Application of targeted next-generation sequencing to identify pathogens in the bronchoalveolar lavage fluid of adults with pulmonary infections

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Methods BALF samples were obtained from 262 adult patients with pulmonary infection and were detected by tNGS, microbial culture, Xpert® MTB/RIF assay, and Aspergillus galactomannan (GM) test. Results In total, 47 potential pathogens were identified in the BALF samples by tNGS, including 21 bacteria, 13 viruses, 11 fungi, 1 parasite, and 1 mycoplasma. The bacterial detection rates of tNGS and ordinary bacterial culture were 74.0% (194/262) and 28.2% (74/262), respectively. The rates of negative, positive, and total consistent and the kappa value between tNGS and bacterial culture were 30.8%, 86.4%, 46.4%, and 0.116, respectively. The positive rate of fungal identification by tNGS was slightly higher than that of fungal culture (31.7% (83/262) and 22.9% (60/262), respectively). The rates of positive, negative, and total consistent and the kappa value between tNGS and fungal culture were 68.9%, 79.1%, 76.7%, and 0.424, respectively. Among the 42 patients with suspected tuberculosis infection, 23 patients showed positive results on both tNGS and Xpert® MTB/RIF assay. The rates of positive, negative, and total consistent and the kappa value between tNGS and pert® MTB/RIF assay were 100.0%, 68.4%, 85.7%, and 0.704, respectively. Finally, the sensitivity and specificity of tNGS versus the GM test were 57.1% and 90.6% versus 71.4% and 82.7%, respectively, when the fungal culture was used as the gold standard for detecting Aspergillus . Additionally, the sensitivity and specificity of tNGS increased to 86.2% and 98.7%, whereas the sensitivity of the GM test decreased to 69.0% when clinically diagnosed Aspergillus infection was used as a reference standard. The read counts of Aspergillus detected by tNGS and the optical density of the GM test were not significantly correlated. Conclusions tNGS is a promising method for detecting pathogens in BALF with a notably higher positive detection rate and a higher sensitivity and/or specificity compared with those of the conventional test. Aspergillus galactomannan test Diagnostics Metagenomics next-generation sequencing Pulmonary infection Targeted next-generation sequencing Xpert Figures Figure 1 Figure 2 Figure 3 Background Among common clinical problems, pulmonary infections exhibit the highest morbidity rates [ 1 , 2 ]. Generally, various pathogens including bacteria, fungi, viruses, mycoplasma, and atypical microbial organisms cause pulmonary infections. In recent years, novel pathogens causing pulmonary infections have emerged, which has resulted in a heavy burden on healthcare systems. Thus, the accurate and timely identification of pathogens causing pulmonary infections is crucial for treatment and prognosis assessment [ 3 , 4 ]. Currently, conventional methods (CMs) for detecting pathogens responsible for pulmonary infections mainly include smear microscopy, microbial culture, immunological tests, and polymerase chain reaction [ 5 ]. However, these methods have some disadvantages, such as low sensitivity, high false-positive rates, long culture cycles, and narrow detection ranges, which do not meet the standards of precision medicine. Moreover, numerous pathogens, such as non-tuberculous mycobacteria, Chlamydia , Rickettsia , rare viruses, and parasites, are difficult to detect using CMs. Therefore, it is imperative to use a superior method to identify pathogens in order to guide disease diagnosis and precise treatment. Metagenomics next-generation sequencing (mNGS) has emerged as a promising method for pathogen detection, which markedly improves the efficiency of etiological diagnosis compared with conventional microbiological tests [ 6 , 7 , 8 ]. However, high costs and difficulties in interpreting mNGS results limit its application in routine clinical examinations. The development of targeted next-generation sequencing (tNGS) in recent years has provided new avenues for pathogen identification [ 9 , 10 ]. tNGS is based on high-throughput sequencing and allows for the rapid and accurate detection of multiple pathogens by selectively amplifying specific genes or regions and providing their genomic information. Compared with mNGS, tNGS is not affected by human genes and allows to perform DNA and RNA dual process detection simultaneously. Moreover, the cost of tNGS in detecting pathogens is only 1/5th of that of mNGS. Additionally, tNGS has a specific pathogen detection spectrum. However, its performance for detecting pathogens in the bronchoalveolar lavage fluid (BALF) of adults with pulmonary infections remains unclear. Herein, we aimed to introduce and explore tNGS application to pathogen identification in the BALF of adults with pulmonary infection compared with CMs including bacterial culture, fungal culture, Xpert® MTB/RIF assay, and Aspergillus galactomannan (GM) test. We hope to provide more accurate and comprehensive information for diagnosing and treating pulmonary infections, which can substantially improve clinical practice. Materials and Methods Study population and sample collection A total of 262 patients with pulmonary infections who underwent fiberoptic bronchoscopy for bronchoalveolar lavage at the Second Affiliated Hospital of Guangxi Medical University between September 2023 and February 2024 were enrolled. Patients with mental disorders, immunodeficiency, rheumatoid diseases, organ transplantation, and cancers (except for lung cancer) and pregnant women were excluded. Among the 262 enrolled patients, 150 were males and 112 were females, and their ages ranged from 19 to 91 years, with a median age of 60 years. Moreover, 31.7% of the basic pulmonary diseases included chronic obstructive pulmonary disease, bronchial stenosis, bronchiectasis, interstitial lung disease, and lung cancer (Table 1 ). BALF samples were collected from all patients and subjected to tNGS, culture, Xpert® MTB/RIF assay, and GM test. This study was approved by the Human Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University, and informed consent was obtained from all patients. TNGS assay The nucleic acid from the BALF samples was extracted using the TIANamp yeast DNA kit (TIANGEN, Beijing, China), following the manufacturer’s instructions. Subsequently, a DNA library for each BALF sample was prepared from the extracted nucleic acids using Pathogeno One Library Kit (BGI Genomics, Shenzhen, China). Briefly, for reverse transcription polymerase chain reaction (RT-PCR), 4 µL of the 5X RT Mix, 10 µL of the nucleic acids, and 6 µL of nuclease-free water were added to PCR tubes. The RT-PCR protocol involved incubation at 25 ℃ for 10 min, 55 ℃ for 15 min, and 85 ℃ for 5 min. Subsequently, the RT-PCR products and nucleic acids isolated from the BALF samples were mixed using the panel mix and 3X enzyme mix to run the first round PCR at an initial denaturation step at 95 ℃ for 3 min, followed by 23 cycles of denaturation at 95 ℃ for 20 s, annealing at 63 ℃ for 2 min, and extension at 72 ℃ for 2 min. The products obtained from the first round of PCR underwent purification and were mixed with 2X BarcodeF, 3X BarcodeR, and 3X enzyme xix for the second round of PCR. The PCR conditions were denaturation at 95 ℃ for 3 min, followed by 12 cycles of denaturation at 95 ℃ for 15 s, annealing at 58 ℃ for 15 s, and extension at 72 ℃ for 1 min. Subsequently, the library was sequenced on the MGISEQ-200 platform. The data were automatically analyzed by mapping