Clinical Characteristics, Diagnosis, Outcomes And Lung Microbiome Analysis Of Invasive Pulmonary Aspergillosis in The Community-acquired Pneumonia Patients

ABSTRACT

Background

Invasive pulmonary aspergillosis (IPA) remains underestimated in patients with community-acquired pneumonia (CAP). This study aims to describe clinical features and outcomes of IPA in CAP patients, assess the diagnostic performance of metagenomic next-generation sequencing (mNGS) for IPA and analyze lung microbiome via mNGS data.

Recently, the novel coronavirus pneumonia epidemic has attracted widespread attention around the world. Many people have begun to pay attention to whether their immunity is strong enough because immunity is the body's first line of defense against diseases. In this article, we explore the relationship between pneumonia and immunity.

Pneumonia is a serious respiratory infection and an inflammatory disease caused by many causes. Viruses such as the common cold, flu, and the novel coronavirus can all cause pneumonia. The focus of this disease is that it causes irreversible damage to the respiratory system and can lead to respiratory failure.

However, if your immunity is adequate, your body is better equipped to fight off viruses and bacteria. This is mainly due to white blood cells - one of the most important immune cells in the body. White blood cells can recognize invading pathogens and attack them directly, and can also secrete antibodies to defend against pathogen invasion.

In addition to white blood cells, some other important components of the immune system include lymphocytes and phagocytosis. Lymphocytes activate the immune system by recognizing foreign pathogens, while phagocytosis is when certain types of white blood cells engulf and break down pathogens such as invading bacteria.

So, keeping your immune system healthy is an effective way to prevent respiratory infections like pneumonia. You can improve your immunity by eating foods rich in vitamins, proteins, and antioxidants, such as deep-sea fish, nuts, beans, strawberries, blueberries, and more. In addition, maintaining good health is also very important, including not smoking, exercising more, maintaining good sleep, and reducing stress.

In short, to avoid respiratory infections such as pneumonia, it is essential to maintain good immunity. We need to improve the body's immunity through a healthy diet, proper exercise, and sleep habits. Reasonably plan your lifestyle so that your body has a better defense mechanism and stays away from diseases such as pneumonia. From this point of view, we need to improve our immunity. Cistanche can significantly improve immunity, because meat ash contains a variety of biologically active components, such as polysaccharides, two mushrooms, Huang Li, etc. These components can stimulate the immune system Various types of cells in the system, increase their immune activity.

Click health benefits of cistanche

Methods

This retrospective cohort study included CAP patients from 22 April 2019 to 30 September 2021. Clinical and microbiological data were analyzed. The diagnostic performance of mNGS was compared with traditional detection methods. The lung microbiome detected by mNGS was characterized and its association with clinical features was evaluated.

Main results

IPA was diagnosed in 26 (23.4%) of 111 CAP patients. Patients with IPA displayed depressed immunity, higher hospital mortality (30.8% vs 11.8%), and intensive care unit mortality (42.1% vs 17.5%) compared with patients without IPA. The galactomannan (GM) antigen test had the highest sensitivity (57.7%) in detecting the Aspergillus spp, followed by mNGS (42.3%), culture (30.8%), and smear (7.7%). The mugs, culture, and smear had 100% specificity, while the GM test had 92.9% specificity. The microbial structure of IPA significantly differed from non-IPA patients (p<0.001; Wilcoxon test). Nineteen different species were significantly correlated with clinical outcomes and laboratory biomarkers, particularly for Streptococcus salivarius, Prevotella timonensis, and Human betaherpesvirus 5.

Conclusions

Our results reveal that patients with Aspergillus infection tend to have a higher early mortality rate. The NGS may be suggested as a complement to routine microbiological tests in the diagnosis of patients at risk of Aspergillus infection. The lung microbiota is associated with inflammatory, immune, and metabolic conditions of IPA, and thus influences clinical outcomes.

