Diagnostic performance of metagenomics sequencing for pulmonary fungal infections: a clinical evaluation using the Nanopore platform

Introduction

In recent years, the incidence of invasive fungal diseases (IFDs), particularly pulmonary fungal infections, has been increasing due to the rising number of patients undergoing solid organ transplantation, hematopoietic stem cell transplantation, or chemotherapy for malignancies, in addition to the widespread use of glucocorticoids and immunosuppressants (1). Additionally, the prevalence of severe viral infections, advanced liver and kidney diseases, diabetes mellitus, and hematological malignancies, all of which impair function, has further contributed to the growing burden of fungal infections (2). The diagnosis of pulmonary fungal infections primarily relies on clinical symptoms, medical history, imaging findings, and laboratory-based pathogen detection (3). However, these infections often present nonspecific clinical manifestations, and imaging-based diagnosis requires differentiation from tuberculosis and nontuberculous mycobacterial infections (4). Furthermore, pulmonary fungal infections may coexist with bacterial or viral coinfections or secondary infections, further complicating the diagnostic process (5). Available fungal detection methods are relatively limited, and there is a lack of clinically applicable single or multiplex molecular diagnostic approaches, such as polymerase chain reaction (PCR) and isothermal amplification assays (6). In this context, metagenomic next-generation sequencing (mNGS) plays a crucial role in the diagnosis of pulmonary fungal infections (7). For example, Mucorales and Aspergillus species both require prolonged culture times, and Mucorales are particularly challenging to cultivate (5,8). Moreover, microscopic examination cannot easily differentiate between these two fungi, yet the clinical management of the pulmonary infections they cause differ significantly. In such cases, mNGS demonstrates a distinct advantage by enabling accurate differentiation between Aspergillus and Mucorales based on pathogen nucleic acid sequences, thereby facilitating timely and targeted antifungal therapy.

Nanopore sequencing (GridION sequencer, Oxford Nanopore Technologies, Oxford, UK) converts the current signal difference generated when nucleic acid molecules pass through nanopores into the bond sequence of chemical signals, realizing the direct reading and sequencing of nucleic acid molecules (9). The nanopore platform exploits the considerable potential of mNGS technology, leveraging the long sequencing of nucleic acid and the real-time system support analysis to provide a short turnaround times (TATs) (10).

This study examined the clinical potential of the nanopore sequencing platform to perform the sequencing and analysis of lower respiratory tract samples from patients with suspected lower respiratory tract fungal infections (LRTFIs). We present this article in accordance with the STARD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1163/rc).

Methods

Study design and participants

Between January 2022 and August 2022, bronchoalveolar lavage fluid (BALF) samples were collected from 253 patients suspected of having LRTFIs at four medical centers in Hangzhou, China. The participating hospitals and the number of enrolled patients are detailed in Table S1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Clinical Research Ethics Committee of the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (No. 20211215-30). All participating hospitals were informed and agreed with this study. Written informed consent was obtained from all participating patients prior to enrollment.

Based on the clinical criteria, suspected LRTFIs was defined as the presence of new or progressive infiltration, consolidation, or ground-glass opacity on chest computed tomography (CT). Subsequently, only samples meeting the following criteria were included: (I) consistency with clinical criteria for suspected LRTFIs; (II) availability of comprehensive information regarding anamnesis, demographic details, and clinical data; (III) sufficient BALF sample volume, with at least 15 mL per individual; and (IV) age ≥18 years (11).

Sampling, processing, and DNA extraction

According to the standard clinical technical specifications, 15-mL sterile thread tubes were used to collect two tubes of BALF sample from patients, with 12 mL in each tube or at least 15 mL in total. The tightness was tested, the tube mouth was sealed with sealing film, and the samples were placed in the sample transport box. The temperature of sample transport and short-term storage was 4 ℃. A sample was cultured with BALF in a routine manner, and the positivity of bacteria and fungi was tested with a drug sensitivity test, and the results were recorded. Nucleic acid extraction was performed on the two samples of BALF according to the AllPrep DNA/RNA Mini Kit instructions (80204; Qiagen, Hilden, Germany).

Library preparation and sequencing

The Rapid Barcoding Kit SQK-RPB004 (DNA concentration <20 ng/mL; Oxford Nanopore Technologies) and SQK-RBK004 (DNA concentration >20 ng/mL; Oxford Nanopore Technologies) were used to construct Nanopore sequencing libraries according to the manufacturer’s instructions, and GridION X5 (Oxford Nanopore Technologies) was used for sequencing. About 0.8 G of data were generated for each sample.