them to a reference fast alignment (FASTA) file containing nucleic acid sequences of pathogens in the database. Culture The BALF samples were cultured by inoculating them onto blood, chocolate, MacConkey, and Sabourauds agar plates. Suspected colonies were identified using the MALDI-Biotyper system (Bruker, Germany). Xpert® MTB/RIF assay Xpert assay was performed following the manufacturer’s instructions. Briefly, 2 mL of the Xpert reagent was mixed with 1 mL of the BALF samples and incubated at room temperature for 10 min. Then, 1 mL of the mixture was transferred to Xpert cartridges and was automatically detected by the Xpert platform. GM test The Platelia Aspergillus Ag kit (BIO-RAD, USA) was used to detect the Aspergillus GM antigen. Briefly, 50 µL of the conjugate (peroxidase-labeled GM monoclonal antibody) was added to microwell strips. Next, 50 µL of the BALF samples was added to each well, followed by incubation in a dry microplate incubator at 37 ℃ for 90 min. The plate was then five times with a microplate washer using 800 µL of the working washing solution. Next, 200 µL of the chromogen TMB solution was rapidly added, followed by incubation in the dark at room temperature for 30 min. Finally, 100 µL of the stopping solution was added, and optical density (OD) was read at 450 nm. Statistical analysis Statistical analysis was performed using SPSS software (version 22.0; SPSS Inc., Chicago, IL, USA). The positive detection rates of the two methods were compared using the chi-square test. An overall agreement was estimated using Cohen’s kappa statistics. A correlation analysis was performed using Spearman’s rank correlation test. Statistical significance was set at P < 0.05. Results Pathogen profile of all patients with pulmonary infections according to tNGS In the 262 patients enrolled, 47 potential pathogens were identified in the BALF samples via tNGS including 21 bacteria, 13 viruses, 11 fungi, 1 parasite, and 1 mycoplasma (Figure 1). The most frequently detected bacteria were Streptococcus pneumoniae (56 cases), Pseudomonas aeruginosa (55 cases), and Haemophilus influenzae (47 cases). The most frequently identified virus was Epstein–Barr virus (EBV), which was detected in 78 patients. The top three fungi identified by tNGS were Candida albicans (59 cases), Aspergillus fumigatus (20 cases), and Candida tropicalis (14 cases). The overall microbial detection rate for tNGS was 99.2% (260/262), and 21.9% (57/260) of it accounted for single-pathogen infection and 78.1% (203/260) accounted for polymicrobial infections. Comparison of tNGS and bacterial culture The bacterial detection rates of tNGS and ordinary bacterial culture were 74.0% (194/262) and 28.2% (74/262), respectively, indicating that the detection rate of tNGS was significantly superior to that of bacterial culture ( P < 0.001). Among the 262 patients, 64 (24.4%) patients showed positive results on both tNGS and ordinary bacterial culture, whereas 58 (22.1%) patients showed negative results on both methods. Furthermore, 130 (49.6%) patients tested positive solely by tNGS, and 10 (3.8%) patients tested positive exclusively by bacterial culture, indicating that the sensitivity of tNGS was significantly higher than that of bacterial culture. Among the 69 patients showing positive results on tNGS and bacterial culture, 21 patients exhibited complete consistency, 43 patients showed partial consistency, and 4 patients displayed complete inconsistency between the tNGS and bacterial culture results. Besides, seven microorganisms, namely Klebsiella oxytoca , Pseudomonas otitidis , Abiotrophia defective , Corynebacterium striatum , Staphylococcus haemolyticus , Shewanella algae, and Aeromonas caviae , were outside the detection range of tNGS in the bacterial culture-positive results (Figure 2). The rates of negative, positive, and total consistent and kappa value between tNGS and bacterial culture were 30.8%, 86.4%, 46.4%, and 0.116, respectively (Table 2). The positive rate of tNGS for fungal identification was slightly higher than that of fungal culture (31.7 % (83/262) and 22.9 % (60/262), respectively). In total, 42 patients showed positive results on both tNGS and fungal culture, whereas 160 patients showed negative results on both. Additionally, 42 patients tested positive solely by tNGS, and 19 patients tested positive solely by fungal culture (Table 2). Among the 42 patients showing positive results on tNGS and fungal culture, 28 patients exhibited complete consistency and 14 patients exhibited partial consistency between the tNGS and bacterial culture results. The rates of positive, negative, and total consistent and kappa value between tNGS and fungal culture were 68.9%, 79.1%, 76.7%, and 0.424, respectively. Consistency between tNGS and Xpert® MTB/RIF assay Herein, 42 patients suspected of tuberculosis infection or with a history of tuberculosis infection were detected by tNGS and Xpert® MTB/RIF assay. The results showed that 23 patients showed positive results for Mycobacterium tuberculosis on both methods. In the Xpert® MTB/RIF assay negative group, 13 patients tested positive for Mycobacterium tuberculosis when detected by tNGS. The rates of positive, negative, and total consistent and kappa value between tNGS and pert® MTB/RIF assay were 100.0%, 68.4%, 85.7%, and 0.704, respectively (Table 2). TNGS and GM test in identifying Aspergillus infection Of the 262 patients, 28 patients tested positive for Aspergillus infection by tNGS, including 20 patients infected with Aspergillus fumigatus and 8 with Aspergillus flavus . Among these 28 patients, 60.7% (17/28) showed positive results and 39.3% (11/28) showed negative results when subjected to the GM test. For proven Aspergillus infection fungal culture, tNGS exhibited a sensitivity of 57.1%, a specificity of 90.6%, a positive predictive value (PPV) of 14.3%, and a negative predictive value (NPV) of 98.7%, whereas the GM test showed a sensitivity of 71.4%, a specificity of 82.7%, a PPV of 71.4%, and an NPV of 99.1%. When clinically diagnosed Aspergillus infection was used as a gold standard, the final sensitivity and specificity of tNGS versus the GM test increased to 86.2% and 98.7% versus 69.0% and 87.6%, respectively (Table 3). Spearman’s correlation coefficient between the read counts of Aspergillus detected by tNGS and the OD value of the GM test was 0.120 ( P > 0.05). Logistic regression analysis showed no significant correlation between the quantitative results of the two methods (Figure 3). Discussion Various testing methods have been used to aid in the diagnosis of pulmonary infectious diseases; however, several cases of pulmonary infection have unidentified etiology. Identifying the etiology to guide targeted therapy remains a formidable challenge. The application of mNGS in pathogen identification has been a significant advancement in the medical field, offering a broad spectrum of pathogen detection and the ability to identify rare and novel pathogens. However, due to the considerably high cost and interpretation challenges associated with mNGS, tNGS has garnered much attention. tNGS is a sequencing technology offering more targeted characteristics at a low cost. Previous studies have reported that there was no significant difference in diagnosing pneumonia between these two technologies [ 11 ]. tNGS is an enrichment sequencing method based on NGS, which includes probe hybridization and PCR amplification. Compared with mNGS, tNGS offers lesser working time due to its standardized workflow. By developing more efficient working steps, detection