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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2025.1652633</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Gut mycobiota dysbiosis and systemic immune dysfunction in Chinese schizophrenia patients with metabolic syndrome</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes" corresp="yes">
<name>
<surname>Ling</surname>
<given-names>Zongxin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Cheng</surname>
<given-names>Yiwen</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Lan</surname>
<given-names>Zhiyong</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Liu</surname>
<given-names>Xia</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhu</surname>
<given-names>Zhangcheng</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Ding</surname>
<given-names>Wenwen</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Xiaocui</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Yu</surname>
<given-names>Pian</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Xiaoxun</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Shao</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="aff8">
<sup>8</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/599355/overview"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Song</surname>
<given-names>Qinghai</given-names>
</name>
<xref ref-type="aff" rid="aff9">
<sup>9</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liao</surname>
<given-names>Rongxian</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, China-Singapore Belt and Road Joint Laboratory on Infection Research and Drug Development, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Yuhang Institute for Collaborative Innovation and Translational Research in Life Sciences and Technology</institution>, <addr-line>Hangzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Jinan Microecological Biomedicine Shandong Laboratory</institution>, <addr-line>Jinan, Shandong</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Psychiatry, Quzhou Third Hospital</institution>, <addr-line>Quzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Intensive Care Unit, The First Affiliated Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Department of Preventive Medicine, School of Public Health and Management, Wenzhou Medical University</institution>, <addr-line>Wenzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff7">
<sup>7</sup>
<institution>Department of Anesthesiology, Affiliated Hospital of Nantong University, Medical School of Nantong University</institution>, <addr-line>Nantong, Jiangsu</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff8">
<sup>8</sup>
<institution>School of Clinical Medicine, The Affiliated Hospital of Hangzhou Normal University</institution>, <addr-line>Hangzhou, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff9">
<sup>9</sup>
<institution>Department of Psychiatry, Lishui Second People&#x2019;s Hospital</institution>, <addr-line>Lishui, Zhejiang</addr-line>,&#xa0;<country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Am&#xe9;lia M. Sarmento, Fernando Pessoa University, Portugal</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/83919/overview">Biljana Bufan</ext-link>, University of Belgrade, Serbia</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3132209/overview">Hui Huang</ext-link>, The First Affiliated Hospital of Chengdu Medical College, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Zongxin Ling, <email xlink:href="mailto:lingzongxin@zju.edu.cn">lingzongxin@zju.edu.cn</email>; Qinghai Song, <email xlink:href="mailto:qinghai_song@126.com">qinghai_song@126.com</email>; Rongxian Liao, <email xlink:href="mailto:rongxian_liao@163.com">rongxian_liao@163.com</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>03</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1652633</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>06</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>19</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Ling, Cheng, Lan, Liu, Zhu, Ding, Xu, Yu, Xu, Shao, Song and Liao.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Ling, Cheng, Lan, Liu, Zhu, Ding, Xu, Yu, Xu, Shao, Song and Liao</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>While bacterial dysbiosis has been extensively studied in schizophrenia with metabolic syndrome (SZ-MetS), the role of gut mycobiota in this comorbidity remains unclear. This study represents the first comprehensive investigation of fungal communities in SZ-MetS patients (n=109) versus healthy controls (HCs, n=101) using ITS1 sequencing and multi-parameter immune profiling. Although global mycobiota structure showed no significant differences, compositional analyses revealed profound taxonomic shifts: pathobionts (<italic>Trichosporon asahii</italic>, <italic>Candida albicans</italic>, <italic>Lodderomyces elongisporus</italic>) were enriched, while putative beneficial species (<italic>Saccharomyces cerevisiae</italic>, <italic>Pleurotus ostreatus</italic>) were reduced in patients. Enterotyping identified two mycobiota clusters (<italic>Candida</italic>-dominant vs <italic>Aspergillus</italic>-dominant), though their distribution was similar between groups. Notably, machine learning revealed a six-species fungal signature with strong diagnostic potential (AUC = 0.86). Species-specific immune correlations were also observed: inflammatory cytokines such as IL-6 and MIP-1&#x3b1; were positively associated with <italic>Ustilago esculenta</italic> and <italic>Trichosporon asahii</italic>, but negatively correlated with <italic>Saccharomyces cerevisiae</italic>. Furthermore, fungal abundances were differentially correlated with metabolic and psychiatric parameters, with <italic>Lodderomyces</italic> linked to elevated triglycerides and <italic>S. cerevisiae</italic> associated with reduced symptom severity. These findings reveal that while overall fungal community structure is preserved, SZ-MetS exhibits distinct mycobiota alterations that interact with host immunity and clinical manifestations, suggesting fungi may contribute to the SZ-MetS vicious cycle through taxon-specific mechanisms.</p>
</abstract>
<kwd-group>
<kwd>schizophrenia</kwd>
<kwd>metabolic syndrome</kwd>
<kwd>mycobiota</kwd>
<kwd>gut-brain axis</kwd>
<kwd>immunological dysfunction</kwd>
</kwd-group>
<counts>
<fig-count count="8"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="57"/>
<page-count count="14"/>