Bioinformatics analyses

High-quality sequencing data were generated via the removal of low-quality reads by Fastap, including adapter contamination, duplicated reads, and short reads (length <500 bp). The Burrows-Wheeler Aligner (BWA) was used to map to a human reference genome (GRCh38) to exclude human sequence data. The remaining sequencing data were aligned to the National Center for Biotechnology Information (NCBI) nucleotide database via Scalable Nucleotide Alignment Program (SNAP). The mapped data were processed for advanced data analysis with in-house scripts, including taxonomy annotation, genome coverage/depth calculation, and abundance calculation (9).

Fungal culture

BALF samples were inoculated onto Sabouraud Dextrose Agar (SDA) and incubated at 28 and 37 ℃ for up to 7 days to support the growth of a broad range of fungal pathogens. The cultures were examined daily for colony formation. Fungal isolates were identified based on colony morphology and microscopic features, and further confirmed by lactophenol cotton blue staining. In selected cases, species-level identification was performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) (10).

Case definitions and LRTFIs adjudication

Due to variations in the DNA extraction efficiency and differences in the intrinsic properties of DNA among different pathogens, this study did not set a pathogen-positive threshold. Instead, it objectively demonstrated the number of pathogen sequences detected by the sequencing technological platforms. Specific nucleic acid sequences judged as pathogens were considered positive detections of that pathogen. For the determination of responsible pathogens, experienced physicians responsible for each center first assessed the comprehensive condition of the patients. Cases that were difficult to determine were further assessed by two clinical experts.

Statistical analysis

Categorical variables were summarized as frequencies and percentages. Diagnostic performance metrics of metagenomic third-generation sequencing (mTGS), including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated using clinical diagnosis as the reference standard. A P value <0.05 was considered statistically significant. Analyses were conducted using Statistical Package for the Social Sciences (SPSS version 26.0).

Results

Sample and patient characteristics

A total of 253 patients with suspected LRTFIs based on imaging evidence were enrolled in this study, and 222 participants met the analysis criteria (Figure 1). The participants were categorized into a fungal infection group and a non-fungal infection group based on clinical diagnosis. In the fungal infection group, there were 28 females (46.7%), whereas in the non-fungal infection group, there were 61 females (37.7%). In the fungal infection group, there were 26 (43.3%) participants aged below 60 years, while there were 88 (54.3%) in the non-fungal infection group. Regarding the distribution of participants across hospital departments, the number of intensive care unit admissions in the fungal and non-fungal infection groups was 2 (3.3%) and 14 (8.6%), respectively. Meanwhile, the number of participants from general wards was 56 (93.3%) in the fungal infection group and 134 (82.7%) in the non-fungal infection group. In terms of lifestyle factors, the number of participants with a history of smoking was 14 (23.3%) in the fungal infection group and 40 (24.7%) in the non-fungal infection group. Additionally, the proportion of participants with a history of alcohol consumption was 10 (16.7%) in the fungal infection group and 31 (19.1%) in the non-fungal infection group. An analysis of underlying comorbidities revealed that 5 participants (8.3%) in the fungal infection group and 14 (8.6%) in the non-fungal infection group had solid tumors. The number of participants with hematologic malignancies was 6 (10.0%) in the fungal infection group and 14 (8.6%) in the non-fungal infection group. Chronic obstructive pulmonary disease (COPD) was present in 2 participants (3.3%) in the fungal infection group and 6 (3.7%) in the non-fungal infection group. The number of participants with interstitial lung disease was 9 (15.0%) in the fungal infection group and 16 (9.9%) in the non-fungal infection group; while tuberculosis was present in 2 (3.3%) and 3 (1.9%) participants, respectively. Moreover, the number of participants with hypertension was 8 (13.3%) in the fungal infection group and 23 (14.2%) in the non-fungal infection group, whereas the number of participants with diabetes mellitus was 6 (10.0%) and 20 (12.4%), respectively. Further details are presented in Table 1.

Figure 1 Flowchart of the study. mTGS, metagenomic third-generation sequencing.

In this study, fungi were detected in 65 samples, yielding a fungal positivity rate of 29.3% (65/222). Based on fungal classification, 11 distinct fungal species were identified, including Pneumocystis jirovecii, Cryptococcus neoformans, Aspergillus fumigatus, Candida albicans, Aspergillus flavus, Talaromyces marneffei, Aspergillus terreus, Candida tropicalis, Schizophyllum commune, Rhizomucor pusillus, and Exophiala dermatitidis. Among these, Pneumocystis jirovecii exhibited the highest detection frequency with 11 occurrences, followed by Cryptococcus neoformans, Aspergillus fumigatus, Candida albicans, Aspergillus flavus, Talaromyces marneffei, Aspergillus terreus, Candida tropicalis, Schizophyllum commune, Rhizomucor pusillus, and Exophiala dermatitidis (Figure 2). Furthermore, 2 fungal species were simultaneously detected in 4 samples, while 1 sample tested positive for 3 fungal species, namely Candida albicans, Cryptococcus neoformans, and Pneumocystis jirovecii.

