Metagenomic next-generation sequencing Reveals Respiratory Pathogen distribution in COVID-19

Metagenomic next-generation sequencing Reveals Respiratory Pathogen distribution in COVID-19 | 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 Metagenomic next-generation sequencing Reveals Respiratory Pathogen distribution in COVID-19 Chong Wang, Shuo Yang, Qi Liu, Hongchao Liu, Sisi Ma, Jing Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6482087/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This retrospective study compared metagenomic next-generation sequencing (mNGS) and traditional culture for pathogen detection in 43 patients with lower respiratory tract infections (LRTI), including 34 COVID-19 cases (14 critical, 20 non-critical) and 9 non-COVID controls. mNGS demonstrated superior sensitivity (95.35% vs. 81.08%) and broader pathogen coverage, identifying 36.36% of bacteria and 74.07% of fungi detected by cultures. Concordance between methods was observed in 63% of cases. Severe COVID-19 patients exhibited reduced respiratory microbiota abundance, potentially linked to viral dominance or therapeutic interventions. Clinical outcomes correlated positively with inflammatory markers (PCT, N-proBNP, neutrophils, LDH, NLR) and negatively with lymphocytes, highlighting systemic inflammation’s role in disease progression. While mNGS offers rapid, high-sensitivity pathogen profiling, limitations include small sample sizes, unresolved specificity concerns and unmeasured confounders . The study underscores mNGS as a promising tool for LRTI diagnosis in COVID-19, though larger prospective cohorts and standardized outcome metrics are needed to validate clinical utility, optimize interpretation, and address cost-effectiveness compared to conventional methods. COVID-19，Respiratory flora，Metagenomic next-generation sequencing，Lower respiratory tract infection，Bacteria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The COVID-19 pandemic has resulted in significant health crises and economic setbacks globally. As of March 2023, the World Health Organization has reported 759 million confirmed cases and over 6.9 million deaths worldwide [WHO Coronavirus (COVID-19) dashboard 2023].Increasing numbers of studies have demonstrated that infection with COVID-19 can result in alterations to the phenotype of certain respiratory tract flora among patients with the virus. These alterations are intricately linked to the treatment and prognosis of COVID-19 patients.[ Wu Y et al. 2021 ; Zuo T et al. 2020 ]. Lower respiratory tract infection (LRTI) poses a significant threat to human life and well-being. The etiology of LRTI is diverse, encompassing bacteria, viruses, and fungi as potential primary causes.[GBD 2015 LRI Collaborators ; Zhu YG et al. 2018 ]. The novel coronavirus is an infectious disease caused by COVID-19 infection with respiratory symptoms and mainly transmitted through respiratory droplets.The most common symptoms of COVID-19 patients are fever, dry cough and shortness of breath.In critically ill patients, sepsis is common, causing damage to the heart, brain, lungs, liver, kidney and blood clotting system [Habas K et al.2020;Huang C et al. 2020 ; Rothan HA et al. 2020; Liu X et al. 2020 ]. The respiratory tract is a crucial site for COVID-19 infection. Multiple studies have demonstrated that the invasion and infection of the respiratory tract by COVID-19 leads to an array of clinical manifestations, which subsequently disrupts the delicate balance of the respiratory flora. Microflora, which reside both on the human surface and within the respiratory tract, play a pivotal role in maintaining human health and homeostasis, including facilitating nutrient absorption and participating in the immune response [Gilbert JA et al. 2018 ; Yano JM et al. 2015 ;Manrique P et al. 2016 ]. The alterations in the microflora, particularly pertaining to its abundance and composition, often bear a significant correlation with the fluctuations in a patient's condition. Numerous clinical investigations have demonstrated that the homeostasis of the respiratory tract microflora among COVID-19 patients is disturbed and perturbed to a certain extent. Such alterations in the respiratory tract microflora may elevate the chances of secondary respiratory tract infections caused by pathogens, thereby enhancing the mortality rates among COVID-19 patients[Bruxvoort KJ et al. 2023 ]. At present, although the conventional methods can quickly provide reliable results for the clinic, however, limited by the culture time and the tester's subjective consciousness, the detection rate and detection speed of the detection results have some limitations, which are gradually difficult to meet the clinical needs [P Rajapaksha et al. 2019]. Blood culture is one of the commonly used clinical testing methods, but it takes a long time, so it is difficult to provide timely detection reports for patients. Blood cultures are also susceptible to contamination, and their authenticity requires clinical identification [Lalezari A et al. 2020 ].Positive blood culture results often indicate a life-threatening condition for patients. This underscores the urgency and importance of fast and accurate detection method in clinical treatment, as it can significantly impact patient outcomes[Kirk F et al. 2023]. Serological antibody testing is a commonly used clinical detection method, but its detection has a window period. For patients in the window period, serological testing is difficult to provide timely test results [P Rajapaksha et al. 2019]. The qPCR can design specific primers or probes for pathogens to achieve specific detection of pathogens, but it lacks the ability to detect unknown pathogens and it is difficult to meet the detection requirements of multiple pathogens [Maartens G et al. 2020 ]. For the detection of fungi, although the non-invasive detection represented by G test (the fungus (1–3)-β-D-glucan test) and GM test (the galactomannan antigen detection test) can achieve rapid detection of fungi, both of them have a high false positive rate. Although combined detection can improve the clinical detection rate, it still needs multiple tests to ensure the detection of fungi [ Köhler JR et al. 2017 ]. Biopsy, as the gold standard for fungal diagnosis, serves as an invasive detection method. However, it entails a considerable duration and lacks specificity in terms of pathogen identification.[Guarner J et al. 2011]. In view of this, conventional detection methods for COVID-19 patients are unable to provide timely and precise reflections of their condition from a macro perspective. Consequently, there is an urgent requirement for novel detection techniques that can analyze the alterations in the respiratory flora of these patients, thereby enhancing the clinical management of their condition. As a representative of second-generation sequencing, mNGS boasts a shortened turnaround time and enhanced detection efficiency, enabling the identification of all pathogens present in samples at the macroscopic level [Serpa PH et al. 2022 ].In recent years, mNGS has been widely used in the diagnosis of hematological, respiratory and central nervous system infections. For difficult infections [Michael R Wilson et al. 2014 ], critical infections [Robert P Dickson et al. 2020 ],special infections [Li Liu et al. 2022 ], mNGS has high diagnostic value, which can be widely used in blood samples[Haibing Liu et al.2022], cerebrospinal fluid samples [Liu H et al. 2022 ], sputum samples [Jie Huang et al. 2020 ], bronchoalveolar lavage fluid samples and other sample types, showing a strong clinical applicability [Dandan Zhang er al. 2022]. However, it must be acknowledged that significant hurdles exist in the clinical utilization of mNGS, particularly in the interpretation of its results. Given the presence of colonizing flora in the lower respiratory tract, the process of obtaining sputum samples from patients cannot be entirely standardized. Consequently, the accurate differentiation between pathogenic bacteria and colonizing flora poses a crucial challenge in the implementation of mNGS. Conventional techniques are capable of identifying prevalent pathogenic bacteria alone, yet they lack the capacity to provide a comprehensive overview of alterations in respiratory tract microflora [P Rajapaksha et al. 2019].The emergence and rapid development of metagenome next-generation sequencing(mNGS) provide new insights for the analysis of clinical diagnosis and treatment of LRTI including COVID-19. At present, there are relatively few studies on the changes of respiratory tract flora in patients with COVID-19. In this study, the sputum samples of 43 patients with LRTI collected from the third Hospital of Peking University from December 2022 to March 2023 were detected by routine microbiological test and mNGS.We detected the respiratory tract flora of the patients by mNGS. Through the analysis of the changes and evolution of respiratory tract flora in patients with COVID-19, we explored the correlation between the changes of respiratory tract flora and clinical events in patients with COVID-19. By constructing a random forest model, the effects of clinical indicators, differential pathogens, microbial flora composition and abundance changes on clinical outcomes in patients with COVID-19 were evaluated. When compared to traditional detection methods, the false positive rate associated with mNGS detection of pathogens is notably elevated. This poses a crucial challenge for mNGS and also represents a significant area of focus for its future development [Jie Huang et al. 2020 , Liu H et al 2022 ]. There is a significant lack of research on the utilization of mNGS in both COVID-19 and LRTI. As an emerging technological, mNGS demonstrates substantial benefits in macro-level etiological detection [Han D et al. 2020;Yang A et al. 2022 ].The initial discovery of COVID-19 is the result of applying mNGS [Zhou P et al. 2020 ; Wu F et al.2020; ]. In this comprehensive study, we conducted a concurrent analysis of both mNGS and routine testing on 43 patient samples, aiming to compare the concordance rates of the two methods in detecting pathogens and antibiotic resistance profiles. Utilizing the hostindex, a metric that assesses the human source of pathogens, we were able to identify the true positive pathogens associated with LRTI and further elucidate which viruses tended to co-infect with bacteria. Additionally, we delved into the microbial shifts observed in patients with COVID-19, examining the alterations in α diversity and β diversity across different patient groups. To further evaluate the prognostic significance of these findings, we employed the Receiver Operating Characteristic (ROC) curve analysis, incorporating clinical indicators and respiratory flora variations to predict disease progression and patient outcomes. Methods Patients and sample collection The 43 sputum samples under examination were residual specimens collected from the clinical laboratory of Peking University Third Hospital between November 2022 and March 2023. Prior to testing, these samples were preserved at a temperature of -80°C. All the patients involved exhibited suspected lung infections or abnormalities in chest imaging findings. The clinical data of 43 patients were extracted from the medical records of 43 patients. The diagnosis of LRTI is based on microbiological examination, microscopic examination and X-ray examination. This study has been approved by the Institutional Review Committee of the third Hospital of Peking University. All samples were obtained with the consent of the patient.Standard