<word-count count="5298"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Multiple Sclerosis and Neuroimmunology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Schizophrenia (SZ) is a severe neuropsychiatric disorder characterized by cognitive impairment and a reduced life expectancy of 15&#x2013;20 years, attributable in part to its frequent comorbidity with metabolic syndrome (MetS) (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). Approximately one-third of SZ patients develop MetS (<xref ref-type="bibr" rid="B3">3</xref>), creating a vicious cycle that exacerbates psychiatric symptoms, accelerates cognitive decline, and increases cardiovascular mortality (<xref ref-type="bibr" rid="B4">4</xref>&#x2013;<xref ref-type="bibr" rid="B6">6</xref>). While antipsychotic side effects and lifestyle factors contribute to this comorbidity (<xref ref-type="bibr" rid="B7">7</xref>), emerging evidence implicates gut microbiome dysbiosis as a potential unifying mechanism bridging metabolic and neuropsychiatric pathology. Extensive research has characterized bacterial dysbiosis in both SZ and MetS separately, revealing altered microbial diversity, short-chain fatty acid production, and gut barrier integrity (<xref ref-type="bibr" rid="B8">8</xref>&#x2013;<xref ref-type="bibr" rid="B14">14</xref>). Our previous work demonstrated distinct fecal bacterial profiles in SZ-MetS patients correlating with immune dysfunction (<xref ref-type="bibr" rid="B12">12</xref>). However, the fungal microbiome (mycobiota) - an equally important component of the gut ecosystem - remains virtually unexplored in SZ-MetS comorbidity, despite its known roles in immune modulation and metabolic regulation (<xref ref-type="bibr" rid="B15">15</xref>&#x2013;<xref ref-type="bibr" rid="B17">17</xref>).</p>
<p>Fungal-bacterial interactions critically maintain gut homeostasis, with mycobiota dysbiosis implicated in inflammatory diseases and metabolic disorders (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B20">20</xref>). Notably, <italic>Candida albicans</italic> overgrowth associates with obesity and insulin resistance (<xref ref-type="bibr" rid="B21">21</xref>), while <italic>Saccharomyces</italic> species modulate host immunity in ways relevant to neuroinflammation (<xref ref-type="bibr" rid="B22">22</xref>). The unique ability of fungi to form biofilms and penetrate intestinal barriers may make them particularly potent modulators of the gut-brain axis (<xref ref-type="bibr" rid="B23">23</xref>). Nevertheless, no studies to date have investigated gut mycobiota alterations in the SZ-MetS overlap, representing a critical knowledge gap given the established links between fungal dysbiosis, systemic inflammation, and neuropsychiatric conditions.</p>
<p>This study represents the first comprehensive analysis of gut fungal communities in Chinese SZ patients with MetS. We enrolled hospitalized SZ-MetS patients and age- and gender-matched healthy controls from Quzhou, China. Using internal transcribed spacer 1 (ITS1) sequencing and multi-parameter immunoassays, we characterize mycobiota profiles distinguishing SZ-MetS patients from matched controls; and examine correlations between fungal taxa and peripheral immune markers. Our findings may reveal novel mycobiota signatures contributing to the SZ-MetS vicious cycle, potentially informing future microbiome-targeted interventions for this high-risk population.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Participants&#x2019; enrollment and sample collection</title>
<p>Building upon our established cohort (<xref ref-type="bibr" rid="B12">12</xref>), this study ultimately included 109 SZ-MetS patients (age 28&#x2013;64 years) and 101 age- and gender-matched healthy controls (HCs) recruited from Quzhou Third People&#x2019;s Hospital between June and November 2023. The study protocol received ethical approval from the hospital&#x2019;s Institutional Review Board (approval no. SY-2023-17), and all participants or their legal guardians provided written informed consent prior to enrollment.</p>
<p>We adhered to the same inclusion and exclusion criteria, as well as participant characteristics, as detailed in our previous work (<xref ref-type="bibr" rid="B12">12</xref>). Notably, individuals with recent use (within 1 month) of any antimicrobial medications&#x2014;including antibacterial, antifungal, antiparasitic, or antiviral agents&#x2014;were excluded. For mycobiota analysis, participants provided approximately 2g of fresh fecal samples collected in sterile containers, which were immediately flash-frozen at -80&#xb0;C within 15 minutes of collection to ensure microbial stability. Concurrently, we obtained fasting venous blood samples during morning hours, with plasma separation completed within 15 minutes of collection followed by storage at -80&#xb0;C until analysis.</p>
</sec>
<sec id="s2_2">
<title>ITS1 sequencing and bioinformatic analysis</title>
<p>Fungal DNA was isolated from 300 mg aliquots of homogenized fecal samples using the QIAamp<sup>&#xae;</sup> DNA Stool Mini Kit (QIAGEN, Germany), with mechanical lysis enhancement through glass-bead disruption (Mini-beadbeater; Thermo Electron Corporation, USA). We amplified the ITS1 region using fungal-specific primers (ITS1F: 5&#x2019;-CTTGGTCATTTAGAGGAAGTAA-3&#x2019;; ITS2R: 5&#x2019;-GCTGCGTTCTTCATCGATGC-3&#x2019;) to construct sequencing libraries. All library preparation and subsequent paired-end sequencing on the Illumina NovaSeq 6000 platform were conducted by Hangzhou KaiTai Bio-lab&#x2019;s technical team, following established protocols (<xref ref-type="bibr" rid="B20">20</xref>).</p>
<p>We processed sequencing data using QIIME2 (v2020.11) with the following workflow: First, raw sequences underwent adapter and barcode removal using Cutadapt (v2.4), followed by quality filtering and chimera elimination through DADA2 to generate high-quality amplicon sequence variants (ASVs) (<xref ref-type="bibr" rid="B24">24</xref>). Taxonomic classification was performed against the UNITE database (Release 9.0, <ext-link ext-link-type="uri" xlink:href="http://unite.ut.ee/index.php">http://unite.ut.ee/index.php</ext-link>) after normalizing samples to equal sequencing depth. For community analysis, we calculated &#x3b1;-diversity (within-sample richness) and &#x3b2;-diversity (between-sample differences) metrics following established fungal microbiota protocols (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B25">25</xref>). Fungal enterotype clustering revealed distinct community structures across samples. To identify diagnostic fungal signatures, we implemented a Random Forest classifier (Mean Decrease Gini for feature importance) and validated discriminative performance using ROC curve analysis via OECloud platform (<ext-link ext-link-type="uri" xlink:href="https://cloud.oebiotech.com">https://cloud.oebiotech.com</ext-link>).</p>
</sec>
<sec id="s2_3">
<title>Systemic immune function analysis</title>
