Microbiome最新文献

{"title":"Challenges in capturing the mycobiome from shotgun metagenome data: lack of software and databases.","authors":"Ekaterina Avershina, Arfa Irej Qureshi, Hanne C Winther-Larsen, Trine B Rounge","doi":"10.1186/s40168-025-02048-3","DOIUrl":"10.1186/s40168-025-02048-3","url":null,"abstract":"<p><strong>Background: </strong>The mycobiome, representing the fungal component of microbial communities, is increasingly acknowledged as an integral part of the gut microbiome. However, research in this area remains relatively limited. The characterization of mycobiome taxa from metagenomic data is heavily reliant on the quality of the software and databases. In this study, we evaluated the feasibility of mycobiome profiling using existing bioinformatics tools on simulated fungal metagenomic data.</p><p><strong>Results: </strong>We identified seven tools claiming to perform taxonomic assignment of fungal shotgun metagenomic sequences. One of these was outdated and required substantial modifications of the code to be functional and was thus excluded. To evaluate the accuracy of identification and relative abundance of the remaining tools (Kraken2, MetaPhlAn4, EukDetect, FunOMIC, MiCoP, and HumanMycobiomeScan), we constructed 18 mock communities of varying species richness and abundance levels. The mock communities comprised up to 165 fungal species belonging to the phyla Ascomycota and Basidiomycota, commonly found in gut microbiomes. Of the tools, FunOMIC and HumanMycobiomeScan needed source code modifications to run. Notably, only one species, Candida orthopsilosis, was consistently identified by all tools across all communities where it was included. Increasing community richness improved precision of Kraken2 and the relative abundance accuracy of all tools on species, genus, and family levels. MetaPhlAn4 accurately identified all genera present in the communities and FunOMIC identified most species. The top three tools for overall accuracy in both identification and relative abundance estimation were EukDetect, MiCoP, and FunOMIC, respectively. Adding 90% and 99% bacterial background did not significantly impact these tools' performance. Among the whole genome reference tools (Kraken2, HMS, and MiCoP), MiCoP exhibited the highest accuracy when the same reference database was used.</p><p><strong>Conclusion: </strong>Our survey of mycobiome-specific software revealed a very limited selection of such tools and their poor robustness due to error-prone software, along with a significant lack of comprehensive databases enabling characterization of the mycobiome. None of the implemented tools fully agreed on the mock community profiles. FunOMIC recognized most of the species, but EukDetect and MiCoP provided predictions that were closest to the correct compositions. The bacterial background did not impact these tools' performance. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"66"},"PeriodicalIF":13.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.","authors":"Ella Rannon, Sagi Shaashua, David Burstein","doi":"10.1186/s40168-025-02055-4","DOIUrl":"10.1186/s40168-025-02055-4","url":null,"abstract":"<p><strong>Background: </strong>Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefits of antibiotics. Therefore, early detection of antimicrobial resistance genes (ARGs), especially in pathogens, is crucial for human health. Most computational methods for ARG detection rely on homology to a predefined gene database and therefore are limited in their ability to discover novel genes.</p><p><strong>Results: </strong>We introduce DRAMMA, a machine learning method for predicting new ARGs with no sequence similarity to known ARGs or any annotated gene. DRAMMA utilizes various features, including protein properties, genomic context, and evolutionary patterns. The model demonstrated robust predictive performance both in cross-validation and an external validation set annotated by an empirical ARG database. Analyses of the high-ranking model-generated candidates revealed a significant enrichment of candidates within the Bacteroidetes/Chlorobi and Betaproteobacteria taxonomic groups.</p><p><strong>Conclusions: </strong>DRAMMA enables rapid ARG identification for global-scale genomic and metagenomic samples, thus holding promise for the discovery of novel ARGs that lack sequence similarity to any known resistance genes. Further, our model has the potential to facilitate early detection of specific ARGs, potentially influencing the selection of antibiotics administered to patients. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"67"},"PeriodicalIF":13.