Given the crucial role of etiology in the diagnosis and treatment of LRTIs, research has increasingly focused on improving microbiological diagnosis. Traditional approaches are often time-consuming, have low pathogen detection rates (below 40%), and involve complex steps16. mNGS offers a promising alternative by enabling broad detection of microorganisms, strain analysis, virulence assessment, resistance gene identification, and diagnosis of infections with unknown causes17. Leveraging short-read sequencing, mNGS rapidly analyzes microbial communities in patient samples, providing unbiased detection, faster turnaround times, and potential for semi-quantitative monitoring. Direct nucleic acid screening of respiratory specimens (sputum, BALF, lung tissue, pleural effusion) enables comprehensive analysis. Therefore, we compared traditional methods and mNGS for LRTI diagnosis by analyzing BALF, blood, tissue, and pleural fluid samples from 165 patients, focusing on detection efficiency and discrepancies.
A significant limitation of our study is that 91.3% of patients had received antimicrobial therapy prior to specimen collection. This factor may substantially bias the comparison between mNGS and traditional methods, particularly affecting culture-based diagnostics. Culture sensitivity is known to decline dramatically after antibiotic initiation, potentially creating an unfair advantage for mNGS, which can detect non-viable organisms and nucleic acid fragments. While this reflects real-world clinical scenarios where patients often receive empirical antibiotics before definitive diagnosis, it may overestimate the relative superiority of mNGS compared to traditional methods. Future studies should include larger cohorts of antibiotic-naive patients to provide more balanced comparisons.
mNGS demonstrated a significantly higher positivity rate (86.7%) than traditional methods (41.8%) (P < 0.05), consistent with prior studies18,19. Discrepancies between mNGS and traditional methods in these cases likely stemmed from mNGS’s superior sensitivity20, broader detection capability21,22, and ability to detect non-viable organisms 23,24. The clinical impact of metagenomics in infection diagnosis continues to expand, with mNGS showing particular advantages in complex cases where traditional methods fail22. Additionally, mNGS’s ability to detect antimicrobial resistance genes provides valuable information for treatment decisions21. The comprehensive nature of metagenomic approaches in clinical microbiology allows for identification of polymicrobial infections and biofilm-associated pathogens that may be missed by conventional methods23,25. While mNGS positivity was higher across all specimen types, the overall positivity rate was not significantly influenced by specimen type, contrasting with Duan et al.26, who hold the opposite view that the sensitivity of mNGS in specimen of blood, BALF and sputum samples. We acknowledge that the smaller sample sizes for blood and sputum specimens could have introduced bias into our results.
Fang27 and Xie19 covered it that mNGS could significantly increase the detecting of the mixed infection. In our mNGS-positive cases, more than one kind of pathogen was detected in 55.2% of patients. mNGS can directly identify various types of pathogens (bacteria, viruses, fungi, mycoplasma, chlamydia, leptospires, parasites, and unknown organisms) because it extracts all nucleic acids from the specimen. Although Streptococcus pneumoniae is typically the dominant pathogen in CAP28, our routine bacterial testing revealed a comparable incidence of NTM. This finding warrants careful interpretation, as it likely reflects the specialized nature of our tertiary referral center, which manages a disproportionate number of patients with chronic lung conditions, bronchiectasis, and other NTM-predisposing factors29. The high prevalence of NTM may not be representative of general LRTI populations and could introduce selection bias into our pathogen spectrum analysis. This finding underscores the advantage of mNGS in detecting multiple pathogens within a single sample, potentially enhancing the diagnosis of mixed infections relative to traditional methods.
The spectrum of pathogens varied significantly between immunocompetent and immunocompromised patients, providing important clinical insights. In immunocompetent patients, bacterial-bacterial co-infections were most common, while bacterial-fungal combinations predominated in immunocompromised patients. This differs from Wu30, who reported bacterial-viral and bacterial-bacterial co-infections as most frequent in immunocompetent patients. These differences may reflect variations in patient populations, geographic factors, and local epidemiology. The predominance of P. jirovecii (20.0%) in our immunocompromised cohort aligns with expected opportunistic infection patterns, while the high frequency of NTM in both groups suggests endemic presence in our region.
While mNGS offers enhanced detection, false positives remain a significant concern due to potential misidentification of contaminants or normal respiratory flora, such as Neisseria mucosa, Roxella roxella, Candida albicans, Malassezia globosa, Prevotella, Acinetobacter, Corynebacterium, HSV, and EBV, which were detected in our mNGS reports31. Given the lack of standardized criteria for distinguishing contamination from true infection, clinicians should carefully evaluate patient presentation, risk factors, and other data when mNGS identifies pathogens missed by traditional methods. Conversely, negative mNGS with positive traditional results should prompt investigation into potential mNGS limitations. To improve interpretation, Marsh32 suggest establishing bacterial load thresholds, and Zhou33 emphasize integrating mNGS with clinical and concurrent lab findings.
A critical limitation of our study is the exclusive focus on DNA sequencing, which precluded detection of important RNA viruses commonly associated with LRTI, including respiratory syncytial virus, influenza virus, rhinovirus, and coronaviruses. This technical limitation may have underestimated the true burden of viral infections in our cohort and reduced the comprehensive diagnostic value of mNGS. The single H1N1 case identified by traditional antigen testing but missed by mNGS exemplifies this limitation. Future studies should incorporate both DNA and RNA sequencing to provide complete pathogen detection coverage, though this would significantly increase costs.