<p>To evaluate participants&#x2019; systemic immune function, we employed a 27-plex human group I cytokine assay kit (Bio-Rad, CA, USA) following our previously established methodology (<xref ref-type="bibr" rid="B12">12</xref>). This magnetic bead-based immunoassay quantified 27 cytokines and chemokines&#x2014;including 16 cytokines, 6 chemokines, and 5 growth factors&#x2014;according to the manufacturer&#x2019;s protocols. Analysis was performed using the Bio-Plex 200 system, with data processed via Bio-Plex Manager v5.0 software. Results were expressed as picograms per milliliter (pg/mL) using integrated standard curves, yielding intra- and inter-assay coefficient of variation (CV) values of 5&#x2013;8% for reproducibility.</p>
</sec>
<sec id="s2_4">
<title>Statistical analysis</title>
<p>For statistical analyses, continuous variables (e.g., &#x3b1;-diversity indices, microbial taxonomic abundances, cytokine levels) were evaluated using independent <italic>t</italic>-tests or Mann-Whitney <italic>U</italic> tests based on normality assessments (Shapiro-Wilk test). Categorical variables were compared via Pearson&#x2019;s chi-square test or Fisher&#x2019;s exact test. Spearman&#x2019;s rank correlation was used to assess associations between microbial abundances, cytokine profiles, and clinical parameters. Differential abundance analyses of microbial taxa were performed using STAMP v2.1.3 with Welch&#x2019;s <italic>t</italic>-test or nonparametric tests, while &#x3b1;-diversity indices (e.g., Chao1, Shannon) were compared using permutation tests. All statistical analyses were conducted in SPSS v24.0 and R packages (e.g., phyloseq, vegan), with visualizations created using GraphPad Prism v6.0. Multiple comparisons were adjusted via the Benjamini-Hochberg procedure to control false discovery rate (FDR), with statistical significance set at FDR &lt; 0.05.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Gut mycobiota characteristics in SZ-MetS patients</title>
<p>We performed comprehensive characterization of gut fungal communities in our cohort of 109 SZ-MetS patients and 101 matched HCs. Following quality filtering of 24,380,498 raw ITS1 sequences, we retained 20,739,649 high-quality reads with an average of 102,532 reads per sample for downstream analysis. After normalizing to an even sequencing depth of 27,214 reads per sample, we identified 4,298 ASVs, with SZ-MetS patients exhibiting 15.5% more ASVs than controls (2,512 vs 2,176). Fecal mycobiota diversity was compared between SZ-MetS patients and HCs based on the relative ASVs table (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). Our analysis revealed no significant structural differences in gut fungal communities between SZ-MetS patients and HCs. Alpha diversity metrics showed comparable fungal diversity between groups, with no significant differences observed in diversity indices (Shannon and Simpson; p &gt; 0.05; <xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1A, B</bold>
</xref>) and richness estimators (ACE, Chao1, and Observed species; p &gt; 0.05; <xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1C&#x2013;E</bold>
</xref>). Similarly, beta diversity analyses using multiple distance metrics such as Bray-Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac algorithms indicated no significant overall differences in community composition between SZ-MetS patients and HCs (all ADONIS p&gt;0.05, <xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1F&#x2013;I</bold>
</xref>). Notwithstanding these similarities, we identified important compositional distinctions. Rank-abundance curves revealed comparable species distributions (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1J</bold>
</xref>), while Venn analysis demonstrated a significant enrichment of unique ASVs in SZ-MetS patients (2122 vs 1786 in HCs) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1K</bold>
</xref>). These findings suggest that while global mycobiota diversity and structure remain similar, SZ-MetS patients exhibit an expanded repertoire of unique fungal species that may contribute to disease pathophysiology.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Comparison of fecal mycobiota structure between SZ-MetS patients and healthy controls. <bold>(A&#x2013;E)</bold> &#x3b1;-diversity indices (Shannon and Simpson) and richness indices (Chao1, ACE, and observed species) were used to assess the overall structure of the fecal mycobiota, with data presented as mean &#xb1; standard deviation. Unpaired two-tailed t-tests were performed for inter-group comparisons. <bold>(F&#x2013;I)</bold> Principal coordinate analysis (PCoA) plots illustrating &#x3b2;-diversity of individual fecal mycobiota based on Bray&#x2013;Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac distances, with each symbol representing an individual sample. <bold>(J)</bold> The rank-abundance curve of fungal amplicon sequence variants (ASVs) shows a higher presence of low-abundance ASVs in the fecal mycobiota of SZ-MetS patients compared to healthy controls. <bold>(K)</bold> Venn diagram illustrating the overlap of ASVs in the fecal mycobiota of SZ-MetS patients and healthy controls.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g001.tif">
<alt-text content-type="machine-generated">Box plots and scatter plots compare microbiome diversity indices and compositions between healthy controls (HCs) and individuals with SZ-MetS. Panels A-E show diversity indices including Shannon, Simpson, Chao1, ACE, and observed species. Panels F-I present PCoA plots for Bray-Curtis, Jaccard, Unweighted UniFrac, and Weighted UniFrac distances, visualizing community differences. Panel J displays rank-abundance curves for both groups. Panel K is a Venn diagram showing shared and unique taxa counts between groups.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_2">
<title>Gut mycobiota composition and enterotype analysis in SZ-MetS patients</title>
<p>Using the UNITE database, ITS sequencing reads were taxonomically assigned to 5 phyla, 158 families, 292 genera, and 478 species. Taxonomic profiling revealed significant differences in gut mycobiota composition between SZ-MetS patients and HCs across various taxonomic levels. At the phylum level, both groups were primarily dominated by Ascomycota and Basidiomycota (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>). Family-level analysis identified differentially abundant taxa with clinical relevance: Saccharomycetales_Incertae_sedis and Phaeosphaeriaceae were enriched in SZ-MetS patients, whereas Aspergillaceae and Pleosporaceae showed decreased abundance compared to HCs (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2B</bold>
</xref>). Genus-level profiling further highlighted significant shifts in <italic>Candida</italic>, <italic>Aspergillus</italic>, <italic>Saccharomyces</italic>, and <italic>Cladosporium</italic> (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2C</bold>
</xref>), with species-specific analyses confirming these trends (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2D</bold>
