Impact of Nocardia Infection on Airway Microbiota in Lung Transplant Recipients: A Single-center, Retrospective Observational Study

Impact of Nocardia Infection on Airway Microbiota in Lung Transplant Recipients: A Single-center, Retrospective Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Nocardia Infection on Airway Microbiota in Lung Transplant Recipients: A Single-center, Retrospective Observational Study Zhibin Xu, Hengyu Zhao, Xiaohua Wang, Yu Xu, Yi Lu, Jiaqi Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6947454/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: To investigate the impact of Nocardia infection on the airway microbiota composition in lung transplant recipients (LTRs) using mNGS and evaluate associations between microbial alterations and clinical parameters.. Methods : This single-center, retrospective cohort study analyzed 679 LTRs (2015–2024), including 20 Nocardia-infected patients (experimental group, EG), 40 matched controls, and 16 post-treatment group (TG) cases. Bronchoalveolar lavage fluid (BALF) was subjected to mNGS to characterize microbial composition. Multi-modal were applied to analyze integrated α/β-diversity metrics, LEfSe biomarker identification, SparCC co-occurrence networks, and clinical parameter correlations (NCT06594133). Results: Microbiome analysis demonstrated comparable alpha diversity but distinct beta diversity profiles (P=0.025) between groups. The EG exhibited significant enrichment of Nocardia species (predominantly N. farcinica and N. cyriacigeorgica ), alongside decreased Rothia mucilaginosa and Burkholderia species versus controls. Therapeutic intervention substantially reduced Nocardia abundance in treated subjects. Network analysis revealed class_Caudoviricetes displayed inverse associations with Nocardia , Achromobacter , Mastadenovirus , and Yersinia . Nocardia-centered subnet showed positive correlations with Enterococcus , Mastadenovirus , and Yersinia , contrasting with negative correlations involving Capnocytophaga , Rothia , and class_Caudoviricetes. In EG, a robust positive correlation was found of CD4⁺ T-cell counts and the CD4/CD8 ratio with serum IgA, IgM, and IgG (each p <0.05). Spearman analysis linked N. farcinica and N. cyriacigeorgica inversely to CD4⁺ count and albumin; N. farcinica also inversely correlated with IgG, IgA, total lymphocyte count, and hemoglobin; and N. cyriacigeorgica inversely correlated with the CD4/CD8 ratio and positively with CD8⁺ T cells. Conclusions: Nocardia infection induces significant alterations in the airway microbiota of LTRs, characterized by Nocardia enrichment and specific commensal depletion. These microbial shifts are strongly associated with host immune and nutritional status. Our findings highlight the critical role of airway microbial dysbiosis in Nocardia infection pathogenesis and suggest microbiota-targeted strategies that down-regulation the immunosuppressants might be a potential adjunct strategy in Nocardia infection in lung transplant recipients. Nocardia infection Lung transplantation Airway microbiome mNGS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung transplantation (LT) remains the definitive treatment for end-stage pulmonary disease, yet post-transplant management is complicated by high infection rates [ 1 ]. Essential chronic immunosuppression compromises host immunity, increasing susceptibility to infections, including opportunistic pathogens like Nocardia species [ 2 ]. Infection risk in lung transplant recipients (LTRs) is multifactorial, involving immunosuppression intensity and alterations in the airway microecology—a key focus of recent research [ 3 ]. The airway microbiota’s equilibrium is vital for respiratory immune homeostasis. Nocardia species are particularly relevant pathogens in LTRs [ 4 ], an immunocompromised population at high risk for nocardiosis [ 5 ]. Nocardia infections can cause severe pulmonary infections and, in some cases, disseminated infections with high mortality rates. Currently, research on Nocardia infections in LTRs is limited, mostly consisting of case reports. Studies indicate that the incidence of Nocardia infection in LTRs ranges from 0.04–35%, with the highest rates observed in lung transplant recipients (0.8–35%) [ 6 ]. Diagnosing and treating Nocardia infections poses significant challenges due to non-specific clinical, laboratory, and radiographic findings, often leading to delayed diagnosis. Traditional culture methods are slow, further hindering timely intervention. While progress has been made in understanding Nocardia clinical features, diagnostics, and treatment [ 7 ], the impact of Nocardia infection on the LTR airway microecology and the mechanisms remain poorly characterized. Our research team has maintained a longstanding commitment to investigating Nocardia infections, achieving several notable contributions to this field. In one study, we characterized the clinical features of Nocardia infections in LTRs, demonstrating that traditional culture methods achieved a diagnostic rate of only 77.8%, whereas metagenomic next-generation sequencing (mNGS) attained 100% diagnostic sensitivity, significantly enhancing diagnostic efficiency and accuracy [ 8 ]. We also characterized typical thoracic imaging features (e.g., multiple nodules, consolidations) and showed that early combination antibiotic therapy improved outcomes in affected LTRs [ 9 ]. Furthermore, we synthesized recent advances in diagnosis and management through a narrative review [ 7 ]. Building on this foundation, the present study utilizes mNGS and 16S rRNA sequencing to investigate the effects of Nocardia infection on the airway microbiota in LTRs. By correlating microbial community shifts with clinical features, we aim to elucidate mechanisms of Nocardia -induced dysbiosis and explore its clinical sequelae. 1. Materials and Methods 1.1 Study Population This retrospective cohort study, approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (Ethics Approval No: ES-2024-K144) and registered with ClinicalTrials.gov (NCT06594133, 2024-11-11), analyzed 679 lung transplant recipients (LTRs, 2015–2024). From these, 20 patients with Nocardia infection were selected as the experimental group (EG) and matched 1:2 with 40 uninfected controls based on age, gender, and primary disease[ 10 ]. All participants provided written informed consent. Follow-up data from 16 EG patients (treatment group, TG) were collected during infection and 4 months post-treatment, categorized as recovered (n = 13) or not recovered (n = 3). Demographic, immune (e.g., lymphocyte count , CD4⁺/CD8⁺ T cells), and nutritional data (e.g., albumin, hemoglobin) were extracted from electronic medical records. Rigorous matching and inclusion criteria minimized bias, ensuring data reliability (Supplementary Fig. 1). 1.1.1 Inclusion Criteria for Experimental Group (EG) ① Age ≥ 18 years; ② Receipt of single lung, bilateral lung, or heart-lung transplantation; ③Diagnosis of Nocardia infection meeting criteria established in “ Nocardia Infections in Solid Organ Transplantation: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice” [ 11 ]; ④ Clinical manifestations of respiratory tract-related symptoms; ⑤ Radiographic evidence of pulmonary lesions; ⑥ Pathogenic confirmation of Nocardia infection through fiberoptic bronchoscopy sampling (e.g., deep sputum, bronchoalveolar lavage fluid) with positive smear, high-throughput sequencing, or culture results. 1.1.2 Exclusion Criteria for Experimental Group (EG) ① LTRs with incomplete clinical data or follow-up information; ② LTRs who declined consent for use of clinical samples in research; ③ Cases with acute or chronic rejection or mortality. 1.1.3 Inclusion Criteria for Control Group (CG) ① Age ≥ 18 years; ② Receipt of single lung, bilateral lung, or heart-lung transplantation; ③ Stable clinical status without Nocardia infection post-transplantation; ④ Matched with EG group LTRs regarding baseline characteristics including gender, age, and primary disease type. With stable clinical status for continuous 3 months without pulmonary complications. 1.2 Nucleic acid extraction, library preparation, and sequencing Total DNA was extracted from 200 µL aliquots of bronchoalveolar lavage fluid (BALF) from LTRs using the QIAamp DNA Micro Kit (QIAGEN, Hilden, Germany) following the manufacturer’s protocol. DNA concentration was quantified via fluorometry (Qubit 3.0, Invitrogen), and integrity was assessed by agarose gel electrophoresis. Metagenomic libraries were prepared from extracted DNA using the QIAseq Ultralow Input Library Kit (QIAGEN). Library concentration was determined (Qubit 3.0), and size distribution and quality were validated using a Bioanalyzer (Agilent 2100, Agilent Technologies, Palo Alto, CA). Indexed, quality-passed libraries were pooled and subjected to paired-end sequencing on an Illumina NextSeq 550 platform. 1.3 Sequencing Data Processing and Microbial Identification Raw sequencing reads were quality-filtered using Trimmomatic (v0.39) [ 12 ] and host reads were mapped to human reference genomes (GRCh38, YH genome) and human sequences within the NCBI NT database by Bowtie2 (v2.4.2) [ 13 ] based on KneadData (v0.7.4). Remaining non-human reads were taxonomically assigned by alignment to microbial sequences (bacteria, fungi, archaea, viruses) in the NCBI NT database using BLASTN (v2.10.1+, megablast algorithm) [ 14 ]. Only reads uniquely mapped to microbes were used for subsequent analyses. 1.3 Donors Organ procurement in this study strictly adhered to China's legislative framework, effective from January 2015, regarding voluntary civilian-based deceased organ donation. All organs were donated following cardiocirculatory or brain death, in compliance with the judicial system[ 15 ]. This approach underscores China's commitment to ethical organ transplantation, with the civilian organ donation program being the sole legal source for such procedures. The law necessitates that all transplant organs be derived from donors, with written informed consent obtained from the donors themselves or their family members, ensuring ethical and legal adherence in organ transplantation processes. 1.4 Statistical Analysis Alpha diversity indices (Shannon, Simpson, Chao1, ACE) were calculated using the R library "vegan". Beta diversity was assessed using Bray-Curtis distance metrics, and visualized with principal coordinate analysis (PCoA) by R library "ape". Differences in continuous variables between groups were evaluated using the Mann-Whitney U test. A nonparametric Spearman’s correlation test was used to assess the relationships between continuous variables. Linear discriminant analysis effect size (LEfSe) identified discriminating microbial taxa between groups (LDA score > 2) [ 16 ]. Statistical analyses were performed using R version 4.1.0, with P < 0.05 considered significant. Generas with more than 1000 reads among all samples were selected to construct the interaction network using SparCC algorithm [ 17 ] and 1000 bootstraps were used to calculate correlations and p values. Networks were produced by retaining edges (correlation coefficient R ranges between − 0.3 and 0.3 and p < 0.05). Networks were visualized using Cytoscape 3.9.1 [ 18 ] . 