time can be further reduced while minimizing interpretation errors. For example, preliminary screening of pathogens based on clinical recommendations allows clinicians to focus on target microorganisms rather than performing broad testing for unknown microorganisms [ 12 ]. In this study, we used tNGS technology to detect 126 pathogens and evaluate their detection performance in BALF samples from adults with pulmonary infection. Compared with CMs, tNGS identified more potential pathogens, including bacteria, viruses, fungi, parasites, and mycoplasma. Many pathogens were detected in most cases revealing the formidable detection capability of tNGS. The detection of multiple pathogens does not indicate polymicrobial infection because the respiratory tract is not a sterile environment and some microorganisms colonized in the body can reactivate in appropriate conditions. Similarly, a positive result does not always indicate infection, which limits the interpretation of tNGS results. Among the enrolled patients, the most common detectable pathogen was EBV, followed by Cytomegalovirus. EBV is a ubiquitous, oncogenic virus that can cause asymptomatic life-long persistence [ 13 ]. EBV acts as a causative factor in the development of diseases, such as infectious mononucleosis, systemic autoimmune diseases, oral diseases, and nasopharyngeal carcinoma [ 14 , 15 , 16 ]. Human Cytomegalovirus is a ubiquitous herpesvirus that establishes latent infection in most people worldwide but can cause severe disease in immunocompromised individuals [ 17 , 18 ]. Both EBV and Cytomegalovirus are colonizing microorganisms rather than pathogenic microorganisms because they seldom cause pulmonary infection. The conclusive assessment should be supplemented by a complete analysis of the patient’s clinical context, imaging findings, culture results, and other etiological examinations. tNGS shows a higher positive detection rate compared with ordinary bacterial culture. Our findings indicate that tNGS usually detects a higher number of bacterial species in the same sample compared with bacterial culture. This difference can be attributed to the limited resources available in culture dishes, often resulting in dominant bacteria overshadowing poorly growing bacterial colonies and subsequently leading to a lower detection rate of bacterial species in bacterial culture. Of the cases tested, 130 (49.6%) were positive by tNGS but negative by bacterial culture, suggesting that a significant portion of patients may shield potential pathogens that are undetectable by bacterial culture. The sensitivity of tNGS in detecting bacteria is significantly higher than that of bacterial culture, despite the broader spectrum of bacterial culture. However, for the identification of fungi, the positive rate of tNGS was only slightly higher than that of fungal culture. The chitin in the fungal cell wall makes it difficult to release nucleic acid during the extraction process, thus reducing the ability of tNGS to detect fungi. Among the enrolled patients, tNGS identified 21 cases of Mycoplasma pneumoniae infection, with cases primarily occurring in young patients. In addition to bacteria, viruses, fungi, and Mycoplasma pneumoniae , tNGS identified one case of Necator americanus infection. Hookworm lung infection is uncommon and challenging to detect with conventional tests [ 19 ]. Tuberculosis remains the leading cause of death from a single infectious agent worldwide, and rapid molecular tests play a crucial role in diagnosing this disease. Xpert MTB/RIF is an automated rapid assay with high sensitivity and specificity for the detection of Mycobacterium tuberculosis . It has been recommended as the initial test for all patients with signs and symptoms of tuberculosis by the World Health Organization as of 2021[ 20 , 21 ]. In this study, we analyzed the consistency between tNGS and the Xpert MTB/RIF assay, with results showing a 100% positive consistency and a 68.4% negative consistency. In the Xpert MTB/RIF assay negative group, 13 cases were observed positive for Mycobacterium tuberculosis by tNGS. Among them, 76.9% (10/13) of patients were suspected of tuberculosis based on imaging evidence. These findings indicate that tNGS outperformed the Xpert MTB/RIF assay in identifying Mycobacterium tuberculosis . Zheng et al. reported that tNGS exhibited higher sensitivity than Xpert for the diagnosis of tuberculosis in children, but the specificity was lower than that of Xpert [ 22 ]. Due to the lack of a reference standard, we did not evaluate the sensitivity and specificity of tNGS and Xpert in diagnosing tuberculosis in adults. Although cultivation is considered the gold standard for identifying tuberculosis, the cultivation period is extremely long, taking approximately four to eight weeks in a solid culture medium or even two to four weeks in a fast liquid culture medium, which necessitates specific growth conditions [ 23 , 24 ]. Therefore, we did not evaluate the diagnostic significance of tNGS compared with culture for Mycobacterium tuberculosis . Invasive pulmonary aspergillosis is a significant cause of morbidity and mortality in immunocompromised patients, whereas chronic pulmonary aspergillosis often goes undetected, resulting in delayed diagnosis and treatment and increased mortality rates [ 25 , 26 ]. Currently, the GM assay, which provides evidence of Aspergillus antigens, is widely used for defining probable aspergillosis. Therefore, we also investigated the diagnostic efficacy of tNGS compared with the GM test in BALF samples for the detection of Aspergillus infection. The GM test outperformed in BALF samples than in serum samples, with a sensitivity of 68% and a specificity of 84% [ 27 ]. In this study, the sensitivity and specificity of tNGS versus the GM test were 57.1% and 90.6% versus 71.4% and 82.7%, respectively, using fungal culture as the gold standard. Additionally, the sensitivity and specificity of tNGS increased to 86.2% and 98.7%, respectively, whereas the sensitivity of the GM test decreased to 69.0% when clinically diagnosed Aspergillus infection was used as the reference standard. This suggests that tNGS had higher sensitivity and specificity compared with GM assays in detecting Aspergillus infections. Furthermore, the read counts of Aspergillus detected by tNGS and the OD value of the GM test showed no significant correlation in this study. Conclusions To conclude, tNGS exhibits a significantly higher positive detection rate and improved sensitivity and specificity for pathogen identification compared with conventional tests. Moreover, tNGS can detect bacteria, fungi, DNA viruses, RNA viruses, and atypical pathogens in a single experimental process, greatly improving efficiency in pathogen detection. However, it is important to acknowledge the limitations of the tNGS technique, particularly its inability to differentiate between colonization, infection, or contamination. This necessitates a systematic and comprehensive analysis to make a conclusive judgment. Nevertheless, tNGS holds promise as a technique for pathogen identification in BALF samples from adults with pulmonary infections. A considerable portion of clinical treatments has been adjusted or implemented based on tNGS results. Abbreviations BALF Bronchoalveolar lavage fluid CM Conventional methods EBV Epstein-barr virus GM Galactomannan mNGS Metagenomic next-generation sequencing OD Optical density PPV Positive predictive value NPV Negative predictive value tNGS Targeted next-generation sequencing Declarations Acknowledgements The authors thank all the clinical and laboratory stuffs contributed in the article. Author contributions SH and LX conceptualized the study design. Data collection and analysis were performed by ZL, JG, WQ, HW. LZ contributed to statistical analysis and graphing. XW helped with the analyses. SH and DL drafted the manuscript which was critically reviewed and approved by all authors. Funding This work was supported by Scientific and Technological Research Project of Guangxi Zhuang Autonomous Region Health Commission (Z-A20230634). Data availability The original data and materials presented in the study are included in the article and supplementary material, further inquiries will be made available on reasonable request. Ethics approval and consent to participate The studies involving human participants were reviewed and approved by the Second Affiliated Hospital of Guangxi Medical University. The study was performance in accordance with the Declaration of Helsinki and all methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all the patients. 