</xref>). Enterotype analysis classified the cohort into two distinct mycobiota clusters: Enterotype 1 (E1), characterized by <italic>Candida</italic> dominance, and Enterotype 2 (E2), marked by <italic>Aspergillus</italic> prevalence (<xref ref-type="fig" rid="f2">
<bold>Figures&#xa0;2E, F</bold>
</xref>). However, the distribution of these enterotypes did not differ significantly between groups (HC: 39 E1/62 E2 vs SZ-MetS: 43 E1/66 E2; <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2G</bold>
</xref>). These findings demonstrate that while SZ-MetS patients exhibit distinct shifts in specific fungal taxa, the overall enterotype distribution remains comparable to HCs, suggesting that disease-associated mycobiota alterations may occur independently of broader community stratification.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Gut mycobiota composition in SZ-MetS patients and healthy controls. <bold>(A)</bold> Phylum; <bold>(B)</bold> Family; <bold>(C)</bold> Genus; <bold>(D)</bold> Species; <bold>(E)</bold> PCoA plot identifying two enterotypes; <bold>(F)</bold> LEfSe analysis of enterotype-specific genera; <bold>(G)</bold> Enterotype distribution.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g002.tif">
<alt-text content-type="machine-generated">Bar charts show the relative abundance of fungi at different taxonomic levels: (A) phylum, (B) family, (C) genus, and (D) species between healthy controls (HCs) and SZ-MetS groups. (E) PCA plot displays separation of groups based on enterotypes E1 and E2. (F) LDA score bar graph compares fungal genera between enterotypes. (G) Bar chart shows the number of samples in enterotypes E1 and E2 for each group.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_3">
<title>Comparative analysis of gut mycobiota between SZ-MetS patients and HCs</title>
<p>LEfSe analysis revealed significant compositional differences in gut fungal communities between SZ-MetS patients and HCs. The cladogram visually highlights differentially abundant taxa across all taxonomic levels, from phylum to species (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). Notably, several fungal species showed significant enrichment in SZ-MetS patients, including <italic>Issatchenkia orientalis</italic>, <italic>Ophiosphaerella aquatica</italic>, and <italic>Trichosporon asahii</italic>. Conversely, multiple potentially beneficial species were significantly reduced in patients, including <italic>Saccharomyces cerevisiae</italic>, <italic>Pleurotus ostreatus</italic>, <italic>Ustilago esculenta</italic>, <italic>Wallemia muriae</italic>, <italic>Penicillium concentricum</italic>, <italic>Aspergillus ruber</italic>, <italic>Debaryomyces udenii</italic>, and <italic>Candida sake</italic> (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Differential fecal mycobiota between SZ-MetS patients and healthy controls. <bold>(A)</bold> LEfSe cladograms showing fungal taxa significantly associated with SZ-MetS patients or healthy controls. Circle size indicates relative abundance, with circles representing taxonomic levels from phylum to species. Statistical significance was determined by Wilcoxon rank-sum test (p &lt; 0.05). <bold>(B)</bold> Histogram of Linear Discriminant Analysis (LDA) scores (&gt; 3.5) for fungal taxa with the largest abundance differences between SZ patients and healthy controls (p &lt; 0.05).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g003.tif">
<alt-text content-type="machine-generated">Phylogenetic tree and bar graph comparing fungal taxa between HCs and SZ-MetS groups. In panel A, the tree shows red highlighting for HCs and blue for SZ-MetS, indicating differential abundance. Panel B displays LDA scores, with bars representing taxa enriched in either HCs (red) or SZ-MetS (blue).</alt-text>
</graphic>
</fig>
<p>Subsequently, we compared the fecal mycobiota between the two groups at specific taxonomic levels using MetaStats 2.0. At the phylum level, SZ-MetS patients exhibited a decrease in Ascomycota and Rozellomycota, while showing an increased abundance of Mortierellomycota (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). At the family level, five families&#x2014;Cryptococcaceae, Malasseziaceae, Trichosporonaceae, Didymellaceae, and Eremotheciaceae&#x2014;were elevated in SZ-MetS patients, while two families, Aspergillaceae and Saccharomycetaceae, were significantly reduced (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>). At the genus level, seven genera, including <italic>Cryptococcus</italic>, <italic>Trichosporon</italic>, <italic>Holleya</italic>, <italic>Ophiosphaerella</italic>, <italic>Lodderomyces</italic>, <italic>Mucor</italic>, and <italic>Issatchenkia</italic>, were more abundant in SZ-MetS patients, while four genera&#x2014;<italic>Aspergillus</italic>, <italic>Coniochaeta</italic>, <italic>Saccharomyces</italic>, and <italic>Ustilago</italic>&#x2014;were reduced (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4C</bold>
</xref>). At the species level, <italic>Candida albicans</italic>, <italic>Trichosporon asahii</italic>, <italic>Holleya sinecauda</italic>, <italic>Leptospora macarangae</italic>, and <italic>Lodderomyces elongisporus</italic> were more abundant in SZ-MetS patients, whereas <italic>Saccharomyces cerevisiae</italic>, <italic>Ustilago esculenta</italic>, <italic>Pleurotus ostreatus</italic>, <italic>Wickerhamomyces anomalus</italic>, <italic>Penicillium concentricum</italic>, and <italic>Candida solani</italic> were significantly reduced (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4D</bold>
</xref>). Additionally, network analysis using SparCC revealed notable differences in fungal ecological relationships, with SZ-MetS patients exhibiting a simpler co-occurrence network compared to HCs (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>). This reduced complexity was especially pronounced among Ascomycota species, indicating disrupted fungal community stability.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>MetaStats2 confirmed key differential fecal fungal taxa between SZ-MetS patients and healthy controls. <bold>(A)</bold> Differential functional phyla; <bold>(B)</bold> Differential functional families; <bold>(C)</bold> Differential functional genera; <bold>(D)</bold> Differential functional species. Data are presented as mean &#xb1; standard deviation. Mann&#x2013;Whitney <italic>U</italic>-tests were used for comparisons between SZ-MetS patients and healthy controls. *p &lt; 0.05 vs. control group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g004.tif">
<alt-text content-type="machine-generated">Bar charts display the relative abundance of fungal taxa across different taxonomic levels: phylum (A), family (B), genus (C), and species (D). Red bars represent healthy controls (HCs), while blue bars represent individuals with schizophrenia metabolic syndrome (SZ-MetS). Significant differences are marked with asterisks.</alt-text>
</graphic>
</fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Co-occurrence network of abundant fecal genera in SZ-MetS patients and controls. The co-occurrence network was constructed using the SparCC algorithm on relative abundance data at the genus level, illustrating ecological interactions within the microbial community. Cytoscape version 3.6.1 was used for network construction. Red and blue lines represent positive and negative correlations, respectively.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g005.tif">
<alt-text content-type="machine-generated">Two circular correlation network diagrams compare HC (Healthy Controls) and SZ-MetS (Schizophrenia with Metabolic Syndrome) groups. Each diagram shows nodes representing fungal species, colored by phylum: black for Ascomycota and blue for Basidiomycota. Lines connecting nodes indicate correlations, with blue for negative and red for positive correlations. The key lists species and a correlation color gradient ranging from negative (blue) to positive (red).</alt-text>
</graphic>
</fig>
<p>We also assessed the potential of key functional fungal species to distinguish SZ-MetS patients from HCs using Random Forest and ROC analysis. Random Forest, a machine learning classification model, helps classify microbial community samples by evaluating the importance of variables using Mean Decrease Gini. A higher value indicates greater significance of a species (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6A</bold>
</xref>). ROC curves were then generated to evaluate the diagnostic performance of fungal species at the species level, with AUC values reflecting their accuracy. Among the differential species, <italic>Saccharomyces cerevisiae</italic> showed moderate diagnostic value (AUC = 0.72), while <italic>Trichosporon asahii</italic>, <italic>Candida albicans</italic>, and <italic>Candida parapsilosis</italic> had limited discriminatory power (AUC = 0.52&#x2013;0.56) (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6B</bold>
</xref>). However, the combined model of six species significantly improved diagnostic accuracy (AUC = 0.86), demonstrating the potential of multivariate approaches in fungal biomarker analysis (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6C</bold>
</xref>). These findings reveal a profound dysbiosis in the gut mycobiota of SZ-MetS patients, characterized by both the expansion of specific fungal taxa and depletion of potentially protective commensals, suggesting possible implications for disease pathophysiology and diagnostic development.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Fungal biomarkers for SZ-MetS diagnosis. <bold>(A)</bold> Random Forest analysis highlighting the importance of various fungal species based on Mean Decrease Gini. <bold>(B)</bold> Receiver Operating Characteristic (ROC) curves for individual fungal species to distinguish SZ-MetS patients from healthy controls. <bold>(C)</bold> ROC curves for a combination of six fungal species to distinguish SZ-MetS patients from healthy controls. AUC represents the area under the ROC curve.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g006.tif">
<alt-text content-type="machine-generated">Panel A displays two graphs: a scatter plot showing species importance ranked by Mean Decrease Gini and a bar chart of their Relative Abundance. Panel B presents ROC curves for multiple species with varying AUC values, showing predictive accuracy. Panel C shows an ROC curve for six species combined, with an AUC of 0.86, indicating model performance.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_4">
<title>Association between key fungal species and systemic inflammatory/clinical markers</title>
<p>Our multiplex cytokine analysis revealed a complex immune dysfunction in SZ-MetS, characterized by elevated levels of several inflammatory cytokines and chemokines relative to HCs. Specifically, the concentrations of cytokines in SZ-MetS versus HCs were as follows: such as IL-1&#x3b2; (3.14 &#xb1; 3.03 pg/mL vs. 0.94 &#xb1; 0.31 pg/mL), IL-1ra (650.79 &#xb1; 546.49 pg/mL vs. 246.95 &#xb1; 125.22 pg/mL), IL-4 (3.84 &#xb1; 2.08 pg/mL vs. 2.59 &#xb1; 0.8 pg/mL), IL-5 (68.69 &#xb1; 45.93 pg/mL vs. 27.1 &#xb1; 25.94 pg/mL), IL-6 (12.07 &#xb1; 10.07 pg/mL vs. 4.18 &#xb1; 2.07 pg/mL), IL-8 (111.94 &#xb1; 162.57 pg/mL vs. 10.26 &#xb1; 6.77 pg/mL), IL-17 (35.36 &#xb1; 14.78 pg/mL vs. 28.03 &#xb1; 6.69 pg/mL), and IFN-&#x3b3; (8.89 &#xb1; 4.13 pg/mL vs. 6.25 &#xb1; 1.36 pg/mL). Similarly, chemokine levels in SZ-MetS compared to HCs were: IP-10 (484.46 &#xb1; 158.12 pg/mL vs. 316.43 &#xb1; 93.4 pg/mL), MCP-1 (30.4 &#xb1; 17.81 pg/mL vs. 19.85 &#xb1; 10.71 pg/mL), MIP-1&#x3b1; (35.63 &#xb1; 22.48 pg/mL vs. 2.66 &#xb1; 1.55 pg/mL), and MIP-1&#x3b2; (227.26 &#xb1; 106.2 pg/mL vs. 158.34 &#xb1; 34.74 pg/mL). Spearman correlation analysis identified significant associations between specific gut fungal species and systemic inflammatory markers as well as clinical parameters. Pro-inflammatory cytokines like IL-6 were positively correlated with <italic>Ustilago esculenta</italic>, while chemokines such as MIP-1&#x3b1; showed positive correlations with <italic>Holleya sinecauda</italic>, <italic>Lodderomyces elongisporus</italic>, and <italic>Trichosporon asahii</italic>, and IP-10 with <italic>Trichosporon asahii</italic>. In contrast, <italic>Saccharomyces cerevisiae</italic>, <italic>Penicillium concentricum</italic>, and <italic>Wickerhamomyces anomalus</italic> exhibited negative correlations with inflammatory markers like MIP-1&#x3b1; and IL-1&#x3b2;, and <italic>Wallemia muriae</italic> negatively correlated with MIP-1, IL-8, IL-1&#x3b2;, and IP-10. Both <italic>Wallemia muriae</italic> and <italic>Wickerhamomyces anomalus</italic> also showed negative correlations with IL-1ra (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7</bold>
</xref>). Additionally, <italic>Lodderomyces elongisporus</italic> was positively correlated with metabolic parameters like triglycerides (TG), whereas <italic>Candida albicans</italic> was negatively correlated with TG. Notably, <italic>Saccharomyces cerevisiae</italic> exhibited inverse correlations with Scale for Assessment of Positive Symptoms (SAPS), while <italic>Candida sake</italic> was negatively correlated with Scale for Assessment of Negative Symptoms (SANS) (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref>). These findings suggest that alterations in gut mycobiota may influence systemic inflammation and clinical manifestations in SZ-MetS patients through species-specific interactions.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Gut fungal species linked to systemic immune dysfunction in SZ-MetS patients. Spearman&#x2019;s correlation heatmap depicting associations between differentially abundant fecal fungal species and circulating immune markers (inflammatory cytokines, chemokines, and growth factors) in SZ-MetS patients. Color intensity represents the strength of correlation, with significance thresholds indicated (p &lt; 0.05). Only statistically significant correlations are annotated. *p &lt; 0.05; **p &lt; 0.05.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g007.tif">