visible flow chart for joining the group.The entry process can be seen in the Figure 1. Clinical sample collection and DNA extraction Sputum samples were collected from each patient with the consent of themselves or their surrogates. DNA extraction and library preparation on these samples were performed by using an NGS Automatic Library Preparation System. The quality of DNA was assessed using a BioAnalyzer 2100 combined with quantitative PCR to measure the adapters before sequencing. Metagenomic next-generation sequencing Qualified DNA libraries were pooled together and subsequently sequenced on Illumina NextSeq500 system (50 bp single-end; San Diego, CA, United States). To control the quality of each sequencing run, a negative control and a positive control were conducted in parallel. A total of 10 - 20 million reads were generated for each sample. The raw sequenced reads were first processed with quality control to remove short (length < 35 bp), low quality and low complexity reads, as well as those corresponding to adapters. Host sequences were filtered out based on the alignment to the human-specific database in NCBI using Bowtie2 (version 2.3.5.1). The clean reads were thus aligned to a manual-curated microbial database using Kraken2 (version 2.1.2; confidence = 0.5) for quick taxonomic classification. The classified reads of interested microorganisms were further validated through a second alignment to the microbial database using Bowtie2. The classification of candidate reads might also be conducted by BLAST (version 2.9.0) whenever the results of Kraken2 and Bowtie2 were inconsistent. Potential pathogens were selected from the results of previous analysis according to the clinical phenotype，and these data were reviewed by senior clinicians. Statistical analysis Student’s t-test and the chi-square test were used to assess the statistical significance of differences in continuous and categorical data, respectively. Statistical significance was set at P ＜ 0.05 ，SPSS software (version25¢0; IBM Corporation, Armonk, NY, USA) was used for the statistical analysis Role of the funding source The funders did not play any role in the study design, data collection, management, analysis, interpretation, review, approval of the manuscript, or the decision to submit the manuscript for publication. Results Patient characteristics In this study, a total of 43 patients were enrolled and divided into three groups based on the recommendations of clinical experts. These groups were the COVID-19 critical group, consisting of 14 patients, the COVID-19 non-critical group with 20 patients, and the control group containing 9 participants.This demographic characteristics of the patients can be seen in the supplementary file. Compared with the control group, the lymphocyte count and lymphocyte percentage in patients with COVID-19 infection decreased significantly, while the percentage of neutrophils, Lactate dehydrogenase (LDH), and neutrophil to lymphocyte ratio (NLR) increased significantly ( P < 0.05 ). Furthermore, when stratifying patients based on disease severity, the COVID-19 critical group exhibited significantly elevated levels of Procalcitonin (PCT), LDH, neutrophil percentage, neutrophil count, and NLR compared to the non-critical group. Conversely, lymphocyte percentage and lymphocyte count were significantly lower in the non-critical group compared to the critical group ( P < 0.05 ). These findings suggest that the immune response in COVID-19 patients is characterized by a shift towards inflammation and away from adaptive immune responses, which may contribute to the pathogenesis of the disease. Detection performances of the Illumina platforms in In sputum specimens The work flow of our routine clinical microbiology test and mNGS for sputum specimen detection is shown in Figure 1. In general, a total of 26 cases (60.46%) were positive for routine culture, including bacterial and fungal culture and identification. Additionally, 18 cases (41.3%) tested positive for other microbial tests such as mycoplasma , chlamydia detection, mass spectrometry, and pathogen nucleic acid detection. Taken together, all microbiological tests were positive in 38 cases (88.37%). The clinical sensitivity of mNGS was 86.67% compared to conventional microbial culture in sputum samples, while the clinical specificity was 7.69%. When compared to a comprehensive reference standard, the clinical sensitivity of mNGS was 40%, and the clinical specificity was 95.35% (Table 1). Notably, the microbiological tests were conducted on English-speaking patients, ensuring that the data represented a diverse population. Table 1 The performance of mNGS relative to culture,all microbiological testing,and composite reference standard(n=43) mNGS positive mNGS negative Agreement Positive by culture（n=30） 26 4 86.67% Negative by culture（n=13） 12 1 7.69% Positive by all microbiological testing（n=38） 31 7 81.57% Negative by all microbiological testing（n=5） 3 2 40% Positive by composite reference standard (n=43) 41 2 95.35% Detailed information of samples with conflicting results 15 kinds of pathogens were isolated from 43 sputum samples, revealing a total of 19 cases complicated by infection. Among these, 22 samples were infected with fungi, including Aspergillus flavus (1 case), Aspergillus fumigatus (2 cases), Candida tropicalis (2 cases), Candida krusei (1 case), Candida albicans (13 cases), and Candida glabrata (6 cases). Additionally, Mucor circinelloids was also detected in 1 case.Furthermore, microbiological tests identified the presence of nine additional pathogens. These included Acinetobacter baumannii, Pseudomonas aeruginosa, Stenotrophomonas maltophilia and Klebsiella pneumoniae . Additionally, methicillin-resistant Staphylococcus and influenza A virus were detected using PCR, while cytomegalovirus and Legionella pneumophila were identified using ELISA. Lastly, Candida krusei was detected through mass spectrometry.The identification of these pathogens highlights the importance of accurate diagnosis and appropriate antimicrobial therapy in the management of respiratory infections. Timely and targeted treatment is crucial to prevent the development of severe complications and promote patient recovery. In contrast, mNGS successfully cultured 44 pathogens from 43 sputum samples, encompassing a diverse range of microorganisms including 24 bacteria, 14 fungi, and 6 viruses. When compared to clinical results, only two of these pathogens were exclusively identified through microbiological testing: Candida krusei (n=1) and Legionella pneumophila (n=2). Additionally, mNGS detected 31 other pathogens that were not identified by standard microbiological methods. Notably, three samples were positive for the presence of pathogens in the mNGS test but were negative in the microbiological test, highlighting the superior sensitivity of mNGS in detecting a broad range of pathogens (Figure 2a and Figure 2b) .This emphasizes the utility of mNGS in clinical diagnostics, particularly in cases where traditional microbiological methods may fail to detect certain pathogens. A total of 38 samples demonstrated positive results in both mNGS and microbiological testing. These samples were distributed across three groups: the control group with 7 cases, the non-critical group with 18 cases, and the severe group with 13 cases. Among these, 27 samples (representing 71.05% of the total) showed consistent results between microbiological and mNGS detection methods. Specifically, the control group had 5 consistent cases, the non-critical group had 14, and the severe group had 8.The remaining 11 samples exhibited inconsistent positive results between the two testing methods. These discordant results are detailed in Table 2.It is important to note that the mNGS , which provides a comprehensive analysis of microbial communities, was found to be highly concordant with traditional microbiological methods in the majority of cases. However, the presence of discordant results highlights the need for further validation and interpretation of test outcomes, especially in critical cases where accurate diagnosis is crucial. Table 2 Samples whose microbial culture is not consistent with the positive result of mNGS.（n=11） Sample number Results of microbiological culture Results of mNGS Control group 3 Acinetobacter baumannii 、 Pseudomonas aeruginosa 、 Candida glabrata Staphylococcus aureus 、 Candida albicans 、 staphylococcus haemolyticus 、 campylpbacter concisus 、 human gammaherpesvirus 4 Control group 6 Pseudomonas aeruginosa Staphylococcus aureus 、 Candida albicans 、 corynebacterium diphtheriae Non-critical group 6 Legionella pneumophila Klebsiella pneumoniae 、 Escherichia coli 、 Pseudomonas aeruginosa 、 Candida albicans Non-critical group 11 Klebsiella pneumoniae 、 Enterococcus faecium 、 Proteus mirabilis Candida albicans 、 Aspergillus fumigatus 、 Candida glabrata 、 candida intermedia 、 penicillium digitatum 、 human gammaherpesvirus 4 Non-critical group 12 Some kind of gram-negative bacilli Aspergillus fumigatus 、 Candida albicans 、 Candida albicans 、 human gammaherpesvirus 4 Non-critical group 14 Acinetobacter baumannii, Candida tropicalis Escherichia coli, Candida albicans Critical group 3 Acinetobacter baumannii Aspergillus fumigatus 、 Candida smooth 、 Staphylococcus aureus 、 Enterococcus faecium 、 Stenotrophomonas maltophilia Critical group 6 Klebsiella pneumoniae Staphylococcus aureus 、 Enterococcus faecium 、 Mycobacterium paragordonae 、 Pichia kudriavzevii Critical group 7 Yeast-like fungi, Escherichia coli Enterococcus faecalis 、 Enterococcus faecium 、 Stenotrophomonas maltophilia Critical group 11 Candida krusei Enterococcus faecalis, candidate parapsilosis Critical group 13 Candida tropicalis Candida albicans Regarding the 5 samples with negative routine test results, it is noteworthy that among them, 2 samples exhibited consistent negative outcomes in both mNGS and microbiological testing. Specifically, one case belonged to the control group, while the other belonged to the non-critical group.On the other hand, 3 samples demonstrated discordant results. These samples tested negative in microbiological testing but positive in mNGS testing. One case fell into the control group, another into the non-critical group, and the final one into the critical group (Table 3 ). Table 3 Samples whose result of mNGS is not consistent with the negative result of microbial culture（n=3） Sample number Results of microbiological culture Results of mNGS Control group 7 Negative Acinetobacter baumannii 、 Candida albicans Non-critical group 3 Negative Escherichia coli 、 Pseudomonas aeruginosa 、 Candida albicans 、 Aspergillus fumigatus Critical group 4 Negative Acinetobacter baumannii 、 Burkholderia cepacian complex 、 Escherichia coli mNGS reveals the distribution of flora in patients with COVID-19 A total of 44 pathogens were detected through mNGS analysis, encompassing 24 bacterial species, 14 fungal strains, and 6 viral types. There were distinct differences in the distribution of these pathogens between COVID-19-infected patients and a control group of non-COVID-19 patients. Specifically, a total of 11 pathogens were shared between the two groups.Upon further investigation, the COVID-19 patient group exhibited 19 unique pathogens, with 4 identified in the critically ill subgroup and 15 in the non-critically ill subgroup. On the other hand, the non-COVID-19 patient group presented with 9 distinct pathogens. A comprehensive overview of the pathogens detected in each group is presented in Figure 2c and Figure 2d. mNGS reveals changes in respiratory microecological diversity in patients with