2. Results 2.1 Microbial Community Composition and Diversity Metagenomic comparative analysis of microbial profiles revealed distinctive patterns among the EG, Control, and TG groups. At the species level, a core microbiome of 2183 microbial species (representing 34.8% of the total species identified) was shared across all three cohorts. The EG and TG groups harbored 966 (15.4%) and 833 (13.3%) unique species, respectively, while the Control group contained 730 (11.6%) exclusive species. At the genus level, 789 genera (43.6% of total) were common to all three groups; the EG, Control, and TG groups possessed 234 (12.9%), 182 (10.1%), and 203 (11.2%) distinct genera, respectively (Fig. 1 A). Taxonomic profiling revealed distinct microbial compositions across the groups (Fig. 1 B). While taxa such as Pseudomonas, Burkholderia, Paraburkholderia , family Anelloviridae , and class_Caudoviricetes were abundant across cohorts, their relative distributions varied significantly. Quantitative analysis of the top 20 genera underscored these differences (Supplementary Table 1): the Control group harbored the highest relative abundances of Burkholderia (mean 17.0%), family Anelloviridae (12.2%), Paraburkholderia (14.0%), and Rothia (4.2%). Conversely, the EG group was characterized by a substantial enrichment of Nocardia (11.1% vs < 0.01% in other groups). The TG group exhibited the highest relative abundance of Pseudomonas (20.5%). Analysis of α-diversity revealed no statistically significant differences in within-sample microbial richness or evenness among the groups (Shannon, Simpson, P>0.05; Chao1, P ≥ 0.44; ACE, P ≥ 0.44; Fig. 1 C). Despite similar α-diversity, β-diversity analysis using Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarities demonstrated clear separation of microbial communities according to group (Fig. 1 D). This visual distinction was statistically significant, as confirmed by PERMANOVA analysis, indicating significantly different overall microbial compositions among the Control, EG, and TG groups (R² = 0.0433, P = 0.025). Statistical analysis showed that although Nocardia remained detectable in the 3 cases in the Not Recovered Group (NRG group), there were no statistically significant differences in Nocardia’s relative abundance or sequencing read counts between the EG and NRG groups, despite apparent visual disparities. These findings suggest that treatment alters but does not fully restore the microbial balance in NRG patients and support the hypothesis that Nocardia infection may act by modulating the abundance of specific microbial communities (Fig. 1 E). 2.2 Microbial Ecological Biomarkers Across Different Groups To systematically identify the distinctive impact of Nocardia infection on airway microbial community structure and its response to treatment, a complementary analytical approach integrating LEfSe and Wilcoxon rank-sum tests for both cross-sectional and longitudinal comparisons was employed. LEfSe analysis (Fig. 2 A) revealed that the EG group was characterized by significant enrichment of Nocardia farcinica and Nocardia cyriacigeorgica (all LDA > 4), as well as increased abundance of commensals such as Ligilactobacillus salivarius , Pantoea ananatis , and Hymenobacter aerilatus (LDA > 2). In the Control group, Rothia mucilaginosa and several Burkholderia species predominated, reflecting a more balanced microbiota. Following treatment (TG), distinct taxa including Pseudopropionibacterium propionicum and Burkholderia metallica emerged, indicating microbial community restructuring in response to intervention. Cross-sectional analysis (Fig. 2 B) further confirmed these findings, with both Nocardia farcinica (P = 1.0×10⁻⁶) and Nocardia cyriacigeorgica (P = 2.9×10⁻⁵) significantly enriched in the EG. Other species such as Human alphaherpesvirus 1 (P = 2.9×10⁻⁵), Staphylococcus capitis (P = 1.0×10⁻⁴), and Ligilactobacillus salivarius (P = 1.0×10⁻⁴) were also elevated in the infection group. Conversely, Rothia mucilaginosa and multiple Burkholderia species (e.g., B. pseudomallei , B. thailandensis ) were reduced in the presence of infection, likely reflecting niche competition following pathogen colonization. Longitudinal analysis (Fig. 2 C) assessed the microbiome dynamics following therapy, demonstrating a significant reduction in Nocardia cyriacigeorgica (P = 0.003) and Nocardia farcinica (P = 0.011) in the TG, alongside shifts in other taxa, including Pseudomonas aeruginosa . These results indicate that antimicrobial treatment not only suppresses pathogenic species, but may also restore ecological balance through broader effects on the airway microbiome. Integrated evaluation of the three analytical approaches (Supplementary Fig. 2) identified 60 taxa with significant intergroup variation. Of these, 24 (40.0%) were unique to LEfSe, 17 (28.3%) to cross-sectional, and 6 (10.0%) to longitudinal analysis. LEfSe and cross-sectional analyses shared 9 taxa (15.0%), while LEfSe and longitudinal analyses overlapped for 2 taxa (3.3%); only 2 taxa (3.3%) were identified by all three methods. This limited overlap, yet notable complementarity, highlights the necessity of a multi-method approach in comprehensively characterizing Nocardia -associated microbiome alterations. 2.3 Microbial Co-occurrence Network Analysis in Nocardia Infection The microbial co-occurrence network was constructed using the SparCC algorithm to investigate inter-taxa associations within the airway microbiota during Nocardia infection. Overall network analysis (Fig. 3 A) revealed a complex network structure comprising 53 nodes (representing microbial taxa) and 273 edges (representing inter-taxa associations), with approximately one-third (33.33%) representing negative correlations. The top 10 nodes by degree, indicating central taxa, included Paraburkholderia , Caballeronia , Trinickia , Cupriavidus , Burkholderia , Bradyrhizobium , Sphingomonas , Mesorhizobium , class_Caudoviricetes , and Achromobacter . Notably, class_Caudoviricetes exhibited extensive negative correlations with several taxa, including Achromobacter , Mastadenovirus , Nocardia , and Yersinia . To specifically examine Nocardia and its direct microbial associates, a one-step subnet centered on Nocardia was extracted and analyzed (Fig. 3 B). This subnet consisted of 25 nodes and 56 edges. Within this subnet, Nocardia showed positive correlations with Enterococcus , Mastadenovirus , and Yersinia , and negative correlations with Capnocytophaga , Rothia , and class_Caudoviricetes . These co-occurrence network analyses provide a visual representation of complex inter-taxa association patterns within the airway microbiota during Nocardia infection and highlight potential key interacting partners. 2.4 Correlation Between Clinical Features and Microbiota Pearson correlation analysis (Fig. 4 A) showed strong positive correlations among white blood cell count, neutrophil count, and neutrophil percentage. CD4⁺ count correlated negatively with CD8⁺ count; the CD4/CD8 ratio correlated positively with CD4⁺ and negatively with CD8⁺. Total lymphocyte count positively correlated with albumin, hemoglobin, and immunoglobulins. Albumin also correlated positively with hemoglobin and CD4⁺ count. Spearman correlation analysis (Fig. 4 B) explored associations between differential microbial taxa abundance and host parameters, visualized in a heatmap including enrichment groups (LEfSe, cross-sectional, longitudinal). In the EG group, Nocardia farcinica and Nocardia cyriacigeorgica , the dominant pathogens, consistently correlated negatively with CD4⁺ and ALB. N. farcinica also showed inverse associations with IgG, IgA, T lymphocytes, HGB, and total lymphocytes, while N. cyriacigeorgica correlated negatively with the CD4/CD8 ratio and positively with CD8⁺. In the TG group, Penicillium rubens correlated negatively with IgG and the CD4/CD8 ratio, and positively with CD8⁺ count. Aspergillus glaucus correlated negatively with NEUT%. Among longitudinal (EG vs TG) differential taxa, Pseudopropionibacterium propionicum correlated significantly negatively with IgM. In cross-sectional analysis (EG vs Control), Human alphaherpesvirus 1 correlated significantly negatively with estimated glomerular filtration rate (eGFR) and ALB, and significantly positively with Cr, NEUT, and WBC. 3. Discussion Respiratory disease has been found to be associated with the airway microbiota’s dysbiosis. This study analyzed the effects of Nocardia infection on the airway microbiota of LTRs and found that changes in the airway microbiota were closely correlated with the patient’s immune status and clinical characteristics. Specifically, we observed significant correlations between Nocardia cyriacigeorgica , Nocardia farcinica , Burkholderia , and Rothia mucilaginosa abundance and various immune markers, suggesting that alterations in the microbial community may influence the progression of post-transplant infections. As far as we know, this is the first study to investigate the association of nocardiosis with airway microbiota. In this study, both the top microbial taxa and the dominant species analysis revealed that specific genera in the EG Group occupied a significantly dominant position. The top genera in the EG group included not only known opportunistic pathogens but also species that are typically suppressed under normal conditions. Further selection by LEfSe demonstrated a notable increase in specific pathogens, such as Nocardia farcinica and Ligilactobacillus_salivarius , in the EG group. The rise of these genera may exacerbate the severity of infection and suggest that Nocardia infection might act synergistically with multiple pathogens, presenting a greater threat to patient health. The phenomenon of co-infection in immunosuppressed patients is of particular concern, as it often exacerbates disease complexity and affects the host’s immune response. As highlighted by Behdenna et al. [ 19 ], multi-pathogen infections can alter transmission efficiency and virulence, ultimately influencing the overall disease outcome. Additionally, co-infection is particularly prevalent in respiratory tract infections, as demonstrated by Kong et al. [ 20 ], who observed significant co-infection of multiple respiratory pathogens, especially in coronavirus infections. A clear divergence in species composition was observed between the EG and control groups, with certain species’ abundance notably higher in the EG group. This differential increase in species may not only be a result of Nocardia infection but may also be amplified by interactions with other microbial communities, worsening the clinical condition. Co-infection with multiple pathogens poses a significant challenge in transplant recipients, increasing the difficulty of infection control. This complex infection mechanism alters the patient’s clinical presentation and reduces the sensitivity and specificity of traditional diagnostic methods, complicating clinical management. These co-infection phenomena present a formidable challenge for infection control in transplant recipients, as co-infections generate complex inter-pathogen interactions, including competition, symbiosis, and coexistence [ 21 ]. In terms of infection control, Susi et al. [ 22 ] indicated that co-infection has a profound impact on epidemiological and population control strategies, as it can drive pathogen community dynamics and increase the risk of disease transmission. Therefore, our study highlights the importance of focusing on