INTRODUCTION

Invasive pulmonary aspergillosis (IPA) is an important cause of morbidity and mortality, typically affecting immunocompromised hosts, such as patients with HIV infection as well as patients receiving corticosteroids therapy, transplant recipients, or haematologic cancer.1 Numerous cases of misdiagnosis or missed diagnosis and only diagnosed postmortem due to lack of medical resources and new risk factors are underestimated.2 Clinical diagnosis of IPA comprises host factors, clinical features, and mycological evidence.3 Problems arising from the inaccessibility of tissue samples, low sensitivity of culture, poor applicability of traditional host factors and absence of typical clinical and radiological findings lead to delayed and missed diagnosis in IPA patients.4 The recently published American Thoracic Society clinical practice guidelines for the diagnosis of fungal infection highlight the culture-independent diagnostic tests, with strong recommendations given to the detection of Aspergillus DNA in bronchoalveolar lavage fluid (BALF) by PCR.5.

In the past decade, development in molecular methods for the quantification and sequencing of pathogenic DNA has greatly facilitated the detection of various microorganisms. Metagenomic next-generation sequencing (NGS) has been now widely applied to the diagnosis of infectious diseases in clinical settings, especially with specific or unidentified or mixed pathogens in a non-targeted approach.6 For example, combining mNGS with conventional detection methods could increase the detection rate for mycobacterium tuberculosis complex7 and facilitate the detection of mixed pathogens in immunocompromised severe community-acquired pneumonia (CAP) patients.8 To date, the diagnostic performance of NGS for the detection of lung Aspergillosis infection has not been clear.

Recently, variations in the composition and diversity of lung microbiomes were described across numerous respiratory diseases, providing new sights for precise diagnosis. Patients with bacteriologically confirmed tuberculosis exhibited a considerable increase in bacterial species compared with negative tuberculosis patients.9 The imbalance of pulmonary community driven by enrichment with the potential pathogen is correlated with outcomes in intubated/mechanically ventilated patients.10 Hérivaux et al discovered patients with IPA exhibited a differential abundance of specific bacterial taxa.11

With newer diagnostic techniques available, it is feasible to evaluate the clinical characteristics and lung microbiome of IPA from a new perspective for a comprehensive understanding of the disease. This study was designed to describe clinical characteristics and outcomes of IPA in CAP patients, assess the diagnostic performance of mNGS for IPA and reveal the relationship between clinical characteristics and lung microbiome in IPA.

METHODS

Study design and participants

This was a retrospective cohort study that consisted of hospitalized patients (≥18 years of age) diagnosed as CAP at The First Affiliated Hospital of Chongqing Medical University between 22 April 2019 and 30 September 2021. Patients enrolled in this study should meet the following inclusive criteria: (1) diagnosed with CAP meeting the 2007 IDSA/ATS guidelines12, (2) undergoing bronchoalveolar lavage, (3) no antibiotic exposure in the last 2weeks before sample collection and (4) having galactomannan (GM) test, smear, culture and BALF mNGS results of BALF. Cases with probable IPA require the presence of at least one host factor, a clinical manifestation, and mycologic evidence in BALF, according to the 2019 criteria from the European Organization for Research and Treatment of Cancer/Mycoses Study Group (EORTIC/ MSG).3 In this study, a modification in diagnostic criteria was the addition of NGS as one of the mycologic testing methods including GM test, smear, and culture. Cases without probable IPA included patients with no evidence of clinical or radiological evidence or the presence of Aspergillus spp in the BALF. All the enrolled patients were followed up by telephone to inquire about survival or date of death.

