 cycle); followed by 55 cycles of 95\u0026deg;C for 15 sec, 57\u0026deg;C for 20 sec, and 78\u0026deg;C for 20 sec; then 40\u0026deg;C for 30 min (1 cycle); and finally 95\u0026deg;C for 2 min and 40\u0026deg;C for 2 min (1 cycle). After amplification, the results were interpreted based on the generated melting curves.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eGraphical plotting and statistical analyses were performed using R version 4.2.3 and Microsoft Office Excel. Continuous variables were analyzed using the Student's t-test and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Inter-group comparisons and diagnostic performance evaluations between detection methods were conducted using McNemar\u0026rsquo;s test, the chi-square test, or Fisher\u0026rsquo;s exact test, as appropriate. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The sensitivity and specificity of each detection method were calculated and reported with 95% confidence intervals (95% CI) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Agreement between different detection methods was assessed using Cohen\u0026rsquo;s Kappa coefficient. The correlation between mNGS read counts and results from MC-PCR as well as culture was evaluated using the Wilcoxon rank-sum test and Spearman\u0026rsquo;s correlation coefficient.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eDemographic Characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 86 BALF samples from patients with clinically suspected mycobacterial pulmonary infection, all of which underwent mNGS testing, were analyzed in this study. The cohort included 41 males (47.7%) and 45 females (52.3%), with an age range of 15 to 89 years. Detailed demographic characteristics are presented in Table\u0026nbsp;1. Comparisons between the TB group and the NTM group across various clinical baseline factors (sex, age, history of previous TB or NTM infection, diabetes), clinical symptoms (cough, fever, fatigue, chest pain/tightness, weight loss), chest imaging findings (pulmonary shadowing, cavity, nodules, bullae, bronchiectasis, bronchial stenosis, pleural effusion), status of immunodeficiency or immunosuppression (reduced immunity, immunodeficiency, immunosuppression), and other relevant factors (prolonged exposure to dust or soil, history of bird contact, smoking) revealed no statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparison between MC-PCR and mNGS Results\u003c/h3\u003e\n\u003cp\u003eAmong the 42 samples in the TB group, 30 (71.43%) were positive for TB by both mNGS and MC-PCR, 5 (11.90%) were TB-positive by mNGS but negative by MC-PCR, and 7 (16.67%) were negative by both methods. In the NTM group (n\u0026thinsp;=\u0026thinsp;44), 31 samples (70.45%) were positive for NTM by both mNGS and MC-PCR, 2 (4.55%) were NTM-positive by mNGS but negative by MC-PCR, 1 (2.27%) was NTM-positive by MC-PCR but negative by mNGS, and 10 (22.73%) were negative by both methods. (Figure 2)\u003c/p\u003e\n\u003cp\u003e(Figure 2)\u003c/p\u003e\n\u003cp\u003eFisher\u0026rsquo;s exact test indicated a statistically significant association between mNGS and MC-PCR results in both groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, the calculated Kappa values demonstrated substantial agreement between the two methods in the TB group (Kappa\u0026thinsp;=\u0026thinsp;0.667) and almost perfect agreement in the NTM group (Kappa\u0026thinsp;=\u0026thinsp;0.824).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eComparison of MC-PCR, mNGS, and Other Laboratory Methods for Mycobacterial Detection\u003c/h2\u003e\n \u003cp\u003eThe results of mNGS were compared with those of MC-PCR, culture, acid-fast staining (AFS), quantitative fluorescent PCR (qPCR), Xpert MTB/RIF, and T-Spot.TB (Fig.\u0026nbsp;3). The diagnostic performance of each method was further evaluated against the final clinical diagnosis (FCD) (Table\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003eIn the TB group, the sensitivities of culture (53.33%, 16/30), qPCR (56%, 14/25), and Xpert MTB/RIF (52.38%, 11/21) were similar and all higher than that of AFS (6.67%, 2/30). In contrast, mNGS (96.67%, 29/30), MC-PCR (83.33%, 25/30), and T-Spot.TB (87.5%, 14/16) exhibited significantly higher sensitivities than the other methods.\u003c/p\u003e\n \u003cp\u003eA similar pattern was observed in the NTM group, where both mNGS (96.77%, 30/31) and MC-PCR (93.55%, 29/31) achieved sensitivities above 90%. Xpert MTB/RIF and T-Spot.TB were not included in the NTM group evaluation, as they are not intended for NTM detection. The specificity of AFS was 100% in both groups, although the comparison between the two groups showed no statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003e(Figure 3)\u003c/p\u003e\n \u003cp\u003eAlthough mNGS results only provide qualitative detection of pathogenic microorganisms, we ranked the sequence read counts from high to low in both the TB and NTM groups and performed correlation analyses with the positivity/negativity results from MC-PCR and culture (Fig.