COVID-19 Upon further analysis, we have discovered that while there was no significant difference in microecological diversity between the COVID-19 patient group (comprising 14 critically ill patients and 20 non-critical patients) and the control group (Figure 3a ,Figure 4a and Figure 4b), significant disparities emerged when we specifically compared the microecological diversity of the COVID-19 critically ill group (n=14) with the non-critically ill group (n=20). Despite some similarities in species composition between the COVID-19 critically ill and non-critical groups, significant differences in ecological diversity were observed between the critically ill group and the control group. Notably, there was no significant difference in microecological diversity between the non-severe group and the control group among COVID-19 patients (Figure 3b,Figure 4c and Figure 4d). In terms of α diversity, the COVID-19 severe group exhibited significantly lower values compared to the control group, indicating a decrease in both microflora abundance and diversity (Figure 4). The relationship between clinical data, differential floras and patient outcome Upon further analysis, we identified a significant difference in the distribution of pathogens among critically ill patients (n = 14) and non-COVID-19 patients (n = 9). Specifically, Enterococcus faecium was predominantly found in the critically ill group, whereas Streptococcus pneumoniae dominated the control group (Figure 5a). The construction of the ROC curve using the random forest model integrated both pathogen presence and clinical indices. The resulting metrics revealed high discriminatory power, with an AUC of 1 in the severe group, indicating perfect discrimination. In the non-severe group, the AUC was 0.933, indicating excellent discrimination. For the control group, the AUC was 0.767, MacroAUC was 0.885, and MicroAUC was 0.905, indicating good to excellent discrimination.(Figure In terms of clinical correlation, Streptococcus pneumoniae, CRP, PCT, N-proBNP, LDH, neutrophil count, neutrophil percentage, and NLR were positively associated with clinical outcomes. Conversely, lymphocyte count and lymphocyte percentage exhibited a negative correlation with clinical outcomes. These findings suggest that certain pathogens and inflammatory markers play a crucial role in determining the prognosis of patients (Figure 5b). Figures 5c and Figure 5d visually depict the disparities in pathogens and clinical correlations between critically ill patients and non-COVID-19 patients, providing valuable insights into the pathogenesis and prognosis of these conditions. In conclusion, our findings highlight the significance of pathogen profiling in predicting clinical outcomes among critically ill patients and non-COVID-19 patients. This information can guide clinicians in developing more targeted therapeutic strategies and enhancing patient care. Discussion In this retrospective study, we delved into the utilization of mNGS in diagnosing sputum samples from COVID-19 patients. Our objective was two-fold: to identify infectious pathogens and to explore the distribution of respiratory pathogens, as well as the microecological shifts in the respiratory flora among COVID-19 patients. In clinical practice, the detection of respiratory and gastrointestinal flora has often been overlooked. However, recent research has highlighted the significance of these microbial shifts in the etiology, progression, and prognosis of numerous diseases [Zhou B et al.2020;Liu S et al. 2023; Tang Q et al. 2021; Mao X et al. 2023; Liu J et al. 2023; Si H et al. 2021].Given the prevalence and impact of COVID-19, understanding the intricacies of its interaction with the respiratory flora is paramount. The application of mNGS provides a more comprehensive understanding of the microbial landscape, enabling more accurate diagnosis and potentially informing targeted therapeutic strategies. In recent studies, sputum, bronchoalveolar lavage fluid, and nasal swabs have been utilized for the diagnosis of COVID-19 infection [Maniruzzaman M et al. 2022]. However, limited research exists on the alterations of the respiratory tract microbiota and the potential distribution patterns of pathogens among COVID-19 patients. Additionally, while mNGS has been extensively employed in the investigation of gastrointestinal infections [Zheng L et al. 2023], bloodstream infections [Jie Huang et al. 2020,Hu B et al. 2023, Jing C et al. 2021], and pulmonary infections [Jie Huang et al. 2020, Haibing Liu et al. 2022;Zhao Z et al. 2023], there is a relative paucity of studies focusing on lower respiratory tract infections primarily caused by COVID-19. In this comprehensive study, we employed mNGS for the first time to elucidate the distribution of respiratory pathogens and the shifts in respiratory tract microbiota among patients with COVID-19. Routine culture, serving as the primary reference during the clinical phase, plays a pivotal role in guiding clinical medication and treatment decisions. However, it's crucial to acknowledge that the precision of this conventional method often hinges on the expertise of the patient's medical institution and the operational proficiency of its personnel. Furthermore, the culture results are typically confined to the identification of one or two dominant pathogens present in the samples, limiting its ability to detect slower-growing or non-dominant pathogens. In contrast, the mNGS offers a rapid and unbiased approach for nucleic acid detection in samples. This advanced sequencing method enables the detection of viruses, bacteria, fungi, and other pathogenic microorganisms without relying on traditional microbial culture media or microtechnology. The strength of mNGS lies in its ability to diagnose complex diseases and identify unknown pathogens. Previous research has highlighted the challenge of mNGS's relatively high false positive rate, attributed to the presence of human information in the samples that is challenging to eliminate[Charles Y Chiu et al. 2019]. Despite this, limited studies have been conducted to assess the concordance between mNGS and conventional detection methods. Understanding the factors that contribute to these false positives and exploring strategies to mitigate them is crucial for enhancing the reliability and accuracy of mNGS in clinical settings. In this comprehensive study, we delved into the concordance between the mNGS analysis of sputum samples and the traditional routine culture methods. Our findings revealed that the level of agreement between mNGS and conventional culture results was somewhat lower than expected, particularly when compared to clinical outcomes. This discrepancy might be attributed to the insufficient microbial reads obtained from the samples.In this study, the Bacteria, fungi, virus detected by mNGS analysis needs to be tested with clinical symptoms, x-ray evidence, traditional microbiological examination, mNGS, serological examination and other tests to determine whether it is a pathogen. The results of our study demonstrated a notably high positive rate of 95.35% for mNGS. In terms of pathogen coverage, mNGS accounted for 36.36% (12/33) of the total bacterial isolates obtained through conventional culture and an impressive 74.07% (20/27) of the fungal isolates. These findings underscore the potential of mNGS in detecting non-dominant pathogenic bacteria that might be overlooked by conventional culture methods. Furthermore, mNGS has the added advantage of identifying rare pathogens and mitigating the interference caused by antibiotics. While there is ample room for improvement in terms of mNGS's concordance with conventional methods, its unique capabilities position it as a promising tool in clinical settings. Its ability to provide a comprehensive analysis of the microbial composition within a sample, including both dominant and non-dominant pathogens, offers invaluable insights for clinicians in treatment decision-making and prognosis assessment [Michael R Wilson et al. 2014]. It is crucial for individuals suffering from fungal infections to achieve prompt diagnosis of fungal pulmonary infection. Recently, there has been a steady rise in the incidence of fungal infections, yet diagnosing early pulmonary fungal infection remains challenging. The current clinical approach to fungal diagnosis primarily relies on the G test and GM test. However, the diagnostic accuracy for early pulmonary infection is not entirely satisfactory, often requiring multiple tests, which can not only exacerbate the patient's discomfort but also delay appropriate treatment. Given its promising performance, mNGS is anticipated to play a pivotal role in the early detection of fungal infections. In a recent study, we observed that mNGS exhibits promising results in the diagnosis of fungal infections, enhancing sensitivity in detecting fungal infections. Notably, there were discrepancies in fungal culture results compared to mNGS findings in 10 cases, as detailed in Table 4. Therefore, it is anticipated that the combined utilization of clinical detection methods such as the G test, GM test, or pathological examination will enhance the detection rate of fungal infections. Table 4 Samples whose MNGS results are not consistent with those of fungi obtained by microbial culture.（n=10） Sample number Results of microbiological culture Results of mNGS Non-critical group 3 Negative Candida albicans 、 Aspergillus fumigatus Non-critical group 4 Negative mucor circinelloides 、 Aspergillus fumigatus 、 Candida albicans Non-critical group 7 Candida albicans Candida albicans 、 Aspergillus fumigatus Non-critical group 11 Negative Candida albicans 、 Aspergillus fumigatus 、 Candida glabrata 、 candida intermedia 、 penicillium digitatum Non-critical group 12 Negative Aspergillus fumigatus 、 Candida albicans 、 Candida albicans Non-critical group 13 Aspergillus flavus 、 Aspergillus fumigatus Aspergillus flavus critical group 3 Negative Aspergillus fumigatus, Candida glabrata critical group 11 Candida krusei Candida teparapsilosis critical group 13 Candida albicans Candida tropicalis Control group 2 yeast-like fungi Candida albicans 、 Candida glabrata 、 Aspergillus Niger It is imperative to emphasize that accurate and timely diagnosis of fungal infections is crucial for effective treatment and patient outcomes. The integration of advanced diagnostic tools like mNGS with traditional methods offers a promising approach to addressing this challenge. Future research should further explore the potential of these combined diagnostic strategies to improve the detection and management of fungal infections. COVID-19 poses a significant threat to human life and health. The pathogenesis of this virus is intricate, often affecting multiple organs and systems, with the respiratory system being the primary target of infection [ Habas K et al. 2020,Peeling RW et al. 2021]. Previous studies have demonstrated that the invasion of COVID-19 into the respiratory system leads to alterations in the abundance and distribution of the respiratory tract microbiota. However, clinical detection methods, such as microbial culture and nucleic acid detection, have limitations in accurately reflecting these changes in patients [Xavier-Santos D et. al 2022]. Recently, the existence of the lower respiratory tract microbiota has been clarified. Advances in genetic analysis have enabled the analysis of the lower respiratory tract microbiome through macrogenomic techniques. These advancements hold the potential to contribute significantly to personalized medicine, offering new biomarkers for diagnosis and treatment [Dong Y