Figure 2 The species, detection frequency, and rate of pathogenic fungi detection of mTGS. The left y-axis (blue bars) represents the absolute number of detected cases for each fungal species. The right y-axis (orange line) indicates the corresponding detection percentage (%) of each fungal species among the total number of cases. mTGS, metagenomic third-generation sequencing.

The comprehensive clinical diagnosis of fungal infection was used as the gold standard to evaluate the diagnostic performance of mTGS in fungal detection. Based on the calculations, mTGS demonstrated a sensitivity of 78.1% [95% confidence interval (CI): 66.0–87.5%], a specificity of 90.5% (95% CI: 84.8–94.7%), a PPV of 76.9% (95% CI: 64.8–86.5%), and a NPV of 91.1% (95% CI: 85.4–95.0%) (Table 2).

Further evaluation of the diagnostic performance of the 3 most frequently detected fungal species indicated that mTGS exhibited high sensitivity in detecting Pneumocystis jirovecii (76.5%) (Table S2) and Cryptococcus neoformans (82.4%) (Table S3), while its sensitivity for Aspergillus spp. was relatively lower (66.7%) (Table S4). Nonetheless, mTGS demonstrated high specificity across all 3 fungal species.

In the fungal-positive samples detected by the mTGS method, multiple co-detected pathogens were identified, including bacteria, viruses, and Chlamydia species (Figure 3). Among the detected viruses, human herpesvirus 5 (HHV-5) and human herpesvirus 4 (HHV-4) exhibited the highest detection frequency. Regarding co-detection of fungi and bacteria, the most frequently co-detected bacterial species were Mycobacterium intracellulare, Mycobacterium tuberculosis, and Streptococcus pneumoniae. Additionally, other frequently co-detected bacterial species included Streptococcus pneumoniae, Mycobacterium kansasii, Enterococcus faecium, Enterococcus faecalis, and Klebsiella pneumoniae. Furthermore, this study identified a single case of co-detection involving Legionella maceachernii and fungi, as well as a co-detection event involving Chlamydia psittaci and fungi.

Figure 3 The species and detection frequency for other pathogens detected alongside pathogenie fungi. The x-axis display the detection frequency of the other pathogens. mTGS, metagenomic third generation sequencing.

Fungi culture was performed on all samples simultaneously, yielding a total of 6 fungal species, which was 5 fewer than the 11 fungal species detected by mTGS. Among the fungi identified by mTGS, Pneumocystis jirovecii, Talaromyces marneffei, Schizophyllum commune, Exophiala dermatitidis, and Rhizomucor pusillus were not detected with culture-based methods. Conversely, Aspergillus fumigatus, Cryptococcus neoformans, Candida albicans, Aspergillus flavus, Aspergillus terreus, and Candida tropicalis were successfully detected by culture, yielding positive results. Notably, the frequency of positive detections for Candida albicans was higher in culture-based methods than in mTGS, whereas the positive detection frequency of all other fungal species was lower in culture than in mTGS. The comparison of positive detection frequencies between the two methodologies is presented in Figure 4.

Figure 4 Comparison of fungal species detected by mTGS and fungal culture methods. mTGS, metagenomic third-generation sequencing.

Through clinical data collection, this study obtained fungal detection results from multiple methodological approaches. Statistical analysis of these methodologies revealed that the positive detection rates for fungal fluorescence staining, Cryptococcus capsular polysaccharide antigen test, galactomannan assay, and [1-3]-β-D-glucan test were 7.2%, 6.9%, 27.3%, and 15.9%, respectively, all of which were lower than the positive detection rate of mTGS (Figure S1).

Among the 65 fungal cases detected using the mTGS approach in this study, 40 patients received corresponding antifungal therapy, and clinical improvement was documented in 36 (90%) of them within 15 days after treatment initiation (Figure S2).

Underlying comorbidities are recognized risk factors for fungal infections. Statistical analysis of the underlying diseases of the study participants revealed that the majority of patients with fungal-positive results had preexisting comorbidities, with diabetes mellitus, hypertension, and hematologic malignancies being the most frequently observed conditions. Furthermore, different underlying diseases exhibited variable fungal detection rates, with positivity rates of 26.3% for malignant solid tumors, 30.0% for hematologic malignancies, 29.4% for hypertension, 28.6% for diabetes mellitus, 27.8% for COPD, 36.8% for interstitial lung disease, and 40.0% for tuberculosis (Figure 5).

