A Microbial World: Could Metagenomic Next-Generation Sequencing Be Involved in Acute Respiratory Failure?

Background The usefulness of metagenomic next-generation sequencing (mNGS) in identifying pathogens is being investigated. We aimed to compare the power of microbial identification between mNGS and various methods in patients with acute respiratory failure. Methods We reviewed 130 patients with respiratory failure, and 184 specimens including blood, bronchoalveolar lavage fluid (BALF), sputum, pleural effusion, ascitic fluid, and urine were tested by mNGS and conventional methods (culture, PCR). We also enrolled 13 patients to evaluate the power of mNGS and pathogen targets NGS (ptNGS) in microbial identifications. Clinical features and microbes detected were analyzed. Results mNGS outperformed the conventional method in the positive detection rate of Mycobacterium tuberculosis (MTB) (OR, ∞; 95% CI, 1–∞; P < 0.05), bacteria (OR, 3.7; 95% CI, 2.4–5.8; P < 0.0001), fungi (OR, 4.37; 95% CI, 2.7–7.2; P < 0.0001), mycoplasma (OR, 10.5; 95% CI, 31.8–115; P = 0.005), and virus (OR, ∞; 95% CI, 180.7–∞; P < 0.0001). We showed that 20 patients (28 samples) were detected with Pneumocystis jirovecii (P. jirovecii) by mNGS, but not by the conventional method, and most of those patients were immunocompromised. Read numbers of Klebsiella pneumoniae (K. pneumoniae), Acinetobacter baumannii (A. baumannii), Pseudomonas aeruginosa (P. aeruginosa), P. jirovecii, cytomegalovirus (CMV), and Herpes simplex virus 1 (HSV1) in BALF were higher than those in other sample types, and the read number of Candida albicans (C. albicans) in blood was higher than that in BALF. We found that orotracheal intubation and type 2 diabetes mellitus (T2DM) were associated with a higher detection rate of bacteria and virus by mNGS, immunosuppression was associated with a higher detection rate of fungi and virus by mNGS, and inflammatory markers were associated with mNGS-positive detection rate of bacteria. In addition, we observed preliminary results of ptNGS. Conclusion mNGS outperformed the conventional method in the detection of MTB, bacteria, fungi, mycoplasma, and virus. Orotracheal intubation, T2DM, immunosuppression, and inflammatory markers were associated with a higher detection rate of bacteria, fungi, and virus by mNGS. In addition, ptNGS results were consistent with the detection of abundant bacteria, fungi, and mycoplasma in our specimens.

Background: The usefulness of metagenomic next-generation sequencing (mNGS) in identifying pathogens is being investigated. We aimed to compare the power of microbial identification between mNGS and various methods in patients with acute respiratory failure.

