Polish journal of microbiology最新文献

{"title":"Microbial Signatures in Head and Neck versus Gastrointestinal Tumors: Identification and Prognostic Modeling.","authors":"Hua Guo, Jihan Wang, Yaqi Niu, Fuqiang Liu","doi":"10.33073/pjm-2026-012","DOIUrl":"10.33073/pjm-2026-012","url":null,"abstract":"<p><p>This study identified key intra-tumor microbial signatures distinguishing head and neck cancers from gastrointestinal cancers and explored their diagnostic and prognostic potential. Intra-tumor microbial data of five cancer types were obtained from the Cancer Microbiome Atlas, and corresponding clinical data were retrieved from the Cancer Genome Atlas. The Wilcoxon test was used to analyze differences in microbial populations. Univariate logistic regression, least absolute shrinkage and selection operator, and recursive feature elimination were sequentially applied to screen optimal microbial markers, and a support vector machine classification model was constructed. A nomogram model and Kaplan-Meier curves were used to validate the predictive and prognostic value of the optimal microbes, respectively. Overall, 463 tumor samples and 47 controls were included. Twenty-three microbes showed significant differences in distribution between head and neck and gastrointestinal tumors; among these, eight overlapping microbes were selected as optimal markers. The SVM model based on these eight microbes achieved AUCs of 0.937 and 0.856 in the training and validation datasets, respectively. The nomogram model constructed with these markers showed high predictive accuracy (C-index = 0.8944 in training, 0.8023 in validation). Kaplan-Meier analysis revealed that high abundance of <i>Capnocytophaga, Lachnospiraceae</i>, and <i>Bacteroidales</i> was significantly associated with longer overall survival in both head and neck tumors and gastrointestinal tumors (all <i>P</i> < 0.05). The eight intra-tumor microbial communities serve as a robust signature for distinguishing head and neck tumors from gastrointestinal tumors. Among these, <i>Capnocytophaga, Lachnospiraceae</i>, and <i>Bacteroidales</i> have potential as prognostic biomarkers to improve survival prediction in cancers.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"123-138"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of Placental Bacterial DNA Communities in Pregnant Women with Gestational Diabetes Mellitus and Association with Early Neonatal Gut Microbiota and Short-Term Perinatal Outcomes.","authors":"Yujia Qian, Xiaowen You, Fangqin Tu, Ye Tao, Minjuan Zhong, Dong Xu","doi":"10.33073/pjm-2026-022","DOIUrl":"10.33073/pjm-2026-022","url":null,"abstract":"<p><p>Gestational diabetes mellitus (GDM) affects maternal metabolism and may be associated with altered placental bacterial DNA community profiles and early neonatal gut microbiota. This prospective cohort study assesses GDM's impact on the placental-neonatal gut microbial axis and links key bacterial taxa to short-term perinatal outcomes and clinical significance. Pregnant women with GDM and healthy controls were enrolled. Placental tissue and first-pass neonatal meconium samples were collected. Bacterial community composition, diversity, and differential genera were analyzed using 5-region 16S rRNA sequencing. FEAST was used to model potential maternal-neonatal microbial sources. Associations among gestational weight gain (GWG), microbial features, and the risk of neonatal hospitalization were also assessed. The GDM and control groups showed significant differences in placental and early neonatal meconium microbial community structures, with increased α-diversity and distinct community separation in both sample types. In placental samples, <i>Brevundimonas</i> was decreased, whereas <i>Lactobacillus</i> and <i>Sphingomonas</i> were enriched. In neonatal meconium, potentially inflammation-related genera, including <i>Staphylococcus, Streptococcus</i>, and <i>Clostridium</i>, were enriched. GWG had limited effects on overall community structure but exerted refined regulation on specific genera. Source tracking showed a reduced placenta-attributed similarity contribution to neonatal meconium microbiota in the GDM group. The abundances of specific placental genera, including <i>Escherichia, Curvibacter</i>, and <i>Pelomonas</i>, were significantly associated with neonatal hospitalization. GDM is associated with altered placental bacterial DNA community profiles and early neonatal meconium microbiota, potentially affecting maternal-neonatal microbial continuity. Specific bacterial genera are associated with early neonatal hospitalization. The study provides a microbial perspective for understanding GDM-related perinatal risk.