<alt-text content-type="machine-generated">Heatmap showing Spearman's correlation between various cytokines and fungal species. Rows represent cytokines like IL-17 and IL-4, while columns represent fungal species such as Candida albicans. Color gradients from red to blue depict correlation strength, with red indicating positive correlation and blue negative. Asterisks denote significant correlations. Dendrograms indicate clustering patterns among both cytokines and fungi.</alt-text>
</graphic>
</fig>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Associations between gut fungal species and clinical indicators in SZ-MetS patients. Heatmap of Spearman&#x2019;s rank correlations between differentially abundant gut fungal species and clinical parameters in SZ-MetS patients. Significant positive correlations (p &lt; 0.05) are shown in red, significant negative correlations (p &lt; 0.05) in blue, and non-significant correlations (p &#x2265; 0.05) in white. *p &lt; 0.05.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1652633-g008.tif">
<alt-text content-type="machine-generated">Heatmap showing Spearman's correlation between microbial species and health metrics BMI, TG, SANS, and SAPS. Correlation values range from -0.2 (blue) to 0.2 (red) with significant correlations marked by asterisks. Rows list microbial species, and columns list health metrics.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>The intricate bidirectional relationship between SZ and MetS presents significant clinical challenges, where antipsychotic medications-while crucial for managing psychiatric symptoms - paradoxically contribute to metabolic dysregulation through mechanisms that go beyond their direct pharmacological effects (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B27">27</xref>). Mounting evidence highlights the gut microbiota as a key mediator in this metabolic-neuropsychiatric crosstalk. Cutting-edge multi-omics research demonstrates that gut microbial imbalances often precede and may drive metabolic abnormalities in SZ patients, regardless of antipsychotic use (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B28">28</xref>). Often referred to as the &#x201c;second brain&#x201d; and &#x201c;metabolic organ&#x201d;, the gut microbiota serves as a vital link between nutritional metabolism and neurobehavioral regulation (<xref ref-type="bibr" rid="B29">29</xref>). Comprising both bacterial and fungal components, it regulates host metabolism through multiple pathways including short-chain fatty acids (SCFAs) production, maintenance of gut barrier function, and immune system regulation (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B31">31</xref>). Current understanding has been predominantly shaped by bacterial research, overlooking the potentially crucial contributions of fungal communities (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B32">32</xref>). The gut mycobiota represents a distinct ecological niche that interacts with host physiology through mechanisms fundamentally different from those of bacteria. Unlike bacterial signaling which primarily occurs through SCFAs production, fungal communities influence host metabolism and immunity through unique pathways via &#x3b2;-glucan recognition by Dectin-1 receptors that modulates both innate and adaptive immune responses (<xref ref-type="bibr" rid="B33">33</xref>), production of neuroactive tryptophan metabolites that can cross the blood-brain barrier (<xref ref-type="bibr" rid="B34">34</xref>), and direct vagus nerve stimulation capable of altering central nervous system activity (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B36">36</xref>). These fungal-specific mechanisms may be particularly relevant to SZ-MetS pathogenesis given the established roles of immune activation and neurotransmitter dysregulation in both SZ and MetS. Furthermore, the ability of certain fungal species to form biofilms and penetrate intestinal barriers positions them as potential amplifiers of gut-derived inflammation -&#xa0;a hypothesized contributor to both neuropsychiatric symptoms and metabolic dysfunction. Despite these plausible mechanistic links, the gut mycobiota remains conspicuously understudied in the context of SZ-MetS comorbidity, representing a critical gap in our understanding of how microbial communities influence the gut-brain-metabolism axis. This knowledge deficit persists even as research continues to elucidate bacterial dysbiosis patterns in SZ-MetS, including the depletion of anti-inflammatory butyrate producers and expansion of pro-inflammatory taxa (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B37">37</xref>, <xref ref-type="bibr" rid="B38">38</xref>). The current study addresses this gap by providing the first systematic investigation of gut mycobiota alterations in SZ-MetS, offering new insights into how fungal communities may contribute to this complex comorbidity through their unique biological properties and host interaction mechanisms.</p>
<p>Our study pioneers the exploration of gut mycobiota in SZ-MetS patients, revealing profound taxonomic alterations despite comparable global &#x3b1;- and &#x3b2;-diversity. Taxonomic profiling uncovered striking shifts in specific fungal taxa, with SZ-MetS patients exhibiting enrichment of <italic>Candida</italic>, <italic>Trichosporon</italic>, and <italic>Issatchenkia</italic> alongside reduction of protective species like <italic>S. cerevisiae</italic> and <italic>P. ostreatus</italic>&#x2014;findings that align with emerging literature on fungal dysbiosis in metabolic and neuroinflammatory disorders. For instance, <italic>C. albicans</italic> overgrowth, a prevalent species within the <italic>Candida</italic> genus, has been mechanistically linked to insulin resistance in MetS by disrupting gut mucosal barriers, increasing permeability, and promoting endotoxemia that impairs insulin signaling in peripheral tissues (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>). Concurrently, the depletion of <italic>S. cerevisiae</italic>&#x2014;a known modulator of tight-junction proteins&#x2014;may exacerbate gut leakage (<xref ref-type="bibr" rid="B41">41</xref>), while its reduction also disrupts SCFAs production critical for metabolic homeostasis (<xref ref-type="bibr" rid="B42">42</xref>, <xref ref-type="bibr" rid="B43">43</xref>). Preclinical models demonstrate that <italic>S. cerevisiae</italic>-depleted mycobiomes correlate with reduced SCFAs levels, dyslipidemia, and altered energy metabolism, mirroring metabolic derangements in SZ-MetS (<xref ref-type="bibr" rid="B44">44</xref>). Mechanistically, these