Figure 5 The number of patients with various typical underlying diseases, along with the detection frequency and detection rate of fungi among them. The left y-axis displays patient counts (orange bars: total patients with underlying diseases; yellow bars: fungus-positive cases). The right y-axis shows the fungal detection rate (blue line, %). COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; HMs, hematologic malignancies; HTN, hypertension; ILD, interstitial lung disease; ST, malignant solid tumor; TB, tuberculosis.

Discussion

In recent years, the emergence and clinical application of mNGS have provided a significant advancement in the diagnosis of pulmonary fungal infections, enriching the diagnostic approaches for LRTFIs and demonstrating substantial clinical value (11,12). Unlike traditional culture-based methods, mNGS does not require the microbial cultivation of clinical specimens. Instead, it directly extracts nucleic acids from samples, constructs a standard sequencing library, performs high-throughput sequencing, and subsequently employs bioinformatics analysis to identify microbial species by aligning genomic sequences, thereby determining the types and relative abundance of microorganisms in the sample (13,14). Consequently, mNGS is fundamentally classified as a molecular diagnostic method. With the continuous technological advancements of the nanopore sequencing platform, its application in pathogen metagenomic sequencing has steadily intensified. The long read-sequencing capability and short TATs of nanopore technology enhance its potential in the field of pathogen sequencing (15,16). Our study had a clear objective: to leverage mTGS technology for the early and rapid screening of potential pathogenic fungi in the lower respiratory tract, thereby providing targeted guidance for antifungal therapy. In the analysis of 222 cases of suspected LRTFIs, a high fungal infection rate was observed, and mTGS demonstrated strong fungal detection capabilities. Additionally, a high rate of co-detection with other pathogens was noted among patients, further highlighting the clinical utility of mTGS in comprehensive pathogen identification.

The currently available fungal detection methods remain relatively limited, with a lack of clinically applicable single or multiplex molecular diagnostic approaches, such as PCR and isothermal amplification techniques (17,18). In this context, mNGS plays a particularly important role in the diagnosis of pulmonary fungal infections. For example, both Mucorales and Aspergillus species require prolonged culture times, with Mucorales being particularly challenging to cultivate (19). Moreover, microscopic examination cannot easily differentiate between these two fungal groups, yet the clinical management of pulmonary infections they cause differs significantly. In such cases, mNGS demonstrates a distinct advantage by enabling the accurate differentiation between Aspergillus and Mucorales based on pathogen nucleic acid sequences, thereby facilitating timely and targeted antifungal therapy (20). Similarly, producing positive results for Pneumocystis jirovecii through culture is difficult, yet the mNGS detection of this pathogen provides valuable clinical guidance (21). In this study, Pneumocystis jirovecii exhibited the highest detection rate, suggesting that traditional diagnostic methods frequently fail to detect this pathogen. Furthermore, in patients with mixed infections or underlying conditions (such as immunosuppression), the detection rate of mNGS was significantly higher than that of conventional methods, indicating it as a valuable complementary diagnostic tool for fungal pneumonia cases that test negative via traditional approaches. However, in cases of respiratory tract colonization by certain fungal species, the clinical significance of detection is often limited. For example, Candida albicans is more commonly associated with candidemia, whereas primary pulmonary candidiasis is exceedingly rare, as human alveolar epithelial cells exhibit innate resistance to Candida infection (22). Therefore, the detection of Candida in respiratory specimens should be interpreted cautiously and regarded primarily as a clinical reference rather than definitive evidence of infection. The partial discordance observed between mTGS and culture highlights the limitations of culture in detecting fastidious or non-viable fungi. Clinical response in patients treated based on mTGS-only findings suggests that these detections were not artifacts or colonization, but likely true pathogens missed by traditional methods.

In immunocompromised patients, opportunistic infections caused by environmental fungi, which are typically nonpathogenic in immunocompetent individuals, are more likely to occur (23). In such cases, clinical symptoms are often atypical, and traditional diagnostic methods face challenges in accurately identifying the causative pathogens. However, mTGS offers a distinct advantage, as it enables direct detection of rare pathogenic fungi through nucleic acid sequencing (24). In this study, two cases of Schizophyllum commune and one case of Exophiala dermatitidis were detected, both of which are opportunistic fungal pathogens commonly found in the environment. Following their detection, clinical evaluation confirmed these fungi as the causative pathogens, and targeted antifungal therapy was subsequently administered.