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Absolute quantification of the living skin microbiome overcomes relic-DNA bias and reveals specific patterns across volunteers.","authors":"Deepan Thiruppathy, Oriane Moyne, Clarisse Marotz, Michael Williams, Perris Navarro, Livia Zaramela, Karsten Zengler","doi":"10.1186/s40168-025-02063-4","DOIUrl":"10.1186/s40168-025-02063-4","url":null,"abstract":"<p><strong>Background: </strong>As the first line of defense against external pathogens, the skin and its resident microbiota are responsible for protection and eubiosis. Innovations in DNA sequencing have significantly increased our knowledge of the skin microbiome. However, current characterizations do not discriminate between DNA from live cells and remnant DNA from dead organisms (relic DNA), resulting in a combined readout of all microorganisms that were and are currently present on the skin rather than the actual living population of the microbiome. Additionally, most methods lack the capability for absolute quantification of the microbial load on the skin, complicating the extrapolation of clinically relevant information.</p><p><strong>Results: </strong>Here, we integrated relic-DNA depletion with shotgun metagenomics and bacterial load determination to quantify live bacterial cell abundances across different skin sites. Though we discovered up to 90% of microbial DNA from the skin to be relic DNA, we saw no significant effect of this on the relative abundances of taxa determined by shotgun sequencing. Relic-DNA depletion prior to sequencing strengthened underlying patterns between microbiomes across volunteers and reduced intraindividual similarity. We determined the absolute abundance and the fraction of population alive for several common skin taxa across body sites and found taxa-specific differential abundance of live bacteria across regions to be different from estimates generated by total DNA (live + dead) sequencing.</p><p><strong>Conclusions: </strong>Our results reveal the significant bias relic DNA has on the quantification of low biomass samples like the skin. The reduced intraindividual similarity across samples following relic-DNA depletion highlights the bias introduced by traditional (total DNA) sequencing in diversity comparisons across samples. The divergent levels of cell viability measured across different skin sites, along with the inconsistencies in taxa differential abundance determined by total vs live cell DNA sequencing, suggest an important hypothesis for certain sites being susceptible to pathogen infection. Overall, our study demonstrates a characterization of the skin microbiome that overcomes relic-DNA bias to provide a baseline for live microbiota that will further improve mechanistic studies of infection, disease progression, and the design of therapies for the skin. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"65"},"PeriodicalIF":13.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virome landscape of wild rodents and shrews in Central China.","authors":"Nailou Zhang, Bing Hu, Li Zhang, Min Gan, Qingwen Ding, Kai Pan, Jinbo Wei, Wen Xu, Dan Chen, Shaolong Zheng, Kun Cai, Zhenhua Zheng","doi":"10.1186/s40168-025-02059-0","DOIUrl":"10.1186/s40168-025-02059-0","url":null,"abstract":"<p><strong>Background: </strong>Wild rodents and shrews serve as vital sentinel species for monitoring zoonotic viruses due to their close interaction with human environments and role as natural reservoirs for diverse viral pathogens. Although several studies have explored viral diversity and assessed pathogenic risks in wild rodents and shrews, the full extent of this diversity remains insufficiently understood.</p><p><strong>Results: </strong>We conducted high-throughput sequencing on 1113 small mammals collected from 97 townships across seven cities in Hubei Province during 2021, supplemented by publicly available data from 2014 and 2016-2017. This analysis revealed a diverse array of novel viruses spanning several viral families, including Arenaviridae, Hepeviridae, Chuviridae, Paramyxoviridae, Arteriviridae, Nodaviridae, Rhabdoviridae, Dicistroviridae, Astroviridae, and Picornaviridae. Phylogenetic analysis and genome structure characterization highlighted the discovery of these novel viruses, enhancing our understanding of viral diversity and evolution. Key host species such as Chodsigoa smithii, Anourosorex squamipes, Niviventer niviventer, and Apodemus agrarius were identified as significant contributors to viral circulation, making them crucial targets for future surveillance. Additionally, the central Plain of Hubei Province was recognized as a critical geographic hub for viral transmission, underscoring its importance in monitoring and controlling viral spread. Machine learning models were employed to assess the zoonotic potential of the identified viruses, revealing that families such as Arenaviridae, Coronaviridae, Hantaviridae, Arteriviridae, Astroviridae, Hepeviridae, Lispiviridae, Nairoviridae, Nodaviridae, Paramyxoviridae, Rhabdoviridae, Picornaviridae, and Picobirnaviridae possess a high likelihood of infecting humans. Notably, rodent-derived Rotavirus A, HTNV, and SEOV displayed almost complete amino acid identity with their human-derived counterparts, indicating a significant risk for human outbreaks.