the pathogenic role of co-infections and their role during the course of Nocardia infection in immunosuppressed patients. Nocardia infection induces complex alterations in the airway microbiota of lung transplant recipients, which may subsequently increase the risk of secondary infections. Spearman correlation analysis showed positive correlations between Nocardia and opportunistic pathogens such as Enterococcus, Mastadenovirus , and Yersinia , while negative correlations were observed with Rothia and the Caudoviricetes family of bacteriophages. These synergistic or inhibitory interactions among genera may increase the complexity and severity of infections in immunosuppressed patients. Genera that exhibited positive correlations, such as Enterococcus and Mastadenovirus , may shift from commensal to opportunistic pathogens under immunosuppressive conditions, thus increasing the patient’s immunological burden and exacerbating the severity of the infection. The immunosuppressive effect increases the invasiveness of these genera in the airway ecosystem, particularly in LTRs. This co-infection is prevalent in clinical practice, as the patient’s compromised immune function diminishes their resistance to bacteria, thereby increasing the risk of secondary infections. Studies indicate that such synergistic effects not only accelerate pathogen spread in the airways but also alter the host’s local immune environment, exacerbating the inflammatory response to infection [ 23 ]. The impact of positive correlation co-infections on airway microbiota disruption occurs primarily through two mechanisms. First, Enterococcus can secrete extracellular polymers and toxins, thereby disrupting the host’s immune defense system and promoting the proliferation of Nocardia and other pathogens [ 24 ]. Specifically, Enterococcus can form biofilms, secreting extracellular polymers that enhance its adhesion to host tissues and persistence [ 25 ]. Furthermore, toxins such as cytolysins produced by Enterococcus can directly damage host cells, weakening immune defenses [ 26 ]. Collectively, these mechanisms create favorable conditions for the colonization and spread of pathogens like Nocardia . Second, while Lactobacillus is generally regarded as a beneficial bacterium, under certain conditions, such as hypoxic environments and nutrient competition, it may further decrease the abundance of beneficial bacteria in the airways through its metabolic byproducts, thus destabilizing the microbial balance [ 27 ]. Recent studies [ 28 ] have shown that Mastadenovirus in immunosuppressed hosts can act as an inflammatory activator, especially in conditions of local imbalance, which increases its pathogenic potential. These synergistic interactions provide opportunities for pathogen proliferation and growth, emphasizing the importance of maintaining microbial balance in the airway microbiota to prevent severe infections. As Nocardia abundance increases, Rothia abundance significantly decreases. This negative correlation suggests that Nocardia proliferation may inhibit the growth of Rothia , thereby disturbing the airway microbiota balance. Rothia , particularly Rothia mucilaginosa , plays a critical protective role in the healthy airway microbiota [ 29 ]. Studies [ 30 – 32 ] have shown that Rothia can produce antimicrobial metabolic products, such as hydrogen peroxide, which inhibit opportunistic pathogens like Staphylococcus aureus , thereby helping to maintain microbiota balance and enhance host immune defenses. Additionally, Rothia mucilaginosa can inhibit the activation of the NF-κB pathway, reducing the production of pro-inflammatory cytokines, thus exerting an anti-inflammatory effect. These functions help maintain the barrier function of airway epithelial cells and induce the host to produce antimicrobial peptides, strengthening local immune defense [ 33 , 34 ]. The significant decline in Rothia abundance may also reflect interference from Nocardia through nutrient competition or the secretion of inhibitory substances, further disrupting the balance. This dysbiosis likely weakens airway defense mechanisms, making patients more susceptible to Nocardia and other pathogens [ 35 ]. Several studies [ 33 , 34 , 36 ] have shown that the decline in Rothia abundance is often associated with a decrease in airway microbiota diversity, which increases the incidence and severity of respiratory infections, particularly in immunocompromised patients. Therefore, in addition to antimicrobial therapy targeting Nocardia , maintaining the abundance of protective genera like Rothia may be a potential adjunct therapy in reducing the risk of co-infections. Future research should explore targeted microbiota interventions, such as specific probiotics or microbiota transplantation techniques, to restore Rothia levels and assist in treating Nocardia infections while lowering the risk of secondary infections. Given these clinical risks, strengthening microbiota management and individualized anti-infection strategies is crucial. Infection monitoring based on metagenomic sequencing techniques can identify synergistic pathogen interaction patterns, providing data support for precise diagnosis. Clinicians can utilize these findings to develop targeted combination therapies, thereby improving the success rate of infection control. Furthermore, appropriate adjustments to immunosuppressive drug use to maintain partial immune function may help reduce the occurrence of co-infections. In conclusion, changes in the airway microbiota play a critical role in immune regulation of post-transplant infections. Different bacterial infections not only alter the balance of immune cells but may also affect the patient’s biochemical markers and immune response, thereby influencing clinical outcomes. We speculate that Nocardia cyriacigeorgica and Nocardia farcinica may disrupt immune homeostasis in lung transplant recipients through specific immune modulation mechanisms, exacerbating complications. 4. Limitations We acknowledge the single-center design and limited sample size, particularly for the refractory group. This preliminary study provides novel insights into Nocardia-driven microbiota changes using mNGS in lung transplant recipients. We propose larger, multicenter studies to enhance the generalizability. 5. Conclusion This study demonstrates that Nocardia infection significantly remodels the airway microbiome in LTRs, shifting the balance towards pathogenic dominance and commensal depletion. This dysbiosis is not isolated but is intimately linked to the host’s immune and nutritional state, with specific microbial taxa showing strong correlations with key clinical indicators. The observed microbial interaction patterns, including potential synergy between Nocardia and opportunistic microbes and antagonism with beneficial taxa, underscore the complexity of post-transplant infections. Our findings establish airway microbial dysbiosis as a critical factor in Nocardia infection pathogenesis in LTRs, suggesting that monitoring and potentially manipulating the airway microbiome could offer valuable strategies for prevention, diagnosis, and treatment. Declarations Ethics Approval and Consent to Participate This retrospective cohort study was conducted in accordance with the ethical principles of the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/) and approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (Approval No: ES-2024-K144). All participants provided written informed consent for the use of their clinical data in research. Patient identities were protected through full anonymization of datasets prior to analysis. Consent for Publication Written informed consent for publication was obtained from all participants. The consent forms explicitly stated that anonymized data derived from their medical records may be included in scientific publications. No personally identifiable information (including names, hospital identification numbers, or images) is presented in this manuscript. Declaration of competing interest The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper. Author’s Contribution Z. Xu and H.Y. Zhao contributed equally to the study. Z. Xu and H.Y. Zhao conceived and designed the study. H.Y. Zhao was responsible for data collection, data analysis, results visualization, and manuscript writing. X.H. Wang and Y. Xu managed the project and participated in data analysis discussions. Y. Lu was involved in data analysis discussions. J.Q. Chen, Q.Y. Ye , and X. Li handled patient follow-up, clinical information collection, and data management. Y.H Wen provided support in data analysis and participated in related discussions. C.R Ju contributed to the conception and design, funding acquisition, project management, and the review and editing of the final manuscript. All authors reviewed and approved the final version of the manuscript and agree to be 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. Funding This study was supported by the Specific Clinical Technology Project of Guangzhou (2023C-TS10), the Guangzhou Medical University Research Capacity Enhancement Programme Major Clinical Research Projects (GMUCR2024-01007), the Clinical Epidemiology Research Program of the State Key Laboratory of Respiratory System Diseases (SKLRD-L-202504), the Chinese Medical Education Association (ZJWYH-2023-YIZHI-003), the National Natural Science Foundation of China, and the Wu Jieping Medical Foundation Research Special Funding Fund (320.6750.2025-01-1). Acknowledgments We gratefully acknowledge HUGO BIOTECH for their invaluable assistance with the mNGS analysis. 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Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. Behdenna A, Lembo T, Calatayud O, Cleaveland S, Halliday JE, Packer C, Lankester F, Hampson K, Craft ME. Czupryna AJPotRSB: Transmission ecology of canine parvovirus in a multi-host, multi-pathogen system. 2019, 286:20182772. Kong D, Zheng Y, Hu L, Chen J, Wu H, Teng Z, Zhou Y, Qiu Q, Lu Y. Pan HJEm, infections: Epidemiological and co-infection characteristics of common human coronaviruses in Shanghai, 2015–2020: a retrospective observational study. 2021, 10:1660–8. Venkatesh N, Koss MJ, Greco C, Nickles G, Wiemann P, Keller NPJM. Secreted secondary metabolites reduce bacterial wilt severity of tomato in bacterial–fungal co-infections. 2021, 9:2123. Susi H, Barrès B, Vale PF, Laine A-LJN. Co-infection alters population dynamics of infectious disease. 2015, 6:5975. 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Taufer CR, da Silva J, Rampelotto PH. The Influence of Probiotic Lactobacilli on COVID-19 and the Microbiota. Nutrients 2024, 16. Rigauts C, Aizawa J, Taylor S, Rogers G, Govaerts M, Cos P, Ostyn L, Sims S, Vandeplassche E, Sze M. The commensal bacterium Rothia mucilaginosa has anti-inflammatory properties in vitro and in vivo, and negatively correlates with sputum pro-inflammatory markers in chronic airway disease. Eur Respiratory Soc; 2021. Milani M, Curia R, Shevlyagina NV, Tatti F. Staphylococcus aureus. Bacterial Degradation of Organic and Inorganic Materials: Staphylococcus aureus Meets the Nanoworld. Springer; 2023. pp. 3–20. Qin B, Dong H, Tang X, Liu Y, Feng G, Wu S, Zhang HJIJBM. Antisense yycF and BMP-2 co-delivery gelatin methacryloyl and carboxymethyl chitosan hydrogel composite for infective bone defects regeneration. 