et al. 2023]. In this study, it was found that the abundance of respiratory tract microflora in COVID-19 critically ill patients was significantly lower than that in COVID-19 (Non-critically ill patients) and control groups without COVID-19 infection, which may be due to the fact that COVID-19 destroyed the distribution of respiratory tract colonization flora. In this paper, the changes of respiratory tract flora among different groups of COVID-19 were studied by mNGS for the first time, and the characteristics of respiratory tract flora of COVID-19 patients were revealed. At present, many studies have proved that the changes of respiratory tract flora are closely related to the occurrence, development and prognosis of the disease. The change trend of respiratory tract flora in patients with COVID-19 may become a new idea for the diagnosis and treatment of COVID-19 [Goto T et al. 2022]. We included mNGS readings in univariate and multivariate analysis of the prognosis of patients with COVID-19. Our study found that the number of microbial sequences and pathogen detection types were not significantly related to the prognosis of COVID-19. CRP, PCT, NT-proBNP, LDH, neutrophil count, neutrophil percentage, NLR and hospitalization days were positively correlated with clinical outcome. PCT, NT-proBNP, neutrophil percentage, LDH, NLR were positively correlated with clinical outcome, while lymphocyte count and lymphocyte percentage were negatively correlated with clinical outcome. This shows that the results of mNGS represent the existence of some bacterial or fungal infections, but have nothing to do with the prognosis of the disease. The present investigation, while providing valuable insights, must be viewed with certain limitations. Firstly, the impact of antibiotic exposure history on routine cultures cannot be overstated, as it significantly contributes to the increased negative rates observed. Secondly, the small sample size utilized in this study limits its generalizability and the ability to draw definitive conclusions. Therefore, future research endeavors should prioritize larger sample sizes to further elucidate the utility of mNGS in COVID-19 and LRTI. Additionally, this study does not capture the unique applications of mNGS in other infectious diseases, especially in challenging cases and the detection of previously unknown pathogens. Future studies should aim to address these gaps in knowledge. In fact, the reported results of mNGS are oriented towards the suspected pathogenic flora, and the normal microbiota is often rarely presented in the reports of mNGS. In the same type of research [Shi CL et al 2020; Zheng Y et al 2021], this defect is inevitable, which may be an important development direction for mNGS in the future. We hope that mNGS can better present normal microbiota and pathogens through technological progress in the future. To summarize, mNGS exhibits remarkable proficiency in rapidly detecting pathogens within LRTI, surpassing the positive rate of conventional culture methods. Furthermore, among critically ill patients with COVID-19, a significant reduction in the abundance of respiratory tract microflora was observed, along with statistically distinct patterns in the distribution of pathogens. Declarations Acknowledgement We thank all participants for their contribution to the present study. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by the National Natural Science Foundation of China (61771022, 62071011), the Key Clinical Specialty Funding Project of Beijing and the Hospital-Enterprise Joint Funding Project. Author Contributions Chong Wang designed and wrote the article, drew the tables. Shuo Yang, Qi Liu and Hongchao Liu wrote and revised this manuscript. Jing Li and Sisi Ma participated in the design of the article. Liyan Cui revised this manuscript and reviewed the figures and tables. All authors gave final approval for publication. Data availability statement The data that support the findings of this study are available on request from the corresponding author, [Liyan Cui], upon reasonable request. Data can be searched in the NCBI sequence reading archive database, temporary submission ID: SUB15283626 Ethical statements The study has been approved by the Ethics Committee of Peking University Third Hospital (M2017032) Approval for human experiments All subjects gave their informed consent to participate in this study for diagnostic and research purposes before inclusion in this study. References WHO Coronavirus (COVID-19) dashboard Geneva: World Health Organization (https:/ /covid19.who.int/data , accessed 14 April 2023 Wu Y, Cheng X, Jiang G, Tang H, Ming S, Tang L, Lu J, Guo C, Shan H, Huang X (2021) Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization. NPJ Biofilms Microbiomes. ;7(1):61. 10.1038/s41522-021-00232-5 . Erratum in: NPJ Biofilms Microbiomes. 2021;7(1):90. 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PMID: 35882258 Shi CL, Han P, Tang PJ, Chen MM, Ye ZJ, Wu MY, Shen J, Wu HY, Tan ZQ, Yu X, Rao GH, Zhang JP (2020) Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect 81(4):567–574 Epub 2020 Aug 5. PMID: 32768450 Zheng Y, Qiu X, Wang T, Zhang J (2021) The Diagnostic Value of Metagenomic Next-Generation Sequencing in Lower Respiratory Tract Infection. Front Cell Infect Microbiol 11:694756. 10.3389/fcimb.2021.694756 PMID: 34568089; PMCID: PMC8458627 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.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. 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07:06:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1619798,"visible":true,"origin":"","legend":"\u003cp\u003eThe mNGS revealed differences in the flora between the groups.\u003c/p\u003e\n\u003cp\u003e(a) Metagenomic sequencing results:Differences in flora distribution between COVID-19 patients and non-COVID-19 patients\u003c/p\u003e\n\u003cp\u003e(b) Metagenomic sequencing results:Differences in flora distribution between COVID-19 patients and non-COVID-19 patients\u003c/p\u003e\n\u003cp\u003e(c) Comparison of the number of floradetected in COVID-19 patients and non-COVID-19 patients\u003c/p\u003e\n\u003cp\u003e(d) Comparison of thenumber of flora detected in COVID-19 patients and non-COVID-19 patients\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/8bfe468053beb4576fa23129.jpg"},{"id":83327988,"identity":"e8a3e055-0337-4911-87d5-2d82bc3f9c33","added_by":"auto","created_at":"2025-05-23 07:06:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1021452,"visible":true,"origin":"","legend":"\u003cp\u003eThe mNGS revealed differences in the relative abundance of flora betweengroups\u003c/p\u003e\n\u003cp\u003e(a) Relative abundance of pathogens in COVID-19 patients and non-COVID-19 patients Relative abundance of pathogens in critically ill patients,non-critically ill patients and non-COVID-19 patients\u003c/p\u003e\n\u003cp\u003e(b) Relative abundance of pathogens in critically ill patients,non-critically ill patients and non-COVID-19patients\u003c/p\u003e\n\u003cp\u003e(c) The results of each group of pathogens detected by mNGS\u003c/p\u003e","description":"","filename":"Figure03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/2ed986bb930e69a2f2a8db90.jpg"},{"id":83327987,"identity":"8f87f43d-d3b3-45f6-9e70-7ac1197a4f82","added_by":"auto","created_at":"2025-05-23 07:06:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":941141,"visible":true,"origin":"","legend":"\u003cp\u003eThe mNGS showed differences in microecological diversity among groups.\u003c/p\u003e\n\u003cp\u003e(a) Microecological diversity of COVID-19 patients and non-COVID-19 patients.The α-diversity anlysis (Shannon,Simpson,Inverse Simpson,Richness,Pielou and chao1 )\u003c/p\u003e\n\u003cp\u003e(b) Microecological diversity of COVID-19 patients and non-COVID-19patients.\u003c/p\u003e\n\u003cp\u003eprincipal co-ordinates analysis/PCOA\u003c/p\u003e\n\u003cp\u003eNon-metric MultidimensionalScaling/NMDS )\u003c/p\u003e\n\u003cp\u003e(c) Microecological diversity of critically ill patients, non-criticallyill patients and control patients.\u003c/p\u003e\n\u003cp\u003eThe α-diversity anlysis (Shannon,Simpson,InverseSimpson,Richness,Pielou and chao1 ).\u003c/p\u003e\n\u003cp\u003e(d) Microecological diversity of critically illpatients,non-critically ill patients and control patients.\u003c/p\u003e\n\u003cp\u003eprincipal co-ordinatesanalysis/PCOA\u003c/p\u003e\n\u003cp\u003eNon-metric Multidimensional Scaling/NMDS ).\u003c/p\u003e","description":"","filename":"Figure04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/98db14b43b984836230e21d9.jpg"},{"id":83329014,"identity":"a15f9173-3020-4454-a06a-472f934f20c9","added_by":"auto","created_at":"2025-05-23 07:14:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1025611,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between clinical data,differential floras and patient outcome\u003c/p\u003e\n\u003cp\u003e(a) Differential pathogen in critical group (\u003cem\u003eEnterococcus faecium\u003c/em\u003e) and Non-COVID-19 (\u003cem\u003eStaphylococcus pneumoniae\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e(b) Relationship between clinical indexes , differential pathogens and clinical outcome\u003c/p\u003e\n\u003cp\u003e(c) ROC curves of random forest classification models(Pathogen +Clinical indicator).\u003c/p\u003e\n\u003cp\u003e(d) ROC curves of random forest classification models(Pathogen).\u003c/p\u003e","description":"","filename":"Figure05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/08e1e3b73fb15dfa6ee8499b.jpg"},{"id":86605980,"identity":"15f26b47-f812-460b-ab14-7fe40cc693c2","added_by":"auto","created_at":"2025-07-13 15:31:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6832769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/1cd9fd78-3e4d-430f-ba87-1d6e262deb2b.pdf"},{"id":83327986,"identity":"9e06a495-017e-40f4-a485-be7a2160374c","added_by":"auto","created_at":"2025-05-23 07:06:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20304,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6482087/v1/296ad06524c8a407ca080c51.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic next-generation sequencing Reveals Respiratory Pathogen distribution in COVID-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has resulted in significant health crises and economic setbacks globally. As of March 2023, the World Health Organization has reported 759\u0026nbsp;million confirmed cases and over 6.9\u0026nbsp;million deaths worldwide [WHO Coronavirus (COVID-19) dashboard 2023].Increasing numbers of studies have demonstrated that infection with COVID-19 can result in alterations to the phenotype of certain respiratory tract flora among patients with the virus. These alterations are intricately linked to the treatment and prognosis of COVID-19 patients.[ Wu Y et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zuo T et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLower respiratory tract infection (LRTI) poses a significant threat to human life and well-being. The etiology of LRTI is diverse, encompassing bacteria, viruses, and fungi as potential primary causes.