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Systematic review and meta-analysis of galactomannan antigen testing in serum and bronchoalveolar lavage for the diagnosis of chronic pulmonary aspergillosis: defining a cutoff. Eur J Clin Microbiol Infect Dis. 2023;42(9):1047-1054. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Zhou","suffix":""},{"id":290418049,"identity":"e83b76a8-0916-4a45-af3c-137f35dbe951","order_by":8,"name":"Li Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIie3PMQrCQBCF4QkL0WJl2w14iJdKA0GvEgikSiHYWC7kEutFtN2QNpg2YJMjJJ1FClN4gLET3L+aYj6YIfL5fjAQBYNGKpUyfCKwPxXbyDo+CfVlbFKYjEl2qoXu0UmQC8apZJDElogtnnInjIiuN85hvUSuF5IYF4oNi3QtmhkPCZdxiStjo+G+IX1xJo1cRraumL90zf2l58NRqaoeJw4hWuMzBIa1v7QauJs+n8/3r70BejA2uKbadhUAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-04-05 14:17:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4223532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4223532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54998980,"identity":"2dd1c3ee-8083-49ae-86ca-68edaa15490a","added_by":"auto","created_at":"2024-04-19 18:27:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57051,"visible":true,"origin":"","legend":"\u003cp\u003ePathogen distribution detected by tNGS among the 262 patients enrolled\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4223532/v1/704dc3dfa8008fee9d6ab837.png"},{"id":54998983,"identity":"5de88d4e-401d-4945-bdce-cad175f49480","added_by":"auto","created_at":"2024-04-19 18:27:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3422566,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of potential bacteria and fungi in the study cohort detected by microbial culture and tNGS\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4223532/v1/defb6ad67b60da960abb51a4.png"},{"id":55000701,"identity":"adb1bfa0-882c-4079-9380-f0517faf26b2","added_by":"auto","created_at":"2024-04-19 18:35:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353042,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the read counts of \u003cem\u003eAspergillus\u003c/em\u003e detected by tNGS and the OD value of the GM test\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4223532/v1/54a9c48d7c15f08919195a1e.png"},{"id":76263885,"identity":"2e9772fd-b368-423a-b778-98a476cbb9eb","added_by":"auto","created_at":"2025-02-14 06:54:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3795355,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223532/v1/812f70e5-9cda-463c-a7fe-ac8a4b9e3b74.pdf"},{"id":54998981,"identity":"25671c07-35c4-4b35-9e92-7141a7392615","added_by":"auto","created_at":"2024-04-19 18:27:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45421,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4223532/v1/7abf9b46b02e07f04bc7f1cd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of targeted next-generation sequencing to identify pathogens in the bronchoalveolar lavage fluid of adults with pulmonary infections","fulltext":[{"header":"Background","content":"\u003cp\u003eAmong common clinical problems, pulmonary infections exhibit the highest morbidity rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Generally, various pathogens including bacteria, fungi, viruses, mycoplasma, and atypical microbial organisms cause pulmonary infections. In recent years, novel pathogens causing pulmonary infections have emerged, which has resulted in a heavy burden on healthcare systems. Thus, the accurate and timely identification of pathogens causing pulmonary infections is crucial for treatment and prognosis assessment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, conventional methods (CMs) for detecting pathogens responsible for pulmonary infections mainly include smear microscopy, microbial culture, immunological tests, and polymerase chain reaction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, these methods have some disadvantages, such as low sensitivity, high false-positive rates, long culture cycles, and narrow detection ranges, which do not meet the standards of precision medicine. Moreover, numerous pathogens, such as non-tuberculous mycobacteria, \u003cem\u003eChlamydia\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, rare viruses, and parasites, are difficult to detect using CMs. Therefore, it is imperative to use a superior method to identify pathogens in order to guide disease diagnosis and precise treatment.\u003c/p\u003e \u003cp\u003eMetagenomics next-generation sequencing (mNGS) has emerged as a promising method for pathogen detection, which markedly improves the efficiency of etiological diagnosis compared with conventional microbiological tests [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, high costs and difficulties in interpreting mNGS results limit its application in routine clinical examinations. The development of targeted next-generation sequencing (tNGS) in recent years has provided new avenues for pathogen identification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. tNGS is based on high-throughput sequencing and allows for the rapid and accurate detection of multiple pathogens by selectively amplifying specific genes or regions and providing their genomic information. Compared with mNGS, tNGS is not affected by human genes and allows to perform DNA and RNA dual process detection simultaneously. Moreover, the cost of tNGS in detecting pathogens is only 1/5th of that of mNGS. Additionally, tNGS has a specific pathogen detection spectrum. However, its performance for detecting pathogens in the bronchoalveolar lavage fluid (BALF) of adults with pulmonary infections remains unclear. Herein, we aimed to introduce and explore tNGS application to pathogen identification in the BALF of adults with pulmonary infection compared with CMs including bacterial culture, fungal culture, Xpert\u0026reg; MTB/RIF assay, and \u003cem\u003eAspergillus\u003c/em\u003e galactomannan (GM) test. We hope to provide more accurate and comprehensive information for diagnosing and treating pulmonary infections, which can substantially improve clinical practice.