2023, 253:127233. Buvelot H, Roth M, Jaquet V, Lozkhin A, Renzoni A, Bonetti EJ, Gaia N, Laumay F, Mollin M, Stasia MJ, et al. Hydrogen Peroxide Affects Growth of S. aureus Through Downregulation of Genes Involved in Pyrimidine Biosynthesis. Front Immunol. 2021;12:673985. Uranga CC, Arroyo P Jr., Duggan BM, Gerwick WH, Edlund A. Commensal Oral Rothia mucilaginosa Produces Enterobactin, a Metal-Chelating Siderophore. mSystems 2020, 5. Stubbendieck RM, Dissanayake E, Burnham PM, Zelasko SE, Temkin MI, Wisdorf SS, Vrtis RF, Gern JE, Currie CR. Rothia from the Human Nose Inhibit Moraxella catarrhalis Colonization with a Secreted Peptidoglycan Endopeptidase. mBio. 2023;14:e0046423. Yang L, Liu T, Liu BC, Liu CT. Severe Pneumonia Advanced to Lung Abscess and Empyema Due to Rothia Mucilaginosa in an Immunocompetent Patient. Am J Med Sci. 2020;359:54–6. Rigauts C, Aizawa J, Taylor SL, Rogers GB, Govaerts M, Cos P, Ostyn L, Sims S, Vandeplassche E, Sze M et al. R othia mucilaginosa is an anti-inflammatory bacterium in the respiratory tract of patients with chronic lung disease. Eur Respir J 2022, 59. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.jpg SupplementaryFigure2.tif SupplementaryTable1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Editor invited by journal 21 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 18 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6947454","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501558574,"identity":"626f613f-6a38-43db-a87b-a33b0494b8d7","order_by":0,"name":"Zhibin Xu","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Xu","suffix":""},{"id":501558575,"identity":"3cec7762-bc20-47e7-9d58-1d66210ac81a","order_by":1,"name":"Hengyu Zhao","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hengyu","middleName":"","lastName":"Zhao","suffix":""},{"id":501558576,"identity":"76e29267-bc56-4b4b-8914-2ccb6bbb071d","order_by":2,"name":"Xiaohua Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Wang","suffix":""},{"id":501558578,"identity":"6e4d7e47-28c6-4eea-9028-01531ae72b8f","order_by":3,"name":"Yu Xu","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Xu","suffix":""},{"id":501558580,"identity":"0ea57000-880c-4121-853b-99fccfd816b4","order_by":4,"name":"Yi Lu","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Lu","suffix":""},{"id":501558582,"identity":"aae2e6c6-7f4b-4a7e-b79b-851f9f62fa57","order_by":5,"name":"Jiaqi Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Chen","suffix":""},{"id":501558587,"identity":"dd5e560c-248c-452c-a973-fdbef4731f8d","order_by":6,"name":"Xuan Li","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Li","suffix":""},{"id":501558591,"identity":"9dc0681e-dbdc-48e2-b723-b64e0edaea50","order_by":7,"name":"Yanhua Wen","email":"","orcid":"","institution":"Hugobiotech Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Wen","suffix":""},{"id":501558594,"identity":"90e9b605-3916-4c26-9f79-a4a2b918cc82","order_by":8,"name":"Chunrong Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxgYGNiiT+QCIBwIGxGphSyBOC0gplOYxIE4L84zkZ495d9yTM+df8/nlzx3bEhvYm7dJMNTcwe2wGWnmxrxnio0tZ7zdZiF55nZiA8+xMgmGY8/waMlhk+ZtS0jccOPsNgPDNqAWiRwzCcaGw8RoOfPMIBGkRf4NsVrO9zA/OAi2hYeAlp5nZpJz2xKMDW6wmTE2tt02buNJK7ZIOIZbi2F78jOJt20JcgbnDz/++LPttmw/++GNNz7U4NHSAGNJJLBJgGhwNCXg1MDAIA9n8R9g/oBH4SgYBaNgFIxgAACpIFmzM2+rpgAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chunrong","middleName":"","lastName":"Ju","suffix":""}],"badges":[],"createdAt":"2025-06-22 03:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6947454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6947454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89542374,"identity":"9418e250-d5b8-422a-89ad-32800d3f5621","added_by":"auto","created_at":"2025-08-21 06:47:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5797467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiome Composition and Diversity Analysis Across Control, EG, and TG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Venn diagrams illustrate the overlaps and unique taxa at the species and genus levels among the three groups. (B) Relative abundances of predominant taxa at the species and genus levels are presented for each group. (C) α-diversity indices (Shannon, Simpson, Chao1, and ACE) are compared among groups; data are shown as box plots with the median and interquartile range; (D) Principal coordinates analysis (PCoA) based on Bray-Curtis distance demonstrates microbial community structure at the species level; group differences were assessed using PERMANOVA, with R² and P values reported. (E) The differences in the relative abundance and reads of \u003cem\u003eNocardia \u003c/em\u003ebetween the 3 NRG samples and the corresponding samples from the infection group of the same patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/f0f9f0f691d547d867dff037.png"},{"id":89543906,"identity":"1a301275-9a0a-494e-b12e-57caab554532","added_by":"auto","created_at":"2025-08-21 06:55:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3231384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-dimensional Analysis of Microbial Community Shifts in Nocardia Infection and Treatment Response.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) LEfSe analysis showing differentially abundant microbial species among control subjects, \u003cem\u003eNocardia\u003c/em\u003e-infected patients (EG), and post-treatment specimens (TG). LDA scores indicate the effect size of each discriminative taxon. (B) Cross-sectional comparison (Control vs EG) of relative microbial abundance showing mean proportions (left), differences with 95% confidence intervals (center), and corresponding P values (right). (C) Longitudinal analysis (EG vs TG) demonstrating treatment-associated changes in microbial abundance with significant reduction in pathogenic \u003cem\u003eNocardia \u003c/em\u003especies following antimicrobial therapy. This complementary analytical approach identified 60 differentially abundant species, with limited overlap between methods, highlighting the value of multi-dimensional profiling in characterizing infection-associated microbiome perturbations.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/14d1e7b7941f1fc4a18f9b1d.png"},{"id":89542375,"identity":"378c2603-849d-4291-bf7f-4e00fd5788ad","added_by":"auto","created_at":"2025-08-21 06:47:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2817388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNocardia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Infection with Microbial Community Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Microbial community network among microbes. Nodes represent microbes, and edges represent associations between microbes. The size of the nodes corresponds to the degree of the microbes. The red node denotes \u003cem\u003eNocardia\u003c/em\u003e. Red edges indicate positive correlations, while blue edges indicate negative correlations. The thickness of the edges reflects the strength of the association between two microbes, with thicker lines representing higher correlations. (B) One-step subnet associated with \u003cem\u003eNocardia\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/301bf698350050fafb84e65d.jpg"},{"id":89542377,"identity":"feda63eb-5d25-411b-a791-5da8a22e6331","added_by":"auto","created_at":"2025-08-21 06:47:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21165455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations Among Host Clinical Characteristics and Microbial Taxa\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/fc644c39067d8f8767fee899.png"},{"id":89545485,"identity":"ee7d0df9-0562-4cd7-aa27-a8198fc605bf","added_by":"auto","created_at":"2025-08-21 07:11:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21837067,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/01356a0e-d252-48a9-88ba-49a78a418b12.pdf"},{"id":89543904,"identity":"fa5fcb4a-d6a7-46b9-8992-eb9b9e80617c","added_by":"auto","created_at":"2025-08-21 06:55:02","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":777314,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/ccdceb170c4863ca03fa59bf.jpg"},{"id":89542368,"identity":"c4b3ecf4-c3f2-4edf-aa0c-b04af72699d3","added_by":"auto","created_at":"2025-08-21 06:47:02","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":796488,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/69534041f01b84beb70de533.tif"},{"id":89544586,"identity":"26322eab-d3fd-4622-a175-2bde4acee1e4","added_by":"auto","created_at":"2025-08-21 07:03:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14555,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6947454/v1/80251fa93f741a9f9514f397.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Nocardia Infection on Airway Microbiota in Lung Transplant Recipients: A Single-center, Retrospective Observational Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung transplantation (LT) remains the definitive treatment for end-stage pulmonary disease, yet post-transplant management is complicated by high infection rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Essential chronic immunosuppression compromises host immunity, increasing susceptibility to infections, including opportunistic pathogens like \u003cem\u003eNocardia\u003c/em\u003e species [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInfection risk in lung transplant recipients (LTRs) is multifactorial, involving immunosuppression intensity and alterations in the airway microecology\u0026mdash;a key focus of recent research [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The airway microbiota\u0026rsquo;s equilibrium is vital for respiratory immune homeostasis. \u003cem\u003eNocardia\u003c/em\u003e species are particularly relevant pathogens in LTRs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], an immunocompromised population at high risk for nocardiosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cem\u003eNocardia\u003c/em\u003e infections can cause severe pulmonary infections and, in some cases, disseminated infections with high mortality rates. Currently, research on \u003cem\u003eNocardia\u003c/em\u003e infections in LTRs is limited, mostly consisting of case reports. Studies indicate that the incidence of \u003cem\u003eNocardia\u003c/em\u003e infection in LTRs ranges from 0.04\u0026ndash;35%, with the highest rates observed in lung transplant recipients (0.8\u0026ndash;35%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDiagnosing and treating Nocardia infections poses significant challenges due to non-specific clinical, laboratory, and radiographic findings, often leading to delayed diagnosis. Traditional culture methods are slow, further hindering timely intervention. While progress has been made in understanding Nocardia clinical features, diagnostics, and treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the impact of \u003cem\u003eNocardia\u003c/em\u003e infection on the LTR airway microecology and the mechanisms remain poorly characterized.