mSystems最新文献

{"title":"Association of body index with fecal microbiome in children cohorts with ethnic-geographic factor interaction: accurately using a Bayesian zero-inflated negative binomial regression model.","authors":"Jian Huang, Yanzhuan Lu, Fengwei Tian, Yongqing Ni","doi":"10.1128/msystems.01345-24","DOIUrl":"https://doi.org/10.1128/msystems.01345-24","url":null,"abstract":"<p><p>The exponential growth of high-throughput sequencing (HTS) data on the microbial communities presents researchers with an unparalleled opportunity to delve deeper into the association of microorganisms with host phenotype. However, this growth also poses a challenge, as microbial data are complex, sparse, discrete, and prone to zero inflation. Herein, by utilizing 10 distinct counting models for analyzing simulated data, we proposed an innovative Bayesian zero-inflated negative binomial (ZINB) regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa. Our proposed model exhibits excellent accuracy compared with conventional Hurdle and INLA models, especially in scenarios characterized by inflation and overdispersion. Moreover, we confirm that dispersion parameters significantly affect the accuracy of model results, with defects gradually alleviating as the number of analyzed samples increases. Subsequently applying our model to amplicon data in real multi-ethnic children cohort, we found that only a subset of taxa were identified as having zero inflation in real data, suggesting that the prevailing understanding and processing of microbial count data in most previous microbiome studies were overly dogmatic. In practice, our pipeline of integrating bacterial differential abundance in microbiome data and relevant covariates is effective and feasible. Taken together, our method is expected to be extended to the microbiota studies of various multi-cohort populations.</p><p><strong>Importance: </strong>The microbiome is closely associated with physical indicators of the body, such as height, weight, age and BMI, which can be used as measures of human health. Accurately identifying which taxa in the microbiome are closely related to indicators of physical development is valuable as microbial markers of regional child growth trajectory. Zero-inflated negative binomial (ZINB) model, a type of Bayesian generalized linear model, can be effectively modeled in complex biological systems. We present an innovative ZINB regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa, and demonstrate that its accuracy is superior to traditional Hurdle and INLA models. Our pipeline of integrating bacterial differential abundance in microbiome data and relevant covariates is effective and feasible.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0134524"},"PeriodicalIF":5.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cigarette smoke-induced disordered microbiota aggravates the severity of influenza A virus infection.","authors":"Tsering Wüthrich, Simone de Brot, Veronica Richina, Nadja Mostacci, Zora Baumann, Nathan G F Leborgne, Aurélie Godel, Marco P Alves, Mohamed Bentires-Alj, Charaf Benarafa, Markus Hilty","doi":"10.1128/msystems.00790-24","DOIUrl":"https://doi.org/10.1128/msystems.00790-24","url":null,"abstract":"<p><p>Cigarette smoke (CS) promotes the development of chronic pulmonary disease and has been associated with increased risk for influenza-related illness. Here, we directly addressed the impact of CS disordered microbiota on the severity of influenza A virus (IAV) infection. Specific and opportunistic pathogen-free (SOPF) C57BL/6J mice were exposed to CS or room air (RA) for 5.5 months. Each exposed mouse was then cohoused with a group of recipient germ-free (GF) mice for 1 month for microbial transfer. Colonized GF mice were then infected intranasally with IAV and disease development was monitored. Upper and lower airway and fecal microbiota were longitudinally investigated by 16S rRNA gene sequencing and bacterial cultures in donor and recipient mice. The bacterial family <i>Streptococcaceae</i> accounted for the largest difference between CS- and RA-exposed microbiota in the oropharynx. Analysis of the oropharynx and fecal microbiota indicated an efficient transfer to coprophagic recipient mice, which replicated the differences in microbiota composition observed in donor mice. Subsequent IAV infection revealed significantly higher weight loss for CS microbiota recipient mice at 8-10 days post infection (dpi) compared to control recipient mice. In addition, H1N1 infection inflicted substantial changes in the microbiota composition, especially at days 4 and 8 after infection. In conclusion, mice with a CS-associated microbiota suffer