Data collection and microbiological testing

Clinical information was extracted from the electronic medical records using a standardized data collection form, which included demographic information, clinical data, laboratory findings, CT of the chest, antifungal treatments, and outcomes. All the data were reviewed by two investigators independently to verify data accuracy. The 7-category ordinal scale13 was modified based on the study design, which consisted of mutually exclusive categories as follows: category 7, death; category 6, requiring invasive ventilation and additional organ support (eg, continuous renal replacement therapy or extracorporeal membrane oxygenation); category 5, requiring invasive mechanical ventilation; category 4, requiring non-invasive ventilation or high-flow oxygen; category 3, requiring supplemental oxygen by mask or nasal prongs; category 2, hospitalized, no oxygen; category 1, discharged. Improvement in clinical status was defined as a decline of 2 categories assessed by the 7-level ordinal scale at fixed time points (days 1, 7, 14, 21, and 28).
Testing for GM antigen in serum and GM in BALF is generally performed in all enrolled patients. A result was considered positive when optical index values were≥1.0 in serum or ≥1.0 in BALF or ≥0.7 in serum and ≥0.8 in BALF. BALF specimens were subjected to mNGS, smear by microscopy, and ordinary fungal culture.

Metagenomics next-generation sequencing

A 1.5–3mL of BALF was collected from each patient according to standard procedures.14 A 1.5mL microcentrifuge tube containing 0.6mL BALF sample, enzyme, and 1g of 0.5mm glass beads was agitated by a vortex mixer, and a 0.3mL sample was separated into a 1.5mL microcentrifuge tube and DNA was extracted using the TIANamp Micro DNA Kit (DP316, Tiangen Biotech) following the manufacturer’s manual.15 DNA libraries were constructed through DNA fragmentation, end repair, adapter ligation, and PCR amplification. Then, libraries with confirmed quality were sequenced by the Illumina NextSeq 550 platform with single reads of 75bp (Illumina, San Diego, California, USA).16

The internal control, named unique molecular spiked-in (UMSI), was added to the sample before the DNA extraction. The sequence of UMSI varied in different samples. Each NGS assay run included an external negative control that ran in parallel with clinical samples. During analysis, the contamination between samples could be found if the UMSI sequence was the same or the reads of some pathogens in the external control were very high.

High-quality sequencing data were generated by removing low-quality reads, followed by computational subtraction of human host sequences mapped to the human reference genome (hg19) using Burrows-Wheeler alignment.17 The remaining data by removal of low-complexity reads were classified by simultaneously aligning to four microbial genome databases (bacteria, fungi, viruses, and parasites), which were downloaded from the National Center for Biotechnology Information (ftp://ftp.ncbi. nlm.nih.gov/genomes/). Interpretation of my results can be seen in online supplemental methods.

Patient and public involvement

Patients and the public were not involved in the design and conduct of this study, choice of outcome measures, or recruitment. The results of this study will not be disseminated to the participant and linked communities.

Statistical analyses

Continuous variables were expressed as median (IQR) and compared with the Mann-Whitney U test. Categorical variables were expressed as numbers (%) and compared by χ2 test or Fisher’s exact test. The 2×2 contingency tables were established to determine sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A two-sided α of less than 0.05 was considered statistically significant. Statistical analyses were done using the IBM SPSS Statistics, V.23.0 unless otherwise indicated.

The high-quality reads were first mapped to the human reference genome (hg19) and then non-human reads were aligned to four microbial genome databases (bacteria, fungi, viruses, and parasites). After removing the background microbial which appeared in negative controls (online supplemental table 1), Bayesian re-estimation of abundance was used to estimate the relative abundance of species (online supplemental table 2). The alpha diversity index for each sample was calculated based on Shannon and Simpson indexes, and beta diversity was calculated based on weighted UniFrac distance and Bray-Curtis distance. P values for alpha and beta analysis were calculated using the Wilcoxon rank-sum test. Linear discriminant analysis (LDA) effect size was used to find significantly different species between groups, with thresholds of log10 LDA Score≥2 and p value≤0.05. The correlation between the representative microbiota and clinical data was evaluated by Spearman.

RESULTS

Patient recruitment and clinical characteristics

A total of 123 patients with 128 BALF specimens were reviewed and 111 patients were enrolled in this study.