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population and sample collection\u003c/h2\u003e\n \u003cp\u003eA total of 262 patients with pulmonary infections who underwent fiberoptic bronchoscopy for bronchoalveolar lavage at the Second Affiliated Hospital of Guangxi Medical University between September 2023 and February 2024 were enrolled. Patients with mental disorders, immunodeficiency, rheumatoid diseases, organ transplantation, and cancers (except for lung cancer) and pregnant women were excluded. Among the 262 enrolled patients, 150 were males and 112 were females, and their ages ranged from 19 to 91 years, with a median age of 60 years. Moreover, 31.7% of the basic pulmonary diseases included chronic obstructive pulmonary disease, bronchial stenosis, bronchiectasis, interstitial lung disease, and lung cancer (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). BALF samples were collected from all patients and subjected to tNGS, culture, Xpert\u0026reg; MTB/RIF assay, and GM test. This study was approved by the Human Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University, and informed consent was obtained from all patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eTNGS assay\u003c/h2\u003e\n \u003cp\u003eThe nucleic acid from the BALF samples was extracted using the TIANamp yeast DNA kit (TIANGEN, Beijing, China), following the manufacturer\u0026rsquo;s instructions. Subsequently, a DNA library for each BALF sample was prepared from the extracted nucleic acids using Pathogeno One Library Kit (BGI Genomics, Shenzhen, China). Briefly, for reverse transcription polymerase chain reaction (RT-PCR), 4 \u0026micro;L of the 5X RT Mix, 10 \u0026micro;L of the nucleic acids, and 6 \u0026micro;L of nuclease-free water were added to PCR tubes. The RT-PCR protocol involved incubation at 25 ℃ for 10 min, 55 ℃ for 15 min, and 85 ℃ for 5 min. Subsequently, the RT-PCR products and nucleic acids isolated from the BALF samples were mixed using the panel mix and 3X enzyme mix to run the first round PCR at an initial denaturation step at 95 ℃ for 3 min, followed by 23 cycles of denaturation at 95 ℃ for 20 s, annealing at 63 ℃ for 2 min, and extension at 72 ℃ for 2 min. The products obtained from the first round of PCR underwent purification and were mixed with 2X BarcodeF, 3X BarcodeR, and 3X enzyme xix for the second round of PCR. The PCR conditions were denaturation at 95 ℃ for 3 min, followed by 12 cycles of denaturation at 95 ℃ for 15 s, annealing at 58 ℃ for 15 s, and extension at 72 ℃ for 1 min. Subsequently, the library was sequenced on the MGISEQ-200 platform. The data were automatically analyzed by mapping them to a reference fast alignment (FASTA) file containing nucleic acid sequences of pathogens in the database.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eCulture\u003c/h2\u003e\n \u003cp\u003eThe BALF samples were cultured by inoculating them onto blood, chocolate, MacConkey, and Sabourauds agar plates. Suspected colonies were identified using the MALDI-Biotyper system (Bruker, Germany).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eXpert\u0026reg; MTB/RIF assay\u003c/h2\u003e\n \u003cp\u003eXpert assay was performed following the manufacturer\u0026rsquo;s instructions. Briefly, 2 mL of the Xpert reagent was mixed with 1 mL of the BALF samples and incubated at room temperature for 10 min. Then, 1 mL of the mixture was transferred to Xpert cartridges and was automatically detected by the Xpert platform.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eGM test\u003c/h2\u003e\n \u003cp\u003eThe Platelia \u003cem\u003eAspergillus\u003c/em\u003e Ag kit (BIO-RAD, USA) was used to detect the \u003cem\u003eAspergillus\u003c/em\u003e GM antigen. Briefly, 50 \u0026micro;L of the conjugate (peroxidase-labeled GM monoclonal antibody) was added to microwell strips. Next, 50 \u0026micro;L of the BALF samples was added to each well, followed by incubation in a dry microplate incubator at 37 ℃ for 90 min. The plate was then five times with a microplate washer using 800 \u0026micro;L of the working washing solution. Next, 200 \u0026micro;L of the chromogen TMB solution was rapidly added, followed by incubation in the dark at room temperature for 30 min. Finally, 100 \u0026micro;L of the stopping solution was added, and optical density (OD) was read at 450 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was performed using SPSS software (version 22.0; SPSS Inc., Chicago, IL, USA). The positive detection rates of the two methods were compared using the chi-square test. An overall agreement was estimated using Cohen\u0026rsquo;s kappa statistics. A correlation analysis was performed using Spearman\u0026rsquo;s rank correlation test. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"533\" height=\"413\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePathogen profile of all patients with pulmonary infections according to tNGS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the 262 patients enrolled, 47 potential pathogens were identified in the BALF samples via tNGS including 21 bacteria, 13 viruses, 11 fungi, 1 parasite, and 1 mycoplasma (Figure 1). The most frequently detected bacteria were \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (56 cases), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (55 cases), and \u003cem\u003eHaemophilus influenzae\u003c/em\u003e (47 cases). The most frequently identified virus was Epstein\u0026ndash;Barr virus (EBV), which was detected in 78 patients. The top three fungi identified by tNGS were \u003cem\u003eCandida albicans\u003c/em\u003e (59 cases), \u003cem\u003eAspergillus fumigatus\u003c/em\u003e (20 cases), and \u003cem\u003eCandida tropicalis\u003c/em\u003e (14 cases).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe overall microbial detection rate for tNGS was 99.2% (260/262), and 21.9% (57/260) of it accounted for single-pathogen infection and 78.1% (203/260)\u003cem\u003e\u0026nbsp;\u003c/em\u003eaccounted for polymicrobial infections. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of tNGS and bacterial culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial detection rates of tNGS and ordinary bacterial culture were 74.0% (194/262) and 28.2% (74/262), respectively, indicating that the detection rate of tNGS was significantly superior to that of bacterial culture (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Among the 262 patients, 64 (24.4%) patients showed positive results on both tNGS and ordinary bacterial culture, whereas 58 (22.1%) patients showed negative results on both methods. Furthermore, 130 (49.6%) patients tested positive solely by tNGS, and 10 (3.8%) patients tested positive exclusively by bacterial culture, indicating that the sensitivity of tNGS was significantly higher than that of bacterial culture. Among the 69 patients showing positive results on tNGS and bacterial culture, 21 patients exhibited complete consistency, 43 patients showed partial consistency, and 4 patients displayed complete inconsistency between the tNGS and bacterial culture results. Besides, seven microorganisms, namely \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e, \u003cem\u003ePseudomonas otitidis\u003c/em\u003e, \u003cem\u003eAbiotrophia defective\u003c/em\u003e, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e, \u003cem\u003eShewanella algae,\u003c/em\u003e and \u003cem\u003eAeromonas caviae\u003c/em\u003e, were outside the detection range of tNGS in the bacterial culture-positive results (Figure 2). The rates of negative, positive, and total consistent and kappa value between tNGS and bacterial culture were 30.8%, 86.4%, 46.4%, and 0.116, respectively (Table 2).