fungal alterations may influence neuropsychiatric function through dual pathways: neurotransmitter regulation and immunomodulation. Altered fungi such as <italic>Trichosporon</italic> promote the release of pro-inflammatory cytokines that disrupt neural plasticity, while <italic>S. cerevisiae</italic> depletion may reduce production of neuroactive metabolites like serotonin and GABA (<xref ref-type="bibr" rid="B45">45</xref>&#x2013;<xref ref-type="bibr" rid="B47">47</xref>). In animal models, <italic>Saccharomyces boulardii</italic> supplementation restores brain serotonin levels and alleviates neuropsychiatric-like behaviors, underscoring fungi&#x2019;s direct impact on the gut-brain axis (<xref ref-type="bibr" rid="B48">48</xref>). Additionally, fungal dysbiosis triggers abnormal immune activation in SZ-MetS, with <italic>Trichosporon</italic>-induced pro-inflammatory responses affecting neural transmission and synaptic function (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B49">49</xref>). Recent research has also shown that fungi-induced changes in gut-associated lymphoid tissue further amplify immune-neuroendocrine crosstalk (<xref ref-type="bibr" rid="B50">50</xref>), emphasizing the complex interaction between gut mycobiota, immunity, and neuropsychiatric dysfunction in this comorbid population. Notably, a six-species fungal signature (<italic>S. cerevisiae</italic>, <italic>T. asahii</italic>, <italic>C. albicans</italic>, <italic>P. ostreatus</italic>, <italic>Lodderomyces elongisporus</italic>, <italic>Wickerhamomyces anomalus</italic>) exhibited exceptional diagnostic accuracy (AUC = 0.86), underscoring the unique utility of mycobiota profiling for identifying disease-specific microbial signatures. This performance aligns with a previous study by Xing et&#xa0;al., in which a decision tree-based model of five bacterial taxa achieved an AUC of 0.94 for discriminating SZ patients with and without MetS (<xref ref-type="bibr" rid="B8">8</xref>). Collectively, these findings highlight the diagnostic potential of gut microbiota&#x2014;encompassing both bacterial and fungal communities&#x2014;for SZ-MetS. Future research should prioritize validation of these mycobiota signatures in independent multicenter cohorts to facilitate their translation into clinical practice.</p>
<p>Our findings demonstrate significant species-specific interactions between gut mycobiota and host physiology in SZ-MetS patients, building upon and extending previous research in this field (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>). Most notably, we observed a strong positive correlation between <italic>T. asahii</italic> abundance and pro-inflammatory chemokines, particularly MIP-1&#x3b1;. This association aligns with established mechanisms of fungal pathogenicity, as <italic>T. asahii</italic> cell wall &#x3b2;-glucans are known to activate Dectin-1 receptors on macrophages, triggering NLRP3 inflammasome assembly and subsequent release of IL-1&#x3b2; and MIP-1&#x3b1; (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B51">51</xref>). These inflammatory mediators may contribute to both the metabolic and neuropsychiatric components of SZ-MetS by promoting insulin resistance and neuroinflammation. Conversely, we found that reduced levels of <italic>S. cerevisiae</italic> were significantly associated with elevated IL-1&#x3b2; and MIP-1&#x3b1;. This inverse relationship supports the emerging understanding of <italic>S. cerevisiae</italic> as an immunomodulatory commensal, capable of attenuating TLR-mediated inflammatory responses through multiple mechanisms, including upregulation of regulatory cytokines and stabilization of gut barrier function (<xref ref-type="bibr" rid="B52">52</xref>). The protective effects of <italic>S. cerevisiae</italic> may be particularly relevant in SZ-MetS, where chronic low-grade inflammation is a hallmark feature. From a metabolic perspective, our data reveal complex fungal-host interactions. <italic>L. elongisporus</italic> showed a strong positive correlation with serum triglyceride levels, consistent with animal studies demonstrating its capacity to enhance hepatic lipid synthesis via PPAR-&#x3b3; activation (<xref ref-type="bibr" rid="B53">53</xref>). Conversely, <italic>C. albicans</italic> abundance was inversely related to triglyceride levels, mirroring observations in NASH patients where <italic>C. albicans</italic> may compete for lipid nutrients (<xref ref-type="bibr" rid="B54">54</xref>). The immunological implications of mycobiota alterations were further underscored by our finding that <italic>W. muriae</italic> depletion correlated with elevated IL-8. This rare basidiomycete has been shown to promote regulatory T cell differentiation in the intestinal mucosa (<xref ref-type="bibr" rid="B55">55</xref>), suggesting its reduction in SZ-MetS may contribute to the disruption of immune homeostasis. Collectively, these observations support a model wherein gut mycobiota dysbiosis participates in a vicious cycle of SZ-MetS pathophysiology: fungal-driven inflammation exacerbates metabolic dysfunction, while resulting metabolic disturbances (e.g., hypertriglyceridemia) create an environment favoring further fungal dysbiosis (<xref ref-type="bibr" rid="B56">56</xref>). This cycle may be amplified by virulence factors such as fungal phospholipases, which directly damage host tissues and potentiate inflammatory responses (<xref ref-type="bibr" rid="B57">57</xref>). Our findings thus position the gut mycobiota as both a contributor to and consequence of SZ-MetS pathology, offering multiple potential intervention targets.</p>
<p>Building on the taxonomic and functional shifts in the gut mycobiota described above, integrating these findings with our prior characterization of gut bacterial communities in SZ-MetS patients provides a more holistic perspective on microbial dysbiosis in this comorbidity (<xref ref-type="bibr" rid="B12">12</xref>). Our prior work identified significant bacterial perturbations, including depletion of anti-inflammatory taxa such as <italic>Bacteroides</italic> and <italic>Faecalibacterium</italic> alongside enrichment of pro-inflammatory genera like <italic>Escherichia-Shigella</italic> and <italic>Klebsiella</italic>. These bacterial shifts are likely to interact with the fungal dysbiosis characterized in the current study, as bacterial and fungal communities in the gut maintain intricate reciprocal relationships that regulate ecosystem stability (<xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Mechanistically, bacterial metabolites such as SCFAs, whose production is compromised in SZ-MetS due to reduced SCFA-producing bacteria (<xref ref-type="bibr" rid="B12">12</xref>), play a role in constraining fungal overgrowth by modulating gut pH and reinforcing intestinal barrier integrity (<xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B42">42</xref>). The