Methods:
We reviewed 130 patients with respiratory failure, and 184 specimens including blood, bronchoalveolar lavage fluid (BALF), sputum, pleural effusion, ascitic fluid, and urine were tested by mNGS and conventional methods (culture, PCR). We also enrolled 13 patients to evaluate the power of mNGS and pathogen targets NGS (ptNGS) in microbial identifications. Clinical features and microbes detected were analyzed.
Results: mNGS outperformed the conventional method in the positive detection rate of Mycobacterium tuberculosis (MTB) (OR, ∞; 95% CI, 1-∞; P < 0.05), bacteria (OR, 3.7; 95% CI, 2.4-5.8; P < 0.0001), fungi (OR, 4.37; 95% CI, 2.7-7.2; P < 0.0001), mycoplasma (OR, 10.5; 95% CI, 31.8-115; P = 0.005), and virus (OR, ∞; 95% CI, 180.7-∞; P < 0.0001). We showed that 20 patients (28 samples) were detected with Pneumocystis jirovecii (P. jirovecii) by mNGS, but not by the conventional method, and most of those patients were immunocompromised. Read numbers of Klebsiella pneumoniae (K. pneumoniae), Acinetobacter baumannii (A. baumannii), Pseudomonas aeruginosa (P. aeruginosa), P. jirovecii, cytomegalovirus (CMV), and Herpes simplex virus 1 (HSV1) in BALF were higher than those in other sample types, and the read number of Candida albicans (C. albicans) in blood was higher than that in BALF. We found that orotracheal intubation and type 2 diabetes mellitus (T2DM) were associated with a higher detection rate of bacteria and virus by mNGS, immunosuppression was associated with a higher detection rate of fungi and virus by mNGS, and inflammatory markers were INTRODUCTION Acute respiratory failure (ARF) is a common cause of admission to an intensive care unit (ICU). Given that the management of ARF varies according to the etiology, a clear understanding of underlying pathophysiology is indispensable for managing these patients properly. The causes of ARF can be categorized into lung parenchymal disease, airway obstruction, and neuromuscular dysfunction (Friedman and Nitu, 2018). In the ICU, impaired gas exchange following pulmonary infection has been found to be the most common cause of ARF (Singh Lamba et al., 2016). Patients with ARF tend to experience infections caused by several organisms at the same time . Therefore, fast and accurate identification of multiple pathogens remains invaluable to successfully manage these patients.
Metagenomic next-generation sequencing (mNGS) is an advanced and attractive method that allows for the simultaneous detection of bacteria, fungi, viruses, and parasites at multiple sites, including the central nervous system, bloodstream, respiratory, gastrointestinal, prosthetic joint, urinary tract, and ocular (Zhou et al., 2016;Burnham et al., 2018;Ivy et al., 2018;Li et al., 2018b;Simner et al., 2018;Thoendel et al., 2018;Wilson et al., 2018). mNGS holds great promise to be able to transition from being a research tool to being a diagnostic method in clinical microbiology laboratories owing to its quick turnaround time. Previous research also reported that mNGS holds a significantly higher clinical sensitivity and specificity than standard diagnostics. For instance, the ability of mNGS to identify pathogens within 24-48 h is a great advantage over the culture method. Li et al. demonstrated that mNGS showed high sensitivity and specificity for bacterial detection (100.0%, 95% CI: 31.0%-100.0%; 76.5%, 95% CI: 49.8%-92.2%) and fungal detection (57.1%, 95% CI: 20.2%-88.2%; 61.5%, 95% CI: 32.2%-84.9%), when compared with the culture method (Li et al., 2018a). Even though the mNGS method has been used for microbial identification in infectious diseases and severe pneumonia, there have been no studies reporting the applications of mNGS in ARF. Herein, we conducted a single-center study involving hospitalized patients with acute respiratory failure in the ICU to evaluate the clinical performance of the mNGS assay in pathogen detection and compare the power of microbial identi!cation between mNGS and various methods.

Study Patients and Sample Collection
A total of 130 patients (184 specimens) collected from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between February 2019 and December 2020 were investigated. Specimens were subjected to regular clinical microbiological assays and mNGS testing in parallel. The study was exempted from ethical review as it was a retrospective study and patient data were anonymized.
The inclusion criteria were in accordance with previous studies, that is a PaO 2 <60 mmHg in room air. Baseline data including demographic characteristics, comorbidities, immunosuppressive state, treatment process, routine blood, and inflammatory markers were recorded. Different specimens, such as sputum, bronchoalveolar lavage fluid (BALF), blood, pleural effusion, ascitic fluid, and urine, were collected.

Microbial identifications of Conventional Methods
1) Bacteria, fungi, and mycobacteria were identified using the culture method as follows. Bacteria: specimens were inoculated into media containing blood agar, brain heart infusion broth, chocolate agar, and thioglycollate. The colonies were used to identify bacterial species using the VITEK 2 compact automated system. Fungi: specimens were inoculated into media containing Sabouraud agar and potato glucose agar media. The fungi were identified according to characteristics of the colonies and microscopic characteristics of hyphae and spores. Mycobacteria: sputum was digested with 4% NaOH digestive juice, and fluid specimens were collected after centrifugation for mycobacterium testing. In a 37°C incubator, 0.5 ml of resuspended sputum pellet and 1 ml fluid were inoculated in MGIT. MGIT was put into BACTEC 960 system (BD Microbiology Systems), which could detect automatically. If a positive result appeared, direct acid-fast staining of the smear was done to confirm the positive samples of the mycobacteria. 2) Viruses, Mycoplasma, and Legionella pneumophila were confirmed using the PCR assay as previously described. Briefly, nucleic acids of Mycoplasma and L. pneumophila were extracted from each specimen using commercial assays (Qiagen, Germany). They were eluted in distilled water and stored at −20°C for future use. PCR reaction was performed using Applied Biosystems 7500 Real-Time PCR system (Applied Biosystems, USA). Distilled water was used as negative control.