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"227-242"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidrug-Resistant <i>Salmonella</i> and <i>Shigella</i>: Epidemiology, Resistance Mechanisms, and Emerging Therapeutic Strategies.","authors":"Abdullah Ali Almrwani, Bassam O Al-Johny, Ali Ahmad Shaibah, Adel M Qumusani, Saleh M Al-Maaqar","doi":"10.33073/pjm-2026-013","DOIUrl":"10.33073/pjm-2026-013","url":null,"abstract":"<p><p>The review article is a critical examination of the growing global problem of multidrug-resistant (MDR) <i>Salmonella</i> and <i>Shigella</i>, focusing on their epidemiology, molecular resistance mechanisms, and clinical effects. It compiles current information on increasing resistance to major antibiotics, such as fluoroquinolones, third-generation cephalosporins, and sulfonamides, that is caused by a variety of factors, including ESBL production and plasmid-mediated gene transfer, especially in Asia, Africa, and the Middle East. The minireview discusses the new therapeutic approaches such as bacteriophage therapy, antimicrobial peptides, antimicrobials based on nanotechnology, and a combination of antibiotic regimens and their mechanisms, efficacy, and some limitations that exist in these approaches. In addition, it discusses the issues of diagnostic challenges, infection control practices, and antibiotic stewardship as part and parcel of resistance management. It will be a focused, evidence-based review to inform research, clinical practices, and policy to turn the trend of resistance around and enhance patient outcomes. The presented multidisciplinary review shows that MDR <i>Salmonella</i> and <i>Shigella</i> infections have become a threatening problem in the world, which requires urgent measures, including multilateral cooperation, improved molecular tracking, fast diagnostics, and new therapeutic options.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"139-156"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In Vitro</i> Antibacterial Activity of Sulbactam-Durlobactam and Eravacycline Against Carbapenem-Resistant <i>Acinetobacter baumannii</i> in China and Analysis of Sulbactam-Durlobactam Resistance Mechanisms.","authors":"Xuelian He, Shang Ma, Yanyan Zhou, Jingjuan Wei, Zhongling Zhuo, Liyan Ma","doi":"10.33073/pjm-2026-019","DOIUrl":"10.33073/pjm-2026-019","url":null,"abstract":"<p><p>The management of carbapenem-resistant <i>Acinetobacter baumannii</i> (CRAB) infections remains a formidable clinical challenge. This study evaluated the <i>in vitro</i> antimicrobial activities of sulbactam-durlobactam (SUL-DUR) and eravacycline (ERV) against CRAB isolates and elucidated the genomic landscapes of resistance and virulence determinants in SUL-DUR-resistant strains to inform therapeutic decision-making. A total of 233 clinical CRAB isolates were collected and screened for susceptibility to SUL-DUR and ERV using the Kirby-Bauer (K-B) disk diffusion assay. Isolates exhibiting resistance to SUL-DUR were further characterized via metagenomic next-generation sequencing (mNGS) to identify key resistance and virulence factors. SUL-DUR and ERV demonstrated robust <i>in vitro</i> activity, with susceptibility rates of 92.3% and 91.4%, respectively. Notably, no isolates exhibited concurrent non-susceptibility to both agents. Genomic analysis of 14 SUL-DUR-resistant strains revealed a complex and heterogeneous distribution of genetic determinants. The presence of <i>bla</i> _NDM-1 was identified as a critical driver of SUL-DUR resistance. Additionally, reduced susceptibility was potentially associated with specific mutations in <i>bla</i> _OXA-23, <i>bla</i> _OXA-66, and <i>bla</i> _TEM-1, while hyperactive efflux systems and altered membrane permeability further synergized to enhance the resistance phenotype. Despite the extensive-drug-resistant (XDR) nature of current CRAB isolates, they maintain high sensitivity to SUL-DUR and ERV. Our findings underscore that SUL-DUR and ERV represent highly promising therapeutic options with significant development potential and broad clinical application prospects for the management of CRAB-related infections.