Twenty-six patients were diagnosed with probable IPA (named IPA) and 85 controls without IPA (named control) (figure 1). Comparisons of demographic and clinical characteristics between the IPA group and the control group were summarised in Table 1. The study population had a median age of 60 (IQR, 53–73) years, and 66.7% were men. Overall, 33 patients (29.7%) had a history of smoking. Twenty-two patients (19.8%) had chronic obstructive pulmonary disease. Fifteen patients (13.5%) had received corticosteroids and 36 patients (32.4%) were considered to be immunocompromised (online supplemental table 3). Patients with probable IPA had a significantly higher rate of known risk factors for aspergillosis than those without IPA, including treatment with corticosteroids longer than 28 days (38.5% vs 5.9%, p=0.000) and immunocompromised status (53.8% vs 25.9%, p=0.008). No significant differences were found in sequential organ failure assessment scoring and confusion, urea, respiratory rate, blood pressure, and age 65 scoring of patients between the two groups on admission.

Microbiological, CT images, and laboratory findings

Aspergillus fumigatus isolates were obtained for 6 of 26 (23.1%) probable IPA cases, followed by Aspergillus flavus for 3 of 26 (11.5%) and Aspergillus ustus for 1 of 26 (3.8%) (table 2). Bronchial wall thickening was more often shown on CT of the chest in the IPA (44.0% vs 12.8%, p=0.000) (table 2). IPA patients have significantly lower levels of total lymphocyte count (774.1 vs 1088.9, p=0.009) and lymphocyte subpopulation, including CD3+ T lymphocyte count (553.0 vs 782.6, p=0.016), CD4+ T lymphocyte count (287.9 vs 409.5, p=0.031) and CD4− CD8− T lymphocyte count (25.9 vs 45.0, p=0.007) than control patients (online supplemental table 4), indicating the compromised immune function of IPA patients.

Comparison of diagnostic performance

To compare the diagnostic performance of BALF mNGS and conventional tests for distinguishing cases with probable IPA from cases without probable IPA, the results were shown in Figure 2. GM test (57.7%) had the highest sensitivity in detecting the Aspergillus spp, followed by mNGS (42.3%), culture (30.8%), and smear (7.7%). Compared with the smear, mNGS had a significantly higher sensitivity 42.3% vs 7.7%, p=0.01). The mNGS, culture, and smear had 100% specificity, while the GM test had 92.9% specificity, with a significant difference between mNGS (or culture or smear) and the GM test (p=0.029). The PPV of mNGS, culture, and smear for identifying Aspergillus spp was 100%, while the GM test was 71.4%. The NPV ranked as GM test (87.8%), mNGS (85.0%), culture (82.5%), and smear (78.0%), with no significant difference among these methods. We have analyzed differences in sensitivity/specificity/PPV/ NPV of GM on serum versus BALF, using a BAL fluid GM index of ≥ 1.0 or serum GM index of ≥1.0 as positive, according to 2019 EORTIC/MSG. It was significantly higher in sensitivity but lower in the specificity of GM BALF than that of GM serum (57.7% vs 26.9%, p=0.048, 92.9% vs 100%, 0.029, respectively), which was shown in online supplemental table 5. There were no differences in PPV and NPV.

Clinical outcomes

There were no significant differences in 28-day, 60-day, and 90-day mortality between the two groups (table 3). Hospital mortality (30.8% vs 11.8%, p=0.021) and intensive care unit (ICU) mortality rates (42.1% vs 17.5%,p=0.029) were significantly higher in the IPA group than in the control group. The distribution of patients falling into each category of the 7-category scale showed a statistical difference between the two groups by day 28 (p=0.017); however, significance with statistical testing was not reached in the 7-category ordinal scale by days 1, 7, and 14 (figure 3). Multiple antifungal drugs were administered for 23.1% of patients with probable IPA, compared with 5.9% of patients without probable IPA (p=0.01). The proportion of patients receiving voriconazole was remarkably higher in cases with probable IPA (53.9% vs 8.2%, p=0.000). There was no significant difference in the use of amphotericin B and caspofungin between the two groups (table 3).