mNGS Sequencing and Bioinformatic Analysis
For mNGS sequencing, about 300 ml plasma was separated from 3 ml of blood, and DNA was extracted using the TIANamp Micro DNA Kit. With regard to other specimens (BALF, sputum, pleural effusion, ascitic fluid, and urine), 600 ml specimen, enzyme, and 1 g 0.5 mm glass bead were mixed and agitated vigorously at 2,800-3,200 rpm for 30 min, then 300 ml specimens were used for DNA extraction using the TIANamp Micro DNA Kit. DNA was fragmented and constructed by an end-repair, adapter-ligation, and PCR amplification. Quality control of the DNA libraries was performed by Agilent 2100, and then quality qualified libraries were sequenced by MGISEQ-2000 platform (Jeon et al., 2014). Next, raw sequencing data were exposed to quality control, including the removal of low-quality reads (Q score cutoff, 20), elimination of human host sequences through mapping to the human reference genome (containing hg19) using Burrows-Wheeler Alignment (Li and Durbin, 2009). The remaining data were aligned to the curated non-redundant Microbial Genome Databases (bacteria, fungi, viruses, parasites). The mapped reads were annotated and the number of species-specific sequences of the specimens were counted.
All of the taxa in the classification reference databases were downloaded from NCBI RefSeq genome database (ftp://ftp.ncbi. nlm.nih.gov/genomes/).

Pathogen Targets NGS
The multiplex PCR-based targeted gene sequencing technology was performed to identify targeted pathogens, and all of the relevant pathogens explored in sequencing are listed in Supplementary Table S5. The workflow was conducted as previously described. Briefly, nucleic acids were extracted according to the instructions of the manufacturer (ZymoBIOMICS ™ DNA/RNA Miniprep Kit, Zymo, R2002). Then, a PCR reaction system (including enzyme mix, DNA, RNA, and nuclease-free water) was performed as follows: denaturation at 95°C (3 min); 25 cycles of denaturation at 95°C (20 s), annealing for 20 s, and extension at 60°C for 4.5 min; final extension at 72°C (5 min). The PCR reaction products were purified after amplification. After sequencing adapter assembly, qualified DNA library was sequenced. The reads were filtered, <60 bp reads or non-specific primer binding was removed, and finally, the clean reads were used for sequencing analysis.

Statistical Analyses
Categorical variables were expressed as percentages, and continuous variables were expressed as mean ± SD if normally distributed. Comparative analysis was conducted by Pearson's test, Fisher's exact test, or the McNemar test where appropriate. Data analysis was performed with SPSS 22.0 and Prism. P < 0.05 was considered significant.

Pathogens Identified by mNGS
Subsequently, we compared the total read numbers of detected microbes in different specimen types. The results show that the sequences of K. pneumoniae in BALF (n = 9) were higher than those in blood (n = 3) and sputum (n = 8). The average sequences of A. baumannii in BALF (n = 10) exceeded the read numbers in blood (n = 3) and sputum (n = 5). We also noted that the identified sequences of P. aeruginosa in BALF outnumbered those in blood and sputum. However, they did not reach statistical difference ( Figure 1B). Among the fungal sequences, the sequences of P. jirovecii in BALF (n = 8) were higher than those in blood (n = 13) and sputum (n = 7). The total reads of C. albicans in blood were significantly higher than those in BALF ( Figure 1C). Besides, we found that, compared with blood and sputum specimens, the viral sequences of CMV were more abundant in BALF specimens, even though it was not statistical significant ( Figure 1D). The read numbers of HSV1 in BALF were significantly higher than those in blood and sputum ( Figure 1D).