</p><p><strong>Conclusion: </strong>This study provides a comprehensive virome landscape for wild rodents and shrews in Central China, highlighting novel viruses and the critical roles of specific host species and regions in viral transmission. By identifying key species and hotspots for viral spread and assessing the zoonotic potential of the discovered viruses, this research enhances our understanding of virus ecology and the factors driving zoonotic disease emergence. The findings emphasize the need for targeted surveillance and proactive strategies to mitigate the risks of zoonotic spillovers, contributing to global public health preparedness. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"63"},"PeriodicalIF":13.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial network inference for longitudinal microbiome studies with LUPINE.","authors":"Saritha Kodikara, Kim-Anh Lê Cao","doi":"10.1186/s40168-025-02041-w","DOIUrl":"10.1186/s40168-025-02041-w","url":null,"abstract":"<p><strong>Background: </strong>The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting.</p><p><strong>Results: </strong>We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE's ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks.</p><p><strong>Conclusions: </strong>LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"64"},"PeriodicalIF":13.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens.","authors":"Fangren Lan, Xiqiong Wang, Qianqian Zhou, Xiaochang Li, Jiaming Jin, Wenxin Zhang, Chaoliang Wen, Guiqin Wu, Guangqi Li, Yiyuan Yan, Ning Yang, Congjiao Sun","doi":"10.1186/s40168-025-02054-5","DOIUrl":"10.1186/s40168-025-02054-5","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The complex interactions between host genetics and the gut microbiome are well documented. However, the specific impacts of gene expression patterns and microbial composition on each other remain to be further explored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Here, we investigated this complex interplay in a sizable population of 705 hens, employing integrative analyses to examine the relationships among the host genome, mucosal gene expression, and gut microbiota. Specific microbial taxa, such as the cecal family Christensenellaceae, which showed a heritability of 0.365, were strongly correlated with host genomic variants. We proposed a novel concept of regulatability ( &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; ), which was derived from h&lt;sup&gt;2&lt;/sup&gt;, to quantify the cumulative effects of gene expression on the given phenotypes. The duodenal mucosal transcriptome emerged as a potent influencer of duodenal microbial taxa, with much higher &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; values (0.17 ± 0.01, mean ± SE) than h&lt;sup&gt;2&lt;/sup&gt; values (0.02 ± 0.00). A comparative analysis of chickens and humans revealed similar average microbiability values of genes (0.18 vs. 0.20) and significant differences in average &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; values of microbes (0.17 vs. 0.04). Besides, cis ( &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;h&lt;/mi&gt; &lt;mrow&gt;&lt;mtext&gt;cis&lt;/mtext&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; ) and trans heritability ( &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;h&lt;/mi&gt; &lt;mrow&gt;&lt;mtext&gt;trans&lt;/mtext&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; ) were estimated to assess the effects of genetic variations inside and outside the cis window of the gene on its expression. Higher &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;h&lt;/mi&gt; &lt;mrow&gt;&lt;mtext&gt;trans&lt;/mtext&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; values than &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;h&lt;/mi&gt; &lt;mrow&gt;&lt;mtext&gt;cis&lt;/mtext&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; values and a greater prevalence of trans-regulated genes than cis-regulated genes underscored the significant role of loci outside the cis window in shaping gene expression levels. Furthermore, our exploration of the regulatory effects of duodenal mucosal genes and the microbiota on 18 complex traits enhanced our understanding of the regulatory mechanisms, in which the CHST14 gene and its regulatory relationships with Lactobacillus salivarius jointly facilitated the deposition of abdominal fat by modulating the concentration of bile salt hydrolase, and further triglycerides, total cholesterol, and free fatty acids absorption and metabolism.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings highlighted a novel concept of &lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msubsup&gt; &lt;/math&gt; to quantify the phenotypic variance attributed to gene expression and emphasize the superior role of intestinal mucosal gene expressions over host genomic variations in elucidating host‒microbe interactions for comp","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"62"},"PeriodicalIF":13.