\u0026nbsp;4). The differences in sequence read counts between positive and negative results from MC-PCR and culture were statistically significant in both groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003e(Figure 4)\u003c/p\u003e\n \u003cp\u003eHowever, in our study, mNGS results were inconsistent with the clinical diagnosis in six cases from the TB group and four cases from the NTM group.A review of medical records revealed that in the TB group, there were five false-positive cases. These were attributed to clinical judgment based on chest imaging features inconsistent with TB infection. One false-negative case was identified, in which the original bioinformatics results showed three reads mapping to Mycobacterium tuberculosis. These were likely unreported due to low matching confidence, and all other test results were negative, leading to a negative report. However, given clinical suspicion of TB infection, empirical anti-tuberculosis therapy was initiated.\u003c/p\u003e\n \u003cp\u003eIn the NTM group, there were three false-positive cases. One case with 114 reads mapping to Mycobacterium abscessus was not considered clinically significant due to the patient\u0026rsquo;s severe underlying conditions (lung cancer and multidrug-resistant bacterial infection), combined with imaging findings. The other two cases, with 7 and 3 reads mapping to Mycobacterium intracellulare, respectively, were also excluded from NTM infection based on inconsistent chest imaging features. Additionally, one false-negative case was noted. The sample, tested on January 25, 2025, initially showed one read mapping to Mycobacterium intracellulare in the original bioinformatics analysis. As other tests were negative, this was considered part of the \u0026ldquo;background microbiota\u0026rdquo; and not reported. However, a positive mycobacterial culture result was obtained on February 7, 2025. Clinical suspicion of mycobacterial infection led to GM-CSF nebulization therapy, with subsequent improvement in respiratory symptoms after three months.\u003c/p\u003e\n \u003cp\u003eFurthermore, one false-positive result was observed with MC-PCR. Review of the original melting curve indicated the detection of a mycobacterial species not covered by the 19-target panel. Verification was not possible due to insufficient residual sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eNTM Species Identification Results\u003c/h2\u003e\n \u003cp\u003eAmong the 33 NTM-positive cases detected by mNGS, Mycobacterium intracellulare was identified in 19 (57.6%), Mycobacterium abscessus in 6 (18.2%), Mycobacterium xenopi in 4 (12.1%), Mycobacterium avium in 2 (6.1%), Mycobacterium kansasii in 2 (6.1%), and a mycobacterial species not covered by the panel in 1 (3.0%). One case showed co-infection with Mycobacterium intracellulare and Mycobacterium kansasii.\u003c/p\u003e\n \u003cp\u003eCompared with the results from MC-PCR (Fig.\u0026nbsp;5), species identification was consistent in 30 cases (90.9%). In two cases (6.1%), MC-PCR was negative, while mNGS detected Mycobacterium avium (4 reads) and Mycobacterium intracellulare (3 reads), respectively. One case (3.0%) was positive by both methods but with discordant species identification: mNGS detected Mycobacterium intracellulare (206 reads), whereas MC-PCR identified Mycobacterium avium and Mycobacterium malmoense.\u003c/p\u003e\n \u003cp\u003eThe distribution of NTM species identified in this study was largely consistent with the epidemiological profile reported in China, predominantly featuring Mycobacterium intracellulare, Mycobacterium abscessus, and Mycobacterium xenopi (22).\u003c/p\u003e\n \u003cp\u003e(Figure 5)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study included a total of 86 patients clinically suspected of mycobacterial pulmonary infection, comprising 42 cases in the TB group and 44 cases in the NTM group. Analysis of clinical characteristics and chest imaging findings revealed no statistically significant differences between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that clinical symptoms and imaging features alone offer limited utility in distinguishing TB from NTM infection (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eT-Spot.TB is widely used in clinical practice as an indicator of tuberculosis infection. In this study, T-Spot.TB demonstrated relatively high sensitivity (87.50%) but relatively low specificity (50.00%). Moreover, it cannot differentiate between active tuberculosis and latent tuberculosis infection (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In the early stages of diagnosis, particularly when patients present with mild symptoms, conventional methods face challenges in accurately discriminating pulmonary tuberculosis from community-acquired pneumonia caused by other pathogens (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the rapid development of molecular detection technologies, metagenomic next-generation sequencing (mNGS) has attracted considerable attention in etiological diagnosis in recent years due to its short turnaround time and high accuracy (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). A positive high-throughput sequencing result is considered clinically meaningful only when at least one additional detection method confirms the identification of the same species (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the results obtained by mNGS and MC-PCR were compared, showing an agreement rate exceeding 85% in both the TB and NTM groups. Both methods were further compared with traditional microbiological assays and other molecular detection techniques. In terms of diagnostic performance, mNGS, T-Spot.TB, and MC-PCR all demonstrated sensitivities exceeding 80% for TB detection. However, the specificity of T-Spot.TB (50.00%) was lower than that of MC-PCR (60.00%), and T-Spot.TB is not applicable for NTM detection. In the NTM group, both mNGS (96.77%) and MC-PCR (93.55%) exhibited significantly higher sensitivity than other methods.