Comparison of mNGS and Conventional Methods
Unlike mNGS, among the microbes isolated by conventional methods, Stenotrophomonas maltophilia (S. maltophilia) was the most common pathogen, followed by A. baumannii, K. pneumoniae, P. aeruginosa, and S. aureus (Figure 2A). The most frequent fungi detected by the conventional method were C. albicans, C. tropicalis, C. glabrata, A. fumigatus, and C. parapsilosis ( Figure 2C). Mycoplasma pneumoniae were the only microbes isolated by the conventional method ( Figure 2D). The viral detection revealed EBV, CMV, and HSV1 as the top 3 viruses, as determined by only mNGS ( Figure 2E).
In detail, E. faecium, P. melaninogenica, Enterococcus faecalis (E. faecalis), Corynebacterium striatum (C. striatum), L. mirabilis, S. pseudopneumoniae, A. nosocomialis, R. mucilaginosa, Rothia dentocariosa (R. dentocariosa), and Streptococcus mitis (S. mitis) exhibited a significantly higher detection rate by the mNGS method compared with that noted by the conventional method (P < 0.05) ( Figure 2A). However, A. baumannii was observed to have a significantly higher yield rate by conventional than by mNGS (P < 0.05) (Figure 2A). The frequent K. pneumoniae and P. aeruginosa showed similar detection rate by both methods (Figure 2A). We also found that mNGS showed a higher detection rate for both G+ and G− bacteria than the conventional method ( Figure 2B).
The percentage of mNGS-positive specimens was higher than that of the conventional method for C. albicans and C. glabrata, although the differences were not significant (P > 0.05). The percentages of positive specimens were similar in the identification of A. fumigatus, C. tropicalis, and C. parapsilosis by both methods ( Figure 2C).
Among all 130 patients, the pathogens of 58.46% patients were uncovered by an empirical antibiotic regimen, and the  antibiotic prescriptions were modified in these patients ( Figure 3B). However, there was no difference in death rate between patients with modified therapy and those without change (P > 0.05) (Supplementary Figure S2). Among the patients with different infection types, most patients had modified therapies, especially mixed infection and viral infection. However, the death rate was not significantly different between those with modified therapy and those without change (P > 0.05) ( Figure 3B and Supplementary Figure S2, data not shown).

mNGS Detection Rate Associated With Clinical Characteristics
mNGS detection rate of bacteria and virus was higher in patients with orotracheal intubation and type 2 diabetes mellitus (T2DM) (Figures 4A, C). Immunocompromised patients were found to have a significantly higher detection rate of fungi and virus, indicating the predisposition of fungal and viral infections in patients with immune deficiencies ( Figure 4B). We also demonstrated a high mNGS-positive rate of bacteria in patients whose detection time was 15 days after being admitted to the hospital (Supplementary Figure S3B). However, the clinical outcome (death) and symptom (fever) were not associated with the mNGS microbial detection rate (Supplementary Figures S3A, C). We further assessed the effect of laboratory examinations on mNGS detection rate. Blood WBC >100 * 10 6 /L and procalcitonin (PCT) >5 mg/L were related with significantly higher mNGS detection rate of bacteria and lower detection rate of fungi ( Figures 4D, F). Creactive protein (CRP) >90 mg/L was associated with lower rate of fungal detection, while there was no relationship between ESR levels and mNGS detection rate ( Figure 4E and Supplementary Figure S3D). These results indicate more common bacterial infections than fungal infections in patients with high WBC, CRP, and PCT. Additionally, further measurement on the influence of IL-1b, IL-2, and IL-6 levels on mNGS detection rate showed that in CNS infections, mNGS detection rate was significantly higher in patients with blood IL-2 >2,000 pg/ml, and IL-6 >40 pg/ml was related with higher mNGS detection rate of fungi or viruses, respectively (Supplementary Figures S3E-G).
For viral detection, ptNGS and mNGS yielded negative results in 3 out of the 13 patients (#004, #007, #009). Both methods only detected very low viral sequences in patients #002 and #006. Both analysis showed low abundance of CMV in patient #001 and high abundance of CMV in patient #010. However, the two methods showed discrepant results in two patients (#003, #008). In patient #003 (sputum), mNGS presented high sequences of EBV, while ptNGS showed low sequences. In patient #008 (sputum), mNGS exhibited low sequences of Torque teno virus and CMV, while ptNGS demonstrated opposite results (Supplementary Table S6).