[GBD 2015 LRI Collaborators ; Zhu YG et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e]. The novel coronavirus is an infectious disease caused by COVID-19 infection with respiratory symptoms and mainly transmitted through respiratory droplets.The most common symptoms of COVID-19 patients are fever, dry cough and shortness of breath.In critically ill patients, sepsis is common, causing damage to the heart, brain, lungs, liver, kidney and blood clotting system [Habas K et al.2020;Huang C et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rothan HA et al. 2020; Liu X et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe respiratory tract is a crucial site for COVID-19 infection. Multiple studies have demonstrated that the invasion and infection of the respiratory tract by COVID-19 leads to an array of clinical manifestations, which subsequently disrupts the delicate balance of the respiratory flora. Microflora, which reside both on the human surface and within the respiratory tract, play a pivotal role in maintaining human health and homeostasis, including facilitating nutrient absorption and participating in the immune response [Gilbert JA et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yano JM et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e;Manrique P et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e]. The alterations in the microflora, particularly pertaining to its abundance and composition, often bear a significant correlation with the fluctuations in a patient's condition. Numerous clinical investigations have demonstrated that the homeostasis of the respiratory tract microflora among COVID-19 patients is disturbed and perturbed to a certain extent. Such alterations in the respiratory tract microflora may elevate the chances of secondary respiratory tract infections caused by pathogens, thereby enhancing the mortality rates among COVID-19 patients[Bruxvoort KJ et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, although the conventional methods can quickly provide reliable results for the clinic, however, limited by the culture time and the tester's subjective consciousness, the detection rate and detection speed of the detection results have some limitations, which are gradually difficult to meet the clinical needs [P Rajapaksha et al. 2019]. Blood culture is one of the commonly used clinical testing methods, but it takes a long time, so it is difficult to provide timely detection reports for patients. Blood cultures are also susceptible to contamination, and their authenticity requires clinical identification [Lalezari A et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].Positive blood culture results often indicate a life-threatening condition for patients. This underscores the urgency and importance of fast and accurate detection method in clinical treatment, as it can significantly impact patient outcomes[Kirk F et al. 2023].\u003c/p\u003e \u003cp\u003eSerological antibody testing is a commonly used clinical detection method, but its detection has a window period. For patients in the window period, serological testing is difficult to provide timely test results [P Rajapaksha et al. 2019]. The qPCR can design specific primers or probes for pathogens to achieve specific detection of pathogens, but it lacks the ability to detect unknown pathogens and it is difficult to meet the detection requirements of multiple pathogens [Maartens G et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. For the detection of fungi, although the non-invasive detection represented by G test (the fungus (1\u0026ndash;3)-β-D-glucan test) and GM test (the galactomannan antigen detection test) can achieve rapid detection of fungi, both of them have a high false positive rate. Although combined detection can improve the clinical detection rate, it still needs multiple tests to ensure the detection of fungi [ K\u0026ouml;hler JR et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. Biopsy, as the gold standard for fungal diagnosis, serves as an invasive detection method. However, it entails a considerable duration and lacks specificity in terms of pathogen identification.[Guarner J et al. 2011]. In view of this, conventional detection methods for COVID-19 patients are unable to provide timely and precise reflections of their condition from a macro perspective. Consequently, there is an urgent requirement for novel detection techniques that can analyze the alterations in the respiratory flora of these patients, thereby enhancing the clinical management of their condition.\u003c/p\u003e \u003cp\u003eAs a representative of second-generation sequencing, mNGS boasts a shortened turnaround time and enhanced detection efficiency, enabling the identification of all pathogens present in samples at the macroscopic level [Serpa PH et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e].In recent years, mNGS has been widely used in the diagnosis of hematological, respiratory and central nervous system infections. For difficult infections [Michael R Wilson et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], critical infections [Robert P Dickson et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e],special infections [Li Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], mNGS has high diagnostic value, which can be widely used in blood samples[Haibing Liu et al.2022], cerebrospinal fluid samples [Liu H et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], sputum samples [Jie Huang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], bronchoalveolar lavage fluid samples and other sample types, showing a strong clinical applicability [Dandan Zhang er al. 2022]. However, it must be acknowledged that significant hurdles exist in the clinical utilization of mNGS, particularly in the interpretation of its results. Given the presence of colonizing flora in the lower respiratory tract, the process of obtaining sputum samples from patients cannot be entirely standardized. Consequently, the accurate differentiation between pathogenic bacteria and colonizing flora poses a crucial challenge in the implementation of mNGS.\u003c/p\u003e \u003cp\u003eConventional techniques are capable of identifying prevalent pathogenic bacteria alone, yet they lack the capacity to provide a comprehensive overview of alterations in respiratory tract microflora [P Rajapaksha et al. 2019].The emergence and rapid development of metagenome next-generation sequencing(mNGS) provide new insights for the analysis of clinical diagnosis and treatment of LRTI including COVID-19. At present, there are relatively few studies on the changes of respiratory tract flora in patients with COVID-19. In this study, the sputum samples of 43 patients with LRTI collected from the third Hospital of Peking University from December 2022 to March 2023 were detected by routine microbiological test and mNGS.We detected the respiratory tract flora of the patients by mNGS. Through the analysis of the changes and evolution of respiratory tract flora in patients with COVID-19, we explored the correlation between the changes of respiratory tract flora and clinical events in patients with COVID-19. By constructing a random forest model, the effects of clinical indicators, differential pathogens, microbial flora composition and abundance changes on clinical outcomes in patients with COVID-19 were evaluated. When compared to traditional detection methods, the false positive rate associated with mNGS detection of pathogens is notably elevated. This poses a crucial challenge for mNGS and also represents a significant area of focus for its future development [Jie Huang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Liu H et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. There is a significant lack of research on the utilization of mNGS in both COVID-19 and LRTI. As an emerging technological, mNGS demonstrates substantial benefits in macro-level etiological detection [Han D et al. 2020;Yang A et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e].The initial discovery of COVID-19 is the result of applying mNGS [Zhou P et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu F et al.2020; ].\u003c/p\u003e \u003cp\u003eIn this comprehensive study, we conducted a concurrent analysis of both mNGS and routine testing on 43 patient samples, aiming to compare the concordance rates of the two methods in detecting pathogens and antibiotic resistance profiles. Utilizing the hostindex, a metric that assesses the human source of pathogens, we were able to identify the true positive pathogens associated with LRTI and further elucidate which viruses tended to co-infect with bacteria. Additionally, we delved into the microbial shifts observed in patients with COVID-19, examining the alterations in α diversity and β diversity across different patient groups. To further evaluate the prognostic significance of these findings, we employed the Receiver Operating Characteristic (ROC) curve analysis, incorporating clinical indicators and respiratory flora variations to predict disease progression and patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and sample collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026nbsp;43 \u0026nbsp;sputum \u0026nbsp; samples \u0026nbsp;under \u0026nbsp;examination \u0026nbsp; were \u0026nbsp;residual \u0026nbsp;specimens \u0026nbsp; collected \u0026nbsp;from \u0026nbsp;the \u0026nbsp; clinical \u0026nbsp;laboratory \u0026nbsp;of \u0026nbsp; Peking \u0026nbsp;University \u0026nbsp;Third \u0026nbsp; Hospital \u0026nbsp;between \u0026nbsp;November 2022 \u0026nbsp; and \u0026nbsp;March 2023. \u0026nbsp;Prior \u0026nbsp; to \u0026nbsp;testing, \u0026nbsp;these \u0026nbsp; samples \u0026nbsp;were \u0026nbsp;preserved \u0026nbsp; at \u0026nbsp;a \u0026nbsp;temperature \u0026nbsp; of \u0026nbsp;-80\u0026deg;C. \u0026nbsp;All \u0026nbsp;the \u0026nbsp; patients \u0026nbsp;involved \u0026nbsp;exhibited \u0026nbsp; suspected \u0026nbsp;lung \u0026nbsp;infections \u0026nbsp; or \u0026nbsp;abnormalities \u0026nbsp;in \u0026nbsp; chest \u0026nbsp;imaging \u0026nbsp;findings.\u003c/p\u003e\n\u003cp\u003eThe clinical data of 43 patients were extracted from the medical records of 43 patients. The diagnosis of LRTI is based on microbiological examination, microscopic examination and X-ray examination. This study has been approved by the Institutional Review Committee of the third Hospital of Peking University. All samples were obtained with the consent of the patient.Standard visible flow chart for joining the group.The entry process can be seen in the Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical sample collection and DNA extraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSputum samples were collected from each patient with the consent of themselves or their surrogates. DNA extraction and library preparation on these samples were performed by using an NGS Automatic Library Preparation System. The quality of DNA was assessed using a BioAnalyzer 2100 combined with quantitative PCR to measure the adapters before sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomic next-generation sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualified DNA libraries were pooled together and subsequently sequenced on Illumina NextSeq500 system (50 bp single-end; San Diego, CA, United States). To control the quality of each sequencing run, a negative control and a positive control were conducted in parallel. A total of 10 - 20 million reads were generated for each sample. The raw sequenced reads were first processed with quality control to remove short (length \u0026lt; 35 bp), low quality and low complexity reads, as well as those corresponding to adapters. Host sequences were filtered out based on the alignment to the human-specific database in NCBI using Bowtie2 (version 2.3.5.1). The clean reads were thus aligned to a manual-curated microbial database using Kraken2 (version 2.1.2; confidence = 0.5) for quick taxonomic classification. The classified reads of interested microorganisms were further validated through a second alignment to the microbial database using Bowtie2. The classification of candidate reads might also be conducted by BLAST (version 2.9.0) whenever the results of Kraken2 and Bowtie2 were inconsistent. Potential pathogens were selected from the results of previous analysis according to the clinical phenotype，and these data were reviewed by senior clinicians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudent\u0026rsquo;s t-test and the chi-square test were used to assess the statistical significance of differences in continuous and categorical data, respectively. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.05\u003c/em\u003e，SPSS software (version25\u0026cent;0; IBM Corporation, Armonk, NY, USA) was used for the statistical analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the funding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders did not play any role in the study design, data collection, management, analysis, interpretation, review, approval of the manuscript, or the decision to submit the manuscript for publication.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 43 patients were enrolled and divided into three groups based on the recommendations of clinical experts. These groups were the COVID-19 critical group, consisting of 14 patients, the COVID-19 non-critical group with 20 patients, and the control group containing 9 participants.This demographic characteristics of the patients can be seen in the supplementary file. Compared with the control group, the lymphocyte count and lymphocyte percentage in patients with COVID-19 infection decreased significantly, while the percentage of neutrophils, Lactate dehydrogenase (LDH), and neutrophil to lymphocyte ratio (NLR) increased significantly (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e). Furthermore, when stratifying patients based on disease severity, the COVID-19 critical group exhibited significantly elevated levels of Procalcitonin (PCT), LDH, neutrophil percentage, neutrophil count, and NLR compared to the non-critical group. Conversely, lymphocyte percentage and lymphocyte count were significantly lower in the non-critical group compared to the critical group (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e). These findings suggest that the immune response in COVID-19 patients is characterized by a shift towards inflammation and away from adaptive immune responses, which may contribute to the pathogenesis of the disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection performances of the Illumina \u0026nbsp;platforms in In sputum specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work flow of our routine clinical microbiology test and mNGS for sputum specimen detection is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eIn general, a total of 26 cases (60.46%) were positive for routine culture, including bacterial and fungal culture and identification. Additionally, 18 cases (41.3%) tested positive for other microbial tests such as\u003cem\u003e\u0026nbsp;mycoplasma\u003c/em\u003e, \u003cem\u003echlamydia\u0026nbsp;\u003c/em\u003edetection, mass spectrometry, and pathogen nucleic acid detection. Taken together, all microbiological tests were positive in 38 cases (88.37%). The clinical sensitivity of mNGS was 86.67% compared to conventional microbial culture in sputum samples, while the clinical specificity was 7.69%. When compared to a comprehensive reference standard, the clinical sensitivity of mNGS was 40%, and the clinical specificity was 95.35% (Table 1). Notably, the microbiological tests were conducted on English-speaking patients, ensuring that the data represented a diverse population.\u003c/p\u003e\n\u003cp\u003eTable 1 The performance of mNGS relative to culture,all microbiological testing,and composite reference standard(n=43) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;mNGS positive \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003emNGS negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e\u0026nbsp;Agreement \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003ePositive by culture（n=30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e86.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003eNegative by culture（n=13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e7.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003ePositive by all microbiological testing（n=38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e31 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e81.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003eNegative by all microbiological testing（n=5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.4176%;\"\u003e\n \u003cp\u003ePositive by composite reference standard (n=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8037%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.975%;\"\u003e\n \u003cp\u003e95.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDetailed information of samples with conflicting results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e15 kinds of pathogens were isolated from 43 sputum samples, revealing a total of 19 cases complicated by infection. Among these, 22 samples were infected with fungi, including \u003cem\u003eAspergillus flavus\u0026nbsp;\u003c/em\u003e(1 case), \u003cem\u003eAspergillus fumigatus\u003c/em\u003e (2 cases), \u003cem\u003eCandida tropicalis\u0026nbsp;\u003c/em\u003e(2 cases), \u003cem\u003eCandida krusei\u003c/em\u003e (1 case), \u003cem\u003eCandida albicans\u0026nbsp;\u003c/em\u003e(13 cases), and \u003cem\u003eCandida glabrata\u003c/em\u003e (6 cases). Additionally, \u003cem\u003eMucor circinelloids\u003c/em\u003e was also detected in 1 case.Furthermore, microbiological tests identified the presence of nine additional pathogens. These included \u003cem\u003eAcinetobacter baumannii, Pseudomonas aeruginosa, Stenotrophomonas maltophilia\u0026nbsp;\u003c/em\u003eand \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. Additionally, \u003cem\u003emethicillin-resistant Staphylococcus\u003c/em\u003e and \u003cem\u003einfluenza A virus\u003c/em\u003e were detected using PCR, while\u003cem\u003e\u0026nbsp;cytomegalovirus\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Legionella pneumophila\u003c/em\u003e were identified using ELISA. Lastly, \u003cem\u003eCandida krusei\u003c/em\u003e was detected through mass spectrometry.The identification of these pathogens highlights the importance of accurate diagnosis and appropriate antimicrobial therapy in the management of respiratory infections. Timely and targeted treatment is crucial to prevent the development of severe complications and promote patient recovery.\u003c/p\u003e\n\u003cp\u003eIn contrast, mNGS successfully cultured 44 pathogens from 43 sputum samples, encompassing a diverse range of microorganisms including 24 bacteria, 14 fungi, and 6 viruses. When compared to clinical results, only two of these pathogens were exclusively identified through microbiological testing: \u003cem\u003eCandida krusei\u003c/em\u003e (n=1) and \u003cem\u003eLegionella pneumophila\u003c/em\u003e (n=2). Additionally, mNGS detected 31 other pathogens that were not identified by standard microbiological methods. Notably, three samples were positive for the presence of pathogens in the mNGS test but were negative in the microbiological test, highlighting the superior sensitivity of mNGS in detecting a broad range of pathogens (Figure 2a and Figure 2b) .This emphasizes the utility of mNGS in clinical diagnostics, particularly in cases where traditional microbiological methods may fail to detect certain pathogens.\u003c/p\u003e\n\u003cp\u003eA total of 38 samples demonstrated positive results in both mNGS and microbiological testing. These samples were distributed across three groups: the control group with 7 cases, the non-critical group with 18 cases, and the severe group with 13 cases. Among these, 27 samples (representing 71.05% of the total) showed consistent results between microbiological and mNGS detection methods. Specifically, the control group had 5 consistent cases, the non-critical group had 14, and the severe group had 8.The remaining 11 samples exhibited inconsistent positive results between the two testing methods. These discordant results are detailed in Table 2.It is important to note that the mNGS , which provides a comprehensive analysis of microbial communities, was found to be highly concordant with traditional microbiological methods in the majority of cases. However, the presence of discordant results highlights the need for further validation and interpretation of test outcomes, especially in critical cases where accurate diagnosis is crucial.\u003c/p\u003e\n\u003cp\u003eTable 2 Samples whose microbial culture is not consistent with the positive result of mNGS.（n=11）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eSample number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003eResults of microbiological culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003eResults of mNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eControl group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida glabrata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003estaphylococcus haemolyticus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ecampylpbacter concisus\u0026nbsp;\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ehuman gammaherpesvirus 4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eControl group 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;corynebacterium diphtheriae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eNon-critical group 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eLegionella pneumophila\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Escherichia coli\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Pseudomonas aeruginosa\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eNon-critical group 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Candida glabrata\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ecandida intermedia\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;penicillium digitatum\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ehuman gammaherpesvirus 4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eNon-critical group 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eSome kind of gram-negative bacilli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Candida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ehuman gammaherpesvirus 4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eNon-critical group 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii, Candida tropicalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli, Candida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eCritical group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida smooth\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Staphylococcus aureus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Stenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eCritical group 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eMycobacterium paragordonae\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ePichia kudriavzevii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eCritical group 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eYeast-like fungi, Escherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Stenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eCritical group 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida krusei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis, candidate parapsilosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8972%;\"\u003e\n \u003cp\u003eCritical group 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9315%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida tropicalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1713%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRegarding the 5 samples with negative routine test results, it is noteworthy that among them, 2 samples exhibited consistent negative outcomes in both mNGS and microbiological testing. Specifically, one case belonged to the control group, while the other belonged to the non-critical group.On the other hand, 3 samples demonstrated discordant results. These samples tested negative in microbiological testing but positive in mNGS testing. One case fell into the control group, another into the non-critical group, and the final one into the critical group (Table 3 ).