from higher disease severity upon IAV infection compared to mice colonized with a normal SOPF microbiota. Our data suggest that independently of CS exposure and concomitant structural lung damage, microbial distortion due to CS exposure may impact the severity of IAV disease course.IMPORTANCEIt has been reported that chronic exposure to CS is associated with a disordered microbiota composition. In this study, we colonized germ-free (GF) mice with the microbiota from SOPF mice which were chronically exposed to CS or RA. This allowed disentangling the effect of the disordered microbiota from the immune-modulating effects of actual CS exposure. We observed a successful transfer of the microbiotas after cohousing including specific microbiota differences induced by CS exposure in formerly GF mice, which were never exposed to CS. We then investigated the effects of IAV infection on the disease course and microbiotas of formerly GF mice. We found that mice with CS-associated microbiota reveal worse disease course compared to the control group. We hypothesize that CS-induced disordering of the microbiota may, indeed, impact the severity of influenza A disease.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0079024"},"PeriodicalIF":5.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.","authors":"Haohong Zhang, Xinghao Xiong, Mingyue Cheng, Lei Ji, Kang Ning","doi":"10.1128/msystems.01395-24","DOIUrl":"https://doi.org/10.1128/msystems.01395-24","url":null,"abstract":"<p><p>The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiome and host gene expressions in tumor microenvironment, as well as their concert effects on patient survival. In this study, we aimed to decode this complex relationship by developing ASD-cancer (autoencoder-based subtypes detector for cancer), a semi-supervised deep learning framework that could extract survival-related features from tumor microbiome and transcriptome data, and identify patients' survival subtypes. By using tissue samples from The Cancer Genome Atlas database, we identified two statistically distinct survival subtypes across all 20 types of cancer Our framework provided improved risk stratification (e.g., for liver hepatocellular carcinoma, [LIHC], log-rank test, <i>P</i> = 8.12E-6) compared to PCA (e.g., for LIHC, log-rank test, <i>P</i> = 0.87), predicted survival subtypes accurately, and identified biomarkers for survival subtypes. Additionally, we identified potential interactions between microbes and host genes that may play roles in survival. For instance, in LIHC, <i>Arcobacter</i>, <i>Methylocella</i>, and <i>Isoptericola</i> may regulate host survival through interactions with host genes enriched in the HIF-1 signaling pathway, indicating these species as potential therapy targets. Further experiments on validation data sets have also supported these patterns. Collectively, ASD-cancer has enabled accurate survival subtyping and biomarker discovery, which could facilitate personalized treatment for broad-spectrum types of cancers.IMPORTANCEUnraveling the intricate relationship between the tumor microbiome, host gene expressions, and their collective impact on cancer outcomes is paramount for advancing personalized treatment strategies. Our study introduces ASD-cancer, a cutting-edge autoencoder-based subtype detector. ASD-cancer decodes the complexities within the tumor microenvironment, successfully identifying distinct survival subtypes across 20 cancer types. Its superior risk stratification, demonstrated by significant improvements over traditional methods like principal component analysis, holds promise for refining patient prognosis. Accurate survival subtype predictions, biomarker discovery, and insights into microbe-host gene interactions elevate ASD-cancer as a powerful tool for advancing precision medicine. These findings not only contribute to a deeper understanding of the tumor microenvironment but also open avenues for personalized interventions across diverse cancer types, underscoring the transformative potential of ASD-cancer in shaping the future of cancer care.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0139524"},"PeriodicalIF":5.