DISCUSSION
In this study, we compared the conventional method and mNGS in a pairwise manner, and mNGS proved to be more advantageous when it comes to microbial detection in clinical practice in some aspects. Firstly, mNGS is better than conventional methods in the detection of bacteria, fungi, mycoplasma, and viruses, more specifically P. jirovecii and CMV. mNGS also demonstrated higher sequencing number of K. pneumoniae, A. baumannii, P. aeruginosa, P. jirovecii, CMV, and HSV1 in BALF than in other specimen types. Of note, P. jirovecii was exclusively detected by mNGS. Secondly, positive mNGS results assisted in targeted antibiotic prescription, especially when confronted with negative conventional results. Thirdly, ptNGS exhibited a good performance in microbial detection. mNGS had the advantages of short detection time and unbiased pathogen detection, and these have allowed for its applications in clinical microbial testing (Gu et al., 2019). Steve et al. demonstrated the highly diagnostic accuracy of mNGS through blinded mNGS testing of cerebrospinal fluid (CSF) from 95 patients which revealed 73% sensitivity and 99% specificity compared with the original clinical test results. Subsequent mNGS performance in a validation cohort further showed overall superior sensitivity and specificity to conventional methods . However, there was no agreement on the advantages of mNGS for etiology diagnosis. in BALF due to significantly higher sensitivity (63.64%) than the culture method (36.36%) (Shi et al., 2020). Zhang et al. demonstrated high sensitivity and specificity of mNGS in identifying S. pneumoniae in pediatric bacterial meningitis (Zhang et al., 2019). Similarly, previous studies showed the diagnostic value of mNGS in bacterial detection. For instance, mNGS exhibited significantly higher percentage of positive specimens in ventilator-associated pneumonia patients than those detected by conventional testing method (Fang et al., 2020). It also showed apparently higher sensitivity and specificity of pathogen analysis than the culture method in pulmonary infections (Li et al., 2018a). However, with regard to fungal infection, some studies revealed that mNGS showed a satisfying diagnostic performance in infectious diseases and had an overall superior detection rate to culture (OR, 4.0; 95% CI, 1.6-10.3; P < 0.01) (Miao et al., 2018), while other research studies noted the comparable detection rate between mNGS and conventional testing methods (OR, 1.42; 95% CI, 0.68-2.97; P = 0.46) (Fang et al., 2020). In general, the present study indicated that mNGS outperformed the conventional method in positive detection rate of MTB, bacteria, and fungi. In detail, E. faecium, P. melaninogenica, E. faecalis, and C. striatum exhibited a higher yield rate by the mNGS method compared with those noted by the conventional method. We also noted the superior feasibility of the conventional method in detecting A. baumannii, which is partly contradictory with the research of Fang et al. (2020).
To date, 20 patients in our study were diagnosed with P. jirovecii pneumonia (PJP). PJP was diagnosed mostly in AIDS patients, and recent years have witnessed increasing populations in less typical scenarios which were prone to PJP, including hematopoietic stem cell transplant recipients, patients receiving immunosuppressive therapies for autoimmune and inflammatory diseases, and patients with pre-existing lung disease Maini et al., 2013;Atwal et al., 2014;Izadi et al., 2014;Avino et al., 2016). Among patients with PJP, most of them were receiving immunosuppressive treatment. Pneumocystis jirovecii could be detected in BALF, blood, and sputum by mNGS, but not by the conventional method, and we found that the read numbers in BALF were significantly higher than those in the other two specimen types. Moreover, these results also led to alterations of previous treatment therapies, presenting the great potential of mNGS in guiding treatment options.