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"210-219"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Driven Prediction of Early Enteral Nutrition Intolerance in ICU Patients Using Intra-Abdominal Pressure and Selected Gut Microbial Taxa.","authors":"Kaihui Zheng, Xuena Zhang, Lishi Qu, Renshu Wang","doi":"10.33073/pjm-2026-023","DOIUrl":"10.33073/pjm-2026-023","url":null,"abstract":"<p><p>To develop and validate a machine learning model integrating intra-abdominal pressure (IAP) and gut microbial characteristics for early identification of enteral nutrition intolerance (EENI) in critically ill ICU patients. This cohort study (January 2023-December 2025) included 300 ICU patients receiving early enteral nutrition. Baseline clinical characteristics, intra-abdominal pressure, laboratory indices, and quantitative gut microbial taxa were collected. Candidate predictors were selected using the least absolute shrinkage and selection operator regression, and independent predictors were incorporated into a multivariable logistic regression model, which was presented as a nomogram. Model performance was assessed using discrimination, calibration, and decision curve analyses. The incidence of EENI was 49.00% (147/300). LASSO selected 16 features: age, analgesic use, serum albumin, glucose, IAP, and the absolute counts of <i>Enterococcus, Bacteroides, Escherichia-Shigella, Klebsiella, Bifidobacterium</i>, and <i>Parabacteroides</i>. The absolute counts of <i>Enterococcus, Bacteroides, Escherichia-Shigella, Klebsiella, Bifidobacterium</i>, and <i>Parabacteroides</i> were significant independent predictors (P < 0.05). The model achieved an AUC of 0.900 (95% CI: 0.863-0.936) in the training set and 0.900 (95% CI: 0.859-0.941) in the validation set. Calibration was good (Hosmer-Lemeshow P = 0.425 and P = 0.423, respectively). DCA demonstrated clinical utility across a wide range of risk thresholds. The developed machine learning model, combining IAP and selected genus-level gut microbial markers, demonstrates strong predictive performance and clinical potential for forecasting and managing early enteral nutrition intolerance in ICU patients.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"243-257"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Longitudinal 16S rRNA Gene Sequencing Study Exploring the Relationship Between Gut Microbiota and Body Composition in Healthy Adults.","authors":"Min-Yen Shih, Yu-Chen Yang, Yun-Ru Liu","doi":"10.33073/pjm-2026-015","DOIUrl":"10.33073/pjm-2026-015","url":null,"abstract":"<p><p>The gut microbiome is linked to body composition, yet most studies involve probiotic or dietary interventions. This study explored relationships between changes in body composition and the fecal microbiota under natural lifestyle conditions. A repeated-measures design involved 15 adults completing four body composition assessments at 3-month intervals. Fecal samples from each time point underwent 16S rRNA gene sequencing. Participants were stratified by body composition parameters, and microbial profiles from initial and final measurements were compared to assess longitudinal patterns. Overweight participants showed lower alpha diversity. Linear mixed models revealed fecal microbiota remained stable across all four time points, with no statistically significant continuous trends observed longitudinally. Exploratory baseline-to-endpoint comparisons across stratified groups and Spearman correlation analyses suggested potential microbiota shifts, though these associations remained statistically non-significant. Preliminary observations exhibited that the OTU identified as <i>Parasutterella excrementihominis</i> tended to associate with higher body fat, whereas the putative species <i>Akkermansia muciniphila</i> showed a potential inverse association. Representative taxa, such as <i>Dialister invisus</i>, appeared enriched in individuals with higher skeletal muscle percentages, whereas the OTU assigned to <i>Bifidobacterium pseudocatenulatum</i> showed the opposite trend. Several associations differed by sex, suggesting modulation by host factors. These preliminary findings suggest possible fecal microbiota patterns associated with body composition, even without targeted interventions. While lacking robust linear associations in this small pilot cohort, the observed directional consistency across statistical approaches highlights the potential of fecal microbes as candidate indicators of metabolic health. These exploratory results require further validation in larger, longitudinal studies with sufficient statistical power.