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alterations in purine and pyrimidine metabolism associated with latent tuberculosis infection: insights from gut microbiome and metabolomics analyses.","authors":"Boyi Yang, Xiaojing Guo, Chongyu Shi, Gang Liu, Xiaoling Qin, Shiyi Chen, Li Gan, Dongxu Liang, Kai Shao, Ruolan Xu, Jieqing Zhong, Yujie Mo, Hai Li, Dan Luo","doi":"10.1128/msystems.00812-24","DOIUrl":"10.1128/msystems.00812-24","url":null,"abstract":"<p><p>Individuals with latent tuberculosis infection (LTBI) account for almost 30% of the population worldwide and have the potential to develop active tuberculosis (ATB). Despite this, the current understanding of the pathogenesis of LTBI is limited. The gut microbiome can be altered in tuberculosis patients, and an understanding of the changes associated with the progression from good health to LTBI to ATB can provide novel perspectives for understanding the pathogenesis of LTBI by identifying microbial and molecular biomarkers associated therewith. In this study, fecal samples from healthy controls (HC), individuals with LTBI and ATB patients were collected for gut microbiome and metabolomics analyses. Compared to HC and LTBI subjects, participants with ATB showed a significant decrease in gut bacterial α-diversity. Additionally, there were significant differences in gut microbial communities and metabolism among the HC, LTBI, and ATB groups. PICRUSt2 analysis revealed that microbiota metabolic pathways involving the degradation of purine and pyrimidine metabolites were upregulated in LTBI and ATB individuals relative to HCs. Metabolomic profiling similarly revealed that purine and pyrimidine metabolite levels were decreased in LTBI and ATB samples relative to those from HCs. Further correlation analyses revealed that the levels of purine and pyrimidine metabolites were negatively correlated with those of gut microbial genera represented by <i>Ruminococcus_gnavus_group</i> (<i>R. gnavus</i>), and the levels of <i>R. gnavus</i> were also positively correlated with adenosine nucleotide degradation II, which is a purine degradation pathway. Moreover, a combined signature including hypoxanthine and xanthine was found to effectively distinguish between LTBI and HC samples (area under the curve [AUC] of training set = 0.796; AUC of testing set = 0.924). Therefore, through gut microbiome and metabolomic analyses, these findings provide valuable clues regarding how alterations in gut purine and pyrimidine metabolism are linked to the pathogenesis of LTBI.IMPORTANCEThis study provides valuable insight into alterations in the gut microbiome and metabolomic profiles in a cohort of adults with LTBI and ATB. Perturbed gut purine and pyrimidine metabolism in LTBI was associated with the compositional alterations of gut microbiota, which may be an impetus for developing novel diagnostic strategies and interventions targeting LTBI.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0081224"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analysis of metabolome and microbiome in a rat model of perimenopausal syndrome.","authors":"Yanqiu Wei, Juanjuan Shi, Jianhua Wang, Zongyan Hu, Min Wang, Wen Wang, Xiujuan Cui","doi":"10.1128/msystems.00623-24","DOIUrl":"10.1128/msystems.00623-24","url":null,"abstract":"<p><p>The objectives of this study are to examine the disparities in serum and intestinal tissue metabolites between a perimenopausal rat model and control rats and to analyze the diversity and functionality of intestinal microorganisms to determine the potential correlation between intestinal flora and metabolites. We established a rat model of perimenopausal syndrome (PMS) and performed an integrated analysis of metabolome and microbiome. Orthogonal partial least-squares discriminant analysis scores and replacement tests indicated distinct separations of anion and cation levels between serum and intestinal samples of the model and control groups. Furthermore, lipids and lipid-like molecules constituted the largest percentage of HMDB compounds in both serum and intestinal tissues, followed by organic acids and derivatives, and organoheterocyclic compounds, with other compounds showing significant variability. Moreover, analysis of diversity and functional enrichment of the intestinal microflora and correlation analysis with metabolites revealed significant variability in the composition of the intestinal flora between the normal control and perimenopausal groups, with these differentially expressed intestinal flora strongly correlated with their metabolites. The findings of this study are expected to contribute to understanding the indications and contraindications for estrogen application in perimenopausal women and to aid in the development of appropriate therapeutic agents.</p><p><strong>Importance: </strong>In this work, we employed 16S ribosomal RNA gene sequencing to analyze the gut microbes in stool samples. In addition, we conducted an ultra-high-performance liquid chromatography-tandem mass spectrometry-based metabolomics approach on gut tissue and serum obtained from rats with perimenopausal syndrome (PMS) and healthy controls. By characterizing the composition and metabolomic properties of gut microbes in PMS rats, we aim to enhance our understanding of their role in women's health, emphasizing the significance of regulating gut microbes in the context of menopausal women's well-being. We aim to provide a theoretical basis for the prevention and treatment of PMS in terms of gut microflora as well as metabolism.