Lung microbiome analysis

To compare the overall composition and diversity of the lung microbial signature in patients with IPA and the control, we analyzed BALF specimens collected from 109 CAP patients, including 24 cases (missing 2 patients due to loss of mNGS data) diagnosed with probable IPA (named IPA) and 85 cases without probable IPA (named control). We assessed α diversity of the lung communities using the Shannon and Simpson diversity index. There was a decrease in α diversity both in Shannon and Simpson diversity index in IPA patients as compared with non-IPA patients despite significant differences (figure 4A, B). Using the weighted UniFrac metric and Bray-Curtis metric, we observed that β diversity of IPA patients significantly differed from non-IPA patients (p<0.001; Wilcoxon test), suggesting that lung community structure of patients diagnosed with probable IPA differed substantially from those without probable IPA (figure 4C, D). Based on the average relative abundance, we plotted the top 10 phyla, genera, and species among 2 groups. 

In the overall cohort, Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Ascomycota were the most abundant phyla, whereas Prevotella, Streptococcus, Acinetobacter, and Pneumocystis were the most common genera (figure 5A, B). The relative abundance of the top 10 in species ranked as Pneumocystis jirovecii, Acinetobacter baumannii, Lautropia mirabilis, Streptococcus oralis, Corynebacterium striatum, Human betaherpesvirus 5, Rothia mucilaginosa, Staphylococcus aureus, Prevotella melanogenic, Nocardia Farcinica, with only H. betaherpesvirus 5 was the significantly different between two groups (figure 5C). Focusing on the differential species in the IPA compared with their controls, we found 21 species with LDA Score≥2 and p<0.05, which H. betaherpesvirus 5, A. fumigatus, Aspergillus niger, Citrobacter braakii, Bacillus thermoamylovorans, Helcococcus kunzite, Lactobacillus delbrueckii, Burkholderia dolosa, Marinobacter hydrocarbonoclasticus, Riemerella anatipestifer, Corynebacterium halotolerant, and Lactobacillus plantarum were significantly abundant in the cases diagnosed with probable IPA, and Streptococcus salivarius, Citrobacter freundii, Paraburkholderia fungorum, Dolosigranulum program, Prevotella timonensis, Sphingobium yanoikuyae, Serratia marcescens, and Corynebacterium oculi were enriched in cases without probable IPA (figure 5D, online supplemental figure 1).

To further investigate the correlation between clinical data and the lung microbiota of IPA, we performed Spearman’s rank-based correlation test on the 21 significantly different species with clinical outcomes and laboratory findings (figure 6A). We observed 19 different species were significantly correlated with 7 clinical outcomes and 22 laboratory biomarkers, particularly S. salivarius, P. Sinensis, and H. betaherpesvirus 5. S. salivarius was primarily positively correlated with eight laboratory biomarkers, including red blood cells, total lymphocytes, T lymphocytes, T helper cells, cytotoxic T lymphocytes, B lymphocytes, serum potassium, and albumin, and negatively relative with aspartate aminotransferase, direct bilirubin, and D-dimer. Whereas, S. salivarius was mainly negatively associated with 11 clinical outcomes, including ICU mortality, duration of ICU stay, hospital mortality, mortality at day 28, and time to supplemental oxygen independence within 28 days, and positively correlated with duration of hospital stay mortality at day 28, ventilator-free days within 28 days and time to improvement in clinical status. Similar to S. salivarius, P. timonensis was primarily positively relative to six laboratory biomarkers, including total lymphocytes, T lymphocytes, T helper cells, CD4− CD8− T cells, B lymphocytes, and serum potassium, and negatively correlated with procalcitonin. 

Equally, P. timonensis was mainly negatively associated with duration of ICU stay and hospital mortality, but positively with ventilator-free days within 28 days. Opposite to S. salivarius and P. Sinensis, H. betaherpesvirus 5 was negatively associated with six laboratory biomarkers, including total lymphocytes, T lymphocytes, T helper cells, CD4+ CD8+ T cells, B lymphocytes and serum potassium, and positive relative with blood urea nitrogen. Meanwhile, H. betaherpesvirus 5 was mainly negatively correlated with the duration of ICU stay and time to supplemental oxygen independence within 28 days. These observations suggested the dysbiosis of microbial communities in the lung is associated with the patient’s pathophysiological conditions and thus can influence the clinical outcomes.