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11871680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roux-en-Y gastric bypass-associated fecal tyramine promotes colon cancer risk via increased DNA damage, cell proliferation, and inflammation.","authors":"Maria Glymenaki, Sophie Curio, Smeeta Shrestha, Qi Zhong, Laura Rushton, Rachael Barry, Mona El-Bahrawy, Julian R Marchesi, Yulan Wang, Nigel J Gooderham, Nadia Guerra, Jia V Li","doi":"10.1186/s40168-025-02049-2","DOIUrl":"10.1186/s40168-025-02049-2","url":null,"abstract":"<p><strong>Background: </strong>Fecal abundances of Enterobacteriaceae and Enterococcaceae are elevated in patients following Roux-en-Y gastric bypass (RYGB) surgery. Concurrently, fecal concentrations of tyramine, derived from gut bacterial metabolism of tyrosine and/or food, increased post-RYGB. Furthermore, emerging evidence suggests that RYGB is associated with increased colorectal cancer (CRC) risk. However, the causal link between RYGB-associated microbial metabolites and CRC risk remains unclear. Hence, this study investigated the tyrosine metabolism of Enterobacteriaceae and Enterococcaceae strains isolated from patients post-RYGB and explored the causal effects of tyramine on the CRC risk and tumorigenesis using both human colonic cancer cell line (HCT 116) and wild-type and Apc^Min/+ mice.</p><p><strong>Results: </strong>We isolated 31 bacterial isolates belonging to Enterobacteriaceae and Enterococcaceae families from the feces of patients with RYGB surgery. By culturing the isolates in tyrosine-supplemented medium, we found that Citrobacter produced phenol as a main product of tyrosine, whereas Enterobacter and Klebsiella produced 4-hydroxyphenylacetate, Escherichia produced 4-hydroxyphenyllactate and 4-hydroxyphenylpyruvate, and Enterococcus and two Klebsiella isolates produced tyramine. These observations suggested the gut bacterial contribution to increased fecal concentrations of tyramine post-RYGB. We subsequently evaluated the impact of tyramine on CRC risk and development. Tyramine induced necrosis and promoted cell proliferation and DNA damage of HCT 116 cells. Daily oral administration of tyramine for 49 days to wild-type mice resulted in visible adenomas in 5 out of 12 mice, accompanied by significantly enhanced DNA damage (γH2AX +) and an increased trend of cell proliferation (Ki67 +) in the ileum, along with an upregulated expression of the cell division cycle gene (Cdc34b) in the colon. To evaluate the impact of tyramine on intestinal tumor growth, we treated Apc^Min/+ mice with the same doses of tyramine and duration. These mice showed larger colonic tumor size and increased intestinal cell proliferation and inflammation (e.g., increased mRNA expression of IL-17A and higher number of Ly6G + neutrophils) compared to water-treated Apc^Min/+ control mice.</p><p><strong>Conclusions: </strong>Our results collectively suggested that RYGB-associated fecal bacteria could contribute to tyramine production and tyramine increased CRC risk by increasing DNA damage, cell proliferation, and pro-inflammatory responses of the gut. Monitoring and modulating tyramine concentrations in high-risk individuals could aid CRC prognosis and management. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"60"},"PeriodicalIF":13.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An in vitro model demonstrating homeostatic interactions between reconstructed human gingiva and a saliva-derived multispecies biofilm.","authors":"Lin Shang, Sanne Roffel, Vera Slomka, Eleanor M D'Agostino, Aline Metris, Mark J Buijs, Bernd W Brandt, Dongmei Deng, Susan Gibbs, Bastiaan P Krom","doi":"10.1186/s40168-025-02033-w","DOIUrl":"10.1186/s40168-025-02033-w","url":null,"abstract":"<p><strong>Background: </strong>In the oral cavity, host-microbe interactions (HMI) continuously occur and greatly impact oral health. In contrast to the well-studied disease-associated HMI during, for example, periodontitis, HMI that are essential in maintaining oral health have been rarely investigated, especially in a human-relevant context. The aim of this study was to extensively characterize homeostatic HMI between saliva-derived biofilms and a reconstructed human gingiva (RHG). RHG was reconstructed following the structure of native gingiva, composed of a multilayered epithelium formed by keratinocytes and a fibroblast-populated compartment. To mimic the oral environment, RHG were inoculated with pooled human saliva resuspended in different saliva substitute media and incubated for 2 or 4 days. The co-cultured biofilms were retrieved and characterized by viable bacterial counting and compositional profiling (16S rRNA gene sequencing). RHG was investigated for metabolic activity (MTT assay), tissue histology (hematoxylin and eosin staining), epithelial proliferation (Ki67 staining), antimicrobial peptide expression, and cytokine secretion.