Fungal infections are often associated with primary diseases, particularly those that result in a compromised immune response (25). In this study, the fungal detection rate was higher in patients who were immunocompromised than in those without primary diseases. Notably, patients with hematologic malignancies and diabetes mellitus had the highest fungal detection rates, suggesting that individuals with immune dysfunction are at greater risk of fungal infections (26). These results highlight the importance of proactive fungal infection prevention in immunocompromised patients. Moreover, in cases where infections occur, fungal pathogens should be strongly considered as potential causative agents to facilitate early and targeted antifungal management.

Due to the differences in sequencing platforms and variation in detection and analysis workflows across manufacturers, standardization of mNGS technology remains challenging (27). As a result, mNGS typically reports the quantity of nucleic acid sequences detected for suspected pathogenic fungi, serving as a clinical reference, while laboratories employ additional confirmatory methods to validate the reliability of mNGS results (28). Given the biological diversity of fungal species, the detection difficulty of mNGS varies across different fungi. The primary factor contributing to these variations in detection efficiency is the structural differences in fungal cell walls. Conventional methods exhibit lower sensitivity for fungal pathogens with thicker cell walls and higher lipid content, making it more challenging to detect such fungi (29). Consequently, a negative mNGS result for these fungi does not necessarily exclude infection, necessitating further diagnostic evaluation.

For patients with pulmonary infections, peripheral blood samples and sputum specimens are more easily obtainable and are commonly used as the primary sample types for the diagnosis of pathogens through traditional culture and immunological assays (30). However, traditional fungal diagnostic methods exhibit relatively low diagnostic efficiency, often requiring multiple tests to obtain a positive result or yielding persistently negative results, particularly in patients receiving empirical antibiotic therapy (31). BALF is highly sensitive for the detection of pulmonary infections and is considered the preferred specimen type, especially for critically ill patients undergoing bronchoscopy (32). BALF can be used for microbial culture, biochemical analysis, cytological examination, parasitological testing, and mNGS detection (33). Therefore, in this study, BALF was selected as the specimen type for mTGS analysis.

In suspected fungal infection cases, clinicians often initiate empirical antifungal therapy while simultaneously selecting appropriate specimens for testing using various traditional fungal diagnostic methods, including culture and microscopy. In most cases, targeted antifungal treatment can only be administered once the causative pathogen has been definitively identified (34). Among traditional fungal diagnostic methods, different approaches exhibit varying TATs. As the gold standard, culture-based methods are often time-consuming, typically requiring 2 to 7 days for results. In contrast, metagenomic approaches significantly reduce the TATs, particularly mTGS based on the nanopore platform, which can shorten the TATs to within 7 hours (35). This rapid pathogen identification allows patients to receive earlier targeted antifungal therapy, thereby improving clinical outcomes and enhancing patient benefits. These findings highlight the potential of mTGS to influence clinical management. The high rate of improvement among patients receiving antifungal therapy based on sequencing results further supports its utility as a diagnostic tool with real clinical impact.

This study has certain limitations that should be acknowledged. First, the sample size was relatively small, and the diversity of detected fungal species was not comprehensive. Future research should expand the sample size to enhance the breadth of fungal detection. Second, this study only included lower respiratory tract samples from several central hospitals in Hangzhou, Zhejiang Province, without incorporating samples from other regions, thereby limiting its generalizability. Third, due to the limited number of cases, we were unable to determine whether the antifungal treatment regimens adopted by clinicians after receiving mTGS results played a decisive role in patient recovery.

Conclusions

In this study, we evaluated the diagnostic performance of mTGS using the Oxford Nanopore platform in detecting fungal pathogens from BALF in patients with LRTFIs. Our findings demonstrate that mTGS offers superior sensitivity and broader pathogen coverage compared to traditional culture-based methods, while significantly reducing the diagnostic TATs. This method is particularly valuable in identifying atypical or rare fungal pathogens that are frequently missed by conventional diagnostics, especially in immunocompromised patient populations. These results support the clinical utility of mTGS as a complementary or alternative approach to standard mycological diagnostics and underscore its potential for early, accurate, and comprehensive detection of fungal infections in routine clinical settings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1163/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1163/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1163/prf

Funding: This study was funded by the Research and Development Program of Zhejiang Province (No. 2023C03068), the National Natural Science Foundation of China (No. 82272338), and the Key R&D Plan of the Ministry of Science and Technology of China (No. 2022YFC2504502).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1163/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Clinical Research Ethics Committee of the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (No. 20211215-30). All participating hospitals were informed and agreed with this study. Informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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