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Propionibacterium freudenreichii (PFR) DSM 20271^T is a bacterium known for its ability to thrive in diverse environments and to produce vitamin B12. Despite its anaerobic preference, recent studies have elucidated its ability to prosper in the presence of oxygen, prompting a deeper exploration of its physiology under aerobic conditions. Here, we investigated the response of DSM 20271^T to aerobic growth by employing comparative transcriptomic and surfaceome analyses alongside metabolite profiling. Cultivation under controlled partial pressure of oxygen (pO₂) conditions revealed significant increases in biomass formation and altered metabolite production, notably of vitamin B12, pseudovitamin-B12, propionate, and acetate, under aerobic conditions. Transcriptomic analysis identified differential expression of genes involved in lactate metabolism, tricarboxylic acid cycle, and electron transport chain, suggesting metabolic adjustments to aerobic environments. Moreover, surfaceome analysis unveiled growth environment-dependent changes in surface protein abundance, with implications for adaptation to atmospheric conditions. Supplementation experiments with key compounds highlighted the potential for enhancing aerobic growth, emphasizing the importance of iron and α-ketoglutarate availability. Furthermore, in liquid culture, FeSO₄ supplementation led to increased heme production and reduced vitamin B12 production, highlighting the impact of oxygen and iron availability on the metabolic pathways. These findings deepen our understanding of PFR's physiological responses to oxygen availability and offer insights for optimizing its growth in industrial applications.

Importance: The study of the response of Propionibacterium freudenreichii to aerobic growth is crucial for understanding how this bacterium adapts to different environments and produces essential compounds like vitamin B12. By investigating its physiological changes under aerobic conditions, we can gain insights into its metabolic adjustments and potential for enhanced growth. These findings not only deepen our understanding of P. freudenreichii's responses to oxygen availability but also offer valuable information for optimizing its growth in industrial applications. This research sheds light on the adaptive mechanisms of this bacterium, providing a foundation for further exploration and potential applications in various fields.

Systemic inflammatory response syndrome (SIRS) is a severe inflammatory response that can lead to organ dysfunction and death. Modulating the gut microbiome is a promising therapeutic approach for managing SIRS. This study assesses the therapeutic potential of the Xuanfei Baidu (XFBD) formula in treating SIRS. The results showed that XFBD administration effectively reduced mortality rates and inflammation in SIRS mice. Using 16S rRNA sequencing and fecal microbiota transplantation (FMT), we substantiated that the therapeutic effects of XFBD are partly attributed to gut microbiota modulation. We conducted in vitro experiments to accurately assess the gut microbiome remodeling effects of 51 compounds isolated from XFBD. These compounds exhibited varying abilities to induce a microbial structure that closely resembles that of the healthy control group. By quantifying their impact on microbial structure and clustering their regulatory patterns, we devised multiple gut microbiome remodeling compound (GMRC) cocktails. GMRC cocktail C, comprising aucubin, gentiopicroside, syringic acid, gallic acid, p-hydroxybenzaldehyde, para-hydroxybenzoic acid, and isoimperatorin, demonstrated superior efficacy in treating SIRS compared to a single compound or to other cocktails. Finally, in vitro experiments showcased that GMRC cocktail C effectively rebalanced bacteria composition in SIRS patients. This study underscores XFBD's therapeutic potential in SIRS and highlights the importance of innovative treatment approaches for this disease by targeting the gut microbiota.IMPORTANCEDeveloping effective treatment strategies for systemic inflammatory response syndrome (SIRS) is crucial due to its severe and often life-threatening nature. While traditional treatments like dexamethasone have shown efficacy, they also come with significant side effects and limitations. This study makes significant strides by demonstrating that the Xuanfei Baidu (XFBD) formula can substantially reduce mortality rates and inflammation in SIRS mice through effective modulation of the gut microbiota. By quantitatively assessing the impact of 51 compounds derived from XFBD on the gut microbiome, we developed a potent gut microbiome remodeling compound cocktail. This cocktail outperformed individual compounds and other mixtures in efficacy against SIRS. These findings highlight the potential of XFBD as a therapeutic solution for SIRS and underscore the critical role of innovative strategies targeting the gut microbiota in addressing this severe inflammatory condition.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

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 Listeria, 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 (P < 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.

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.

Importance: 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.

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.

Importance: 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 Pseudomonas aeruginosa and Staphylococcus aureus, 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.

Gut microbiota and associated metabolites have been linked to breast carcinogenesis. Evidences demonstrate blood microbiota primarily originates from the gut and may act as a biomarker for breast cancer. We aimed to characterize the microbiota-gut microbial metabolites cross-talk in blood and develop a composite diagnostic panel for breast cancer. We performed 16S rRNA gene sequencing and metabolomics profiling on blood samples from 107 breast cancer cases and 107 age-paired controls. We found that the alpha diversity of the blood microbiota was decreased in breast cancer compared to controls. There were significantly different profiles of microbiota and gut microbial metabolites in blood between these two groups, with nine bacterial genera and four gut microbial metabolites increased in patients, while thirty-nine bacterial genera and two gut microbial metabolites increased in controls. Some breast cancer-associated gut microbial metabolites were linked to differential blood microbiota, and a composite microbiota-metabolite diagnostic panel was further developed with an area under the curve of 0.963 for breast cancer. This study underscored the pivotal role of microbiota and gut microbial metabolites in blood and their interactions for breast carcinogenesis, as well as the potential of a composite diagnostic panel as a non-invasive biomarker for breast cancer.IMPORTANCEOur integrated analysis demonstrated altered profiles of microbiota and gut microbial metabolites in blood for breast cancer patients. The extensive correlation between microbiota and gut microbial metabolites in blood assisted the understanding of the pathogenesis of breast cancer. The good performance of a composite microbiota-gut microbial metabolites panel in blood suggested a non-invasive approach for breast cancer detection and a novel strategy for better diagnosis and prevention of breast cancer in the future.