\u003c/p\u003e \u003cp\u003eFurthermore, given the relative difficulty and prolonged time required for mycobacterial culture, both mNGS and MC-PCR enable species identification within a much shorter timeframe. This facilitates rapid diagnosis and targeted treatment of tuberculosis and NTM infections in clinical practice. Overall, the combined use of mNGS and MC-PCR demonstrates superior feasibility in the diagnosis of mycobacterial pulmonary disease.\u003c/p\u003e \u003cp\u003eBy comparing the sequence read counts from mNGS with the positivity rates of MC-PCR and culture, we observed a significant correlation between the number of mycobacterial reads detected by mNGS and the positivity rates of both MC-PCR and culture (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The number of sequences detected by mNGS can be influenced by multiple factors, including sample quality, contamination, experimental procedures, host DNA ratio, and bioinformatics analysis. Relevant consensus guidelines also indicate that read counts should only be interpreted qualitatively (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Based on the findings of this study, we hypothesize that under standardized and rigorously controlled mNGS experimental conditions, a higher number of detected mycobacterial reads is associated with an increased likelihood of a positive MC-PCR result and a higher probability of successful mycobacterial culture.\u003c/p\u003e \u003cp\u003eAlthough mNGS demonstrates excellent performance in detecting pathogenic microorganisms, it has certain limitations. In this study, a total of eight false-positive and two false-negative results were observed with mNGS.\u003c/p\u003e \u003cp\u003eDuring the bioinformatics analysis phase of mNGS, factors such as patient symptoms, host DNA ratio, and background microbiota are considered. However, for high-priority pathogens like TB\u0026mdash;a highly pathogenic microorganism\u0026mdash;or NTM species not typically present in the background microbiota, even a single read may be reported as positive (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Consequently, when issues such as sample contamination, data analysis ambiguities, atypical clinical presentations, or prior antibiotic use leading to negative results by other methods arise, distinguishing true positives from false positives or negatives based solely on mNGS can be challenging (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The relatively high cost of mNGS testing may impose a financial burden on economically disadvantaged patients, which also limits the possibility of repeated testing for result verification (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In contrast, MC-PCR is more cost-effective. Using MC-PCR to verify mNGS results can help reduce false positives without substantially increasing overall testing costs.\u003c/p\u003e \u003cp\u003eFurthermore, in this study, the concordance rate for NTM species identification between mNGS and MC-PCR reached 90.9%. When mNGS detects NTM sequences but conventional culture yields false-negative results due to factors such as insufficient incubation time or low microbial load, MC-PCR can serve as an alternative method to support and confirm the species identification of NTM.\u003c/p\u003e \u003cp\u003eDespite the valuable findings obtained in this study, several limitations should be acknowledged. First, some cases were from outpatient settings or were transferred to other hospitals for further treatment. Patient compliance and subsequent therapeutic outcomes could not be fully evaluated, and the possibility that the final clinical diagnosis may have been influenced by the mNGS results cannot be excluded.\u003c/p\u003e \u003cp\u003eSecond, the MC-PCR assay used (Xiamen Zhishan Technology) can only detect MTB and 18 specific NTM species. NTM strains outside this panel cannot be identified to the species level.\u003c/p\u003e \u003cp\u003eFinally, although both mNGS and MC-PCR demonstrated high sensitivity and substantial agreement in detecting mycobacterial pulmonary disease, neither method can distinguish between true pulmonary NTM infection and colonization. Clinical judgment based on the overall presentation remains essential for final interpretation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs an emerging detection technology, mNGS offers unique advantages among various mycobacterial diagnostic methods and provides a basis for the early diagnosis and treatment of tuberculosis and NTM infections. Our study validated the application of mNGS and real-time PCR with melting curve analysis (MC-PCR) in the auxiliary diagnosis of mycobacterial pulmonary disease, demonstrating the feasibility of both approaches. We hope that by optimizing these methodologies, new diagnostic strategies can be developed for pulmonary tuberculosis and NTM infections, thereby contributing to global tuberculosis control efforts and to the detection and management of non-tuberculous mycobacterial diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetagenomics next-generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMC-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereal-time PCR with melting curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBALF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBronchoalveolar lavage fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-tuberculous