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0062324"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The choice of 16S rRNA gene sequence analysis impacted characterization of highly variable surface microbiota in dairy processing environments.","authors":"Sarah E Daly, Jingzhang Feng, Devin Daeschel, Jasna Kovac, Abigail B Snyder","doi":"10.1128/msystems.00620-24","DOIUrl":"10.1128/msystems.00620-24","url":null,"abstract":"<p><p>Accurate knowledge of the microbiota collected from surfaces in food processing environments is important for food quality and safety. This study assessed discrepancies in taxonomic composition and alpha and beta diversity values generated from eight different bioinformatic workflows for the analysis of 16S rRNA gene sequences extracted from the microbiota collected from surfaces in dairy processing environments. We found that the microbiota collected from environmental surfaces varied widely in density (0-9.09 log₁₀ CFU/cm²) and Shannon alpha diversity (0.01-3.40). Consequently, depending on the sequence analysis method used, characterization of low-abundance genera (i.e., below 1% relative abundance) and the number of genera identified (114-173 genera) varied considerably. Some low-abundance genera, including <i>Listeria</i>, varied between the amplicon sequence variant (ASV) and operational taxonomic unit (OTU) methods. Centered log-ratio transformation inflated alpha and beta diversity values compared to rarefaction. Furthermore, the ASV method also inflated alpha and beta diversity values compared to the OTU method (<i>P</i> < 0.05). Therefore, for sparse, uneven, low-density data sets, the OTU method and rarefaction are better for taxonomic and ecological characterization of surface microbiota.IMPORTANCECulture-dependent environmental monitoring programs are used by the food industry to identify foodborne pathogens and spoilage biota on surfaces in food processing environments. The use of culture-independent 16S rRNA amplicon sequencing to characterize this surface microbiota has been proposed as a tool to enhance environmental monitoring. However, there is no consensus on the most suitable bioinformatic analyses to accurately capture the diverse levels and types of bacteria on surfaces in food processing environments. Here, we quantify the impact of different bioinformatic analyses on the results and interpretation of 16S rRNA amplicon sequences collected from three cultured dairy facilities in New York State. This study provides guidance for the selection of appropriate 16S rRNA analysis procedures for studying environmental microbiota in dairy processing environments.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0062024"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stable, multigenerational transmission of the bean seed microbiome despite abiotic stress.","authors":"Abby Sulesky-Grieb, Marie Simonin, A Fina Bintarti, Brice Marolleau, Matthieu Barret, Ashley Shade","doi":"10.1128/msystems.00951-24","DOIUrl":"10.1128/msystems.00951-24","url":null,"abstract":"<p><p>Microbiota that originate in the seed can have consequences for the education of the plant immune system, competitive exclusion of pathogens from the host tissue, and host access to critical nutrients. Our research objective was to investigate the consequences of the environmental conditions of the parent plant for bacterial seed microbiome assembly and transmission across plant generations. Using a fully factorial, three-generational experimental design, we investigated endophytic seed bacterial communities of common bean lines (<i>Phaseolus vulgaris</i> L.) grown in the growth chamber and exposed to either control conditions, drought, or excess nutrients at each generation. We applied 16S rRNA microbiome profiling to the seed endophytes and measured plant health outcomes. We discovered stable transmission of 22 bacterial members, regardless of the parental plant condition. This study shows the maintenance of bacterial members of the plant microbiome across generations, even under environmental stress. Overall, this work provides insights into the ability of plants to safeguard microbiome members, which has implications for crop microbiome management in the face of climate change.IMPORTANCESeed microbiomes initiate plant microbiome assembly and thus have critical implications for the healthy development and performance of crops. However, the consequences of environmental conditions of the parent plant for seed microbiome assembly and transmission are unknown, but this is critical information, given the intensifying stressors that crops face as the climate crisis accelerates. This study provides insights into the maintenance of plant microbiomes across generations, with implications for durable plant microbiome maintenance in agriculture on the changing planet.