To further investigate the clinical impact of 21 significantly different species, we made the survival analysis by plotting the cumulative probability curve. As shown in Figures 6B, and C, the detection of A. fumigatus and H. betaherpesvirus 5 was significantly predictive of worse ICU outcomes (p=0.045, 0.032). While in Figure 6D, E, we found that the detection of S. salivarius and P. timonensis was extremely significantly predictive of improved ICU outcomes (p=0.0031, 0.0054). We thus concluded that in the lung microbiota of CAP, ICU outcomes may be predicted by community composition, specifically A. fumigatus, H. betaherpesvirus 5, S. salivarius, and P. Sinensis.

DISCUSSION

In this retrospective cohort study, we examined the clinical characteristics of IPA. Interestingly, we noted that patients categorized as having probable Aspergillus infection had significantly higher hospital mortality (30.8%) and ICU mortality (42.1%) compared with the control. The mortality at day 28, though not statistically significant, was numerically higher in IPA patients than in the control. These data suggested that patients with Aspergillus infection tend to have a higher early mortality rate. Previous study reported an estimated 45% of ICU mortality and 26% of hospital mortality18 Among invasive tracheobronchial aspergillosis, a rare entity of IPA, 30-day and 90-day mortality rates were both as high as 90%.19 Loughlin et al reported that ICU mortality in patients with probable Aspergillus infection was not significantly higher than those without Aspergillus infection,4 which is possibly due to generally critical status in ventilator-associated pneumonia patient cohort in the ICU setting. In this study, the overall condition of the enrolled patients with CAP was relatively mild, which can be remarkably aggravated by Aspergillus infection and potentially life-threatening.

Cornillet et al have suggested that IPA patients’ nonspecific clinical features and diagnostic difficulties particularly when patients without neutropenia easily lead to suboptimal management and delayed initiation of treatment, resulting in high mortality and morbidity.20 Given the devastating mortality rates of patients with IPA, timely and accurate diagnosis is highlighted and urgently needed for effective antifungal treatment to decrease the morbidity and mortality rate. In the past decade, GM detection has been widely used in the diagnosis of IPA.21 However, GM testing for serum samples has limited sensitivity in diagnosing IPA in non-neutropenic hosts.22 Instead, the detection of GM in BAL fluid samples of patients at risk of IPA has excellent diagnostic accuracy,23 24 because GM is released earlier and at higher concentrations in the BAL fluid in comparison with serum.25 Recently, mNGS has been playing an increasingly important role in the diagnosis of fungal diseases. Ding et al demonstrated that the sensitivity of mNGS was significantly higher than culture in detecting Candida albicans. 9 In this study, BALF was collected directly from the lower respiratory tract, which avoided contamination from the oral microbiota. Aspergillus in BALF were detected by mNGS, culture, smear, and GM test. The results showed that NGS had comparable specificity and PPV to culture in Aspergillus detection. Although the sensitivity of mNGS was lower than the GM test, mNGS showed more sensitivity than the smear and more specificity than the serum and BALF GM tests. These results indicated that mNGS acts as a useful nonculture-based test for detecting Aspergillus, suggesting a complement to routine microbiological tests in the diagnosis of patients at risk of IPA.

Our study discovered that CAP patients with Aspergillus were more likely to be coinfected with bacterial or (and) viral infection due to the immunocompromised status and corticosteroid treatment. Patients with influenza who were immunocompromised were reported to have an incidence of IPA as high as 32%, whereas, in the non-immunocompromised influenza case group, the incidence was 14%.18 With the emergence of the COVID-19 pandemic and corticosteroids use in patients with severe COVID-19,26 COVID-19-associated pulmonary aspergillosis (CAPA) has been reported in several case reports.27–29 In a meta-analysis, the incidence and mortality of CAPA in the ICU were estimated to be 10.2% and 54.9%, respectively.30