\u003c/p\u003e\n\u003cp\u003eTable 3 \u0026nbsp;Samples whose result of mNGS is not consistent with the negative result of microbial culture（n=3）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eSample number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eResults of microbiological culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003eResults of mNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eControl group 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eCritical group 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eBurkholderia cepacian complex\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003emNGS reveals the distribution of flora in patients with COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 44 pathogens were detected through mNGS analysis, encompassing 24 bacterial species, 14 fungal strains, and 6 viral types. There were distinct differences in the distribution of these pathogens between COVID-19-infected patients and a control group of non-COVID-19 patients. Specifically, a total of 11 pathogens were shared between the two groups.Upon further investigation, the COVID-19 patient group exhibited 19 unique pathogens, with 4 identified in the critically ill subgroup and 15 in the non-critically ill subgroup. On the other hand, the non-COVID-19 patient group presented with 9 distinct pathogens. A comprehensive overview of the pathogens detected in each group is presented in Figure 2c and Figure 2d.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emNGS reveals changes in respiratory microecological diversity in patients with COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon further analysis, we have discovered that while there was no significant difference in microecological diversity between the COVID-19 patient group (comprising 14 critically ill patients and 20 non-critical patients) and the control group (Figure 3a ,Figure 4a and Figure 4b), significant disparities emerged when we specifically compared the microecological diversity of the COVID-19 critically ill group (n=14) with the non-critically ill group (n=20). Despite some similarities in species composition between the COVID-19 critically ill and non-critical groups, significant differences in ecological diversity were observed between the critically ill group and the control group. Notably, there was no significant difference in microecological diversity between the non-severe group and the control group among COVID-19 patients (Figure 3b,Figure 4c and Figure 4d).\u003c/p\u003e\n\u003cp\u003eIn terms of \u0026alpha; diversity, the COVID-19 severe group exhibited significantly lower values compared to the control group, indicating a decrease in both microflora abundance and diversity (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relationship between clinical data, differential floras and patient outcome\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUpon further analysis, we identified a significant difference in the distribution of pathogens among critically ill patients (n = 14) and non-COVID-19 patients (n = 9). Specifically, \u003cem\u003eEnterococcus faecium\u003c/em\u003e was predominantly found in the critically ill group, whereas \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e dominated the control group (Figure 5a).\u003c/p\u003e\n\u003cp\u003eThe construction of the ROC curve using the random forest model integrated both pathogen presence and clinical indices. The resulting metrics revealed high discriminatory power, with an AUC of 1 in the severe group, indicating perfect discrimination. In the non-severe group, the AUC was 0.933, indicating excellent discrimination. For the control group, the AUC was 0.767, MacroAUC was 0.885, and MicroAUC was 0.905, indicating good to excellent discrimination.(Figure\u003c/p\u003e\n\u003cp\u003eIn terms of clinical correlation, Streptococcus pneumoniae, CRP, PCT, N-proBNP, LDH, neutrophil count, neutrophil percentage, and NLR were positively associated with clinical outcomes. Conversely, lymphocyte count and lymphocyte percentage exhibited a negative correlation with clinical outcomes. These findings suggest that certain pathogens and inflammatory markers play a crucial role in determining the prognosis of patients (Figure 5b).\u003c/p\u003e\n\u003cp\u003eFigures 5c and Figure 5d visually depict the disparities in pathogens and clinical correlations between critically ill patients and non-COVID-19 patients, providing valuable insights into the pathogenesis and prognosis of these conditions.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings highlight the significance of pathogen profiling in predicting clinical outcomes among critically ill patients and non-COVID-19 patients. This information can guide clinicians in developing more targeted therapeutic strategies and enhancing patient care.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we delved into the utilization of mNGS in diagnosing sputum samples from COVID-19 patients. Our objective was two-fold: to identify infectious pathogens and to explore the distribution of respiratory pathogens, as well as the microecological shifts in the respiratory flora among COVID-19 patients. In clinical practice, the detection of respiratory and gastrointestinal flora has often been overlooked. However, recent research has highlighted the significance of these microbial shifts in the etiology, progression, and prognosis of numerous diseases [Zhou B et al.2020;Liu S et al. 2023; Tang Q et al. 2021; Mao X et al. 2023; Liu J et al. 2023; Si H et al. 2021].Given the prevalence and impact of COVID-19, understanding the intricacies of its interaction with the respiratory flora is paramount. The application of mNGS provides a more comprehensive understanding of the microbial landscape, enabling more accurate diagnosis and potentially informing targeted therapeutic strategies.\u003c/p\u003e\n\u003cp\u003eIn recent studies, sputum, bronchoalveolar lavage fluid, and nasal swabs have been utilized for the diagnosis of COVID-19 infection [Maniruzzaman M et al. 2022]. However, limited research exists on the alterations of the respiratory tract microbiota and the potential distribution patterns of pathogens among COVID-19 patients. Additionally, while mNGS has been extensively employed in the investigation of gastrointestinal infections [Zheng L et al. 2023], bloodstream infections [Jie Huang et al. 2020,Hu B et al. 2023, Jing C et al. 2021], and pulmonary infections [Jie Huang et al. 2020, Haibing Liu et al. 2022;Zhao Z et al. 2023], there is a relative paucity of studies focusing on lower respiratory tract infections primarily caused by COVID-19. In this comprehensive study, we employed mNGS for the first time to elucidate the distribution of respiratory pathogens and the shifts in respiratory tract microbiota among patients with COVID-19.\u003c/p\u003e\n\u003cp\u003eRoutine culture, serving as the primary reference during the clinical phase, plays a pivotal role in guiding clinical medication and treatment decisions. However, it\u0026apos;s crucial to acknowledge that the precision of this conventional method often hinges on the expertise of the patient\u0026apos;s medical institution and the operational proficiency of its personnel. Furthermore, the culture results are typically confined to the identification of one or two dominant pathogens present in the samples, limiting its ability to detect slower-growing or non-dominant pathogens.\u003c/p\u003e\n\u003cp\u003eIn contrast, the mNGS offers a rapid and unbiased approach for nucleic acid detection in samples. This advanced sequencing method enables the detection of viruses, bacteria, fungi, and other pathogenic microorganisms without relying on traditional microbial culture media or microtechnology. The strength of mNGS lies in its ability to diagnose complex diseases and identify unknown pathogens.\u003c/p\u003e\n\u003cp\u003ePrevious research has highlighted the challenge of mNGS\u0026apos;s relatively high false positive rate, attributed to the presence of human information in the samples that is challenging to eliminate[Charles Y Chiu et al. 2019]. Despite this, limited studies have been conducted to assess the concordance between mNGS and conventional detection methods. Understanding the factors that contribute to these false positives and exploring strategies to mitigate them is crucial for enhancing the reliability and accuracy of mNGS in clinical settings.\u003c/p\u003e\n\u003cp\u003eIn this comprehensive study, we delved into the concordance between the mNGS analysis of sputum samples and the traditional routine culture methods. Our findings revealed that the level of agreement between mNGS and conventional culture results was somewhat lower than expected, particularly when compared to clinical outcomes. This discrepancy might be attributed to the insufficient microbial reads obtained from the samples.In this study, the Bacteria, fungi, virus detected by mNGS analysis needs to be tested with clinical symptoms, x-ray evidence, traditional microbiological examination, mNGS, serological examination and other tests to determine whether it is a pathogen.\u003c/p\u003e\n\u003cp\u003eThe results of our study demonstrated a notably high positive rate of 95.35% for mNGS. In terms of pathogen coverage, mNGS accounted for 36.36% (12/33) of the total bacterial isolates obtained through conventional culture and an impressive 74.07% (20/27) of the fungal isolates. These findings underscore the potential of mNGS in detecting non-dominant pathogenic bacteria that might be overlooked by conventional culture methods. Furthermore, mNGS has the added advantage of identifying rare pathogens and mitigating the interference caused by antibiotics.\u003c/p\u003e\n\u003cp\u003eWhile there is ample room for improvement in terms of mNGS\u0026apos;s concordance with conventional methods, its unique capabilities position it as a promising tool in clinical settings. Its ability to provide a comprehensive analysis of the microbial composition within a sample, including both dominant and non-dominant pathogens, offers invaluable insights for clinicians in treatment decision-making and prognosis assessment [Michael R Wilson et al. 2014].\u003c/p\u003e\n\u003cp\u003eIt is crucial for individuals suffering from fungal infections to achieve prompt diagnosis of fungal pulmonary infection. Recently, there has been a steady rise in the incidence of fungal infections, yet diagnosing early pulmonary fungal infection remains challenging. The current clinical approach to fungal diagnosis primarily relies on the G test and GM test. However, the diagnostic accuracy for early pulmonary infection is not entirely satisfactory, often requiring multiple tests, which can not only exacerbate the patient\u0026apos;s discomfort but also delay appropriate treatment. Given its promising performance, mNGS is anticipated to play a pivotal role in the early detection of fungal infections.