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"157-167"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of Gut Microbiota in Patients with Severe Pneumonia and its Potential Clinical Relevance.","authors":"Fangchao Zhong, Maosen Huang, Xiaoxia Wei, Lihua Fu, Linhai Yan","doi":"10.33073/pjm-2026-001","DOIUrl":"10.33073/pjm-2026-001","url":null,"abstract":"<p><p>Gut microbiota is associated with a variety of diseases, but its relationship with severe pneumonia remains to be explored. This study primarily analyzed the intestinal microbiota of patients with severe pneumonia and examined its association with clinical data. We collected clinical data from 96 patients with severe pneumonia for differential analysis and identified prognostic factors using logistic regression. Fecal samples from patients with severe pneumonia and healthy controls were collected and analyzed using 16S rRNA sequencing. We applied three machine learning algorithms (LASSO, Random Forest, and SVM) to identify microbial markers associated with severe pneumonia. The patients were grouped by \"discharge status\". Significant differences were observed in age (<i>p</i> = 0.025), total length of hospital stay (<i>p</i> < 0.001), and C-reactive protein (CRP) (<i>p</i> < 0.001). Logistic regression analysis identified age (<i>p</i> = 0.024) and total hospital stay (<i>p</i> < 0.001) as factors influencing the likelihood of improvement and discharge. Diversity analysis of collected stool samples revealed differences between the two groups. LDA Effect Size (LEfSe) analysis highlighted significant microbial differences at various taxonomic levels between the two populations. Three machine learning algorithms identified 9 microbial markers for severe pneumonia. A diagnostic prediction model was constructed, with an area under the Receiver Operating Characteristic (ROC) curve of 0.969 (95% CI: 0.946-0.992). Patients with severe pneumonia exhibit unique intestinal microbiota characteristics, which may be regulated by age and total length of hospital stay, thereby influencing the disease's prognosis.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"109-122"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct Gut Microbiome and Metabolome Profiles Associate with Differential Responses to Immunotherapy in Colorectal Cancer.","authors":"Fang Liang, Jing Li, Yingkun Yue, Jiaxin Pan, Chenyu Liu, Duo Cheng, Nan Zhang, Kunkun Li, Feifei Chu, Huili Wu","doi":"10.33073/pjm-2026-016","DOIUrl":"10.33073/pjm-2026-016","url":null,"abstract":"<p><p>The composition of the intestinal microbiome has been identified as a key factor influencing the efficacy of immune checkpoint inhibitors. This study aimed to systematically evaluate the potential associations among gut microbiota, metabolic profiles, and clinical outcomes in patients with MSI-H advanced colorectal cancer (CRC) treated with immunotherapy. Twenty advanced CRC patients receiving immunotherapy were enrolled and categorized into clinical benefit response (CBR) and non-benefit (NCB) groups based on treatment efficacy. Fecal samples were analyzed using metagenomic sequencing and untargeted metabolomics. The results revealed significant enrichments of <i>s_Clostridium unclassified</i> and metabolites such as guanosine, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid, and quercetin 3-(6\"-malonyl-glucoside) in the CBR group, suggesting their potential positive predictive value for immunotherapy response. Conversely, the NCB group showed significant enrichments of <i>s_Roseburia hominis, s_Marseilla massiliensis</i>, and metabolites including pyrophosphate, riboflavin, and PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:0), indicating a possible association with treatment resistance. By integrating fecal metagenomics and metabolomics, this study reveals distinctive \"flora-metabolite\" interactions linked to therapeutic response in advanced CRC patients undergoing immunotherapy. Specific microbial and metabolic profiles were positively or negatively correlated with immunotherapy outcomes, highlighting their potential not only as predictive biomarkers but also as a theoretical foundation for developing individualized immunotherapy strategies based on microecological modulation.