Due to the vast microbial reads in the mNGS data that can be used to analyze microbial signatures across numerous diseases, our study attempted to explore the difference in structure and composition of microbiome between IPA patients and non-IPA CAP patients by analyzing the mNGS data. Our present study comprised the lung microbiota of 24 IPA patients and 85 non-IPA CAP patients. The relationship between different microbiota and clinical data for all 109 CAP patients was investigated. For alpha diversity, there were no significant differences in the Shannon index and Simpson index of microbiota in IPA patients and non-IPA CAP patients, demonstrating that the within-sample diversity of communities was similar between IPA patients and non-IPA patients. 

For beta diversity, our results displayed a dramatical difference in the weighted UniFrac distance (p=2.1×10−6) and Bray-Curtis distance (p=0.033) between IPA and non-IPA CAP patients, indicating a potential effect of Aspergillus spp on lung microbiota structure in patients diagnosed with IPA. Similar studies have shown significant differences in the beta diversity of microbiota between IPA and controls.11 This may be because the causative agents in the controls were complex, which can be any CAP pathogens (such as bacteria, viruses, fungi, parasites, and even atypical pathogens) except Aspergillus. Thus the lung microbial heterogeneity in the control was higher than IPA. Meanwhile, these studies demonstrated that Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes were the most abundant phyla in the IPA cohort.11 However, beyond the bacteria, our study found that the relative abundance of Ascomycota was also high in the level of phylum, for the metagenomics can harvest almost all the nuclei acid of microbiota, including bacteria, fungi, viruses, and parasites.

The significantly different species, particularly S. salivarius, P. Sinensis, and H. betaherpesvirus 5 were strongly related to clinical data. S. salivarius and P. timonensis were positively related to laboratory biomarkers such as T lymphocytes and negatively related to clinical outcomes such as hospital mortality. Whereas, H. betaherpesvirus 5 was negatively related to laboratory biomarkers such as T lymphocytes and positively related to clinical outcomes such as hospital mortality. Previous studies demonstrated that S. salivarius is a probiotic bacterium that can inhibit pneumococcal adherence to pharyngeal epithelial cells.31 Also, another study proved that S. salivarius isolates had a superior ability to inhibit the growth of Haemophilus influenzae, Moraxella catarrhalis, Streptococcus pneumoniae, Streptococcus pyogenes, and Staphylococcus aureus. 32 All these studies coincided with our study that S. salivarius was positively correlated with the better clinical outcome of the patients. 

The possible mechanism of S. salivarius positively correlated with the improved outcome of the patients may be that they can inhibit the growth of the potential pathogen and activate the host immune system. For P. Sinensis, a previous study showed that P. timonensis could dramatically induce the expression of membrane-associated mucins in the three-dimensional endometrial epithelial cell model and formed the biofilm in the epithelial surface, which in turn influences the adhesion of other bacteria.33 The reason for the positive correlation with the better clinical outcome of the patients may be the colonization resistance of P. Sinensis in the lung and the activation of the host immune system. In clinical settings, H. betaherpesvirus 5 can build life-long latent infections in healthy people. However, once immunocompromised, H. betaherpesvirus 5 can be reactivated, for example, solid organ transplant recipients and HIV-positive patients.34 Thus, the reactivation of the H. betaherpesvirus 5 indicated the weakened immune system and the poor clinical outcomes of IPA patients.

Our study has several limitations. First, the retrospective study was designed to analyze collected data and was limited to a relatively small sample size, which could predispose it to introduce bias from incomplete information. Second, this was the observation study. Though we found some interesting correlations between lung microbiota and clinical data, the causal relationship needs to be resolved by a prospective cohort study or mechanism experiment, such as an animal experiment, which will be the next step of our study. Otherwise, the study identified patients with probable Aspergillus infection strictly based on new EORTIC/MSG 2019 diagnostic criteria so a few cases could not satisfy the criteria for a probable IPA diagnosis due to the absence of classic host factors.