</p><p><strong>Results: </strong>Viable biofilms were detected up to day 4 of co-culturing. Bacterial counts indicated biofilm growth from the inoculation to day 2 and maintained thereafter at a similar level until day 4. All biofilms shared similar composition throughout 4 days, independent of co-culture time and different saliva substitute media used during inoculation. Biofilms were diverse with Streptococcus, Haemophilus, and Neisseria being the dominating genera. While supporting biofilm development, RHG displayed no significant changes in metabolic activity, tissue histology, or epithelial proliferation. However, in the presence of biofilms, the antimicrobial peptides elafin and human β-defensin-2 were upregulated, and the secretion of cytokines IL-6, CXCL1, CXCL8, CCL5, and CCL20 increased.</p><p><strong>Conclusion: </strong>This model mimicked homeostatic HMI where a healthy gingiva supported a viable, diverse, and stable microbial community, incorporating bacterial genera found on native gingiva. The gingiva model maintained its tissue integrity and exerted protective responses in the presence of biofilms over time. This study adds to the evidence that shows the important role of the host in maintaining homeostatic HMI that are essential for oral health. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"58"},"PeriodicalIF":13.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virome specific to tick genus with distinct ecogeographical distribution.","authors":"Di Tian, Run-Ze Ye, Yu-Yu Li, Ning Wang, Wan-Ying Gao, Bai-Hui Wang, Zhe-Tao Lin, Wen-Jie Zhu, Qiu-Shi Wang, Ya-Ting Liu, Hua Wei, Yi-Fei Wang, Yi Sun, Xiao-Yu Shi, Na Jia, Jia-Fu Jiang, Xiao-Ming Cui, Wu-Chun Cao, Zhi-Hong Liu","doi":"10.1186/s40168-025-02061-6","DOIUrl":"10.1186/s40168-025-02061-6","url":null,"abstract":"<p><strong>Background: </strong>The emergence of tick-borne pathogens poses a serious threat to both human and animal health. There remains controversy about virome diversity in relation to tick genus and ecogeographical factors.</p><p><strong>Results: </strong>We conducted the meta‑transcriptomic sequencing of 155 pools of ticks encompassing 7 species of 3 genera collected from diverse geographical fauna of Ningxia Province, China. Two species of Dermacentor genus were distributed in the predominantly grassland areas of the central and eastern regions, with the lowest viral diversity. Two species of Hyalomma ticks were found in the predominantly desert areas of the northern regions, with intermediate viral diversity. Three species of Haemaphysalis ticks were concentrated in the predominantly forested areas of the southern regions, exhibiting the highest viral diversity. We assembled 348 viral genomes of 63 species in 14 orders, including 26 novel viruses. The identified viruses were clearly specific to tick genus: 22 virus species were exclusive to Dermacentor, 12 to Hyalomma, and 27 to Haemaphysalis.</p><p><strong>Conclusions: </strong>The associations between tick genera and geographical distribution, viral richness, and composition provide new insights into tick-virus interactions, offering clues to identify high-risk regions for different tick-borne viruses. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"57"},"PeriodicalIF":13.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbiota-derived urolithin A in monoclonal gammopathies and multiple myeloma therapy.","authors":"Alba Rodríguez-García, Raquel Ancos-Pintado, Roberto García-Vicente, Alejandra Ortiz-Ruiz, Andrés Arroyo, Miguel Ángel Navarro, María Luz Morales, Patricia Guevara-Ramirez, Pablo Justo, Nieves López-Muñoz, José Sánchez-Pina, Rafael Alonso, María Victoria Selma, María Dolores Frutos-Lisón, Rocío García-Villalba, Francisco A Tomás-Barberán, Rosa Ayala, Joaquín Martínez-López, María Linares","doi":"10.1186/s40168-025-02045-6","DOIUrl":"10.1186/s40168-025-02045-6","url":null,"abstract":"<p><strong>Background: </strong>Gut microbiota-derived urolithins may influence multiple myeloma (MM) disease progression and treatment. We analyzed urolithins and their associated microbiota in a retrospective cohort of 45 patients with active MM or premalignant disease using mass spectrometry and 16S rRNA gene sequencing.</p><p><strong>Results: </strong>Patients with detectable levels of urolithin in serum and stool and a higher abundance of urolithin-related microbiota had a better outcome. Analysis of the effects of urolithin A (UroA) treatment ex vivo, in vitro, and in vivo revealed that UroA is cytotoxic against MM cell lines and modulates the cell cycle and mitochondrial activity. Notably, UroA inhibits the proliferation of primary MM cells in vitro and in a xenograft mouse model, improving overall survival. Finally, combination therapy with UroA and bortezomib has a synergistic effect in vitro, even in the presence of bortezomib resistance, and modulates signaling pathways involved in MM development.</p><p><strong>Conclusions: </strong>UroA might be a potential therapeutic agent to halt MM disease progression or to overcome resistance when used in combination. Video Abstract.</p>","PeriodicalId":18447,"journal":{"name":"Microbiome","volume":"13 1","pages":"56"},"PeriodicalIF":13.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}