depletion of <italic>S. cerevisiae</italic> observed in our current study&#x2014; a species known to synergize with SCFA-producing bacteria to stabilize mucosal barriers&#x2014;may thus be exacerbated by the concurrent loss of bacterial SCFA sources. Conversely, the enrichment of opportunistic fungi like <italic>C. albicans</italic> and <italic>T. asahii</italic> in SZ-MetS could disrupt bacterial homeostasis: these fungi secrete phospholipases and proteases that degrade intestinal barriers (<xref ref-type="bibr" rid="B57">57</xref>), creating a permissive environment for pro-inflammatory bacteria to translocate and amplify systemic inflammation. Notably, both bacterial and fungal dysbiosis in SZ-MetS correlate with elevated pro-inflammatory cytokines (e.g., IL-6, MIP-1&#x3b1;), suggesting a convergent impact on immune activation. For instance, bacterial lipopolysaccharides and fungal &#x3b2;-glucans may synergistically activate TLR4 and Dectin-1 signaling pathways (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B51">51</xref>), driving the chronic low-grade inflammation that links metabolic dysfunction and neuropsychiatric symptoms. Such cross-kingdom interactions likely contribute to the &#x201c;vicious cycle&#x201d; of gut barrier impairment, immune dysregulation, and clinical manifestations in SZ-MetS, highlighting the need to consider multi-kingdom microbial dynamics in future mechanistic investigations. While these observations support potential bacterial-fungal crosstalk in SZ-MetS, the current study did not explicitly investigate the direct molecular or ecological mechanisms underlying such interactions. This represents an important avenue for future research, as disentangling these relationships could reveal novel therapeutic targets that simultaneously modulate both microbial kingdoms.</p>
<p>While our findings provide important insights, several limitations must be acknowledged. First, the cross-sectional design precludes establishing causal relationships between mycobiota alterations and disease progression. Second, while we controlled for major confounders, the potential effects of antipsychotic medications on fungal communities require dedicated investigation through medication-na&#xef;ve studies. Third, our single-center design and specific ethnic composition may limit the generalizability of findings to other populations. Fourth, the diagnostic value of key functional fungal taxa identified in this study is solely based on results from the discovery cohort, with no independent validation cohort to confirm their reliability and robustness, which hinders the direct translation of these findings into clinical applications. Finally, the functional consequences of observed mycobiota changes warrant verification through mechanistic studies employing gnotobiotic models or multi-omics approaches.</p>
</sec>
<sec id="s5" sec-type="conclusion">
<title>Conclusion</title>
<p>This study comprehensively characterizes gut mycobiota dysbiosis in Chinese SZ-MetS patients, revealing distinct taxonomic shifts including enrichment of <italic>Candida</italic>, <italic>Trichosporon</italic>, and <italic>Issatchenkia</italic>, alongside reduction of protective taxa like <italic>S. cerevisiae</italic> and <italic>P. ostreatus</italic>. Despite comparable global diversity, these compositional changes correlate with systemic immune dysfunction, as evidenced by elevated pro-inflammatory cytokines and species-specific associations with metabolic/clinical parameters. A six-species fungal signature demonstrates robust diagnostic accuracy, highlighting mycobiota&#x2019;s potential as novel biomarkers for SZ-MetS. Collectively, these findings establish gut mycobiota dysbiosis as a critical contributor to the SZ-MetS vicious cycle. Future research should prioritize longitudinal studies to track mycobiota dynamics during disease progression, mechanistic investigations using gnotobiotic models to clarify fungal-mediated immune-metabolic crosstalk, and clinical trials evaluating <italic>S. cerevisiae</italic>-based probiotics to restore gut homeostasis. Additionally, integrating metatranscriptomic and metabolomic approaches to explore fungal-bacterial interactions may unveil innovative therapeutic targets for this complex comorbidity.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: <uri xlink:href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov/</uri>, PRJNA1280732.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Quzhou Third People&#x2019;s Hospital Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>ZXL: Funding acquisition, Validation, Formal analysis, Supervision, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing,&#xa0;Conceptualization. YC: Data curation, Validation, Conceptualization, Funding acquisition, Writing &#x2013; original draft, Formal analysis. ZYL: Formal analysis, Writing &#x2013; original draft, Data curation, Validation, Conceptualization, Methodology. XL: Conceptualization, Validation, Data curation, Formal analysis, Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Supervision, Methodology. ZZ: Data curation, Methodology, Investigation, Writing &#x2013; original draft, Conceptualization, Formal analysis. WD: Investigation, Data curation, Writing &#x2013; original draft, Methodology, Formal analysis. XCX: Formal analysis, Data curation, Writing &#x2013; original draft, Methodology, Investigation. PY: Formal analysis, Data curation, Writing &#x2013; original draft, Methodology, Investigation. XXX:&#xa0;Formal analysis, Methodology, Writing &#x2013; original draft, Investigation, Data curation. LS: Data curation, Investigation, Writing &#x2013; original draft, Methodology, Formal analysis, Project administration. QS: Investigation, Conceptualization, Formal analysis, Writing &#x2013; original draft, Methodology, Data curation. RL: Formal analysis, Methodology, Supervision, Data curation, Conceptualization, Investigation, Funding acquisition, Writing &#x2013; original draft.</p>
</sec>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research and/or publication of this article. This present work was funded by the grants of the National S&amp;T Major Project of China (2023YFC2308400), Key R&amp;D Program of Quzhou (2023K187), Shandong Provincial Laboratory Project (SYS202202), the Fundamental Research Funds for the Central Universities (2025ZFJH003), the Taishan Scholar Foundation of Shandong Province (tsqn202103119), and the Foundation of China&#x2019;s State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (ZZ202316 and ZZ202319).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The 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.</p>
</sec>
<sec id="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec id="s12" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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