mycobacteria\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efinal clinical diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. smegmatis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium smegmatis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. bovis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium bovis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. scrofulaceum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium scrofulaceum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. chelonae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium chelonae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. simiae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium simiae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. lentiflavum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium lentiflavum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. gordonae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium gordonae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. kansasii\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium kansasii\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. abscessus\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium abscessus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. fortuitum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium fortuitum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. marinum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium marinum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. ulcerans\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium ulcerans\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. terrae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium terrae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. nonchromogenicum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium nonchromogenicum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. szulgai\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium szulgai\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. malmoense\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium malmoense\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. xenopi\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium xenopi\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. intracellulare\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium intracellulare\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM. avium\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium avium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Review Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences (Approval No. : I-24PJ2561).The studies were conducted in accordance with local legislation and institutional requirements. Written informed consent was obtained from the patients for publication of this article and any accompanying images.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Statement\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eWritten informed consent was obtained from the patient for publication of the details of their medical case and any accompanying images.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eWe confirm that this manuscript has not been published else where and is not under simultaneous consideration by any other journal. Furthermore, all authors have agreed to submit this manu script and declare no potential conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Key Laboratory of Molecular Diagnostics and Individualized Therapy of Huangshi City (Project No.: FZ2026007).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**Zhou Lv:** Data curation, Visualization, Writing \u0026ndash; original draft. **Ziran Wang:** Writing \u0026ndash; review \u0026amp; editing. **Xinfei Chen:** Initial experiments, Bioinformatics analysis. **Ziyi Wang:** Sample collection, Initial experiments. **Yujie Sun:** Sample collection, Initial experiments. **Huiting Su:** Initial experiments, Data curation. **Jiayu Guo:** Initial experiments, Data curation. **Minya Lu:** Initial experiments, Data curation. **Chenglin Yang:** Sample collection, Initial experiments. **Wan Huang:** Sample collection, Initial experiments. **Lina Guo:** Methodology, Resources. **Juan Du:** Guidance, Supervision, Review. **Qiwen Yang:** Supervision, Resources.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are thankful to all the study participants and their families. We also gratefully acknowledge the generous financial support provided by the Joint Fund of the Key Laboratory of Molecular Diagnostics and Individualized Therapy of Huangshi City.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe datasets generated during the current study are not publicly available due to privacy/ethical restrictions, but are available from the corresponding author on reasonable request for academic, non-commercial purposes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEstaji F, Kamali A, Keikha M. Strengthening the global response to tuberculosis: insights from the 2024 WHO global TB report. J Clin Tuberc Other Mycobact Dis. 2025;39:100522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopalaswamy R, Shanmugam S, Mondal R, Subbian S. Of tuberculosis and non-tuberculous mycobacterial infections \u0026ndash; a comparative analysis of epidemiology, diagnosis and treatment. J Biomed Sci. 2020;27(1):74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Chen Y, Wang Q, Pan J, Bao R, Jin W, et al. Comparison of molecular testing methods for diagnosing non-tuberculous mycobacterial infections. Eur J Clin Microbiol Infect Dis. 2025;44(1):109\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin J, Zhao Y, Wei S, Dai Z, Lin S. Evaluation of the MeltPro myco assay for the identification of non-tuberculous mycobacteria. Infect Drug Resist. 