\u003c/p\u003e\u003cp\u003eOur research team has maintained a longstanding commitment to investigating \u003cem\u003eNocardia\u003c/em\u003e infections, achieving several notable contributions to this field. In one study, we characterized the clinical features of \u003cem\u003eNocardia\u003c/em\u003e infections in LTRs, demonstrating that traditional culture methods achieved a diagnostic rate of only 77.8%, whereas metagenomic next-generation sequencing (mNGS) attained 100% diagnostic sensitivity, significantly enhancing diagnostic efficiency and accuracy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We also characterized typical thoracic imaging features (e.g., multiple nodules, consolidations) and showed that early combination antibiotic therapy improved outcomes in affected LTRs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, we synthesized recent advances in diagnosis and management through a narrative review [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on this foundation, the present study utilizes mNGS and 16S rRNA sequencing to investigate the effects of \u003cem\u003eNocardia\u003c/em\u003e infection on the airway microbiota in LTRs. By correlating microbial community shifts with clinical features, we aim to elucidate mechanisms of \u003cem\u003eNocardia\u003c/em\u003e-induced dysbiosis and explore its clinical sequelae.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Study Population\u003c/h2\u003e\u003cp\u003e This retrospective cohort study, approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (Ethics Approval No: ES-2024-K144) and registered with ClinicalTrials.gov (NCT06594133, 2024-11-11), analyzed 679 lung transplant recipients (LTRs, 2015\u0026ndash;2024). From these, 20 patients with Nocardia infection were selected as the experimental group (EG) and matched 1:2 with 40 uninfected controls based on age, gender, and primary disease[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All participants provided written informed consent. Follow-up data from 16 EG patients (treatment group, TG) were collected during infection and 4 months post-treatment, categorized as recovered (n\u0026thinsp;=\u0026thinsp;13) or not recovered (n\u0026thinsp;=\u0026thinsp;3). Demographic, immune (e.g., \u003cem\u003elymphocyte count\u003c/em\u003e, CD4⁺/CD8⁺ T cells), and nutritional data (e.g., albumin, hemoglobin) were extracted from electronic medical records. Rigorous matching and inclusion criteria minimized bias, ensuring data reliability (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e1.1.1 Inclusion Criteria for Experimental Group (EG)\u003c/h2\u003e\u003cp\u003e① Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years;\u003c/p\u003e\u003cp\u003e② Receipt of single lung, bilateral lung, or heart-lung transplantation;\u003c/p\u003e\u003cp\u003e③Diagnosis of \u003cem\u003eNocardia\u003c/em\u003e infection meeting criteria established in \u0026ldquo;\u003cem\u003eNocardia\u003c/em\u003e Infections in Solid Organ Transplantation: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice\u0026rdquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e];\u003c/p\u003e\u003cp\u003e④ Clinical manifestations of respiratory tract-related symptoms;\u003c/p\u003e\u003cp\u003e⑤ Radiographic evidence of pulmonary lesions;\u003c/p\u003e\u003cp\u003e⑥ Pathogenic confirmation of \u003cem\u003eNocardia\u003c/em\u003e infection through fiberoptic bronchoscopy sampling (e.g., deep sputum, bronchoalveolar lavage fluid) with positive smear, high-throughput sequencing, or culture results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e1.1.2 Exclusion Criteria for Experimental Group (EG)\u003c/h2\u003e\u003cp\u003e① LTRs with incomplete clinical data or follow-up information;\u003c/p\u003e\u003cp\u003e② LTRs who declined consent for use of clinical samples in research;\u003c/p\u003e\u003cp\u003e③ Cases with acute or chronic rejection or mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e1.1.3 Inclusion Criteria for Control Group (CG)\u003c/h2\u003e\u003cp\u003e① Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years;\u003c/p\u003e\u003cp\u003e② Receipt of single lung, bilateral lung, or heart-lung transplantation;\u003c/p\u003e\u003cp\u003e③ Stable clinical status without \u003cem\u003eNocardia\u003c/em\u003e infection post-transplantation;\u003c/p\u003e\u003cp\u003e④ Matched with EG group LTRs regarding baseline characteristics including gender, age, and primary disease type. With stable clinical status for continuous 3 months without pulmonary complications.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Nucleic acid extraction, library preparation, and sequencing\u003c/h2\u003e\u003cp\u003eTotal DNA was extracted from 200 \u0026micro;L aliquots of bronchoalveolar lavage fluid (BALF) from LTRs using the QIAamp DNA Micro Kit (QIAGEN, Hilden, Germany) following the manufacturer\u0026rsquo;s protocol. DNA concentration was quantified via fluorometry (Qubit 3.0, Invitrogen), and integrity was assessed by agarose gel electrophoresis. Metagenomic libraries were prepared from extracted DNA using the QIAseq Ultralow Input Library Kit (QIAGEN). Library concentration was determined (Qubit 3.0), and size distribution and quality were validated using a Bioanalyzer (Agilent 2100, Agilent Technologies, Palo Alto, CA). Indexed, quality-passed libraries were pooled and subjected to paired-end sequencing on an Illumina NextSeq 550 platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Sequencing Data Processing and Microbial Identification\u003c/h2\u003e\u003cp\u003eRaw sequencing reads were quality-filtered using Trimmomatic (v0.39) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and host reads were mapped to human reference genomes (GRCh38, YH genome) and human sequences within the NCBI NT database by Bowtie2 (v2.4.2) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] based on KneadData (v0.7.4). Remaining non-human reads were taxonomically assigned by alignment to microbial sequences (bacteria, fungi, archaea, viruses) in the NCBI NT database using BLASTN (v2.10.1+, megablast algorithm) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Only reads uniquely mapped to microbes were used for subsequent analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Donors\u003c/h2\u003e\u003cp\u003eOrgan procurement in this study strictly adhered to China's legislative framework, effective from January 2015, regarding voluntary civilian-based deceased organ donation. All organs were donated following cardiocirculatory or brain death, in compliance with the judicial system[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This approach underscores China's commitment to ethical organ transplantation, with the civilian organ donation program being the sole legal source for such procedures. The law necessitates that all transplant organs be derived from donors, with written informed consent obtained from the donors themselves or their family members, ensuring ethical and legal adherence in organ transplantation processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAlpha diversity indices (Shannon, Simpson, Chao1, ACE) were calculated using the R library \"vegan\". Beta diversity was assessed using Bray-Curtis distance metrics, and visualized with principal coordinate analysis (PCoA) by R library \"ape\". Differences in continuous variables between groups were evaluated using the Mann-Whitney U test. A nonparametric Spearman\u0026rsquo;s correlation test was used to assess the relationships between continuous variables. Linear discriminant analysis effect size (LEfSe) identified discriminating microbial taxa between groups (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Statistical analyses were performed using R version 4.1.0, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e\u003cp\u003eGeneras with more than 1000 reads among all samples were selected to construct the interaction network using SparCC algorithm [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and 1000 bootstraps were used to calculate correlations and p values. Networks were produced by retaining edges (correlation coefficient R ranges between \u0026minus;\u0026thinsp;0.3 and 0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Networks were visualized using Cytoscape 3.9.1 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] .\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Microbial Community Composition and Diversity\u003c/h2\u003e\u003cp\u003eMetagenomic comparative analysis of microbial profiles revealed distinctive patterns among the EG, Control, and TG groups. At the species level, a core microbiome of 2183 microbial species (representing 34.8% of the total species identified) was shared across all three cohorts. The EG and TG groups harbored 966 (15.4%) and 833 (13.3%) unique species, respectively, while the Control group contained 730 (11.6%) exclusive species. At the genus level, 789 genera (43.6% of total) were common to all three groups; the EG, Control, and TG groups possessed 234 (12.9%), 182 (10.1%), and 203 (11.2%) distinct genera, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTaxonomic profiling revealed distinct microbial compositions across the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). While taxa such as \u003cem\u003ePseudomonas, Burkholderia, Paraburkholderia\u003c/em\u003e, family \u003cem\u003eAnelloviridae\u003c/em\u003e, and \u003cem\u003eclass_Caudoviricetes\u003c/em\u003e were abundant across cohorts, their relative distributions varied significantly. Quantitative analysis of the top 20 genera underscored these differences (Supplementary Table\u0026nbsp;1): the Control group harbored the highest relative abundances of \u003cem\u003eBurkholderia\u003c/em\u003e (mean 17.0%), family \u003cem\u003eAnelloviridae\u003c/em\u003e (12.2%), \u003cem\u003eParaburkholderia\u003c/em\u003e (14.0%), and \u003cem\u003eRothia\u003c/em\u003e (4.2%). Conversely, the EG group was characterized by a substantial enrichment of \u003cem\u003eNocardia\u003c/em\u003e (11.1% vs\u0026thinsp;\u0026lt;\u0026thinsp;0.01% in other groups). The TG group exhibited the highest relative abundance of \u003cem\u003ePseudomonas\u003c/em\u003e (20.5%).\u003c/p\u003e\u003cp\u003eAnalysis of α-diversity revealed no statistically significant differences in within-sample microbial richness or evenness among the groups (Shannon, Simpson, P\u0026gt;0.05; Chao1, P\u0026thinsp;\u0026ge;\u0026thinsp;0.44; ACE, P\u0026thinsp;\u0026ge;\u0026thinsp;0.44; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Despite similar α-diversity, β-diversity analysis using Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarities demonstrated clear separation of microbial communities according to group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). This visual distinction was statistically significant, as confirmed by PERMANOVA analysis, indicating significantly different overall microbial compositions among the Control, EG, and TG groups (R\u0026sup2; = 0.0433, P\u0026thinsp;=\u0026thinsp;0.025). Statistical analysis showed that although Nocardia remained detectable in the 3 cases in the Not Recovered Group (NRG group), there were no statistically significant differences in Nocardia\u0026rsquo;s relative abundance or sequencing read counts between the EG and NRG groups, despite apparent visual disparities. These findings suggest that treatment alters but does not fully restore the microbial balance in NRG patients and support the hypothesis that \u003cem\u003eNocardia\u003c/em\u003e infection may act by modulating the abundance of specific microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Microbial Ecological Biomarkers Across Different Groups\u003c/h2\u003e\u003cp\u003eTo systematically identify the distinctive impact of \u003cem\u003eNocardia\u003c/em\u003e infection on airway microbial community structure and its response to treatment, a complementary analytical approach integrating LEfSe and Wilcoxon rank-sum tests for both cross-sectional and longitudinal comparisons was employed. LEfSe analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed that the EG group was characterized by significant enrichment of \u003cem\u003eNocardia farcinica\u003c/em\u003e and \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e (all LDA\u0026thinsp;\u0026gt;\u0026thinsp;4), as well as increased abundance of commensals such as \u003cem\u003eLigilactobacillus salivarius\u003c/em\u003e, \u003cem\u003ePantoea ananatis\u003c/em\u003e, and \u003cem\u003eHymenobacter aerilatus\u003c/em\u003e (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2). In the Control group, \u003cem\u003eRothia mucilaginosa\u003c/em\u003e and several \u003cem\u003eBurkholderia\u003c/em\u003e species predominated, reflecting a more balanced microbiota. Following treatment (TG), distinct taxa including \u003cem\u003ePseudopropionibacterium propionicum\u003c/em\u003e and \u003cem\u003eBurkholderia metallica\u003c/em\u003e emerged, indicating microbial community restructuring in response to intervention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCross-sectional analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) further confirmed these findings, with both \u003cem\u003eNocardia farcinica\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;1.0\u0026times;10⁻⁶) and \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;2.9\u0026times;10⁻⁵) significantly enriched in the EG. Other species such as \u003cem\u003eHuman alphaherpesvirus 1\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;2.9\u0026times;10⁻⁵), \u003cem\u003eStaphylococcus capitis\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;1.0\u0026times;10⁻⁴), and \u003cem\u003eLigilactobacillus salivarius\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;1.0\u0026times;10⁻⁴) were also elevated in the infection group. Conversely, \u003cem\u003eRothia mucilaginosa\u003c/em\u003e and multiple \u003cem\u003eBurkholderia\u003c/em\u003e species (e.g., \u003cem\u003eB. pseudomallei\u003c/em\u003e, \u003cem\u003eB. thailandensis\u003c/em\u003e) were reduced in the presence of infection, likely reflecting niche competition following pathogen colonization.\u003c/p\u003e\u003cp\u003eLongitudinal analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) assessed the microbiome dynamics following therapy, demonstrating a significant reduction in \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.003) and \u003cem\u003eNocardia farcinica\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.011) in the TG, alongside shifts in other taxa, including \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. These results indicate that antimicrobial treatment not only suppresses pathogenic species, but may also restore ecological balance through broader effects on the airway microbiome.\u003c/p\u003e\u003cp\u003eIntegrated evaluation of the three analytical approaches (Supplementary Fig.\u0026nbsp;2) identified 60 taxa with significant intergroup variation. Of these, 24 (40.0%) were unique to LEfSe, 17 (28.3%) to cross-sectional, and 6 (10.0%) to longitudinal analysis. LEfSe and cross-sectional analyses shared 9 taxa (15.0%), while LEfSe and longitudinal analyses overlapped for 2 taxa (3.3%); only 2 taxa (3.3%) were identified by all three methods. This limited overlap, yet notable complementarity, highlights the necessity of a multi-method approach in comprehensively characterizing \u003cem\u003eNocardia\u003c/em\u003e-associated microbiome alterations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Microbial Co-occurrence Network Analysis in \u003cem\u003eNocardia\u003c/em\u003e Infection\u003c/h2\u003e\u003cp\u003eThe microbial co-occurrence network was constructed using the SparCC algorithm to investigate inter-taxa associations within the airway microbiota during Nocardia infection. Overall network analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) revealed a complex network structure comprising 53 nodes (representing microbial taxa) and 273 edges (representing inter-taxa associations), with approximately one-third (33.33%) representing negative correlations. The top 10 nodes by degree, indicating central taxa, included \u003cem\u003eParaburkholderia\u003c/em\u003e, \u003cem\u003eCaballeronia\u003c/em\u003e, \u003cem\u003eTrinickia\u003c/em\u003e, \u003cem\u003eCupriavidus\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e, \u003cem\u003eclass_Caudoviricetes\u003c/em\u003e, and \u003cem\u003eAchromobacter\u003c/em\u003e. Notably, \u003cem\u003eclass_Caudoviricetes\u003c/em\u003e exhibited extensive negative correlations with several taxa, including \u003cem\u003eAchromobacter\u003c/em\u003e, \u003cem\u003eMastadenovirus\u003c/em\u003e, \u003cem\u003eNocardia\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e. To specifically examine \u003cem\u003eNocardia\u003c/em\u003e and its direct microbial associates, a one-step subnet centered on \u003cem\u003eNocardia\u003c/em\u003e was extracted and analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This subnet consisted of 25 nodes and 56 edges. Within this subnet, \u003cem\u003eNocardia\u003c/em\u003e showed positive correlations with \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eMastadenovirus\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e, and negative correlations with \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eclass_Caudoviricetes\u003c/em\u003e. These co-occurrence network analyses provide a visual representation of complex inter-taxa association patterns within the airway microbiota during Nocardia infection and highlight potential key interacting partners.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Correlation Between Clinical Features and Microbiota\u003c/h2\u003e\u003cp\u003ePearson correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) showed strong positive correlations among white blood cell count, neutrophil count, and neutrophil percentage. CD4⁺ count correlated negatively with CD8⁺ count; the CD4/CD8 ratio correlated positively with CD4⁺ and negatively with CD8⁺. Total lymphocyte count positively correlated with albumin, hemoglobin, and immunoglobulins. Albumin also correlated positively with hemoglobin and CD4⁺ count.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpearman correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) explored associations between differential microbial taxa abundance and host parameters, visualized in a heatmap including enrichment groups (LEfSe, cross-sectional, longitudinal). In the EG group, \u003cem\u003eNocardia farcinica\u003c/em\u003e and \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e, the dominant pathogens, consistently correlated negatively with CD4⁺ and ALB. \u003cem\u003eN. farcinica\u003c/em\u003e also showed inverse associations with IgG, IgA, T lymphocytes, HGB, and total lymphocytes, while \u003cem\u003eN. cyriacigeorgica\u003c/em\u003e correlated negatively with the CD4/CD8 ratio and positively with CD8⁺. In the TG group, \u003cem\u003ePenicillium rubens\u003c/em\u003e correlated negatively with IgG and the CD4/CD8 ratio, and positively with CD8⁺ count. \u003cem\u003eAspergillus glaucus\u003c/em\u003e correlated negatively with NEUT%. Among longitudinal (EG vs TG) differential taxa, \u003cem\u003ePseudopropionibacterium propionicum\u003c/em\u003e correlated significantly negatively with IgM. In cross-sectional analysis (EG vs Control), \u003cem\u003eHuman alphaherpesvirus 1\u003c/em\u003e correlated significantly negatively with estimated glomerular filtration rate (eGFR) and ALB, and significantly positively with Cr, NEUT, and WBC.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eRespiratory disease has been found to be associated with the airway microbiota\u0026rsquo;s dysbiosis. This study analyzed the effects of \u003cem\u003eNocardia\u003c/em\u003e infection on the airway microbiota of LTRs and found that changes in the airway microbiota were closely correlated with the patient\u0026rsquo;s immune status and clinical characteristics. Specifically, we observed significant correlations between \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e, \u003cem\u003eNocardia farcinica\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, and \u003cem\u003eRothia mucilaginosa\u003c/em\u003e abundance and various immune markers, suggesting that alterations in the microbial community may influence the progression of post-transplant infections. As far as we know, this is the first study to investigate the association of nocardiosis with airway microbiota.\u003c/p\u003e\u003cp\u003eIn this study, both the top microbial taxa and the dominant species analysis revealed that specific genera in the EG Group occupied a significantly dominant position. The top genera in the EG group included not only known opportunistic pathogens but also species that are typically suppressed under normal conditions. Further selection by LEfSe demonstrated a notable increase in specific pathogens, such as \u003cem\u003eNocardia farcinica\u003c/em\u003e and \u003cem\u003eLigilactobacillus_salivarius\u003c/em\u003e, in the EG group. The rise of these genera may exacerbate the severity of infection and suggest that \u003cem\u003eNocardia\u003c/em\u003e infection might act synergistically with multiple pathogens, presenting a greater threat to patient health. The phenomenon of co-infection in immunosuppressed patients is of particular concern, as it often exacerbates disease complexity and affects the host\u0026rsquo;s immune response. As highlighted by Behdenna et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], multi-pathogen infections can alter transmission efficiency and virulence, ultimately influencing the overall disease outcome. Additionally, co-infection is particularly prevalent in respiratory tract infections, as demonstrated by Kong et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], who observed significant co-infection of multiple respiratory pathogens, especially in coronavirus infections. A clear divergence in species composition was observed between the EG and control groups, with certain species\u0026rsquo; abundance notably higher in the EG group. This differential increase in species may not only be a result of \u003cem\u003eNocardia\u003c/em\u003e infection but may also be amplified by interactions with other microbial communities, worsening the clinical condition. Co-infection with multiple pathogens poses a significant challenge in transplant recipients, increasing the difficulty of infection control. This complex infection mechanism alters the patient\u0026rsquo;s clinical presentation and reduces the sensitivity and specificity of traditional diagnostic methods, complicating clinical management.\u003c/p\u003e\u003cp\u003eThese co-infection phenomena present a formidable challenge for infection control in transplant recipients, as co-infections generate complex inter-pathogen interactions, including competition, symbiosis, and coexistence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In terms of infection control, Susi et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] indicated that co-infection has a profound impact on epidemiological and population control strategies, as it can drive pathogen community dynamics and increase the risk of disease transmission. Therefore, our study highlights the importance of focusing on the pathogenic role of co-infections and their role during the course of \u003cem\u003eNocardia\u003c/em\u003e infection in immunosuppressed patients. \u003cem\u003eNocardia\u003c/em\u003e infection induces complex alterations in the airway microbiota of lung transplant recipients, which may subsequently increase the risk of secondary infections. Spearman correlation analysis showed positive correlations between \u003cem\u003eNocardia\u003c/em\u003e and opportunistic pathogens such as \u003cem\u003eEnterococcus, Mastadenovirus\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e, while negative correlations were observed with \u003cem\u003eRothia\u003c/em\u003e and the \u003cem\u003eCaudoviricetes\u003c/em\u003e family of bacteriophages. These synergistic or inhibitory interactions among genera may increase the complexity and severity of infections in immunosuppressed patients.\u003c/p\u003e\u003cp\u003eGenera that exhibited positive correlations, such as \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eMastadenovirus\u003c/em\u003e, may shift from commensal to opportunistic pathogens under immunosuppressive conditions, thus increasing the patient\u0026rsquo;s immunological burden and exacerbating the severity of the infection. The immunosuppressive effect increases the invasiveness of these genera in the airway ecosystem, particularly in LTRs. This co-infection is prevalent in clinical practice, as the patient\u0026rsquo;s compromised immune function diminishes their resistance to bacteria, thereby increasing the risk of secondary infections. Studies indicate that such synergistic effects not only accelerate pathogen spread in the airways but also alter the host\u0026rsquo;s local immune environment, exacerbating the inflammatory response to infection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The impact of positive correlation co-infections on airway microbiota disruption occurs primarily through two mechanisms. First, \u003cem\u003eEnterococcus\u003c/em\u003e can secrete extracellular polymers and toxins, thereby disrupting the host\u0026rsquo;s immune defense system and promoting the proliferation of \u003cem\u003eNocardia\u003c/em\u003e and other pathogens [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Specifically, \u003cem\u003eEnterococcus\u003c/em\u003e can form biofilms, secreting extracellular polymers that enhance its adhesion to host tissues and persistence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, toxins such as cytolysins produced by \u003cem\u003eEnterococcus\u003c/em\u003e can directly damage host cells, weakening immune defenses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Collectively, these mechanisms create favorable conditions for the colonization and spread of pathogens like \u003cem\u003eNocardia\u003c/em\u003e. Second, while \u003cem\u003eLactobacillus\u003c/em\u003e is generally regarded as a beneficial bacterium, under certain conditions, such as hypoxic environments and nutrient competition, it may further decrease the abundance of beneficial bacteria in the airways through its metabolic byproducts, thus destabilizing the microbial balance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recent studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] have shown that \u003cem\u003eMastadenovirus\u003c/em\u003e in immunosuppressed hosts can act as an inflammatory activator, especially in conditions of local imbalance, which increases its pathogenic potential. These synergistic interactions provide opportunities for pathogen proliferation and growth, emphasizing the importance of maintaining microbial balance in the airway microbiota to prevent severe infections.\u003c/p\u003e\u003cp\u003eAs \u003cem\u003eNocardia\u003c/em\u003e abundance increases, \u003cem\u003eRothia\u003c/em\u003e abundance significantly decreases. This negative correlation suggests that \u003cem\u003eNocardia\u003c/em\u003e proliferation may inhibit the growth of \u003cem\u003eRothia\u003c/em\u003e, thereby disturbing the airway microbiota balance. \u003cem\u003eRothia\u003c/em\u003e, particularly \u003cem\u003eRothia mucilaginosa\u003c/em\u003e, plays a critical protective role in the healthy airway microbiota [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] have shown that \u003cem\u003eRothia\u003c/em\u003e can produce antimicrobial metabolic products, such as hydrogen peroxide, which inhibit opportunistic pathogens like \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, thereby helping to maintain microbiota balance and enhance host immune defenses. Additionally, \u003cem\u003eRothia mucilaginosa\u003c/em\u003e can inhibit the activation of the NF-κB pathway, reducing the production of pro-inflammatory cytokines, thus exerting an anti-inflammatory effect. These functions help maintain the barrier function of airway epithelial cells and induce the host to produce antimicrobial peptides, strengthening local immune defense [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The significant decline in \u003cem\u003eRothia\u003c/em\u003e abundance may also reflect interference from \u003cem\u003eNocardia\u003c/em\u003e through nutrient competition or the secretion of inhibitory substances, further disrupting the balance. This dysbiosis likely weakens airway defense mechanisms, making patients more susceptible to \u003cem\u003eNocardia\u003c/em\u003e and other pathogens [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Several studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] have shown that the decline in \u003cem\u003eRothia\u003c/em\u003e abundance is often associated with a decrease in airway microbiota diversity, which increases the incidence and severity of respiratory infections, particularly in immunocompromised patients. Therefore, in addition to antimicrobial therapy targeting \u003cem\u003eNocardia\u003c/em\u003e, maintaining the abundance of protective genera like \u003cem\u003eRothia\u003c/em\u003e may be a potential adjunct therapy in reducing the risk of co-infections. Future research should explore targeted microbiota interventions, such as specific probiotics or microbiota transplantation techniques, to restore \u003cem\u003eRothia\u003c/em\u003e levels and assist in treating Nocardia infections while lowering the risk of secondary infections.\u003c/p\u003e\u003cp\u003eGiven these clinical risks, strengthening microbiota management and individualized anti-infection strategies is crucial. Infection monitoring based on metagenomic sequencing techniques can identify synergistic pathogen interaction patterns, providing data support for precise diagnosis. Clinicians can utilize these findings to develop targeted combination therapies, thereby improving the success rate of infection control. Furthermore, appropriate adjustments to immunosuppressive drug use to maintain partial immune function may help reduce the occurrence of co-infections.\u003c/p\u003e\u003cp\u003eIn conclusion, changes in the airway microbiota play a critical role in immune regulation of post-transplant infections. Different bacterial infections not only alter the balance of immune cells but may also affect the patient\u0026rsquo;s biochemical markers and immune response, thereby influencing clinical outcomes. We speculate that \u003cem\u003eNocardia cyriacigeorgica\u003c/em\u003e and \u003cem\u003eNocardia farcinica\u003c/em\u003e may disrupt immune homeostasis in lung transplant recipients through specific immune modulation mechanisms, exacerbating complications.\u003c/p\u003e"},{"header":"4. Limitations","content":"\u003cp\u003eWe acknowledge the single-center design and limited sample size, particularly for the refractory group. This preliminary study provides novel insights into Nocardia-driven microbiota changes using mNGS in lung transplant recipients. We propose larger, multicenter studies to enhance the generalizability.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that \u003cem\u003eNocardia\u003c/em\u003e infection significantly remodels the airway microbiome in LTRs, shifting the balance towards pathogenic dominance and commensal depletion. This dysbiosis is not isolated but is intimately linked to the host\u0026rsquo;s immune and nutritional state, with specific microbial taxa showing strong correlations with key clinical indicators. The observed microbial interaction patterns, including potential synergy between \u003cem\u003eNocardia\u003c/em\u003e and opportunistic microbes and antagonism with beneficial taxa, underscore the complexity of post-transplant infections. Our findings establish airway microbial dysbiosis as a critical factor in Nocardia infection pathogenesis in LTRs, suggesting that monitoring and potentially manipulating the airway microbiome could offer valuable strategies for prevention, diagnosis, and treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was conducted in accordance with the ethical principles of the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/) and approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (Approval No: ES-2024-K144). All participants provided written informed consent for the use of their clinical data in research. Patient identities were protected through full anonymization of datasets prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants. The consent forms explicitly stated that anonymized data derived from their medical records may be included in scientific publications. No personally identifiable information (including names, hospital identification numbers, or images) is presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZ. Xu\u003c/em\u003e and \u003cem\u003eH.Y. Zhao\u003c/em\u003e contributed equally to the study. \u003cem\u003eZ. Xu\u003c/em\u003e and \u003cem\u003eH.Y. Zhao\u003c/em\u003e conceived and designed the study. \u003cem\u003eH.Y. Zhao\u003c/em\u003e was responsible for data collection, data analysis, results visualization, and manuscript writing.\u003cem\u003e\u0026nbsp;X.H. Wang\u003c/em\u003e and \u003cem\u003eY. Xu\u0026nbsp;\u003c/em\u003emanaged the project and participated in data analysis discussions. \u003cem\u003eY. Lu\u003c/em\u003e was involved in data analysis discussions. \u003cem\u003eJ.Q. Chen, Q.Y. Ye\u003c/em\u003e, and \u003cem\u003eX. Li\u003c/em\u003e handled patient follow-up, clinical information collection, and data management. \u003cem\u003eY.H Wen\u003c/em\u003e provided support in data analysis and participated in related discussions. \u003cem\u003eC.R Ju\u003c/em\u003e contributed to the conception and design, funding acquisition, project management, and the review and editing of the final manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript and agree to be 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Specific Clinical Technology Project of Guangzhou (2023C-TS10), the Guangzhou Medical University Research Capacity Enhancement Programme Major Clinical Research Projects (GMUCR2024-01007), the Clinical Epidemiology Research Program of the State Key Laboratory of Respiratory System Diseases (SKLRD-L-202504), the Chinese Medical Education Association (ZJWYH-2023-YIZHI-003), the National Natural Science Foundation of China, and the Wu Jieping Medical Foundation Research Special Funding Fund (320.6750.2025-01-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge HUGO BIOTECH for their invaluable assistance with the mNGS analysis. Their expert technical support was instrumental in generating the comprehensive sequencing data that underpinned this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The dataset(s) supporting the conclusions of this article is(are) available in the Synapse repository, syn68724324 (https://doi.org/10.7303/syn68724324).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrachuk P, Bartash R, Abbasi M, Keene A. Infectious Complications in Lung Transplant Recipients. Lung. 2020;198:879\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar R, Ison MG. Opportunistic Infections in Transplant Patients. Infect Dis Clin North Am. 2019;33:1143\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Cx, Lv M, Liu H, Yx L, Jb P, Cx Y, Su J. 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Eur J Clin Microbiol Infect Dis. 2014;33:689\u0026ndash;702.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChunrong J, Men T, Xue W, Shiyue LJOT. Progress on the diagnosis and treatment of nocardiosis in organ transplant recipients. 2024, 15:868\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Y, Lian QY, Chen A, Zhang JH, Xu X, Huang DX, He JX, Ju CR. Clinical characteristics and treatment strategy of nocardiosis in lung transplant recipients: A single-center experience. IDCases. 2023;32:e01758.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLIAN Q, CHEN A, XU X, WEI B, HUANG D, KUANG M, CAI Y, HE J. JU CJCJoOT: Clinical analysis ofnocardia infection in lung transplant recipient: a report of five cases. 2021:417\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIwagami M, Shinozaki T. Introduction to Matching in Case-Control and Cohort Studies. 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The national program for deceased organ donation in China. Transplantation. 2013;96:5\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8:e1002687.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBehdenna A, Lembo T, Calatayud O, Cleaveland S, Halliday JE, Packer C, Lankester F, Hampson K, Craft ME. Czupryna AJPotRSB: Transmission ecology of canine parvovirus in a multi-host, multi-pathogen system. 2019, 286:20182772.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKong D, Zheng Y, Hu L, Chen J, Wu H, Teng Z, Zhou Y, Qiu Q, Lu Y. Pan HJEm, infections: Epidemiological and co-infection characteristics of common human coronaviruses in Shanghai, 2015\u0026ndash;2020: a retrospective observational study. 2021, 10:1660\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVenkatesh N, Koss MJ, Greco C, Nickles G, Wiemann P, Keller NPJM. Secreted secondary metabolites reduce bacterial wilt severity of tomato in bacterial\u0026ndash;fungal co-infections. 2021, 9:2123.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSusi H, Barr\u0026egrave;s B, Vale PF, Laine A-LJN. Co-infection alters population dynamics of infectious disease. 2015, 6:5975.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoncheva I, Poudrier J, Falcone EL. Role of the intestinal microbiota in host defense against respiratory viral infections. Curr Opin Virol. 2024;66:101410.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang S, Meng X, Zhen Y, Baima Q, Wang Y, Jiang X, Xu ZJFC, Microbiology I. Strategies and mechanisms targeting Enterococcus faecalis biofilms associated with endodontic infections: a comprehensive review. 2024, 14:1433313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSangiorgio G, Calvo M, Migliorisi G, Campanile F, Stefani S. The Impact of Enterococcus spp. in the Immunocompromised Host: A Comprehensive Review. Pathogens 2024, 13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLanka S, Katta A, Kovvali M, Pandrangi S. Enterococcus faecium Virulence Factors and Biofilm Components: Synthesis, Structure, Function, and Inhibitors. ESKAPE Pathogens: Detection, Mechanisms and Treatment Strategies. Springer; 2024. pp. 209\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatsuda T, Yano T, Maruyama A, Kumagai HJNSKG. Antimicrobial activities of organic acids determined by minimum inhibitory concentrations at different pH ranged from 4.0 to 7.0. 1994, 41:687\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaufer CR, da Silva J, Rampelotto PH. The Influence of Probiotic Lactobacilli on COVID-19 and the Microbiota. Nutrients 2024, 16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRigauts C, Aizawa J, Taylor S, Rogers G, Govaerts M, Cos P, Ostyn L, Sims S, Vandeplassche E, Sze M. The commensal bacterium Rothia mucilaginosa has anti-inflammatory properties in vitro and in vivo, and negatively correlates with sputum pro-inflammatory markers in chronic airway disease. Eur Respiratory Soc; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilani M, Curia R, Shevlyagina NV, Tatti F. Staphylococcus aureus. Bacterial Degradation of Organic and Inorganic Materials: Staphylococcus aureus Meets the Nanoworld. Springer; 2023. pp. 3\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQin B, Dong H, Tang X, Liu Y, Feng G, Wu S, Zhang HJIJBM. Antisense yycF and BMP-2 co-delivery gelatin methacryloyl and carboxymethyl chitosan hydrogel composite for infective bone defects regeneration. 2023, 253:127233.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuvelot H, Roth M, Jaquet V, Lozkhin A, Renzoni A, Bonetti EJ, Gaia N, Laumay F, Mollin M, Stasia MJ, et al. Hydrogen Peroxide Affects Growth of S. aureus Through Downregulation of Genes Involved in Pyrimidine Biosynthesis. Front Immunol. 2021;12:673985.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUranga CC, Arroyo P Jr., Duggan BM, Gerwick WH, Edlund A. Commensal Oral Rothia mucilaginosa Produces Enterobactin, a Metal-Chelating Siderophore. \u003cem\u003emSystems\u003c/em\u003e 2020, 5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStubbendieck RM, Dissanayake E, Burnham PM, Zelasko SE, Temkin MI, Wisdorf SS, Vrtis RF, Gern JE, Currie CR. Rothia from the Human Nose Inhibit Moraxella catarrhalis Colonization with a Secreted Peptidoglycan Endopeptidase. mBio. 2023;14:e0046423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L, Liu T, Liu BC, Liu CT. Severe Pneumonia Advanced to Lung Abscess and Empyema Due to Rothia Mucilaginosa in an Immunocompetent Patient. Am J Med Sci. 2020;359:54\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRigauts C, Aizawa J, Taylor SL, Rogers GB, Govaerts M, Cos P, Ostyn L, Sims S, Vandeplassche E, Sze M et al. R othia mucilaginosa is an anti-inflammatory bacterium in the respiratory tract of patients with chronic lung disease. Eur Respir J 2022, 59.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nocardia infection, Lung transplantation, Airway microbiome, mNGS","lastPublishedDoi":"10.21203/rs.3.rs-6947454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6947454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the impact of Nocardia infection on the airway microbiota composition in lung transplant recipients (LTRs) using mNGS and evaluate associations between microbial alterations and clinical parameters..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This single-center, retrospective cohort study analyzed 679 LTRs (2015–2024), including 20 Nocardia-infected patients (experimental group, EG), 40 matched controls, and 16 post-treatment group (TG) cases. Bronchoalveolar lavage fluid (BALF) was subjected to mNGS to characterize microbial composition. Multi-modal were applied to analyze integrated α/β-diversity metrics, LEfSe biomarker identification, SparCC co-occurrence networks, and clinical parameter correlations (NCT06594133).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eMicrobiome analysis demonstrated comparable alpha diversity but distinct beta diversity profiles (P=0.025) between groups. The EG exhibited significant enrichment of \u003cem\u003eNocardia\u003c/em\u003e species (predominantly \u003cem\u003eN. farcinica\u003c/em\u003e and \u003cem\u003eN. cyriacigeorgica\u003c/em\u003e), alongside decreased \u003cem\u003eRothia mucilaginosa\u003c/em\u003e and \u003cem\u003eBurkholderia\u003c/em\u003especies versus controls. Therapeutic intervention substantially reduced \u003cem\u003eNocardia \u003c/em\u003eabundance in treated subjects. Network analysis revealed \u003cem\u003eclass_Caudoviricetes \u003c/em\u003edisplayed inverse associations with \u003cem\u003eNocardia\u003c/em\u003e, \u003cem\u003eAchromobacter\u003c/em\u003e, \u003cem\u003eMastadenovirus\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e. Nocardia-centered subnet showed positive correlations with \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eMastadenovirus\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e, contrasting with negative correlations involving \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eclass_Caudoviricetes. \u003c/em\u003eIn EG, a robust positive correlation was found of CD4⁺ T-cell counts and the CD4/CD8 ratio with serum IgA, IgM, and IgG (each \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Spearman analysis linked \u003cem\u003eN. farcinica\u003c/em\u003e and \u003cem\u003eN. cyriacigeorgica\u003c/em\u003e inversely to CD4⁺ count and albumin; \u003cem\u003eN. farcinica \u003c/em\u003ealso inversely correlated with IgG, IgA, total lymphocyte count, and hemoglobin; and \u003cem\u003eN. cyriacigeorgica\u003c/em\u003einversely correlated with the CD4/CD8 ratio and positively with CD8⁺ T cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e \u003cem\u003eNocardia\u003c/em\u003e infection induces significant alterations in the airway microbiota of LTRs, characterized by Nocardia enrichment and specific commensal depletion. These microbial shifts are strongly associated with host immune and nutritional status. Our findings highlight the critical role of airway microbial dysbiosis in \u003cem\u003eNocardia \u003c/em\u003einfection pathogenesis and suggest microbiota-targeted strategies that down-regulation the immunosuppressants might be a potential adjunct strategy in Nocardia infection in lung transplant recipients.\u003c/p\u003e","manuscriptTitle":"Impact of Nocardia Infection on Airway Microbiota in Lung Transplant Recipients: A Single-center, Retrospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 06:46:57","doi":"10.21203/rs.3.rs-6947454/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-28T11:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86681950119965819843036148128704570708","date":"2025-08-20T10:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T03:45:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T18:30:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-21T08:27:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-19T00:51:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-07-19T00:48:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3543ece8-229d-4628-8fb9-fd5a2f04011a","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-21T06:46:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 06:46:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6947454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6947454","identity":"rs-6947454","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}