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":"75 2","pages":"168-194"},"PeriodicalIF":1.6,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Detection of Pathogens and Resistance Genes in Positive Blood Cultures Using BioFire^® FilmArray BCID2.","authors":"Canberk Çinar, Yeliz Tanriverdi Çayci, Zehranur Atakcan, İlknur Biyik, Kemal Bilgin, Demet Gür Vural, Asuman Birinci","doi":"10.33073/pjm-2026-020","DOIUrl":"10.33073/pjm-2026-020","url":null,"abstract":"<p><p>Bloodstream infections cause significant morbidity and mortality, and rapid identification of the causative microorganisms, along with their antimicrobial resistance profiles, is crucial for appropriate treatment. Molecular diagnostic systems such as the BioFire FilmArray Blood Culture Identification 2 (BCID2) panel provide faster results than conventional culture-based methods. This study evaluated the performance of the BCID2 panel for rapid detection of microorganisms and antimicrobial resistance genes in 50 positive blood cultures collected at a university hospital between January 2023 and September 2025. All samples were obtained from peripheral blood cultures, and repeat or catheter-related cultures were excluded. Samples were analyzed using conventional culture methods, mass spectrometry-based identification, and automated antimicrobial susceptibility testing, in parallel with the BCID2 panel. Resistance genes detected by the panel were confirmed using polymerase chain reaction. At least one microorganism was detected in forty-seven samples (94%), showing strong agreement between molecular and culture-based methods. Complete concordance was observed for <i>Escherichia coli</i> and <i>Pseudomonas aeruginosa</i>, while <i>Klebsiella pneumoniae</i> showed slightly lower but acceptable agreement. Polymicrobial results were detected by BCID2 in 20 cases (40%). Detected resistance genes correlated well with phenotypic resistance results. However, the absence of clinical correlation data and repeat blood culture information limits the interpretation of these findings, particularly for coagulase-negative staphylococci. The BCID2 panel enables rapid and reliable identification of bloodstream pathogens and relevant resistance genes, serving as a valuable complement to conventional diagnostic methods when interpreted alongside clinical data.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":" ","pages":"220-226"},"PeriodicalIF":1.6,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148355228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal Changes in Selected Gut Microorganisms During Gluten-Free Diet in Pediatric Coeliac Disease.","authors":"Aleksandra Zięba, Kamil Drożdż, Agnieszka Krawczyk","doi":"10.33073/pjm-2026-017","DOIUrl":"10.33073/pjm-2026-017","url":null,"abstract":"<p><p>Alterations in gut microbiota have been reported in coeliac disease (CeD). However, longitudinal evidence distinguishing disease-related effects from diet-driven changes after gluten-free diet initiation remains scarce, particularly in pediatric cohorts. The present study aimed to evaluate time-dependent changes in selected intestinal microorganisms in children with CeD before and during adherence to a gluten-free diet and to compare these findings with those of healthy controls. Microbial DNA isolates from stool samples of pediatric patients with CeD (n = 24) and healthy children (n = 24) were analyzed by quantitative real-time PCR. Samples from CeD patients were categorized into four time points: pre-diet, 6-month, 1-year, and 2-year follow-up. The healthy controls were assessed once. The prevalence and microbial load of selected microorganisms were evaluated. <i>Bifidobacterium</i> spp. remained highly prevalent across all time points, although their abundance varied significantly over follow-up, with the lowest levels at one year and higher levels at two years, comparable to those in controls. The prevalence of <i>Candida tropicalis</i> increased significantly during dietary treatment, reaching levels similar to those of healthy children after two years. <i>Saccharomyces cerevisiae</i> showed a gradual rise in prevalence and abundance, whereas <i>Methanobrevibacter smithii</i> remained infrequent with low and fluctuating microbial load. The duration of adherence to a gluten-free diet appears to influence gut microbial profiles in pediatric CeD. The selected gut microbiota microorganisms' assessment may serve as a complementary tool for understanding intestinal adaptation during dietary treatment, however, its routine clinical application requires further validation.</p>","PeriodicalId":94173,"journal":{"name":"Polish journal of microbiology","volume":" ","pages":"195-209"},"PeriodicalIF":1.6,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148355199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}