2022;15:3287\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryant JM, Grogono DM, Greaves D, Foweraker J, Roddick I, Inns T, et al. Whole-genome sequencing to identify transmission of Mycobacterium abscessus between patients with cystic fibrosis: a retrospective cohort study. Lancet. 2013;381(9877):1551\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim KJ, Chang Y, Yun SG, Nam MH, Cho Y. Evaluation of a commercial multiplex real-time PCR with melting curve analysis for the detection of mycobacterium tuberculosis complex and five nontuberculous mycobacterial species. Microorganisms. 2024;13(1):26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin KL, Sarmiento ME, Alvarez-Cabrera N, Norazmi MN, Acosta A. Pulmonary non-tuberculous mycobacterial infections: current state and future management. Eur J Clin Microbiol Infect Dis. 2020;39(5):799\u0026ndash;826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma SK, Upadhyay V. Epidemiology, diagnosis \u0026amp; treatment of non-tuberculous mycobacterial diseases. Indian J Med Res. 2020;152(3):185\u0026ndash;226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhanushali J, Jadhav U, Ghewade B, Wagh P. Unveiling the clinical diversity in nontuberculous mycobacteria (NTM) infections: a comprehensive review. Cureus [Internet]. 2023 Nov 4 [cited 2025 Jun 30]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cureus.com/articles/193379-unveiling-the-clinical-diversity-in-nontuberculous-mycobacteria-ntm-infections-a-comprehensive-review\u003c/span\u003e\u003cspan address=\"https://www.cureus.com/articles/193379-unveiling-the-clinical-diversity-in-nontuberculous-mycobacteria-ntm-infections-a-comprehensive-review\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;rlős Z, Lőrinczi LK, Antus B, Barta I, Mikl\u0026oacute;s Z, Horv\u0026aacute;th I. Epidemiology, microbiology and clinical impacts of non-tuberculous mycobacteria in adult patients with cystic fibrosis. Heliyon. 2025;11(1):e41324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Zhu Y, Zhang Y, Liu Z, Zhang M, Chen J, et al. Evaluation and Comparison of Laboratory Methods in Diagnosing \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e and Nontuberculous Mycobacteria in 3012 Sputum Samples. Clin Respiratory J. 2025;19(3):e70071.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi CL, Han P, Tang PJ, Chen MM, Ye ZJ, Wu MY, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect. 2020;81(4):567\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Chen Q, Zhang D, Hu L, Li S, Lu M et al. Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients. Chen F, editor. Microbiol Spectrum. 2025;e01563-24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu P, Yang K, Yang L, Wang Z, Jin F, Wang Y, et al. Next-generation metagenome sequencing shows superior diagnostic performance in acid-fast staining sputum smear-negative pulmonary tuberculosis and non-tuberculous mycobacterial pulmonary disease. Front Microbiol. 2022;13:898195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenoit P, Brazer N, De Lorenzi-Tognon M, Kelly E, Servellita V, Oseguera M, et al. Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections. Nat Med. 2024;30(12):3522\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Boheemen S, Van Rijn AL, Pappas N, Carbo EC, Vorderman RHP, Sidorov I, et al. Retrospective validation of a metagenomic sequencing protocol for combined detection of RNA and DNA viruses using respiratory samples from pediatric patients. J Mol Diagn. 2020;22(2):196\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao Qing Y, Yumeng P, Jue B, Rong W, Qingqing L. Etiological diagnostic value of metagenomic next-generation sequencing in non-tuberculous mycobacteria infection. Chin J Clin Med. 2020;27(4):559\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaston DC. Clinical metagenomics for infectious diseases: progress toward operational value. Simner PJ, editor. J Clin Microbiol. 2023;61(2):e01267-22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai X, Xu K, Tong Y, Li J, Dai L, Shi J, et al. Application of targeted next-generation sequencing in bronchoalveolar lavage fluid for the detection of pathogens in pulmonary infections. Infect Drug Resist. 2025;18:511\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolzheimer M, Buter J, Minnaard AJ. Chemical synthesis of cell wall constituents of \u003cem\u003emycobacterium tuberculosis\u003c/em\u003e. Chem Rev. 2021;121(15):9554\u0026ndash;643.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhosravi AD, Meghdadi H, Hashemzadeh M, Alami A, Tabandeh MR. Application of a new designed high resolution melting analysis for mycobacterial species identification. BMC Microbiol. 