\u003c/p\u003e\n\u003cp\u003eIn a recent study, we observed that mNGS exhibits promising results in the diagnosis of fungal infections, enhancing sensitivity in detecting fungal infections. Notably, there were discrepancies in fungal culture results compared to mNGS findings in 10 cases, as detailed in Table 4. Therefore, it is anticipated that the combined utilization of clinical detection methods such as the G test, GM test, or pathological examination will enhance the detection rate of fungal infections.\u003c/p\u003e\n\u003cp\u003eTable 4 Samples whose MNGS results are not consistent with those of fungi obtained by microbial culture.（n=10）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eSample number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eResults of microbiological culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003eResults of mNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Aspergillus fumigatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003emucor circinelloides\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida glabrata\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003ecandida intermedia\u0026nbsp;\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003epenicillium digitatum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eNon-critical group 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus flavus\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus flavus\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003ecritical group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus fumigatus, Candida glabrata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003ecritical group 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida krusei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida teparapsilosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003ecritical group 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida tropicalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.3935%;\"\u003e\n \u003cp\u003eControl group 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1928%;\"\u003e\n \u003cp\u003e\u003cem\u003eyeast-like fungi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38.4137%;\"\u003e\n \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eCandida glabrata\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003e\u0026nbsp;Aspergillus Niger\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIt is imperative to emphasize that accurate and timely diagnosis of fungal infections is crucial for effective treatment and patient outcomes. The integration of advanced diagnostic tools like mNGS with traditional methods offers a promising approach to addressing this challenge. Future research should further explore the potential of these combined diagnostic strategies to improve the detection and management of fungal infections.\u003c/p\u003e\n\u003cp\u003eCOVID-19 poses a significant threat to human life and health. The pathogenesis of this virus is intricate, often affecting multiple organs and systems, with the respiratory system being the primary target of infection [ Habas K et al. 2020,Peeling RW et al. 2021]. Previous studies have demonstrated that the invasion of COVID-19 into the respiratory system leads to alterations in the abundance and distribution of the respiratory tract microbiota. However, clinical detection methods, such as microbial culture and nucleic acid detection, have limitations in accurately reflecting these changes in patients [Xavier-Santos D et. al 2022].\u003c/p\u003e\n\u003cp\u003eRecently, the existence of the lower respiratory tract microbiota has been clarified. Advances in genetic analysis have enabled the analysis of the lower respiratory tract microbiome through macrogenomic techniques. These advancements hold the potential to contribute significantly to personalized medicine, offering new biomarkers for diagnosis and treatment [Dong Y et al. 2023].\u003c/p\u003e\n\u003cp\u003eIn this study, it was found that the abundance of respiratory tract microflora in COVID-19 critically ill patients was significantly lower than that in COVID-19 (Non-critically ill patients) and control groups without COVID-19 infection, which may be due to the fact that COVID-19 destroyed the distribution of respiratory tract colonization flora. In this paper, the changes of respiratory tract flora among different groups of COVID-19 were studied by mNGS for the first time, and the characteristics of respiratory tract flora of COVID-19 patients were revealed. At present, many studies have proved that the changes of respiratory tract flora are closely related to the occurrence, development and prognosis of the disease. The change trend of respiratory tract flora in patients with COVID-19 may become a new idea for the diagnosis and treatment of COVID-19 [Goto T et al. 2022].\u003c/p\u003e\n\u003cp\u003eWe included mNGS readings in univariate and multivariate analysis of the prognosis of patients with COVID-19. Our study found that the number of microbial sequences and pathogen detection types were not significantly related to the prognosis of COVID-19. CRP, PCT, NT-proBNP, LDH, neutrophil count, neutrophil percentage, NLR and hospitalization days were positively correlated with clinical outcome. PCT, NT-proBNP, neutrophil percentage, LDH, NLR were positively correlated with clinical outcome, while lymphocyte count and lymphocyte percentage were negatively correlated with clinical outcome. This shows that the results of mNGS represent the existence of some bacterial or fungal infections, but have nothing to do with the prognosis of the disease.\u003c/p\u003e\n\u003cp\u003eThe present investigation, while providing valuable insights, must be viewed with certain limitations. Firstly, the impact of antibiotic exposure history on routine cultures cannot be overstated, as it significantly contributes to the increased negative rates observed. Secondly, the small sample size utilized in this study limits its generalizability and the ability to draw definitive conclusions. Therefore, future research endeavors should prioritize larger sample sizes to further elucidate the utility of mNGS in COVID-19 and LRTI. Additionally, this study does not capture the unique applications of mNGS in other infectious diseases, especially in challenging cases and the detection of previously unknown pathogens. Future studies should aim to address these gaps in knowledge. In fact, the reported results of mNGS are oriented towards the suspected pathogenic flora, and the normal microbiota is often rarely presented in the reports of mNGS. In the same type of research [Shi CL et al 2020; Zheng Y et al 2021], this defect is inevitable, which may be an important development direction for mNGS in the future. We hope that mNGS can better present \u0026nbsp;normal microbiota and pathogens through technological progress in the future.\u003c/p\u003e\n\u003cp\u003eTo summarize, mNGS exhibits remarkable proficiency in rapidly detecting pathogens within LRTI, surpassing the positive rate of conventional culture methods. Furthermore, among critically ill patients with COVID-19, a significant reduction in the abundance of respiratory tract microflora was observed, along with statistically distinct patterns in the distribution of pathogens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants for their contribution to the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (61771022, 62071011), the Key Clinical Specialty Funding Project of Beijing and the Hospital-Enterprise Joint Funding Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChong Wang designed and wrote the article, drew the tables. Shuo Yang, Qi Liu and Hongchao Liu wrote and revised this manuscript. Jing Li and Sisi Ma participated in the design of the article. Liyan Cui revised this manuscript and reviewed the figures and tables. All authors gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, [Liyan Cui], upon reasonable request. Data can be searched in the NCBI sequence reading archive database, temporary submission ID: SUB15283626\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the Ethics Committee of Peking University Third Hospital (M2017032)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval for human experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects gave their informed consent to participate in this study for diagnostic and research purposes before inclusion in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e \u003cli\u003e\u003cspan\u003eWHO Coronavirus (COVID-19) dashboard Geneva: World Health Organization (https:/ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/covid19.who.int/data\u003c/span\u003e\u003cspan address=\"http:///covid19.who.int/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed 14 April 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Cheng X, Jiang G, Tang H, Ming S, Tang L, Lu J, Guo C, Shan H, Huang X (2021) Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization. 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Front Cell Infect Microbiol 11:694756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2021.694756\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2021.694756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 34568089; PMCID: PMC8458627\u003c/span\u003e\u003c/li\u003e\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":"COVID-19，Respiratory flora，Metagenomic next-generation sequencing，Lower respiratory tract infection，Bacteria","lastPublishedDoi":"10.21203/rs.3.rs-6482087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6482087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis retrospective study compared metagenomic next-generation sequencing (mNGS) and traditional culture for pathogen detection in 43 patients with lower respiratory tract infections (LRTI), including 34 COVID-19 cases (14 critical, 20 non-critical) and 9 non-COVID controls. mNGS demonstrated superior sensitivity (95.35% vs. 81.08%) and broader pathogen coverage, identifying 36.36% of bacteria and 74.07% of fungi detected by cultures. Concordance between methods was observed in 63% of cases. Severe COVID-19 patients exhibited reduced respiratory microbiota abundance, potentially linked to viral dominance or therapeutic interventions. Clinical outcomes correlated positively with inflammatory markers (PCT, N-proBNP, neutrophils, LDH, NLR) and negatively with lymphocytes, highlighting systemic inflammation’s role in disease progression. While mNGS offers rapid, high-sensitivity pathogen profiling, limitations include small sample sizes, unresolved specificity concerns and unmeasured confounders . The study underscores mNGS as a promising tool for LRTI diagnosis in COVID-19, though larger prospective cohorts and standardized outcome metrics are needed to validate clinical utility, optimize interpretation, and address cost-effectiveness compared to conventional methods.\u003c/p\u003e","manuscriptTitle":"Metagenomic next-generation sequencing Reveals Respiratory Pathogen distribution in COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 07:05:57","doi":"10.21203/rs.3.rs-6482087/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":"8b988fd5-8afd-49dd-88af-ac14fbef3b13","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-13T15:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 07:05:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6482087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6482087","identity":"rs-6482087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}