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0095124"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction for Kwan et al., \"Gut phageome in Mexican Americans: a population at high risk for metabolic dysfunction-associated steatotic liver disease and diabetes\".","authors":"Suet-Ying Kwan, Caroline M Sabotta, Lorenzo R Cruz, Matthew C Wong, Nadim J Ajami, Joseph B McCormick, Susan P Fisher-Hoch, Laura Beretta","doi":"10.1128/msystems.01297-24","DOIUrl":"10.1128/msystems.01297-24","url":null,"abstract":"","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0129724"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.","authors":"Kristen L Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda","doi":"10.1128/msystems.00840-24","DOIUrl":"10.1128/msystems.00840-24","url":null,"abstract":"<p><p>The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional scaling, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16S rDNA sequencing data sets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stages, and seasons. Our results indicate that the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality.</p><p><strong>Importance: </strong>We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0084024"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E.PathDash, pathway activation analysis of publicly available pathogen gene expression data.","authors":"Lily Taub, Thomas H Hampton, Sharanya Sarkar, Georgia Doing, Samuel L Neff, Carson E Finger, Kiyoshi Ferreira Fukutani, Bruce A Stanton","doi":"10.1128/msystems.01030-24","DOIUrl":"10.1128/msystems.01030-24","url":null,"abstract":"<p><p>E.PathDash facilitates re-analysis of gene expression data from pathogens clinically relevant to chronic respiratory diseases, including a total of 48 studies, 548 samples, and 404 unique treatment comparisons. The application enables users to assess broad biological stress responses at the KEGG pathway or gene ontology level and also provides data for individual genes. E.PathDash reduces the time required to gain access to data from multiple hours per data set to seconds. Users can download high-quality images such as volcano plots and boxplots, differential gene expression results, and raw count data, making it fully interoperable with other tools. Importantly, users can rapidly toggle between experimental comparisons and different studies of the same phenomenon, enabling them to judge the extent to which observed responses are reproducible. As a proof of principle, we invited two cystic fibrosis scientists to use the application to explore scientific questions relevant to their specific research areas. Reassuringly, pathway activation analysis recapitulated results reported in original publications, but it also yielded new insights into pathogen responses to changes in their environments, validating the utility of the application. All software and data are freely accessible, and the application is available at scangeo.dartmouth.edu/EPathDash.</p><p><strong>Importance: </strong>Chronic respiratory illnesses impose a high disease burden on our communities and people with respiratory diseases are susceptible to robust bacterial infections from pathogens, including <i>Pseudomonas aeruginosa</i> and <i>Staphylococcus aureus</i>, that contribute to morbidity and mortality. Public gene expression datasets generated from these and other pathogens are abundantly available and an important resource for synthesizing existing pathogenic research, leading to interventions that improve patient outcomes. However, it can take many hours or weeks to render publicly available datasets usable; significant time and skills are needed to clean, standardize, and apply reproducible and robust bioinformatic pipelines to the data. Through collaboration with two microbiologists, we have shown that E.PathDash addresses this problem, enabling them to elucidate pathogen responses to a variety of over 400 experimental conditions and generate mechanistic hypotheses for cell-level behavior in response to disease-relevant exposures, all in a fraction of the time.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0103024"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}