2024;24(1):205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Li H, Jiang G, Zhao L, Ma Y, Javid B, et al. Prevalence and drug resistance of nontuberculous mycobacteria, northern China, 2008\u0026ndash;2011. Emerg Infect Dis. 2014;20(7):1252\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Nna, Gao L, lu, Liu M, Zhang W, min, Zhang X ke, Chen L et al. Analysis of non-tuberculous mycobacteria types in high tuberculosis endemic areas. J Health Popul Nutr. 2025;44(1):54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZellweger JP, Sotgiu G, Block M, Dore S, Altet N, Blunschi R, et al. Risk assessment of tuberculosis in contacts by IFN-γ release assays. A tuberculosis network european trials group study. Am J Respir Crit Care Med. 2015;191(10):1176\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCudahy P, Shenoi SV. Diagnostics for pulmonary tuberculosis. Postgrad Med J. 2016;92(1086):187\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong R, Lin S, Zhang S, Yi Y, Li L, Yang H, et al. Pathogen spectrum and microbiome in lower respiratory tract of patients with different pulmonary diseases based on metagenomic next-generation sequencing. Front Cell Infect Microbiol. 2024;14:1320831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Xing L. Metagenomic next-generation sequencing assistance in identifying non-tuberculous mycobacterial infections. Front Cell Infect Microbiol. 2023;13:1253020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExpert Group on Consensus for High throughput Sequencing. Expert consensus on the application of highthroughput sequencing technology in the diagnosis of mycobacterial diseases. Chin J Infect Dis. 2023;41(3):175\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol: Mech Dis. 2019;14(1):319\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang ZB, Leng EL, Cao WF, Liu SM, Zhou YL, Luo CQ, et al. A systematic review and meta-analysis of the diagnostic accuracy of metagenomic next-generation sequencing for diagnosing tuberculous meningitis. Front Immunol. 2023;14:1223675.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\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-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mycobacterial pulmonary infection, Non-tuberculous mycobacteria identification, Metagenomic next-generation sequencing (mNGS), Real-time PCR with melting curve analysis (MC-PCR)","lastPublishedDoi":"10.21203/rs.3.rs-8693419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8693419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eRapid and accurate identification of \u003cem\u003eMycobacteria\u003c/em\u003e is of critical importance in clinical practice. With the advancement of molecular diagnostics, techniques such as metagenomic next-generation sequencing (\u003cb\u003emNGS\u003c/b\u003e) and real-time PCR with melting curve analysis (\u003cb\u003eMC-PCR\u003c/b\u003e) are being increasingly employed for the diagnosis of mycobacterial infections. This study aimed to evaluate the diagnostic value of these two methods in the context of pulmonary mycobacterial disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBronchoalveolar lavage fluid (BALF) samples from 86 patients suspected of pulmonary mycobacterial infection were analyzed using both mNGS and MC-PCR. The concordance between the results of these two methods was compared. Using a comprehensive clinical diagnosis as the reference standard, the sensitivity, specificity, and agreement of these two molecular techniques, alongside conventional detection methods, were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the group suspected of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (TB) infection, mNGS and MC-PCR demonstrated substantial agreement (Kappa\u0026thinsp;=\u0026thinsp;0.667). The sensitivities were 96.67% (29/30) and 83.33% (25/30), respectively. In the group suspected of non-tuberculous mycobacteria (NTM) infection, the two methods showed a high level of agreement (Kappa\u0026thinsp;=\u0026thinsp;0.824), with sensitivities of 96.77% (30/31) and 93.55% (29/31), respectively. The concordance rate for NTM species identification between the two methods was 90.9% (30/33).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn the diagnosis of mycobacterial pulmonary infection and the identification of non-tuberculous mycobacteria (NTM) species, both mNGS and MC-PCR exhibited higher sensitivity and superior consistency compared to the other methods evaluated in this study. The combined application of these techniques with conventional detection methods may provide a novel and effective approach for the diagnosis of mycobacterial pulmonary infections.\u003c/p\u003e","manuscriptTitle":"On the Application of Metagenomic Next-Generation Sequencing and Real-time PCR with Melting Curve Analysis in the Auxiliary Diagnosis of Mycobacterial Pulmonary Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 14:20:45","doi":"10.21203/rs.3.rs-8693419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-15T07:28:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256135649882829487398987922846890960623","date":"2026-03-18T04:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292270147752415106520924449371351008825","date":"2026-02-27T12:57:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T14:07:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T14:43:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T08:19:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T12:36:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-02-04T12:20:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e75b1acd-4d33-4f46-89d0-1a809b1a0956","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T14:20:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 14:20:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8693419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8693419","identity":"rs-8693419","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}