Diagnosing LRTI etiology requires comprehensive clinical and laboratory assessment, and future guidelines are needed to refine mNGS data interpretation and differentiate contamination from true pathogens.
Our study demonstrated that mNGS offered superior detection of NTM, Prevotella, Streptococcus, and anaerobes compared to traditional methods. This is likely due to several factors. Isolating, culturing, and identifying NTM strains is time-consuming and often yields low positivity rates, hindering timely clinical diagnosis and treatment34, leading to infrequent NTM detection in our hospital using traditional methods. Prevotella, a black-pigmented anaerobic species and a component of the oral microbiome, is linked to oral diseases35 and is easily detected in patients with chronic pulmonary infections and compromised immunity. However, Prevotella identification via traditional methods requires culture isolation followed by identification using the rapid ID 32A system, a lengthy and costly process36. Anaerobic bacteria detection necessitates a strict anaerobic environment during sample collection and analysis, often leading to these organisms being missed by routine clinical microbiology laboratories.
For mycobacterial infections, mNGS can rapidly identify the Mycobacterium species, whereas conventional tests require more time. However, mNGS may sometimes miss Mycobacterium tuberculosis (MTB) or NTM. As Li14 noted, the detection of MTB and NTM can be lower with mNGS because MTB is an intracellular bacterium, making it difficult to detect its extracellular nucleic acids. Our results also showed that pathology confirmed 6 out of 20 MTB cases missed by mNGS, highlighting the value of combining mNGS with pathology or culture for MTB diagnosis. Other research8 also suggests integrating mNGS with culture or Xpert assays for tuberculosis diagnosis. Furthermore, mNGS was advantageous for detecting rare pathogens like O. tsutsugamushi, Chlamydia, and Legionella, which were only detected by mNGS in our study. mNGS also aids in Cryptococcus identification, which, in combination with culture and CrAg assays, significantly improves diagnostic accuracy and guides appropriate antifungal treatment selection37. In our research, we identified three cases of Pneumocystis jirovecii by both mNGS and traditional methods.
The consistency rate between traditional methods and mNGS for pathogen identification was only 32.3% in LRTI, with 20 cases (32.3%) showing inconsistencies. This included 5 missed cases of NTM, 4 of MTB, 2 of Enterococcus, 2 of Pseudomonas aeruginosa, and single cases of Staphylococcus hemolyticus, Paragonimus, H1N1, Haemophilus influenzae, anaerobic bacteria, Klebsiella, Myxococcus aureus, and Cryptococcus, all missed by either mNGS or traditional methods. The antigen test detected H1N1 and Paragonimus, and the Cryptococcal Antigen Latex Agglutination System detected Cryptococcus, which were missed by mNGS. Another possibility is that we may have considered normal flora as suspected pathogens when sequence reads were near the set threshold for mNGS in traditional-positive patients. Furthermore, incomplete disruption of the Mycobacterium cell wall during DNA extraction for mNGS might have resulted in missed MTB cases. Regardless of downstream molecular methods, effective sample pretreatment remains essential for pathogen recovery.
mNGS’s speed and accuracy in identifying infectious pathogens significantly reduces diagnostic turnaround time, providing valuable clinical guidance for subsequent diagnosis and treatment38. In our study, 72.13% of patients who were receiving antibiotics during hospitalization had their anti-infective regimens modified based on mNGS results. This high rate of treatment modification demonstrates the substantial clinical impact of mNGS on antimicrobial stewardship. The ability to de-escalate therapy in 32.73% of patients not only reduces unnecessary antibiotic exposure but also helps combat antimicrobial resistance—a critical global health concern. The escalation of therapy in 39.39% of patients, driven by detection of specific pathogens such as Aspergillus, P. jirovecii, or polymicrobial infections, highlights mNGS’s value in identifying complex infections requiring specialized treatment.
Despite the promising results, several barriers must be addressed before routine clinical implementation of mNGS. Cost remains a significant limitation, with mNGS testing typically costing 5–10 times more than traditional methods. The requirement for specialized bioinformatics expertise and infrastructure may limit accessibility in resource-constrained settings. Turnaround time, while faster than culture, still requires 24–48 h, which may not meet the needs for urgent clinical decisions. Additionally, the interpretation of results requires clinical expertise to distinguish pathogenic organisms from colonizers or contaminants, necessitating specialized training for clinicians.
Future research should focus on developing standardized interpretation guidelines, cost-effectiveness analyses, and identification of optimal patient populations for mNGS testing. Integration with rapid point-of-care diagnostics and development of simplified reporting systems could enhance clinical utility. Furthermore, studies incorporating both DNA and RNA sequencing, larger sample sizes stratified by antibiotic exposure, and long-term patient outcome data would strengthen the evidence base for mNGS implementation.
Moving forward, several strategies can be implemented in clinical practice to enhance LRTI diagnosis and address the limitations identified in this study. Firstly, supplementing DNA sequencing with RNA sequencing will broaden the scope of pathogen detection, particularly for RNA viruses. Secondly, expanding sample sizes, especially for blood, tissue, and pleural fluid specimens, will increase the statistical power and sensitivity of pathogen identification. Crucially, establishing standardized criteria for interpreting mNGS results is essential for differentiating between true infections and the presence of contaminants or colonizing organisms. Finally, validating positive mNGS findings with confirmatory testing methods, such as cultures, immunological assays, or PCR, will improve the accuracy and reliability of LRTI diagnoses.