Mixed infection is very common in clinical experience and 70% of patients in the current study were diagnosed with mixed infection: 44.3%, 35.7%, 7.1%, 5.7%, 5.7%, and 1.4% patients presented mixed bacterial-fungal, bacterial-fungal-viral, bacterial-viral, bacteria-mycoplasma, bacterial-viral, bacterial-MTB, and fungal-viral infections. mNGS could assess a wide range of organism and microbial profiles, which were often absent in conventional results. Notably, mNGS showed superiority in the simultaneous identification of bacteria, fungi, and viruses when compared with the conventional method, which has also been validated in former research (Fang et al., 2020). Therefore, these results indicated the potential role of mNGS in detecting a broad spectrum of pathogens simultaneously and aiding the clinicians with disease diagnosis and targeted therapeutic schedule. In our study, the therapies of most patients were modified according to the findings of the mNGS. Miao et al. found that 61% patients in their study had initial diagnosis modified based on mNGS findings, and the pathogens were more frequently uncovered (58.6%) by an empirical antibiotic regimen (Miao et al., 2018). In our study, the data showed that more than half of the patients had modified therapies according to the mNGS identification; however, there was no difference of death rate between patients with modified therapy and those without change. Therefore, mNGS could emerge as a promising technology for disease diagnosis on the premise of proper patient selection and data analysis; however, the effect of mNGS-guided therapy on clinical outcomes still needs large-scale studies to delineate.
Apart from mNGS, during recent years, ptNGS was individually developed to facilitate the rapid, cost-effective, sensitive, and specific identification of MTB, a major NTM species (Li et al., 2021); specific microorganisms; and antibiotic resistance markers (Hu et al., 2016;Feigelman et al., 2017;Nimmo et al., 2017;Chao et al., 2020). ptNGS uses a selection process before library preparation and sequencing to enrich for microbial sequences of interest. In our study, the enrichment was achieved by multiplex PCR for specific genes, which has been described in blood, sputum, and tissue specimens (Briese et al., 2015;Allicock et al., 2018;Chao et al., 2020;Li et al., 2021). We tried a preliminary exploration on the role of ptNGS in pathogen detection and compared the results of ptNGS and mNGS pairwise in 2 blood specimens, 7 sputum specimens, and 4 BALF specimens (13 patients). We found consistent results of abundant microbial detection by both methods, including A. baumannii, E. faecium, P. aeruginosa, S. aureus, K. pneumoniae, and M. hominis. This indicated the clinical utility of this new approach for pathogen detection, which is also worthy of larger studies to delineate.
Despite these promising results in our study, there were also some limitations. Firstly, the research was limited by the relatively small sample size and the single-center and retrospective nature. For instance, the types of specimens being analyzed in Figure 2 are mixed and we could not analyze different results from different specimen types. Therefore, a large-scale study is worthy of further research to expect comparisons of positive detections between mNGS and conventional methods in different specimen types. Secondly, the absence of RNA sequencing in our study may lead to reporting bias and false-negative results of RNA viruses. Thirdly, we observed inconsistent results between mNGS and the conventional test. The establishment of the thresholds for mNGSpositive identification is worthy of further research and can assist clinicians to diagnose pathogens. In addition, we noted that the BALF and sputum were from the respiratory tract, which is often mixed with commensal organisms and microbial colonization. For instance, the false-positive results in a previous study suggested the likely colonization of E. faecium, A. baumannii, and P. aeruginosa in the sputum of patients with infectious disease (Miao et al., 2018). The incapability of directly differentiating pathogenic infection and colonization, to some degree, challenged the precision of the mNGS results. Therefore, it not only warned us of the false-positive results of mNGS but also alerted us of the necessity of comprehensive consideration of clinical features and sequencing results in clinical practice.
The clinician should interpret the mNGS results with caution when confronted with the inconsistent results of conventional methods by combining clinical features. mNGS could also emerge as a promising technology for precision diagnosis and tailored therapy in clinical infectious diseases.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI and accession PRJNA760800 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA760800).

ETHICS STATEMENT
Ethical review and approval was not required for the study on human participants in accordance with local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with national legislation and institutional requirements.

AUTHOR CONTRIBUTIONS
Conception and design of the work: GS and YF. Data analysis: