The Gut Mycobiome and Inter-kingdom Microbial Networks are linked to COPD Severity in Lung Cancer Patients

The Gut Mycobiome and Inter-kingdom Microbial Networks are linked to COPD Severity in Lung Cancer Patients | 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 Article The Gut Mycobiome and Inter-kingdom Microbial Networks are linked to COPD Severity in Lung Cancer Patients György Szklenarik, David Dora, Sára Szincsak, Clement Kojo Acquah, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7992928/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic disorder affecting host–microbiome interactions beyond the airways. Although bacterial alterations in COPD have been documented, the gut mycobiome and its ecological integration with bacterial communities remain unexplored. In this study, we profiled the gut mycobiome of 61 non-small-cell lung cancer (NSCLC) patients stratified by COPD severity using ITS2 sequencing and analyzed 47 overlapping patients with available metagenomic data to construct cross-kingdom bacterial–fungal networks. Alpha diversity, assessed by Shannon, Simpson, and Chao1 indices, did not differ significantly between patients with and without severe COPD. Partial least squares discriminant analysis (PLS-DA) revealed partial separation of the two groups, with COPD severity explaining 6% of overall compositional variance (R²=0.06, p = 0.058). COPD-severe patients exhibited a significantly reduced Ascomycota/Basidiomycota ratio (p = 0.039) and lower relative abundance of Mucoromycota. Analysis of compositions of microbiomes (ANCOM) identified Myrothecium and Lasiodiplodia crassispora enriched in severe COPD, while Helotiales_unclassified and Phallus atrovolvatus were more abundant in non-severe cases. Fungal co-occurrence networks demonstrated reduced connectivity and modularity in severe COPD (28 nodes, 39 edges) compared with non-severe COPD (33 nodes, 64 edges). Cross-kingdom analyses integrating bacterial genera revealed strengthened Candida–Enterococcus/Clostridium hubs and weakened Faecalibacterium/Roseburia–yeast associations in severe disease. Keystone analysis showed increased centrality for Candida, Aspergillus, Enterococcus, and Clostridium, and decreased centrality for Akkermansia and Roseburia. A compositional balance classifier achieved high discriminatory power (AUC = 0.88) in distinguishing COPD-severe from non-severe patients. These findings indicate that COPD severity is not characterized by major diversity loss but by guild-specific compositional shifts and extensive network rewiring, favoring oxygen-tolerant, opportunistic taxa over short-chain fatty acid–associated commensals. Health sciences/Diseases Biological sciences/Microbiology Gut mycobiome COPD Cross-kingdom networks Fungal ecology Gut-lung axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Chronic obstructive pulmonary disease (COPD) is one of the three leading causes of death, affecting around 10% of the population, with an expected increase by 23% from 2020 to 2050, including females by 47.1% [ 1 , 2 ]. COPD is characterized by exposure to inhaled particulate matter, including smoking or air pollutants, in combination with genetic, developmental, and social factors. [ 3 ]. The identification of “treatable traits” is crucial through the identification of “phenotypes” and “endotypes”. Therefore, a better understanding of molecular mechanisms might reveal novel associations beyond our current understanding of gene-environment interactions [ 4 , 5 ]. The gut-lung axis may help extend our current understanding of the dynamics of maintenance, repair, and cumulative tissue injury, as well as aging, in the pathology of COPD. The gastrointestinal and respiratory tracts share similar cellular components, but their distribution varies to support organ-specific functions. Mucosa-associated lymphoid tissue (MALT) comprises gut-associated lymphoid tissue (GALT) and inducible bronchus-associated lymphoid tissue (iBALT). The microbiome contributes to mucosal and systemic immune homeostasis through the mononuclear phagocyte system (MPS), which mediates host–microbe crosstalk [ 6 ]. A notable example of the mutual changes in the gut-lung axis microbiome is the increase in Streptococcus species in both organs among smokers [ 7 ]. Lactobacillus was shown to play an immunomodulatory role in the management of patients with chronic respiratory diseases by balancing lung immunity and promoting respiratory health through the bidirectional gut-lung axis. [ 8 ] Regarding metabolites, SCFAs produced by anaerobic bacteria in the gut have a beneficial effect on the immune system's response to infections, including COPD [ 9 ]. Other studies have raised the possibility of further microbiota dysbiosis, with alterations in the gut microbiota, specifically in the Fusobacteriaceae, Prevotellaceae, and Bacteroidaceae genera, and a reduced Prevotella/Moraxella ratio in the lungs [ 10 ]. Further research has shown that the abundance of Rothia, Romboutsia, Intestinibacter, Escherichia, Lachnospiraceae, Aerococcus, and Fusobacterium increased in COPD, but that Bacteroides, Roseburia, Lachnospira, Ruminococcaceae, and Lachnoclostridium decreased [ 11 ]. Of the millions of species of fungi that exist, around 300 are present in the human body. The mycobiome, the fungal community of the gut, comprises less than 1% of the human gut microbiota. According to the Human Microbiome Project, the fungal communities were characterized by a high prevalence of Saccharomyces, Malassezia, and Candida, in addition to S. cerevisiae, M. Restricta, and C. albicans. The Ascomycota and Basidiomycota phyla are almost exclusively present in the human intestinal tract, whereas other phyla are mostly found in pathological conditions [ 12 ]. The significance of the A/B ratio and Candida ssp. are raised in cases of gut pre-cancerous (IBDs and IBS) and cancerous lesions [ 12 , 13 – 15 ]. The A/B ratio is also increased in metabolic diseases such as type 2 diabetes and obesity [ 16 ]. The gut-lung axis is bidirectional, and the communication pathway can be direct (via the oral cavity or sputum) or indirect, involving the bloodstream in relation to the gut bacteriome. SCFAs are also beneficial for inflammatory conditions of the lung via bone marrow hematopoietic precursors and circulating monocytes/neutrophils, enhancing their regulatory and anti-inflammatory programming. They also signal via airway epithelial and immune cells (e.g., alveolar macrophages, Tregs) through GPR41/43 and HDAC inhibition to promote barrier integrity and dampen lung inflammation [ 17 ]. While both the bacterial gut metagenome [ 7 ] and metatranscriptome [ 18 ] have been recently linked to COPD, the gut mycobiome and its ecological network with bacteria have not yet been elucidated. In this study, we aim to analyze the gut mycobiome in non-small cell lung cancer (NSCLC) patients according to their COPD status with ITS sequencing. Moreover, we utilized the metagenome data from our earlier validation cohort [ 19 ] to create bacterial-fungal co-occurrence networks and to investigate ecological hubs and rewiring. Our approach aims to provide novel insights into the gut–lung axis by characterizing fungal bacterial interactions in COPD. METHODS Study population A total of 61 patients diagnosed with stage IV NSCLC and receiving standard-of-care therapy approved by the Institutional Oncology Team were enrolled in our study cohort between 2017 and 2020 at the County Hospital of Pulmonology, Torokbalint, Hungary. Clinicopathological data included age, gender, smoking pack year (PY), and COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage at the time of lung cancer diagnosis. Patients were classified as COPD severe (GOLD stadium C and D) and COPD mild / no COPD (GOLD A and B and no comorbidity). All patients were assessed with an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1 at the time of fecal sampling. Before sampling, all COPD patients received standard-of-care therapy contemporary guidelines in accordance with the current GOLD guidelines. Patients receiving systemic antibiotic therapy or an acute exacerbation within 30 days of fecal sampling were excluded from the study cohort. Internal transcribed sequencing (ITS) For fungal community profiling, we performed ITS2 amplicon sequencing on genomic DNA extracted from stool samples of lung cancer patients stratified by COPD status. DNA concentration was measured using a Qubit fluorometer, and 200 ng of DNA the PCR input for each sample. Amplification targeted the ITS2 region using a primer set specific to fungal taxa, with unique barcodes assigned to each sample. PCR products were purified, and equimolar amounts were pooled to generate the sequencing library. Libraries underwent A-tailing, end polishing, and adapter ligation, followed by bead-based purification and PCR amplification to obtain fully double-stranded products. Size distribution and concentration were assessed via real-time PCR. Sequencing was performed on an Illumina HiSeq 2500 platform as previously described. Metagenomic sequencing For bacterial abundances, we used metagenomic shotgun sequencing data from n = 47 patients overlapping with the ITS2 cohort, corresponding to the validation cohort of our previously published study [ 20 ]. High-quality reads were taxonomically profiled using MetaPhlAn2 (version 2.7.7) with default parameters to generate relative abundance estimates at multiple taxonomic levels. For the current analysis, genus-level abundances were extracted and matched with corresponding fungal genus-level profiles from ITS2 sequencing. Only taxa present in at least 10% of samples and with a minimum of 1% total abundance threshold were retained. The merged bacterial–fungal abundance table served as the input for cross-kingdom correlation analyses and network construction. Diversity analyses Alpha-diversity was quantified with four complementary indices calculated in phyloseq v1.44: Chao1, Shannon entropy, and Simpson. For each index, values were compared between COPD-severe and mild or no-COPD groups using a two-tailed Wilcoxon rank-sum test; p-values < 0.05 were considered significant. Rarefaction curves confirmed saturation of all metrics at the chosen depth. To capture overall compositional turnover, we used a partial-least-squares (PLS) distance as a single surrogate for conventional β-diversity measures. Briefly, centred log-ratio (CLR)–transformed genus and species tables were subjected to PLS regression with COPD status (severe vs mild or no COPD) as the response in mixOmics v6.22. The first two latent components (PLS1 + PLS2) explained 65% of the between-sample variance in the species data and 69% in the genus data, respectively. A Euclidean distance matrix was then calculated based on these component scores; this “PLS distance” integrates both compositional dissimilarity and group-discriminative structure and has been shown to parallel the Bray–Curtis distance while maximizing clinical separability [ 20 ]. Group centroid separation was tested with PERMANOVA. All analyses were run in R 4.3. ANCOM analysis ANCOM analysis was performed to identify differentially abundant fungal taxa between COPD non-severe (0) and COPD severe (1) groups. The abundance data were first filtered to exclude samples without COPD Severe classification. Total Sum Scaling (TSS) normalization was applied to mitigate compositional bias. For genus-level analysis, data were aggregated by summing abundances across species. A Wilcoxon rank-sum test was conducted for each taxon, and Benjamini-Hochberg FDR correction was applied to account for multiple testing. ANCOM detects subtle compositional differences by leveraging log-ratio transformations across all taxa pairs, capturing consistent shifts that absolute or relative abundance comparisons may overlook. This framework enhances sensitivity to nuanced community changes, even when individual taxa vary modestly, making it well-suited for distinguishing subtle microbiome alterations [21,22]. Fungal ecology Genera-resolved ecological profiles were visualised with a two-layer sunburst diagram. First, the top 40 abundant genera, regardless of patient group, were retained. Each genus was annotated to a primary ecological guild using the FUNGuild v1.3 reference database (confidence ≥ “probable”); unresolved taxa were labelled “Undefined Saprotroph”. Guild names formed the inner ring, and genera formed the outer ring. Abundances were centre-log-ratio normalised, converted to relative percentages, and exported to Plotly 5.20. To compare guild composition between COPD-severe and mild or no COPD patients, each sample’s genera were collapsed to the guild level, summed, and expressed as a percentage of total ITS reads. Medians for the two patient groups were plotted. Statistical differences were screened with two-tailed Wilcoxon rank-sum tests (α = 0.05). Correlation and network analysis Spearman pairwise correlations were computed and edges with |ρ| >0.4 and Benjamini–Hochberg-adjusted p < 0.05 were retained to build an undirected weighted graph in networkx; node size scaled to degree and edge width to |ρ|. Separate matrices were generated for COPD-severe and mild or no COPD samples, and two subnetworks were rendered with identical layout seeds. Betweenness and closeness centralities were calculated for each genus in both graphs; shifts (Δcentrality = value₁ – value₀) were exported, ranked, and visualised as lollipop plots (matplotlib) and spring-layout differential networks (red = stronger, blue = weaker in COPD severe). The cleaned ITS table (fungi) and the MetaPhlan genus table (bacteria) were vertically concatenated after CLR transformation and aligned on 47 overlapping samples. Inter-kingdom Spearman correlations were computed; only bacteria–fungus pairs with |ρ| >0.4 were retained. Guild information for fungi (FUNGuild) and the phylum for bacteria coloured nodes in the network visualization. Fisher-z statistics (n₀ = 27, n₁ = 20) were calculated for each edge to compare COPD groups; Δz and raw p values provided an edge-rewiring score. Edges with p 0.4 for each genus in both groups; Δdegree values were sorted and the top 30 taxa plotted as colour-coded lollipops (purple = fungi, green = bacteria). A second lollipop summarised the 30 strongest rewired pairs (largest |Δz|). All correlations used pair-wise complete ranks. Supervised compositional balance analysis ITS and metagenome-derived abundances (n = 47) were offset with a pseudocount (0.5) and log-transformed; the balance score for each sample was defined as the difference between the mean log-abundance of a numerator set and a denominator set of genera (i.e., log of the ratio of geometric means). We identified these sets via a greedy forward selection (selbal-like): starting from the best discriminating 1-vs-1 pair for COPD-severe (1) vs mild or no COPD (0) and iteratively adding genera to the numerator or denominator when cross-sectional AUC improved by ≥ 0.01, with a cap of eight genera total to limit model complexity. Performance was summarised by the ROC AUC with bootstrap 95% CI (1,000 resamples) and an exact permutation test (labels shuffled 2,000 times) using a fixed random seed. Stability was assessed by repeated stratified resampling (20 half-splits), recording the selection frequency of each genus in the numerator/denominator. The analysis was implemented in Python (pandas, scikit-learn, matplotlib). RESULTS Fecal ITS sequencing data were available for n = 61 lung cancer patients diagnosed with severe COPD (Gold C-D stadium) or non-severe, defined as mild / no COPD (Gold A-B or no COPD comorbidity), who were included to conduct this study. After excluding patients (n = 4) who received systemic antibiotic treatment within 30 days of sampling, 57 patients were analyzed. Patients were categorized as having severe COPD (n = 18), or non-severe COPD (n = 39). Mycobiome diversity and composition according to COPD status Alpha diversity was measured at the level of fungal species and genera, where we compared Shannon (Fig. 1 A, D), Simpson (Fig. 1 B, E), and Chao1 (Fig. 1 C, F) indices between the COPD severe (1) and COPD mild / no COPD (0) groups, where none of the comparisons showed a significant difference. Next, we aimed to assess whether there was a significant compositional shift between patients with severe vs non-severe COPD. A sensitive analysis, Partial Least Squares Discriminant Analysis (PLS-DA) was used to reveal subtle difference in composition data between the two patient groups. PLS-DA maximizes the separation between the two groups by finding the optimal combination of taxa that best differentiates them. Although there was some overlap (Fig. 2 A-B), PLS-DA revealed a separation pattern linked to COPD status among genera and species (Fig. 2 A-B). Permanova on the PLS distance matrix yielded a borderline-significant effect of COPD status (pseudo-F = 2.1, R² = 0.06, p = 0.058), indicating that 6% of overall fungal community variance is attributable to disease severity. Next, we compared the fungal macrocomposition of the two patient groups, listing key phyla and the ratio of Ascomycota to Basidiomycota (A/B ratio). The relative abundance of Ascomycota (90.68% vs 95.06%) to Basidiomycota (8.71% vs 1.46%) was significantly lower in patients with severe COPD (vs non-severe COPD, p = 0.0394, Fig. 2 C-D). In addition, the abundance of phylum Mucoromycota also decreased in patients with severe COPD compared to patients with mild or no COPD (0.25% vs 3.21%). Analysis of Compositions of Microbiomes with Bias Correction (ANCOM) was performed to identify differentially abundant fungal taxa between the two clinical patient groups. ANCOM identified genera Myrothecium (overrepresented in severe COPD) and Helotiales_unclassified (overrepresented in mild or no COPD) with an above-log10 (FDR) value of 2. At the species level, the top signals included Lasiodiplodia crassispora (enriched in COPD-severe) and Phallus atrovolvatus (enriched in non-severe). Other species enriched in severe cases were Trichosporon asahii, Talaromyces aculeatus, and Chaetomium aureum. In contrast, species such as Leucocoprinus cretaceus, Trichoderma virens, and Cosmospora sp. were more abundant in patients with non-severe conditions. Correlation of fungal genera and fungal networks in COPD We constructed and analyzed genus-level co-occurrence networks derived from the fungal ITS data, stratified by the presence or absence of severe COPD. We applied Spearman correlation analysis (Fig. 3 A), retaining only robust associations (|ρ| >0.4, FDR-adjusted p < 0.01) to construct a weighted, undirected network that included all patients (Fig. 3 B). The ITS genus–genus matrix was dominated by positive co-occurrences. ρ ≥ 0.8 pairs were: Myxocephala–Roussoella (ρ = 0.86), Fusarium–Tetracladium (ρ = 0.82), Arachnopeziza–Thelidium (ρ = 0.82), Arachnopeziza–Meliniomyces (ρ = 0.81), Clonostachys–Roussoella (ρ = 0.8), Clonostachys–Myxocephala (ρ = 0.8), Leptodontidium–Tetracladium (ρ = 0.8), Fusarium–Mortierella (ρ = 0.8). The remaining 60 pairs with ρ = 0.60–0.8 repeatedly linked plant/soil and litter-associated genera (Debaryomyces, Kazachstania, Cladosporium, Aspergillus, Penicillium) with root- or wood-associated saprotrophs/mycoparasites (Leptodontidium, Trichoderma, Tetracladium, Clonostachys, Mortierella). In contrast, no negative correlations exceeded |ρ| = 0.6; the strongest antagonistic trends were Mucor–Saitozyma (ρ=−0.545), Clonostachys–Mucor (ρ=−0.512), Mucor–Trichoderma (ρ=−0.508), and Aspergillus–Saccharomyces (ρ=−0.498). Collectively, the signal suggests co-varying environmental guilds—wood/leaf-litter saprotrophs, mycoparasites, and root-associated fungi—move together in the gut (likely reflecting shared sources or trophic coupling), while Mucorales show niche exclusion with filamentous Ascomycetes and basidiomycetous yeasts. Separate networks were generated for patients without severe COPD (n = 39) and with severe COPD (n = 18). The non-severe COPD network exhibited higher complexity, comprising 33 fungal genera connected by 64 significant edges, with a network density of 0.121, average degree of 3.88, and average clustering coefficient of 0.403. In contrast, the severe COPD network was sparser, with only 28 nodes and 39 edges (density: 0.101; average degree: 2.79; clustering coefficient: 0.329). These differences indicate reduced co-occurrence connectivity in patients with severe COPD. Edge-level comparison revealed that only 21 correlations were shared between groups, while 43 and 18 were unique to the COPD = 0 and COPD = 1 networks, respectively. The COPD = 0 network contained a greater proportion of strong positive associations, suggesting network stability, whereas the COPD severe network included several negative correlations, indicative of potential antagonistic interactions or niche exclusion. Modularity analysis further demonstrated that the non-severe COPD network had more distinct and interconnected community modules, whereas the severe COPD network showed reduced modularity and fewer hub taxa. Collectively, these findings suggest that severe COPD is associated with a less complex and more fragmented structure of fungal co-occurrence. Altogether, the COPD-severe-associated network is characterized by a reduction in complexity, loss of connectivity, and a shift toward more isolated interactions among fungal taxa. Gut fungal ecology and the bacteria-fungal network in COPD To further explore the association between the gut mycobiome and COPD, we aimed to investigate which ecological niches and fungal guilds are enriched in patients with severe vs non-severe disease. Sunburst diagram shows the taxonomic breakdown at the phylum and genus level of the gut mycobiome in our cohort in the context of major taxonomical guilds (Fig. 4 A). Major ecological guilds include Plant pathogens, Lichenized fungi, Fungal parasites, Freshwater saprotrophs, Fermenters, Ericoid mycorrhizals, Animal pathogens, Soil saprotrophs, Ectomycorrhizals, Wood saprotrophs, Root endophytes, Plant saprotrophs, and unidentified saprotrophs that couldn’t be classified to neither guild. When comparing the two patient groups, we found that animal pathogens (28% vs 14%, p = 0.007) and ectomycorrhizal fungi (4% vs 0.2%, p < 0.001) are significantly more prevalent in patients with severe COPD. In contrast, fibre-degrading soil saprotrophs were overrepresented in mild or no COPD (7% vs 2%, p < 0.032) (Fig. 4 B). To establish a correlation and network map between bacteria and fungi, we used the metagenome data-derived bacterial abundances of the 47 overlapping patients from our NSCLC cohort, published previously for validation purposes [ 19 ]. Concatenated and normalized abundances of top genera (1% cut-off) were correlated using Spearman’s rank correlation (Fig. 4 C). Then, a co-occurrence network was created, adjusted for ecological guilds (Fig. 4 D). The heatmap and hierarchical clustering revealed distinct co-occurrence modules. Notably, some fungal taxa (Saccharomyces, Debaryomyces, Candida) tend to cluster with bacterial genera typically found in disturbed or inflamed gut environments (e.g., Enterococcus, Escherichia/Shigella, Klebsiella, and Clostridium), suggesting a possible shared association with gut dysbiosis or environments related to inflammation. Clusters of bacterial genera involved in fiber degradation and SCFA production—such as Ruminococcus, Eubacterium, and Butyricicoccus—appear inversely related to fungi typically classified as pathobionts or stress-tolerant (e.g., Kazachstania, Malassezia). Several fungal genera show consistent anti-correlation with taxa known to maintain gut homeostasis (Faecalibacterium, Blautia, Roseburia). Co-occurrence analysis revealed four major cross-kingdom ecological communities: a fermentative dysbiosis niche, that combines probiotic and fermentative bacteria with ascomycetous yeasts and plant-associated fungi (Lactococcus, Paenibacillus, Saccharomyces, Fusarium, Anaerostipes, brown, Fig. 4 D); an anaerobic Core and gut barrier modulator niche with abundant SCFA producers (Clostridium, Blautia), co-occurring or anti-correlated with fungal pathobionts like Candida (blue, Fig. 4 D); a community of soil-derived immunomodulators, genera involved in secondary metabolite production and often found in inhaled particles or soil-contaminated foods (Streptomyces, Cryptococcus, Actinomyces, Dysosmobacter, Thermothelymices, green, Fig. 4 D); and a default commensal axis including key commensal, high-abundance anaerobes and low-abundance environmental fungi (Bacteroides, Parabacteroides, Roseburia, Malassezia, gray, Fig. 4 D). In the case of the Keystone analysis, centrality increased for Candida, Aspergillus, Enterococcus, and Clostridium, while it decreased for Akkermansia and Roseburia. These results indicate a shift from fiber-associated, butyrate-linked networks toward oxygen-tolerant, fermentative/proteolytic consortia in COPD-severe cases (Fig. 4 E). Cross-kingdom edge rewiring showed more edges strengthening in COPD-severe than weakening. Stronger correlations concentrated on the yeast Candida and facultative anaerobes Enterococcus and Clostridium, with additional positive links to Akkermansia and Lactobacillus. Weaker correlations centered on SCFA-associated taxa (Faecalibacterium, Roseburia) and saprotrophic fungi (Debaryomyces, Saccharomyces) (Fig. 4 F). Predictive function of compositional balance We used a supervised compositional balance classifier to derive a single, interpretable per-patient score that contrasts “pro-severe COPD” versus “anti-severe COPD” genera according to COPD. This method is suitable for microbiome data (which are inherently relative), avoids spurious correlations, and directly tests the network-level hypothesis of hub replacement. In the 47 matched ITS and metagenome sequenced samples, higher scores were observed in COPD-severe (Fig. 5 A), yielding strong discrimination (AUC = 0.88 with restricted candidates; 0.93 when all genera were allowed, Fig. 5 B). The balance selected Sporisorium in the numerator and Alistipes, Eubacterium, Kazachstania, and Kluyveromyces in the denominator, and significance by permutation. Stability resampling repeatedly recovered the same pattern (Sporisorium as the most frequent numerator; Alistipes/Eubacterium/Kazachstania/Kluyveromyces as denominators), indicating a robust direction of effect even if individual taxa enter with moderate frequencies (Fig. 5 C). Biologically, the balance captures a tilt toward opportunistic fungi relative to commensal, fermentation-linked bacteria/yeasts, consistent with our network findings: strengthened co-occurrence around aerotolerant, inflammation-adapted taxa and weakening of SCFA-associated partners. DISCUSSION Despite comprising a small share of the gut ecosystem, fungi deliver potent cell-wall ligands and metabolites that reset mucosal immunity and propagate signals along the gut–lung axis [23, 10, 6]. In COPD, gut-derived molecular signals can alter exacerbation risk and modulate responses to infection and therapy [ 10 , 7 ]. When fungal communities rewire cross-kingdom networks and erode SCFA–centered trophic chains, barrier instability and systematic inflammation can follow, amplifying lung pathology. Conversely, network reconstitution—via diet, prebiotics, or targeted microbiome strategies—might alleviate disease. Our data, therefore, position the gut mycobiome as a context-dependent biomarker of pulmonary homeostasis and severe COPD, with implications for risk stratification and future microbiome-based interventions. In the current study, we profiled the gut mycobiome in patients with lung cancer and those with and without severe COPD, and integrated bacterial metagenome data from the same cohort. Alpha-diversity showed no significant differences in the two patient groups, but composition and structure shifted with COPD severity. Severe COPD showed a higher Ascomycota/Basidiomycota ratio, enrichment of opportunistic fungi, and a simpler, less connected fungal network. Cross-kingdom links favored Candida/Aspergillus with Enterobacteriaceae, while ties around SCFA-associated bacteria and commensal yeasts weakened. Overall, our results support a network “rewiring” rather than the effects of differentially abundant single-taxon species. In our cohort, fungal alpha diversity did not differ across COPD categories (severe vs mild or no-COPD), while composition showed a modest but reproducible separation when using PLS, a pattern is consistent with many bacterial gut studies in COPD that report limited alpha shifts but measurable community turnover [ 7 , 24 ]. By contrast, airway mycobiome work sometimes finds higher alpha diversity or distinct clustering in COPD—especially in frequent exacerbators—in line with a compartment-specific signal [ 25 , 26 ]. For stool fungi, comparative data remain scarce and variable, likely reflecting low biomass, high inter-individual variation, and technical constraints of ITS profiling. Altogether, COPD severity aligns with subtle compositional drift, not drastic diversity changes. In phylum-level analyses, we observed a shift in the A/B ratio, with severe COPD showing relatively lower Ascomycota and higher Basidiomycota, suggesting a broad ecological tilt. Similar A/B distortions are reported in inflammatory bowel disease, GI cancers and other inflammatory states, though directionality varies across cohorts, methods, and sampling types [12,27,28,29,30]. Several studies describe the enrichment of Basidiomycota and the depletion of Ascomycota in gut inflammation, as well as in lung cancer, aligning with our pattern, while others report mixed results [ 31 ]. Biologically, an A/B shift may reflect oxygenation, bile acid, and immune pressures that favor aerotolerant or lipid-adapted Basidiomycota (e.g., Malassezia) and alter interactions with bacteria and host pattern-recognition pathways [ 30 ]. Because Ascomycota also contains both commensals and opportunists, the A/B ratio should be read as a coarse ecosystem marker that compresses complex mycobiome changes into a tractable metric for longitudinal assessment [ 12 ]. Across COPD groups, fungal genus correlations reconfigured: severe COPD showed fewer and weaker positive edges, more isolated nodes, and lower modularity/robustness—hallmarks of ecosystems under inflammatory stress [32,33,34]. Hubs shifted toward opportunistic taxa, while commensal/food-associated yeasts lost centrality. Cross-kingdom networks mirrored this: in severe COPD, Candida/Aspergillus strengthened associations with Enterobacteriaceae and other facultative aerobes, whereas ties linking SCFA-producing bacteria (Faecalibacterium and Roseburia) to benign yeasts weakened or reversed. These patterns indicate network-level rewiring, not single-taxon effects. Mechanistically, inflammation-driven epithelial metabolic shifts may increase mucosal oxygen and nitrate levels, favoring facultative taxa over strict anaerobes [ 35 , 36 ]. Because SCFAs stabilize lung immune tone, erosion of SCFA-centered consortia offers a plausible gut–lung pathway for exacerbation-prone phenotypes [ 37 ]. Cross-kingdom studies further support fungi as active modulators rather than bystanders [38,39]. COPD shows strong association with smoking (there were only two non-smokers in our cohort) and smoking-/inflammation-driven immune–trophic feedback explain our patterns. Reduced colonocyte β-oxidation and PPAR-γ signaling increase mucosal O 2 and host-derived nitrate, favoring facultative bacteria and aerotolerant yeasts over strict butyrate producers [ 36 ]. The strengthened Candida–Enterococcus/Clostridium edges and weakened Faecalibacterium/Roseburia–benign yeast edges are consistent with metabolic reprogramming, characterized by lower butyrate production and enhanced lactate/ethanol/amine fluxes that reinforce inflammation and barrier stress [ 40 , 41 , 42 , 43 ]. Fecal metabolomics and further multi-omic approaches, combined with longitudinal studies, are needed to confirm these findings. In our study, instead of classifying patients as diagnosed vs. not diagnosed with COPD, we stratified patients as severe COPD vs. mild/no COPD. Severity-based grouping more accurately reflects the biology that drives microbiome changes. Severity is associated with exacerbation burden, systemic inflammation, hypoxemia, comorbid load, and exposure to therapies, including inhaled corticosteroids and antibiotics, all of which are potent modifiers of the microbiome [ 26 , 44 ]. As COPD worsens, systemic inflammation rises [45], neutrophils are more primed [46], and hypoxemia plus catabolic stress reshape epithelial metabolism and barrier function—factors that influence gut oxygen leak, bile acids, and cross-kingdom networks [ 10 , 47 , 48 ]. Exacerbation burden also perturb the bacterial–fungal balance [49,50,51]. These gradients create a more precise dose–response in gut–lung signaling than a binary “COPD yes/no,” which mixes biologically near-normal mild cases with profoundly dysregulated severe cases. Our study is limited by its cross-sectional design. Moreover, the sample size constrained multivariate and subgroup analyses; therefore, we focused on ecological and network differences rather than strict clinical or biomarker endpoints—a choice that precludes causal inference and allows residual confounding (diet, medications, inhaled corticosteroids/antibiotics, oral health). Still, converging clinical and experimental data support a reciprocal gut–lung axis in COPD: gut metabolites shape airway immunity, while pulmonary inflammation and therapies reshape gut communities [ 48 , 52 ]. CONCLUSION Taken together, our data suggest that COPD severity is mirrored not by distinct gain or loss of specific fungal taxa, but by re-wiring of cross-kingdom networks toward opportunistic, inflammation-adapted consortia and away from SCFA-producing ecosystems. Our study extends COPD microbiome work to the mycobiome and nominates network features—not single taxa—as candidate readouts to explain alteration of gut commensals in pulmonary inflammatory conditions. Prospective, multi-omics, and interventional studies should test whether restoring SCFA-centric ecological niches improves pulmonary outcomes. Declarations Data availability The datasets generated and analysed during the current study have been deposited in the NCBI sequence read archive (SRA) database under accession code PRJNA811494. Funding This study was supported by National Research, Development and Innovation Office, grants #146775 (Zoltan Lohinai), #142287 (David Dora). Contributions Conceptualization, GS., and Z.L.; data curation, G.S., D.D., S.S., C.K.A. and M.H.; formal analysis, D.D., G.S., S.S. and A.B.; funding acquisition, D.D., G.G. and Z.L.; investigation, G.S., D.D., S.S., A.B., and Z.L.; methodology, G.S., D.D., C.K.A. and M.H.; project administration, G.G., and Z.L.; resources, D.D., G.G., and Z.L.; software, G.S., D.D., A.B. and M.H.; supervision, D.D., and Z.L.; validation, C.K.A., A.B. and M.H.; visualization, GS., D.D., and M.H.; writing—original draft, G.S., D.D., and Z.L.; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript. Ethics approval and consent to participate In the current study, we followed the principles of the Declaration of Helsinki established by the World Medical Association. 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PMID: 12106623. Dang, A. T. & Marsland, B. J. Microbes, metabolites, and the gut-lung axis. Mucosal Immunol. ;12(4):843–850. (2019). 10.1038/s41385-019-0160-6 . Epub 2019 Apr 11. PMID: 30976087. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Editor invited by journal 07 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 06 Nov, 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. 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08:27:16","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":461144,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/c8852bed9e66b63cfbb3dad5.png"},{"id":97127276,"identity":"df848a8f-6d32-487c-83a1-6e60ce70f301","added_by":"auto","created_at":"2025-12-01 08:27:16","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126934,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/e7d018939598711b44fa72f3.png"},{"id":97141994,"identity":"f7c625b6-9c1e-4de9-b7bd-71d9ac783bee","added_by":"auto","created_at":"2025-12-01 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10:07:43","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213565,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/757ca5d57f592222827225eb.png"},{"id":97142195,"identity":"cb24f637-0e9b-45a1-bb7b-4f086de61e50","added_by":"auto","created_at":"2025-12-01 10:07:23","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":461144,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/f152a4e0ca3831b7dcf97f23.png"},{"id":97127281,"identity":"cb54772e-bc96-490d-8802-49aa27a63fcf","added_by":"auto","created_at":"2025-12-01 08:27:16","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":515592,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/78e64f09a6e6edb7d681d897.png"},{"id":97127280,"identity":"35b14bb4-674a-4aa7-9a07-95255e6ca124","added_by":"auto","created_at":"2025-12-01 08:27:16","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":241650,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/15d90fee133e07ef46e1670c.png"},{"id":97127283,"identity":"f0a586fa-cccb-4c31-9fdb-e344cba09948","added_by":"auto","created_at":"2025-12-01 08:27:16","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126934,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/345319ad9b03f7e53d0b95d6.png"},{"id":97127278,"identity":"8c1a1491-8cea-41ed-bcb4-554b986b0d30","added_by":"auto","created_at":"2025-12-01 08:27:16","extension":"xml","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127477,"visible":true,"origin":"","legend":"","description":"","filename":"533c7ba8522a46fba5e3694cb37eeb111structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/b7b52115b8955aaa92a39630.xml"},{"id":97142691,"identity":"ea81675c-8eeb-440e-8369-2cebecdbd9e9","added_by":"auto","created_at":"2025-12-01 10:07:52","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145663,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/d46797a74e5079285b09be84.html"},{"id":97127251,"identity":"28965842-dc2f-470a-9ec4-fe6279bf249d","added_by":"auto","created_at":"2025-12-01 08:27:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1033289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity of the gut mycobiome according to COPD. \u003c/strong\u003eShannon, Simpson and Chao1 alpha diversity metrics were calculated for patients with severe COPD (1), and no- or mild COPD (0) at species and genus levels for gut mycobiota (A-F). Statistical comparison of diversity indices were performed using Welch’s test. Statistical significance *p \u0026lt; 0.05; **p \u0026lt; 0.01, ***p \u0026lt; .001, all p-values were two-sided\u003c/p\u003e","description":"","filename":"FIGURE11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/481a1652ac0eb17888c2e85a.jpg"},{"id":97127254,"identity":"c3a44620-6eec-4205-90b4-bb159d2c11f1","added_by":"auto","created_at":"2025-12-01 08:27:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2577371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompositional differences of the gut mycobiome according to COPD. \u003c/strong\u003ePartial least squares discriminant analysis (PLS-DA) performed on abundance data is shown at the species (A) and genus (B) levels. Axes represent the first two latent components that maximize between-group variance, demonstrating partial but reproducible separation despite overlap. Group differences were evaluated using PERMANOVA on the PLS distance matrix, yielding a borderline significant effect of COPD status (pseudo-F = 2.1, R² = 0.06, p = 0.058). Phylum-level profiles (C) illustrate relative contributions of Ascomycota, Basidiomycota, and Mucoromycota, with values expressed as mean percentage abundance across groups. The Ascomycota/Basidiomycota ratio (D) was visualized using boxplots, confirming a significant reduction in severe COPD (p = 0.0394). Differential abundance was further examined using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM), a log-ratio framework that accounts for compositionality and controls false discovery. Results are displayed for species (E) and genera (F), ranked by −log10(FDR). ANCOM significance threshold was set to −log10(FDR) \u0026gt; 2. Bars indicate directionality, with purple denoting taxa overrepresented in COPD-severe and pink indicating enrichment in non-severe patients.\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/8f935ffed9afec3553e2d84b.jpg"},{"id":97142590,"identity":"3e2651b5-7853-4ddb-92ff-e73d09c8e0ca","added_by":"auto","created_at":"2025-12-01 10:07:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10477953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of fungal genera and the gut mycobiome network in COPD. \u003c/strong\u003ePanel A shows the Spearman correlation matrix of fungal genera after filtering for |ρ| \u0026gt; 0.4 and p \u0026lt; 0.01. A weighted undirected gut mycobiota network is dominated by positive correlations. Panels C and D present stratified networks for patients with and without severe COPD, respectively. The non-severe network exhibited higher modularity and a greater proportion of strong positive correlations, while the severe network was more fragmented and included several negative associations. Red and blue colored edges depict positive and negative correlations, respectively. The size of each node is proportional to its degree in the network (the number of connections it has), shared node colors classify correlating genera to co-occurrence clusters. Panels E and F show the top 20 shifts in betweenness and closeness centrality, respectively. Positive bars show genera more central in patients with severe COPD, whereas negative bars show genera more central in patients with no severe COPD.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/3fc4b7f080dc40fdb77511df.jpg"},{"id":97141431,"identity":"df083d3d-83e3-4aa2-85b4-4ca7ba30c5df","added_by":"auto","created_at":"2025-12-01 10:06:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7302282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross-kingdom ecology and co-occurrence network perspective of the gut mycobiome in COPD. \u003c/strong\u003ePanel A shows a sunburst diagram of the gut mycobiome at the phylum and genus level, with outer segments color-coded by ecological guild. The relative abundance of major fungal guilds across patient groups is shown in a bar chart (B), indicating significant enrichment of animal pathogens and ectomycorrhizal fungi in COPD-severe patients. At the same time, soil saprotrophs were more abundant in non-severe cases. Panel C presents a hierarchical clustered correlation heatmap of bacterial and fungal genera derived from overlapping metagenome and ITS datasets, including the 126 significant associations with Spearman correlation |ρ| \u0026gt; 0.4 and p \u0026lt; 0.05. Distinct modules are visible, with clusters of bacterial SCFA producers (Ruminococcus, Eubacterium, Butyricicoccus) showing negative correlations with fungal pathobionts (Kazachstania, Malassezia), whereas Candida, Debaryomyces, and Saccharomyces align with pro-inflammatory bacterial taxa such as Enterococcus, Escherichia/Shigella, and Klebsiella. Panel D depicts the bacteria–fungi correlation network, organized by guilds into four ecological communities: a fermentative dysbiosis niche (brown), an anaerobic SCFA-rich core (blue), a soil-derived immunomodulator cluster (green), and a commensal axis containing Bacteroides, Parabacteroides, Roseburia, and Malassezia (gray). Node sizes represent abundance, and edges are colored red for positive and blue for negative correlations. Lollipop charts show the top 30 keystone degree shifts between COPD-severe and non-severe patients (E), where taxa such as Candida, Aspergillus, Enterococcus, and Clostridium gained centrality, while Akkermansia and Roseburia lost centrality. The top 30 significantly rewired genus pairs, according to COPD (F), showed strengthened correlations in severe cases, concentrated around Candida and facultative anaerobes, while weakened edges predominantly involved SCFA-associated bacteria and saprotrophic fungi. X-axis represents the magnitude of network change between COPD-severe and non-severe groups: difference in keystone degree centrality values (gain or loss of connectivity importance for each genus, E), and the Δρ correlation strength for each significantly rewired bacterial–fungal genus pair (F).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/c5ebe7160da0ecca7363abbe.jpg"},{"id":97142841,"identity":"661d194b-05e8-4036-89d3-9420aa1f9fee","added_by":"auto","created_at":"2025-12-01 10:07:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":853084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive function of cross-kingdom co-occurrence niches.\u003c/strong\u003eBalance score is the difference between the mean log-abundance of a numerator set and a denominator set of genera (score = mean[log numerator] − mean[log denominator]; pseudocount 0.5). Sets were selected using a greedy forward procedure seeded with the best 1-vs-1 pair and expanded until the AUC improved by ≥ 0.01, capped at a total of eight genera. (A). Performance was summarised by the ROC AUC with bootstrap 95% CI (1,000 resamples) and an exact permutation test (labels shuffled 2,000 times) using a fixed random seed (B). Stability was assessed by repeated stratified resampling (20 half-splits), recording the selection frequency of each genus in the numerator/denominator (C).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/f4d1810df22549c0af7d2cff.jpg"},{"id":108437629,"identity":"a408ebd6-80de-4d66-aec3-72c7470313cf","added_by":"auto","created_at":"2026-05-04 16:00:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22570140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7992928/v1/2f88d4b0-2dd0-49f0-a9b2-70dae7ce021a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Gut Mycobiome and Inter-kingdom Microbial Networks are linked to COPD Severity in Lung Cancer Patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is one of the three leading causes of death, affecting around 10% of the population, with an expected increase by 23% from 2020 to 2050, including females by 47.1% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. COPD is characterized by exposure to inhaled particulate matter, including smoking or air pollutants, in combination with genetic, developmental, and social factors. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The identification of \u0026ldquo;treatable traits\u0026rdquo; is crucial through the identification of \u0026ldquo;phenotypes\u0026rdquo; and \u0026ldquo;endotypes\u0026rdquo;. Therefore, a better understanding of molecular mechanisms might reveal novel associations beyond our current understanding of gene-environment interactions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe gut-lung axis may help extend our current understanding of the dynamics of maintenance, repair, and cumulative tissue injury, as well as aging, in the pathology of COPD. The gastrointestinal and respiratory tracts share similar cellular components, but their distribution varies to support organ-specific functions. Mucosa-associated lymphoid tissue (MALT) comprises gut-associated lymphoid tissue (GALT) and inducible bronchus-associated lymphoid tissue (iBALT). The microbiome contributes to mucosal and systemic immune homeostasis through the mononuclear phagocyte system (MPS), which mediates host\u0026ndash;microbe crosstalk [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A notable example of the mutual changes in the gut-lung axis microbiome is the increase in Streptococcus species in both organs among smokers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Lactobacillus was shown to play an immunomodulatory role in the management of patients with chronic respiratory diseases by balancing lung immunity and promoting respiratory health through the bidirectional gut-lung axis. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Regarding metabolites, SCFAs produced by anaerobic bacteria in the gut have a beneficial effect on the immune system's response to infections, including COPD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Other studies have raised the possibility of further microbiota dysbiosis, with alterations in the gut microbiota, specifically in the Fusobacteriaceae, Prevotellaceae, and Bacteroidaceae genera, and a reduced Prevotella/Moraxella ratio in the lungs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Further research has shown that the abundance of Rothia, Romboutsia, Intestinibacter, Escherichia, Lachnospiraceae, Aerococcus, and Fusobacterium increased in COPD, but that Bacteroides, Roseburia, Lachnospira, Ruminococcaceae, and Lachnoclostridium decreased [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf the millions of species of fungi that exist, around 300 are present in the human body. The mycobiome, the fungal community of the gut, comprises less than 1% of the human gut microbiota. According to the Human Microbiome Project, the fungal communities were characterized by a high prevalence of Saccharomyces, Malassezia, and Candida, in addition to S. cerevisiae, M. Restricta, and C. albicans. The Ascomycota and Basidiomycota phyla are almost exclusively present in the human intestinal tract, whereas other phyla are mostly found in pathological conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The significance of the A/B ratio and Candida ssp. are raised in cases of gut pre-cancerous (IBDs and IBS) and cancerous lesions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The A/B ratio is also increased in metabolic diseases such as type 2 diabetes and obesity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e The gut-lung axis is bidirectional, and the communication pathway can be direct (via the oral cavity or sputum) or indirect, involving the bloodstream in relation to the gut bacteriome. SCFAs are also beneficial for inflammatory conditions of the lung via bone marrow hematopoietic precursors and circulating monocytes/neutrophils, enhancing their regulatory and anti-inflammatory programming. They also signal via airway epithelial and immune cells (e.g., alveolar macrophages, Tregs) through GPR41/43 and HDAC inhibition to promote barrier integrity and dampen lung inflammation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While both the bacterial gut metagenome [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and metatranscriptome [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have been recently linked to COPD, the gut mycobiome and its ecological network with bacteria have not yet been elucidated.\u003c/p\u003e\u003cp\u003eIn this study, we aim to analyze the gut mycobiome in non-small cell lung cancer (NSCLC) patients according to their COPD status with ITS sequencing. Moreover, we utilized the metagenome data from our earlier validation cohort [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to create bacterial-fungal co-occurrence networks and to investigate ecological hubs and rewiring. Our approach aims to provide novel insights into the gut\u0026ndash;lung axis by characterizing fungal bacterial interactions in COPD.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003e A total of 61 patients diagnosed with stage IV NSCLC and receiving standard-of-care therapy approved by the Institutional Oncology Team were enrolled in our study cohort between 2017 and 2020 at the County Hospital of Pulmonology, Torokbalint, Hungary. Clinicopathological data included age, gender, smoking pack year (PY), and COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage at the time of lung cancer diagnosis. Patients were classified as COPD severe (GOLD stadium C and D) and COPD mild / no COPD (GOLD A and B and no comorbidity). All patients were assessed with an Eastern Cooperative Oncology Group (ECOG) performance status of 0\u0026ndash;1 at the time of fecal sampling. Before sampling, all COPD patients received standard-of-care therapy contemporary guidelines in accordance with the current GOLD guidelines. Patients receiving systemic antibiotic therapy or an acute exacerbation within 30 days of fecal sampling were excluded from the study cohort.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInternal transcribed sequencing (ITS)\u003c/h3\u003e\n\u003cp\u003eFor fungal community profiling, we performed ITS2 amplicon sequencing on genomic DNA extracted from stool samples of lung cancer patients stratified by COPD status. DNA concentration was measured using a Qubit fluorometer, and 200 ng of DNA the PCR input for each sample. Amplification targeted the ITS2 region using a primer set specific to fungal taxa, with unique barcodes assigned to each sample. PCR products were purified, and equimolar amounts were pooled to generate the sequencing library. Libraries underwent A-tailing, end polishing, and adapter ligation, followed by bead-based purification and PCR amplification to obtain fully double-stranded products. Size distribution and concentration were assessed via real-time PCR. Sequencing was performed on an Illumina HiSeq 2500 platform as previously described.\u003c/p\u003e\n\u003ch3\u003eMetagenomic sequencing\u003c/h3\u003e\n\u003cp\u003eFor bacterial abundances, we used metagenomic shotgun sequencing data from n\u0026thinsp;=\u0026thinsp;47 patients overlapping with the ITS2 cohort, corresponding to the validation cohort of our previously published study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. High-quality reads were taxonomically profiled using MetaPhlAn2 (version 2.7.7) with default parameters to generate relative abundance estimates at multiple taxonomic levels. For the current analysis, genus-level abundances were extracted and matched with corresponding fungal genus-level profiles from ITS2 sequencing. Only taxa present in at least 10% of samples and with a minimum of 1% total abundance threshold were retained. The merged bacterial\u0026ndash;fungal abundance table served as the input for cross-kingdom correlation analyses and network construction.\u003c/p\u003e\n\u003ch3\u003eDiversity analyses\u003c/h3\u003e\n\u003cp\u003eAlpha-diversity was quantified with four complementary indices calculated in phyloseq v1.44: Chao1, Shannon entropy, and Simpson. For each index, values were compared between COPD-severe and mild or no-COPD groups using a two-tailed Wilcoxon rank-sum test; p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Rarefaction curves confirmed saturation of all metrics at the chosen depth.\u003c/p\u003e\u003cp\u003eTo capture overall compositional turnover, we used a partial-least-squares (PLS) distance as a single surrogate for conventional β-diversity measures. Briefly, centred log-ratio (CLR)\u0026ndash;transformed genus and species tables were subjected to PLS regression with COPD status (severe vs mild or no COPD) as the response in mixOmics v6.22. The first two latent components (PLS1\u0026thinsp;+\u0026thinsp;PLS2) explained 65% of the between-sample variance in the species data and 69% in the genus data, respectively. A Euclidean distance matrix was then calculated based on these component scores; this \u0026ldquo;PLS distance\u0026rdquo; integrates both compositional dissimilarity and group-discriminative structure and has been shown to parallel the Bray\u0026ndash;Curtis distance while maximizing clinical separability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Group centroid separation was tested with PERMANOVA. All analyses were run in R 4.3.\u003c/p\u003e\n\u003ch3\u003eANCOM analysis\u003c/h3\u003e\n\u003cp\u003eANCOM analysis was performed to identify differentially abundant fungal taxa between COPD non-severe (0) and COPD severe (1) groups. The abundance data were first filtered to exclude samples without COPD Severe classification. Total Sum Scaling (TSS) normalization was applied to mitigate compositional bias. For genus-level analysis, data were aggregated by summing abundances across species. A Wilcoxon rank-sum test was conducted for each taxon, and Benjamini-Hochberg FDR correction was applied to account for multiple testing. ANCOM detects subtle compositional differences by leveraging log-ratio transformations across all taxa pairs, capturing consistent shifts that absolute or relative abundance comparisons may overlook. This framework enhances sensitivity to nuanced community changes, even when individual taxa vary modestly, making it well-suited for distinguishing subtle microbiome alterations [21,22].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFungal ecology\u003c/h2\u003e\u003cp\u003eGenera-resolved ecological profiles were visualised with a two-layer sunburst diagram. First, the top 40 abundant genera, regardless of patient group, were retained. Each genus was annotated to a primary ecological guild using the FUNGuild v1.3 reference database (confidence \u0026ge; \u0026ldquo;probable\u0026rdquo;); unresolved taxa were labelled \u0026ldquo;Undefined Saprotroph\u0026rdquo;. Guild names formed the inner ring, and genera formed the outer ring. Abundances were centre-log-ratio normalised, converted to relative percentages, and exported to Plotly 5.20. To compare guild composition between COPD-severe and mild or no COPD patients, each sample\u0026rsquo;s genera were collapsed to the guild level, summed, and expressed as a percentage of total ITS reads. Medians for the two patient groups were plotted. Statistical differences were screened with two-tailed Wilcoxon rank-sum tests (α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation and network analysis\u003c/h3\u003e\n\u003cp\u003eSpearman pairwise correlations were computed and edges with |ρ| \u0026gt;0.4 and Benjamini\u0026ndash;Hochberg-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained to build an undirected weighted graph in networkx; node size scaled to degree and edge width to |ρ|. Separate matrices were generated for COPD-severe and mild or no COPD samples, and two subnetworks were rendered with identical layout seeds. Betweenness and closeness centralities were calculated for each genus in both graphs; shifts (Δcentrality\u0026thinsp;=\u0026thinsp;value₁ \u0026ndash; value₀) were exported, ranked, and visualised as lollipop plots (matplotlib) and spring-layout differential networks (red\u0026thinsp;=\u0026thinsp;stronger, blue\u0026thinsp;=\u0026thinsp;weaker in COPD severe).\u003c/p\u003e\u003cp\u003eThe cleaned ITS table (fungi) and the MetaPhlan genus table (bacteria) were vertically concatenated after CLR transformation and aligned on 47 overlapping samples. Inter-kingdom Spearman correlations were computed; only bacteria\u0026ndash;fungus pairs with |ρ| \u0026gt;0.4 were retained. Guild information for fungi (FUNGuild) and the phylum for bacteria coloured nodes in the network visualization. Fisher-z statistics (n₀ = 27, n₁ = 20) were calculated for each edge to compare COPD groups; Δz and raw p values provided an edge-rewiring score. Edges with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 formed a rewiring network whose line thickness scaled with |Δρ|. In parallel, the degree was recalculated at |ρ| \u0026gt;0.4 for each genus in both groups; Δdegree values were sorted and the top 30 taxa plotted as colour-coded lollipops (purple\u0026thinsp;=\u0026thinsp;fungi, green\u0026thinsp;=\u0026thinsp;bacteria). A second lollipop summarised the 30 strongest rewired pairs (largest |Δz|). All correlations used pair-wise complete ranks.\u003c/p\u003e\n\u003ch3\u003eSupervised compositional balance analysis\u003c/h3\u003e\n\u003cp\u003eITS and metagenome-derived abundances (n\u0026thinsp;=\u0026thinsp;47) were offset with a pseudocount (0.5) and log-transformed; the balance score for each sample was defined as the difference between the mean log-abundance of a numerator set and a denominator set of genera (i.e., log of the ratio of geometric means). We identified these sets via a greedy forward selection (selbal-like): starting from the best discriminating 1-vs-1 pair for COPD-severe (1) vs mild or no COPD (0) and iteratively adding genera to the numerator or denominator when cross-sectional AUC improved by \u0026ge;\u0026thinsp;0.01, with a cap of eight genera total to limit model complexity. Performance was summarised by the ROC AUC with bootstrap 95% CI (1,000 resamples) and an exact permutation test (labels shuffled 2,000 times) using a fixed random seed. Stability was assessed by repeated stratified resampling (20 half-splits), recording the selection frequency of each genus in the numerator/denominator. The analysis was implemented in Python (pandas, scikit-learn, matplotlib).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFecal ITS sequencing data were available for n\u0026thinsp;=\u0026thinsp;61 lung cancer patients diagnosed with severe COPD (Gold C-D stadium) or non-severe, defined as mild / no COPD (Gold A-B or no COPD comorbidity), who were included to conduct this study. After excluding patients (n\u0026thinsp;=\u0026thinsp;4) who received systemic antibiotic treatment within 30 days of sampling, 57 patients were analyzed. Patients were categorized as having severe COPD (n\u0026thinsp;=\u0026thinsp;18), or non-severe COPD (n\u0026thinsp;=\u0026thinsp;39).\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMycobiome diversity and composition according to COPD status\u003c/h2\u003e\u003cp\u003eAlpha diversity was measured at the level of fungal species and genera, where we compared Shannon (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, D), Simpson (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, E), and Chao1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, F) indices between the COPD severe (1) and COPD mild / no COPD (0) groups, where none of the comparisons showed a significant difference.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we aimed to assess whether there was a significant compositional shift between patients with severe vs non-severe COPD. A sensitive analysis, Partial Least Squares Discriminant Analysis (PLS-DA) was used to reveal subtle difference in composition data between the two patient groups. PLS-DA maximizes the separation between the two groups by finding the optimal combination of taxa that best differentiates them. Although there was some overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B), PLS-DA revealed a separation pattern linked to COPD status among genera and species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Permanova on the PLS distance matrix yielded a borderline-significant effect of COPD status (pseudo-F\u0026thinsp;=\u0026thinsp;2.1, R\u0026sup2; = 0.06, p\u0026thinsp;=\u0026thinsp;0.058), indicating that 6% of overall fungal community variance is attributable to disease severity.\u003c/p\u003e\u003cp\u003eNext, we compared the fungal macrocomposition of the two patient groups, listing key phyla and the ratio of Ascomycota to Basidiomycota (A/B ratio). The relative abundance of Ascomycota (90.68% vs 95.06%) to Basidiomycota (8.71% vs 1.46%) was significantly lower in patients with severe COPD (vs non-severe COPD, p\u0026thinsp;=\u0026thinsp;0.0394, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). In addition, the abundance of phylum Mucoromycota also decreased in patients with severe COPD compared to patients with mild or no COPD (0.25% vs 3.21%).\u003c/p\u003e\u003cp\u003eAnalysis of Compositions of Microbiomes with Bias Correction (ANCOM) was performed to identify differentially abundant fungal taxa between the two clinical patient groups. ANCOM identified genera Myrothecium (overrepresented in severe COPD) and Helotiales_unclassified (overrepresented in mild or no COPD) with an above-log10 (FDR) value of 2. At the species level, the top signals included Lasiodiplodia crassispora (enriched in COPD-severe) and Phallus atrovolvatus (enriched in non-severe). Other species enriched in severe cases were Trichosporon asahii, Talaromyces aculeatus, and Chaetomium aureum. In contrast, species such as Leucocoprinus cretaceus, Trichoderma virens, and Cosmospora sp. were more abundant in patients with non-severe conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation of fungal genera and fungal networks in COPD\u003c/h2\u003e\u003cp\u003eWe constructed and analyzed genus-level co-occurrence networks derived from the fungal ITS data, stratified by the presence or absence of severe COPD. We applied Spearman correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), retaining only robust associations (|ρ| \u0026gt;0.4, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) to construct a weighted, undirected network that included all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The ITS genus\u0026ndash;genus matrix was dominated by positive co-occurrences. ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.8 pairs were: Myxocephala\u0026ndash;Roussoella (ρ\u0026thinsp;=\u0026thinsp;0.86), Fusarium\u0026ndash;Tetracladium (ρ\u0026thinsp;=\u0026thinsp;0.82), Arachnopeziza\u0026ndash;Thelidium (ρ\u0026thinsp;=\u0026thinsp;0.82), Arachnopeziza\u0026ndash;Meliniomyces (ρ\u0026thinsp;=\u0026thinsp;0.81), Clonostachys\u0026ndash;Roussoella (ρ\u0026thinsp;=\u0026thinsp;0.8), Clonostachys\u0026ndash;Myxocephala (ρ\u0026thinsp;=\u0026thinsp;0.8), Leptodontidium\u0026ndash;Tetracladium (ρ\u0026thinsp;=\u0026thinsp;0.8), Fusarium\u0026ndash;Mortierella (ρ\u0026thinsp;=\u0026thinsp;0.8). The remaining 60 pairs with ρ\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.8 repeatedly linked plant/soil and litter-associated genera (Debaryomyces, Kazachstania, Cladosporium, Aspergillus, Penicillium) with root- or wood-associated saprotrophs/mycoparasites (Leptodontidium, Trichoderma, Tetracladium, Clonostachys, Mortierella). In contrast, no negative correlations exceeded |ρ| = 0.6; the strongest antagonistic trends were Mucor\u0026ndash;Saitozyma (ρ=\u0026minus;0.545), Clonostachys\u0026ndash;Mucor (ρ=\u0026minus;0.512), Mucor\u0026ndash;Trichoderma (ρ=\u0026minus;0.508), and Aspergillus\u0026ndash;Saccharomyces (ρ=\u0026minus;0.498). Collectively, the signal suggests co-varying environmental guilds\u0026mdash;wood/leaf-litter saprotrophs, mycoparasites, and root-associated fungi\u0026mdash;move together in the gut (likely reflecting shared sources or trophic coupling), while Mucorales show niche exclusion with filamentous Ascomycetes and basidiomycetous yeasts.\u003c/p\u003e\u003cp\u003eSeparate networks were generated for patients without severe COPD (n\u0026thinsp;=\u0026thinsp;39) and with severe COPD (n\u0026thinsp;=\u0026thinsp;18). The non-severe COPD network exhibited higher complexity, comprising 33 fungal genera connected by 64 significant edges, with a network density of 0.121, average degree of 3.88, and average clustering coefficient of 0.403. In contrast, the severe COPD network was sparser, with only 28 nodes and 39 edges (density: 0.101; average degree: 2.79; clustering coefficient: 0.329). These differences indicate reduced co-occurrence connectivity in patients with severe COPD. Edge-level comparison revealed that only 21 correlations were shared between groups, while 43 and 18 were unique to the COPD\u0026thinsp;=\u0026thinsp;0 and COPD\u0026thinsp;=\u0026thinsp;1 networks, respectively. The COPD\u0026thinsp;=\u0026thinsp;0 network contained a greater proportion of strong positive associations, suggesting network stability, whereas the COPD severe network included several negative correlations, indicative of potential antagonistic interactions or niche exclusion.\u003c/p\u003e\u003cp\u003eModularity analysis further demonstrated that the non-severe COPD network had more distinct and interconnected community modules, whereas the severe COPD network showed reduced modularity and fewer hub taxa. Collectively, these findings suggest that severe COPD is associated with a less complex and more fragmented structure of fungal co-occurrence. Altogether, the COPD-severe-associated network is characterized by a reduction in complexity, loss of connectivity, and a shift toward more isolated interactions among fungal taxa.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGut fungal ecology and the bacteria-fungal network in COPD\u003c/h2\u003e\u003cp\u003eTo further explore the association between the gut mycobiome and COPD, we aimed to investigate which ecological niches and fungal guilds are enriched in patients with severe vs non-severe disease. Sunburst diagram shows the taxonomic breakdown at the phylum and genus level of the gut mycobiome in our cohort in the context of major taxonomical guilds (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Major ecological guilds include Plant pathogens, Lichenized fungi, Fungal parasites, Freshwater saprotrophs, Fermenters, Ericoid mycorrhizals, Animal pathogens, Soil saprotrophs, Ectomycorrhizals, Wood saprotrophs, Root endophytes, Plant saprotrophs, and unidentified saprotrophs that couldn\u0026rsquo;t be classified to neither guild. When comparing the two patient groups, we found that animal pathogens (28% vs 14%, p\u0026thinsp;=\u0026thinsp;0.007) and ectomycorrhizal fungi (4% vs 0.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are significantly more prevalent in patients with severe COPD. In contrast, fibre-degrading soil saprotrophs were overrepresented in mild or no COPD (7% vs 2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.032) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTo establish a correlation and network map between bacteria and fungi, we used the metagenome data-derived bacterial abundances of the 47 overlapping patients from our NSCLC cohort, published previously for validation purposes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Concatenated and normalized abundances of top genera (1% cut-off) were correlated using Spearman\u0026rsquo;s rank correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Then, a co-occurrence network was created, adjusted for ecological guilds (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The heatmap and hierarchical clustering revealed distinct co-occurrence modules. Notably, some fungal taxa (Saccharomyces, Debaryomyces, Candida) tend to cluster with bacterial genera typically found in disturbed or inflamed gut environments (e.g., Enterococcus, Escherichia/Shigella, Klebsiella, and Clostridium), suggesting a possible shared association with gut dysbiosis or environments related to inflammation. Clusters of bacterial genera involved in fiber degradation and SCFA production\u0026mdash;such as Ruminococcus, Eubacterium, and Butyricicoccus\u0026mdash;appear inversely related to fungi typically classified as pathobionts or stress-tolerant (e.g., Kazachstania, Malassezia). Several fungal genera show consistent anti-correlation with taxa known to maintain gut homeostasis (Faecalibacterium, Blautia, Roseburia).\u003c/p\u003e\u003cp\u003eCo-occurrence analysis revealed four major cross-kingdom ecological communities: a fermentative dysbiosis niche, that combines probiotic and fermentative bacteria with ascomycetous yeasts and plant-associated fungi (Lactococcus, Paenibacillus, Saccharomyces, Fusarium, Anaerostipes, brown, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD); an anaerobic Core and gut barrier modulator niche with abundant SCFA producers (Clostridium, Blautia), co-occurring or anti-correlated with fungal pathobionts like Candida (blue, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD); a community of soil-derived immunomodulators, genera involved in secondary metabolite production and often found in inhaled particles or soil-contaminated foods (Streptomyces, Cryptococcus, Actinomyces, Dysosmobacter, Thermothelymices, green, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD); and a default commensal axis including key commensal, high-abundance anaerobes and low-abundance environmental fungi (Bacteroides, Parabacteroides, Roseburia, Malassezia, gray, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eIn the case of the Keystone analysis, centrality increased for Candida, Aspergillus, Enterococcus, and Clostridium, while it decreased for Akkermansia and Roseburia. These results indicate a shift from fiber-associated, butyrate-linked networks toward oxygen-tolerant, fermentative/proteolytic consortia in COPD-severe cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Cross-kingdom edge rewiring showed more edges strengthening in COPD-severe than weakening. Stronger correlations concentrated on the yeast Candida and facultative anaerobes Enterococcus and Clostridium, with additional positive links to Akkermansia and Lactobacillus. Weaker correlations centered on SCFA-associated taxa (Faecalibacterium, Roseburia) and saprotrophic fungi (Debaryomyces, Saccharomyces) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePredictive function of compositional balance\u003c/h2\u003e\u003cp\u003eWe used a supervised compositional balance classifier to derive a single, interpretable per-patient score that contrasts \u0026ldquo;pro-severe COPD\u0026rdquo; versus \u0026ldquo;anti-severe COPD\u0026rdquo; genera according to COPD. This method is suitable for microbiome data (which are inherently relative), avoids spurious correlations, and directly tests the network-level hypothesis of hub replacement. In the 47 matched ITS and metagenome sequenced samples, higher scores were observed in COPD-severe (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), yielding strong discrimination (AUC\u0026thinsp;=\u0026thinsp;0.88 with restricted candidates; 0.93 when all genera were allowed, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The balance selected Sporisorium in the numerator and Alistipes, Eubacterium, Kazachstania, and Kluyveromyces in the denominator, and significance by permutation. Stability resampling repeatedly recovered the same pattern (Sporisorium as the most frequent numerator; Alistipes/Eubacterium/Kazachstania/Kluyveromyces as denominators), indicating a robust direction of effect even if individual taxa enter with moderate frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Biologically, the balance captures a tilt toward opportunistic fungi relative to commensal, fermentation-linked bacteria/yeasts, consistent with our network findings: strengthened co-occurrence around aerotolerant, inflammation-adapted taxa and weakening of SCFA-associated partners.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDespite comprising a small share of the gut ecosystem, fungi deliver potent cell-wall ligands and metabolites that reset mucosal immunity and propagate signals along the gut\u0026ndash;lung axis [23, 10, 6]. In COPD, gut-derived molecular signals can alter exacerbation risk and modulate responses to infection and therapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. When fungal communities rewire cross-kingdom networks and erode SCFA\u0026ndash;centered trophic chains, barrier instability and systematic inflammation can follow, amplifying lung pathology. Conversely, network reconstitution\u0026mdash;via diet, prebiotics, or targeted microbiome strategies\u0026mdash;might alleviate disease. Our data, therefore, position the gut mycobiome as a context-dependent biomarker of pulmonary homeostasis and severe COPD, with implications for risk stratification and future microbiome-based interventions.\u003c/p\u003e\u003cp\u003eIn the current study, we profiled the gut mycobiome in patients with lung cancer and those with and without severe COPD, and integrated bacterial metagenome data from the same cohort. Alpha-diversity showed no significant differences in the two patient groups, but composition and structure shifted with COPD severity. Severe COPD showed a higher Ascomycota/Basidiomycota ratio, enrichment of opportunistic fungi, and a simpler, less connected fungal network. Cross-kingdom links favored Candida/Aspergillus with Enterobacteriaceae, while ties around SCFA-associated bacteria and commensal yeasts weakened. Overall, our results support a network \u0026ldquo;rewiring\u0026rdquo; rather than the effects of differentially abundant single-taxon species.\u003c/p\u003e\u003cp\u003eIn our cohort, fungal alpha diversity did not differ across COPD categories (severe vs mild or no-COPD), while composition showed a modest but reproducible separation when using PLS, a pattern is consistent with many bacterial gut studies in COPD that report limited alpha shifts but measurable community turnover [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By contrast, airway mycobiome work sometimes finds higher alpha diversity or distinct clustering in COPD\u0026mdash;especially in frequent exacerbators\u0026mdash;in line with a compartment-specific signal [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For stool fungi, comparative data remain scarce and variable, likely reflecting low biomass, high inter-individual variation, and technical constraints of ITS profiling. Altogether, COPD severity aligns with subtle compositional drift, not drastic diversity changes.\u003c/p\u003e\u003cp\u003eIn phylum-level analyses, we observed a shift in the A/B ratio, with severe COPD showing relatively lower Ascomycota and higher Basidiomycota, suggesting a broad ecological tilt. Similar A/B distortions are reported in inflammatory bowel disease, GI cancers and other inflammatory states, though directionality varies across cohorts, methods, and sampling types [12,27,28,29,30]. Several studies describe the enrichment of Basidiomycota and the depletion of Ascomycota in gut inflammation, as well as in lung cancer, aligning with our pattern, while others report mixed results [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Biologically, an A/B shift may reflect oxygenation, bile acid, and immune pressures that favor aerotolerant or lipid-adapted Basidiomycota (e.g., Malassezia) and alter interactions with bacteria and host pattern-recognition pathways [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Because Ascomycota also contains both commensals and opportunists, the A/B ratio should be read as a coarse ecosystem marker that compresses complex mycobiome changes into a tractable metric for longitudinal assessment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcross COPD groups, fungal genus correlations reconfigured: severe COPD showed fewer and weaker positive edges, more isolated nodes, and lower modularity/robustness\u0026mdash;hallmarks of ecosystems under inflammatory stress [32,33,34]. Hubs shifted toward opportunistic taxa, while commensal/food-associated yeasts lost centrality. Cross-kingdom networks mirrored this: in severe COPD, Candida/Aspergillus strengthened associations with Enterobacteriaceae and other facultative aerobes, whereas ties linking SCFA-producing bacteria (Faecalibacterium and Roseburia) to benign yeasts weakened or reversed. These patterns indicate network-level rewiring, not single-taxon effects. Mechanistically, inflammation-driven epithelial metabolic shifts may increase mucosal oxygen and nitrate levels, favoring facultative taxa over strict anaerobes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Because SCFAs stabilize lung immune tone, erosion of SCFA-centered consortia offers a plausible gut\u0026ndash;lung pathway for exacerbation-prone phenotypes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Cross-kingdom studies further support fungi as active modulators rather than bystanders [38,39].\u003c/p\u003e\u003cp\u003eCOPD shows strong association with smoking (there were only two non-smokers in our cohort) and smoking-/inflammation-driven immune\u0026ndash;trophic feedback explain our patterns. Reduced colonocyte β-oxidation and PPAR-γ signaling increase mucosal O\u003csub\u003e2\u003c/sub\u003e and host-derived nitrate, favoring facultative bacteria and aerotolerant yeasts over strict butyrate producers [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The strengthened Candida\u0026ndash;Enterococcus/Clostridium edges and weakened Faecalibacterium/Roseburia\u0026ndash;benign yeast edges are consistent with metabolic reprogramming, characterized by lower butyrate production and enhanced lactate/ethanol/amine fluxes that reinforce inflammation and barrier stress [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Fecal metabolomics and further multi-omic approaches, combined with longitudinal studies, are needed to confirm these findings.\u003c/p\u003e\u003cp\u003eIn our study, instead of classifying patients as diagnosed vs. not diagnosed with COPD, we stratified patients as severe COPD vs. mild/no COPD. Severity-based grouping more accurately reflects the biology that drives microbiome changes. Severity is associated with exacerbation burden, systemic inflammation, hypoxemia, comorbid load, and exposure to therapies, including inhaled corticosteroids and antibiotics, all of which are potent modifiers of the microbiome [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. As COPD worsens, systemic inflammation rises [45], neutrophils are more primed [46], and hypoxemia plus catabolic stress reshape epithelial metabolism and barrier function\u0026mdash;factors that influence gut oxygen leak, bile acids, and cross-kingdom networks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Exacerbation burden also perturb the bacterial\u0026ndash;fungal balance [49,50,51]. These gradients create a more precise dose\u0026ndash;response in gut\u0026ndash;lung signaling than a binary \u0026ldquo;COPD yes/no,\u0026rdquo; which mixes biologically near-normal mild cases with profoundly dysregulated severe cases.\u003c/p\u003e\u003cp\u003eOur study is limited by its cross-sectional design. Moreover, the sample size constrained multivariate and subgroup analyses; therefore, we focused on ecological and network differences rather than strict clinical or biomarker endpoints\u0026mdash;a choice that precludes causal inference and allows residual confounding (diet, medications, inhaled corticosteroids/antibiotics, oral health). Still, converging clinical and experimental data support a reciprocal gut\u0026ndash;lung axis in COPD: gut metabolites shape airway immunity, while pulmonary inflammation and therapies reshape gut communities [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eTaken together, our data suggest that COPD severity is mirrored not by distinct gain or loss of specific fungal taxa, but by re-wiring of cross-kingdom networks toward opportunistic, inflammation-adapted consortia and away from SCFA-producing ecosystems. Our study extends COPD microbiome work to the mycobiome and nominates network features\u0026mdash;not single taxa\u0026mdash;as candidate readouts to explain alteration of gut commensals in pulmonary inflammatory conditions. Prospective, multi-omics, and interventional studies should test whether restoring SCFA-centric ecological niches improves pulmonary outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study have been deposited in the NCBI sequence read archive (SRA) database under accession code PRJNA811494.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Research, Development and Innovation Office, grants #146775 (Zoltan Lohinai), #142287 (David Dora).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, GS., and Z.L.; data curation, G.S., D.D., S.S., C.K.A. and M.H.; formal analysis, D.D., G.S., S.S. and A.B.; funding acquisition, D.D., G.G. and Z.L.; investigation, G.S., D.D., S.S., A.B., and Z.L.; methodology, G.S., D.D., C.K.A. and M.H.; project administration, G.G., and Z.L.; resources, D.D., G.G., and Z.L.; software, G.S., D.D., A.B. and M.H.; supervision, D.D., and Z.L.; validation, C.K.A., A.B. and M.H.; visualization, GS., D.D., and M.H.; writing\u0026mdash;original draft, G.S., D.D., and Z.L.; writing\u0026mdash;review and editing, all authors. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the current study, we followed the principles of the Declaration of Helsinki established by the World Medical Association. The research protocol received formal approval from the national ethics authority, namely the Hungarian Scientific and Research Ethics Committee of the Medical Research Council (ETT TUKEB; approval number 50302-2/2017/EKU). All patients provided informed consent prior to participation. To ensure confidentiality, patient identifiers were removed after collecting clinicopathological data, thereby preventing both direct and indirect identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBoers, E. et al. Global burden of chronic obstructive pulmonary disease through 2050. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e2346598 (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKahnert, K. et al. 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PMID: 30976087.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut mycobiome, COPD, Cross-kingdom networks, Fungal ecology, Gut-lung axis","lastPublishedDoi":"10.21203/rs.3.rs-7992928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7992928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic disorder affecting host\u0026ndash;microbiome interactions beyond the airways. Although bacterial alterations in COPD have been documented, the gut mycobiome and its ecological integration with bacterial communities remain unexplored. In this study, we profiled the gut mycobiome of 61 non-small-cell lung cancer (NSCLC) patients stratified by COPD severity using ITS2 sequencing and analyzed 47 overlapping patients with available metagenomic data to construct cross-kingdom bacterial\u0026ndash;fungal networks. Alpha diversity, assessed by Shannon, Simpson, and Chao1 indices, did not differ significantly between patients with and without severe COPD. Partial least squares discriminant analysis (PLS-DA) revealed partial separation of the two groups, with COPD severity explaining 6% of overall compositional variance (R\u0026sup2;=0.06, p\u0026thinsp;=\u0026thinsp;0.058). COPD-severe patients exhibited a significantly reduced Ascomycota/Basidiomycota ratio (p\u0026thinsp;=\u0026thinsp;0.039) and lower relative abundance of Mucoromycota. Analysis of compositions of microbiomes (ANCOM) identified Myrothecium and Lasiodiplodia crassispora enriched in severe COPD, while Helotiales_unclassified and Phallus atrovolvatus were more abundant in non-severe cases. Fungal co-occurrence networks demonstrated reduced connectivity and modularity in severe COPD (28 nodes, 39 edges) compared with non-severe COPD (33 nodes, 64 edges). Cross-kingdom analyses integrating bacterial genera revealed strengthened Candida\u0026ndash;Enterococcus/Clostridium hubs and weakened Faecalibacterium/Roseburia\u0026ndash;yeast associations in severe disease. Keystone analysis showed increased centrality for Candida, Aspergillus, Enterococcus, and Clostridium, and decreased centrality for Akkermansia and Roseburia. A compositional balance classifier achieved high discriminatory power (AUC\u0026thinsp;=\u0026thinsp;0.88) in distinguishing COPD-severe from non-severe patients. These findings indicate that COPD severity is not characterized by major diversity loss but by guild-specific compositional shifts and extensive network rewiring, favoring oxygen-tolerant, opportunistic taxa over short-chain fatty acid\u0026ndash;associated commensals.\u003c/p\u003e","manuscriptTitle":"The Gut Mycobiome and Inter-kingdom Microbial Networks are linked to COPD Severity in Lung Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:27:11","doi":"10.21203/rs.3.rs-7992928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-25T01:49:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-25T01:46:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-07T12:58:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T21:42:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-06T21:39:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79baa0d8-6b82-4e4c-a013-9c43eee5b161","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58586194,"name":"Health sciences/Diseases"},{"id":58586195,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-04T15:59:54+00:00","versionOfRecord":{"articleIdentity":"rs-7992928","link":"https://doi.org/10.1038/s41598-026-47296-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-30 15:57:21","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-12-01 08:27:11","video":"","vorDoi":"10.1038/s41598-026-47296-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47296-x","workflowStages":[]},"version":"v1","identity":"rs-7992928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7992928","identity":"rs-7992928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}