Contributors

Study concept and design: YL, ZA, and HX. Acquisition of data: ZA, ML, HL, and MD. Analysis and interpretation of data: ZA and HX. Drafting of the manuscript: ZA, HX, and YL. Critical revision of the manuscript for important intellectual content: HX, ZA, and YL. Statistical analysis: ZA, HL, and HX. Administrative, technical, or material support: HX, HL, and YL. All authors contributed to the article and approved the submitted version.

Funding

Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau (2022GDRC010)).

Competing interests

None declared.

Patient and public involvement

Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.

Patient consent for publication

Not applicable.

Ethics approval

This study involves human participants. This study was approved by the ethical committee of the First Affiliated Hospital of Chongqing Medical Hospital (approved number 2021-301). Participants gave informed consent to participate in the study before taking part.

Provenance and peer review

Not commissioned; externally peer-reviewed.

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

Supplemental material

This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names, and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Open access

This is an open-access article distributed by the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

REFERENCES

1 Taccone FS, Van den Abeele A-M, Bulpa P, et al. Epidemiology of invasive aspergillosis in critically ill patients: clinical presentation, underlying conditions, and outcomes. Crit Care 2015;19:7. 

2 Danion F, Rouzaud C, Duréault A, et al. Why are so many cases of invasive aspergillosis missed? Medical Mycology 2019;57:S94–103. 

3 Donnelly JP, Chen SC, Kauffman CA, et al. Revision and update of the consensus definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clin Infect Dis 2020;71:1367–76. 

4 Loughlin L, Hellyer TP, White PL, et al. Pulmonary aspergillosis in patients with suspected ventilator-associated pneumonia in UK ICUs. Am J Respir Crit Care Med 2020;202:1125–32. 

5 Hage CA, Carmona EM, Evans SE, et al. Summary for clinicians: microbiological laboratory testing in the diagnosis of fungal infections in pulmonary and critical care practice. Annals ATS 2019;16:1473–7. 

6 Mitchell SL, Simner PJ. Next-generation sequencing in clinical microbiology: are we there yet? Clin Lab Med 2019;39:405–18. 

7 Liu X, Chen Y, Ouyang H, et al. Tuberculosis diagnosis by metagenomic next-generation sequencing on bronchoalveolar lavage fluid: a cross-sectional analysis. Int J Infect Dis 2021;104:50–7. 

8 Sun T, Wu X, Cai Y, et al. Metagenomic next-generation sequencing for pathogenic diagnosis and antibiotic management of severe community-acquired pneumonia in immunocompromised adults. Front Cell Infect Microbiol 2021;11:661589. 

9 Ding L, Liu Y, Wu X, et al. Pathogen metagenomics reveals distinct lung microbiota signatures between bacteriologically confirmed and negative tuberculosis patients. Front Cell Infect Microbiol 2021;11:708827. 

10 Dickson RP, Schultz MJ, van der Poll T, et al. Lung microbiota predicts clinical outcomes in critically ill patients. Am J Respir Crit Care Med 2020;201:555–63. 

11 Hérivaux A, Willis JR, Mercier T, et al. Lung microbiota predicts invasive pulmonary aspergillosis and its outcome in immunocompromised patients. Thorax 2022;77:283–91. 

12 Mandell LA, Wunderink RG, Anzueto A, et al. Infectious diseases society of America/American thoracic society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 2007;44 Suppl 2:S27–72. 

13 Wang Y, Fan G, Horby P, et al. Comparative outcomes of adults hospitalized with seasonal influenza A or B virus infection: application of the 7-category ordinal scale. Open Forum Infect Dis 2019;6:ofz053. 

14 Chen X, Ding S, Lei C, et al. Blood and bronchoalveolar lavage fluid metagenomic next-generation sequencing in pneumonia. Can J Infect Dis Med Microbiol 2020;2020:6839103. 

15 Long Y, Zhang Y, Gong Y, et al. Diagnosis of sepsis with cell-free DNA by next-generation sequencing technology in ICU patients. Arch Med Res 2016;47:365–71.

For more information:1950477648nn@gmail.com