\u003c/p\u003e\n\u003cp\u003eThe positive rate of tNGS for fungal identification was slightly higher than that of fungal culture (31.7 % (83/262) and 22.9 % (60/262), respectively). In total, 42 patients showed positive results on both tNGS and fungal culture, whereas 160 patients showed negative results on both. Additionally, 42 patients tested positive solely by tNGS, and 19 patients tested positive solely by fungal culture (Table 2). Among the 42 patients showing positive results on tNGS and fungal culture, 28 patients exhibited complete consistency and 14 patients exhibited partial consistency between the tNGS and bacterial culture results. The rates of positive, negative, and total consistent and kappa value between tNGS and fungal culture were 68.9%, 79.1%, 76.7%, and 0.424, respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" width=\"994\" height=\"404\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsistency between tNGS and Xpert\u0026reg; MTB/RIF assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHerein, 42 patients suspected of tuberculosis infection or with a history of tuberculosis infection were detected by tNGS and Xpert\u0026reg; MTB/RIF assay. The results showed that 23 patients showed positive results for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e on both methods. In the Xpert\u0026reg; MTB/RIF assay negative group, 13 patients tested positive for \u003cem\u003eMycobacterium tuberculosis\u0026nbsp;\u003c/em\u003ewhen\u003cem\u003e\u0026nbsp;\u003c/em\u003edetected by tNGS. The rates of positive, negative, and total consistent and kappa value between tNGS and pert\u0026reg; MTB/RIF assay were 100.0%, 68.4%, 85.7%, and 0.704, respectively (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNGS and GM test in identifying \u003cem\u003eAspergillus\u003c/em\u003e infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 262 patients, 28 patients tested positive for \u003cem\u003eAspergillus\u003c/em\u003e infection by tNGS, including 20 patients infected with \u003cem\u003eAspergillus fumigatus\u003c/em\u003e and 8 with \u003cem\u003eAspergillus flavus\u003c/em\u003e. Among these 28 patients, 60.7% (17/28) showed positive results and 39.3% (11/28) showed negative results when subjected to the GM test. For proven \u003cem\u003eAspergillus\u003c/em\u003e infection fungal culture, tNGS exhibited a sensitivity of 57.1%, a specificity of 90.6%, a positive predictive value (PPV) of 14.3%, and a negative predictive value (NPV) of 98.7%, whereas the GM test showed a sensitivity of 71.4%, a specificity of 82.7%, a PPV of 71.4%, and an NPV of 99.1%. When clinically diagnosed \u003cem\u003eAspergillus\u003c/em\u003e infection was used as a gold standard, the final sensitivity and specificity of tNGS versus the GM test increased to 86.2% and 98.7% versus 69.0% and 87.6%, respectively (Table 3). Spearman\u0026rsquo;s correlation coefficient between the read counts of \u003cem\u003eAspergillus\u003c/em\u003e detected by tNGS and the OD value of the GM test was 0.120 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Logistic regression analysis showed no significant correlation between the quantitative results of the two methods (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"1069\" height=\"310\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVarious testing methods have been used to aid in the diagnosis of pulmonary infectious diseases; however, several cases of pulmonary infection have unidentified etiology. Identifying the etiology to guide targeted therapy remains a formidable challenge. The application of mNGS in pathogen identification has been a significant advancement in the medical field, offering a broad spectrum of pathogen detection and the ability to identify rare and novel pathogens. However, due to the considerably high cost and interpretation challenges associated with mNGS, tNGS has garnered much attention. tNGS is a sequencing technology offering more targeted characteristics at a low cost. Previous studies have reported that there was no significant difference in diagnosing pneumonia between these two technologies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. tNGS is an enrichment sequencing method based on NGS, which includes probe hybridization and PCR amplification. Compared with mNGS, tNGS offers lesser working time due to its standardized workflow. By developing more efficient working steps, detection time can be further reduced while minimizing interpretation errors. For example, preliminary screening of pathogens based on clinical recommendations allows clinicians to focus on target microorganisms rather than performing broad testing for unknown microorganisms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we used tNGS technology to detect 126 pathogens and evaluate their detection performance in BALF samples from adults with pulmonary infection. Compared with CMs, tNGS identified more potential pathogens, including bacteria, viruses, fungi, parasites, and mycoplasma. Many pathogens were detected in most cases revealing the formidable detection capability of tNGS. The detection of multiple pathogens does not indicate polymicrobial infection because the respiratory tract is not a sterile environment and some microorganisms colonized in the body can reactivate in appropriate conditions. Similarly, a positive result does not always indicate infection, which limits the interpretation of tNGS results. Among the enrolled patients, the most common detectable pathogen was EBV, followed by Cytomegalovirus. EBV is a ubiquitous, oncogenic virus that can cause asymptomatic life-long persistence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. EBV acts as a causative factor in the development of diseases, such as infectious mononucleosis, systemic autoimmune diseases, oral diseases, and nasopharyngeal carcinoma [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Human Cytomegalovirus is a ubiquitous herpesvirus that establishes latent infection in most people worldwide but can cause severe disease in immunocompromised individuals [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Both EBV and Cytomegalovirus are colonizing microorganisms rather than pathogenic microorganisms because they seldom cause pulmonary infection. The conclusive assessment should be supplemented by a complete analysis of the patient\u0026rsquo;s clinical context, imaging findings, culture results, and other etiological examinations.\u003c/p\u003e \u003cp\u003etNGS shows a higher positive detection rate compared with ordinary bacterial culture. Our findings indicate that tNGS usually detects a higher number of bacterial species in the same sample compared with bacterial culture. This difference can be attributed to the limited resources available in culture dishes, often resulting in dominant bacteria overshadowing poorly growing bacterial colonies and subsequently leading to a lower detection rate of bacterial species in bacterial culture. Of the cases tested, 130 (49.6%) were positive by tNGS but negative by bacterial culture, suggesting that a significant portion of patients may shield potential pathogens that are undetectable by bacterial culture. The sensitivity of tNGS in detecting bacteria is significantly higher than that of bacterial culture, despite the broader spectrum of bacterial culture. However, for the identification of fungi, the positive rate of tNGS was only slightly higher than that of fungal culture. The chitin in the fungal cell wall makes it difficult to release nucleic acid during the extraction process, thus reducing the ability of tNGS to detect fungi. Among the enrolled patients, tNGS identified 21 cases of \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e infection, with cases primarily occurring in young patients. In addition to bacteria, viruses, fungi, and \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, tNGS identified one case of \u003cem\u003eNecator americanus\u003c/em\u003e infection. Hookworm lung infection is uncommon and challenging to detect with conventional tests [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTuberculosis remains the leading cause of death from a single infectious agent worldwide, and rapid molecular tests play a crucial role in diagnosing this disease. Xpert MTB/RIF is an automated rapid assay with high sensitivity and specificity for the detection of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e. It has been recommended as the initial test for all patients with signs and symptoms of tuberculosis by the World Health Organization as of 2021[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we analyzed the consistency between tNGS and the Xpert MTB/RIF assay, with results showing a 100% positive consistency and a 68.4% negative consistency. In the Xpert MTB/RIF assay negative group, 13 cases were observed positive for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e by tNGS. Among them, 76.9% (10/13) of patients were suspected of tuberculosis based on imaging evidence. These findings indicate that tNGS outperformed the Xpert MTB/RIF assay in identifying \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e. Zheng et al. reported that tNGS exhibited higher sensitivity than Xpert for the diagnosis of tuberculosis in children, but the specificity was lower than that of Xpert [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the lack of a reference standard, we did not evaluate the sensitivity and specificity of tNGS and Xpert in diagnosing tuberculosis in adults. Although cultivation is considered the gold standard for identifying tuberculosis, the cultivation period is extremely long, taking approximately four to eight weeks in a solid culture medium or even two to four weeks in a fast liquid culture medium, which necessitates specific growth conditions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, we did not evaluate the diagnostic significance of tNGS compared with culture for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eInvasive pulmonary aspergillosis is a significant cause of morbidity and mortality in immunocompromised patients, whereas chronic pulmonary aspergillosis often goes undetected, resulting in delayed diagnosis and treatment and increased mortality rates [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Currently, the GM assay, which provides evidence of \u003cem\u003eAspergillus\u003c/em\u003e antigens, is widely used for defining probable aspergillosis. Therefore, we also investigated the diagnostic efficacy of tNGS compared with the GM test in BALF samples for the detection of \u003cem\u003eAspergillus\u003c/em\u003e infection. The GM test outperformed in BALF samples than in serum samples, with a sensitivity of 68% and a specificity of 84% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, the sensitivity and specificity of tNGS versus the GM test were 57.1% and 90.6% versus 71.4% and 82.7%, respectively, using fungal culture as the gold standard. Additionally, the sensitivity and specificity of tNGS increased to 86.2% and 98.7%, respectively, whereas the sensitivity of the GM test decreased to 69.0% when clinically diagnosed \u003cem\u003eAspergillus\u003c/em\u003e infection was used as the reference standard. This suggests that tNGS had higher sensitivity and specificity compared with GM assays in detecting \u003cem\u003eAspergillus\u003c/em\u003e infections. Furthermore, the read counts of \u003cem\u003eAspergillus\u003c/em\u003e detected by tNGS and the OD value of the GM test showed no significant correlation in this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo conclude, tNGS exhibits a significantly higher positive detection rate and improved sensitivity and specificity for pathogen identification compared with conventional tests. Moreover, tNGS can detect bacteria, fungi, DNA viruses, RNA viruses, and atypical pathogens in a single experimental process, greatly improving efficiency in pathogen detection. However, it is important to acknowledge the limitations of the tNGS technique, particularly its inability to differentiate between colonization, infection, or contamination. This necessitates a systematic and comprehensive analysis to make a conclusive judgment. Nevertheless, tNGS holds promise as a technique for pathogen identification in BALF samples from adults with pulmonary infections. A considerable portion of clinical treatments has been adjusted or implemented based on tNGS results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBALF Bronchoalveolar lavage fluid\u003c/p\u003e\n\u003cp\u003eCM Conventional methods \u003c/p\u003e\n\u003cp\u003eEBV Epstein-barr virus \u003c/p\u003e\n\u003cp\u003eGM Galactomannan\u003c/p\u003e\n\u003cp\u003emNGS Metagenomic next-generation sequencing\u003c/p\u003e\n\u003cp\u003eOD Optical density \u003c/p\u003e\n\u003cp\u003ePPV Positive predictive value \u003c/p\u003e\n\u003cp\u003eNPV Negative predictive value\u003c/p\u003e\n\u003cp\u003etNGS Targeted next-generation sequencing \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the clinical and laboratory stuffs contributed in the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH and LX conceptualized the study design. Data collection and analysis were performed by\u003c/p\u003e\n\u003cp\u003eZL, JG, WQ, HW. LZ contributed to statistical analysis and graphing. XW helped with the analyses. SH and DL drafted the manuscript which was critically reviewed and approved by all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Scientific and Technological Research Project of Guangxi Zhuang Autonomous Region Health Commission (Z-A20230634).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data and materials presented in the study are included in the article and supplementary material, further inquiries will be made available on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Second Affiliated Hospital of Guangxi Medical University. The study was performance in accordance with the Declaration of Helsinki and all methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223-1249. \u003c/li\u003e\n\u003cli\u003eCavallazzi R, Ramirez J. Community-acquired pneumonia in chronic obstructive pulmonary disease. Curr Opin Infect Dis. 2020;33(2):173-181.\u003c/li\u003e\n\u003cli\u003eHo DK, Nichols BLB, Edgar KJ, Murgia X, Loretz B, Lehr CM. Challenges and strategies in drug delivery systems for treatment of pulmonary infections. Eur J Pharm Biopharm. 2019; 144:110-124.\u003c/li\u003e\n\u003cli\u003eBrixey AG, Reddy R, Giovanni SP. Nonimaging Diagnostic Tests for Pneumonia. Radiol Clin North Am. 2022;60(3):521-534.\u003c/li\u003e\n\u003cli\u003eRiccobono E, Bussini L, Giannella M, Viale P, Rossolini GM. Rapid diagnostic tests in the management of pneumonia. Expert Rev Mol Diagn. 2022;22(1):49-60.\u003c/li\u003e\n\u003cli\u003eWei Y, Zhang T, Ma Y, Yan J, Zhan J, Zheng J, et al. Clinical Evaluation of Metagenomic Next-Generation Sequencing for the detection of pathogens in BALF in severe community acquired pneumonia, Ital J Pediatr. 2023; 49:25.\u003c/li\u003e\n\u003cli\u003eTsang HF, Yu ACS, Jin N, Yim AKY, Leung WMS, Lam KW, et al. The clinical application of metagenomic next-generation sequencing for detecting pathogens in bronchoalveolar lavage fluid: case reports and literature review, Expert Rev Mol Diagn. 2022; 22:575-582.\u003c/li\u003e\n\u003cli\u003eLu D, Abudouaini M, Kerimu M, Leng Q, Wu H, Aynazar A, et al. Clinical Evaluation of Metagenomic Next-Generation Sequencing and Identification of Risk Factors in Patients with Severe Community-Acquired Pneumonia, Infect Drug Resist. 2023; 16:5135-5147.\u003c/li\u003e\n\u003cli\u003eHuang C, Huang Y, Wang Z, Lin Y, Li Y, Chen Y, et al. Multiplex PCR-based next generation sequencing as a novel, targeted and accurate molecular approach for periprosthetic joint infection diagnosis, Front Microbiol. 2023; 14:1181348.\u003c/li\u003e\n\u003cli\u003eGao D, Hu Y, Jiang X, Pu H, Guo Z, Zhang Y. Applying the pathogen-targeted next-generation sequencing method to pathogen identification in cerebrospinal fluid, Ann Transl Med. 2021; 9:1675.\u003c/li\u003e\n\u003cli\u003eGaston DC, Miller HB, Fissel JA, Jacobs E, Gough E, Wu J, et al. Evaluation of Metagenomic and Targeted Next-Generation Sequencing Workflows for Detection of Respiratory Pathogens from Bronchoalveolar Lavage Fluid Specimens. J Clin Microbiol. 2022;60(7):e0052622.\u003c/li\u003e\n\u003cli\u003eSchlaberg R, Chiu CY, Miller S, Procop GW, Weinstock G. Validation of Metagenomic Next-Generation Sequencing Tests for Universal Pathogen Detection. Arch Pathol Lab Med. 2017;141(6):776-786.\u003c/li\u003e\n\u003cli\u003eDamania B, Kenney SC, Raab-Traub N. Epstein-Barr virus: Biology and clinical disease. Cell. 2022;185(20):3652-3670.\u003c/li\u003e\n\u003cli\u003eTonoyan L, Vincent-Bugnas S, Olivieri CV, Doglio A. New Viral Facets in Oral Diseases: The EBV Paradox. Int J Mol Sci. 2019;20(23):5861.\u003c/li\u003e\n\u003cli\u003eYu H, Robertson ES. Epstein-Barr Virus History and Pathogenesis. Viruses. 2023;15(3):714.\u003c/li\u003e\n\u003cli\u003eHouen G, Trier NH. Epstein-Barr Virus and Systemic Autoimmune Diseases. Front Immunol. 2021;11:587380.\u003c/li\u003e\n\u003cli\u003eSemmes EC, Hurst JH, Walsh KM, Permar SR. Cytomegalovirus as an immunomodulator across the lifespan. Curr Opin Virol. 2020;44:112-120.\u003c/li\u003e\n\u003cli\u003eKrstanović F, Britt WJ, Jonjić S, Brizić I. Cytomegalovirus Infection and Inflammation in Developing Brain. Viruses. 2021;13(6):1078.\u003c/li\u003e\n\u003cli\u003eSharma A, Noon JB, Kontodimas K, Garo LP, Platten J, Quinton LJ, et al. IL-27 Enhances \u0026gamma;\u0026delta; T Cell-Mediated Innate Resistance to Primary Hookworm Infection in the Lungs. J Immunol. 2022;208(8):2008-2018.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO operational handbook on tuberculosis, module 3 diagnosis, rapid diagnostics for tuberculosis detection. 2021.\u003c/li\u003e\n\u003cli\u003eDorman SE, Schumacher SG, Alland D, Nabeta P, Armstrong DT, King B, et al. Xpert MTB/RIF Ultra for detection of Mycobacterium tuberculosis and rifampicin resistance: a prospective multicentre diagnostic accuracy study. Lancet Infect Dis. 2018;18(1):76-84.\u003c/li\u003e\n\u003cli\u003eZheng H, Yang H, Wang Y, Li F, Xiao J, Guo Y, et al. Diagnostic value of tNGS vs Xpert MTB/RIF in childhood TB. Heliyon. 2023;10(1):e23217. \u003c/li\u003e\n\u003cli\u003ePfyffer GE, Wittwer F. Incubation time of mycobacterial cultures: how long is long enough to issue a final negative report to the clinician? J Clin Microbiol. 2012;50(12):4188-9. \u003c/li\u003e\n\u003cli\u003eRitchie SR, Harrison AC, Vaughan RH, Calder L, Morris AJ. New recommendations for duration of respiratory isolation based on time to detect Mycobacterium tuberculosis in liquid culture. Eur Respir J. 2007;30(3):501-7.\u003c/li\u003e\n\u003cli\u003eUllmann AJ, Aguado JM, Arikan-Akdagli S, Denning DW, Groll AH, Lagrou K, et al. Diagnosis and management of Aspergillus diseases: executive summary of the 2017 ESCMID-ECMM-ERS guideline. Clin Microbiol Infect. 2018;24 Suppl 1:e1-e38.\u003c/li\u003e\n\u003cli\u003eZarif A, Thomas A, Vayro A. Chronic Pulmonary Aspergillosis: A Brief Review. Yale J Biol Med. 2021;94(4):673-679.\u003c/li\u003e\n\u003cli\u003e.de Oliveira VF, Silva GD, Taborda M, Levin AS, Magri MMC. Systematic review and meta-analysis of galactomannan antigen testing in serum and bronchoalveolar lavage for the diagnosis of chronic pulmonary aspergillosis: defining a cutoff. Eur J Clin Microbiol Infect Dis. 2023;42(9):1047-1054.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aspergillus galactomannan test, Diagnostics, Metagenomics next-generation sequencing, Pulmonary infection, Targeted next-generation sequencing, Xpert","lastPublishedDoi":"10.21203/rs.3.rs-4223532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4223532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTargeted next-generation sequencing (tNGS) has emerged as an efficient diagnostic method for pathogens identification. herein, we aimed to evaluate its performance in pathogen detection in bronchoalveolar lavage fluid (BALF).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBALF samples were obtained from 262 adult patients with pulmonary infection and were detected by tNGS, microbial culture, Xpert\u0026reg; MTB/RIF assay, and \u003cem\u003eAspergillus\u003c/em\u003e galactomannan (GM) test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn total, 47 potential pathogens were identified in the BALF samples by tNGS, including 21 bacteria, 13 viruses, 11 fungi, 1 parasite, and 1 mycoplasma. The bacterial detection rates of tNGS and ordinary bacterial culture were 74.0% (194/262) and 28.2% (74/262), respectively. The rates of negative, positive, and total consistent and the kappa value between tNGS and bacterial culture were 30.8%, 86.4%, 46.4%, and 0.116, respectively. The positive rate of fungal identification by tNGS was slightly higher than that of fungal culture (31.7% (83/262) and 22.9% (60/262), respectively). The rates of positive, negative, and total consistent and the kappa value between tNGS and fungal culture were 68.9%, 79.1%, 76.7%, and 0.424, respectively. Among the 42 patients with suspected tuberculosis infection, 23 patients showed positive results on both tNGS and Xpert\u0026reg; MTB/RIF assay. The rates of positive, negative, and total consistent and the kappa value between tNGS and pert\u0026reg; MTB/RIF assay were 100.0%, 68.4%, 85.7%, and 0.704, respectively. Finally, the sensitivity and specificity of tNGS versus the GM test were 57.1% and 90.6% versus 71.4% and 82.7%, respectively, when the fungal culture was used as the gold standard for detecting \u003cem\u003eAspergillus\u003c/em\u003e. Additionally, the sensitivity and specificity of tNGS increased to 86.2% and 98.7%, whereas the sensitivity of the GM test decreased to 69.0% when clinically diagnosed \u003cem\u003eAspergillus\u003c/em\u003e infection was used as a reference standard. The read counts of \u003cem\u003eAspergillus\u003c/em\u003e detected by tNGS and the optical density of the GM test were not significantly correlated.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003etNGS is a promising method for detecting pathogens in BALF with a notably higher positive detection rate and a higher sensitivity and/or specificity compared with those of the conventional test.\u003c/p\u003e","manuscriptTitle":"Application of targeted next-generation sequencing to identify pathogens in the bronchoalveolar lavage fluid of adults with pulmonary infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:27:52","doi":"10.21203/rs.3.rs-4223532/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f589633f-4935-4232-9ebb-0c0de694e61b","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-14T06:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:27:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4223532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4223532","identity":"rs-4223532","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}