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Microbial transformation is a favored approach for environmental remediation. However, the effectiveness of microbial remediation has been limited by the lack of chassis cells with satisfactory contaminant degradation performance. Pseudomonas putida B6-2, with a wide substrate spectrum and high solvent tolerance, is a chassis strain with great potential for application in environmental remediation. Here, guided by bioinformatic analyses and genome-scale metabolic model (GEM) predictions, we successfully optimized P. putida B6-2 by rationally reducing its nonessential genetic components and generating a more robust genome-streamlined strain, P. putida BGR4. Several improvements were observed compared with the original P. putida B6-2 strain, including a 1.4 × 10⁵-fold increase in electroporation efficiency, an 8.3-fold increase in conjugation efficiency, improved glycerol utilization capability, and increased phenol utilization after heterologous expression of the phenol monooxygenase encoded by dmpKLMNOP. Additionally, P. putida BGR4 exhibited enhanced tolerance to several stressors, including starvation, oxidative stress, and DNA damage. Transcriptomic analysis revealed that genome streamlining led to the upregulation of genes involved in the "carbon metabolism" and "tricarboxylic acid cycle" pathways in P. putida BGR4, which likely contributed to the superior phenotype of P. putida BGR4 in terms of carbon source utilization and contaminant degradation capabilities. Furthermore, the absence of four prophages was identified as a potential cause of the enhanced stress resistance observed in P. putida BGR4. Overall, we developed a combined genome-streamlining strategy involving bioinformatic analyses and GEM predictions and generated a more robust chassis strain, P. putida BGR4, which expands the repertoire of chassis cells for environmental remediation.IMPORTANCEDespite the development of many chassis cells, there is still a lack of robust chassis cells with satisfactory contaminant degradation performance. Targeted genome streamlining is an effective way to provide powerful chassis cells. However, genome streamlining does not always lead to the improved phenotypes of genome-streamlined chassis cells. In this research, a novel procedure that combined bioinformatic analyses and GEM predictions was proposed to guide genome streamlining and predict the effects of genome streamlining. This genome streamlining procedure was successfully applied to Pseudomonas putida B6-2, which was a chassis cell with great potential for application in environmental remediation and resulted in the generation of a more robust chassis cell, P. putida BGR4, thereby providing a superior chassis cell for efficient and sustainable environmental remediation and a valuable framework for guiding the genome streamlining of strains for other applications.

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, P = 8.12E-6) compared to PCA (e.g., for LIHC, log-rank test, P = 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, Arcobacter, Methylocella, and Isoptericola 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.

Clostridioides difficile is the leading cause of healthcare-associated diarrhea in industrialized countries. Many questions remain to be answered about the mechanisms governing its interaction with the host during infection. Non-coding RNAs (ncRNAs) contribute to shape virulence in many pathogens and modulate host responses; however, their role in C. difficile infection (CDI) has not been explored. To better understand the dynamics of ncRNA expression contributing to C. difficile infectious cycle and host response, we used a dual RNA-seq approach in a conventional murine model. From the pathogen side, this transcriptomic analysis revealed the upregulation of virulence factors, metabolism, and sporulation genes, as well as the identification of 61 ncRNAs differentially expressed during infection that correlated with the analysis of available raw RNA-seq data sets from two independent studies. From these data, we identified 118 potential new transcripts in C. difficile, including 106 new ncRNA genes. From the host side, we observed the induction of several pro-inflammatory pathways, and among the 185 differentially expressed ncRNAs, the overexpression of microRNAs (miRNAs) previously associated to inflammatory responses or unknown long ncRNAs and miRNAs. A particular host gene expression profile could be associated to the symptomatic infection. In accordance, the metatranscriptomic analysis revealed specific microbiota changes accompanying CDI and specific species associated with symptomatic infection in mice. This first adaptation of in vivo dual RNA-seq to C. difficile contributes to unravelling the regulatory networks involved in C. difficile infectious cycle and host response and provides valuable resources for further studies of RNA-based mechanisms during CDI.IMPORTANCEClostridioides difficile is a major cause of nosocomial infections associated with antibiotic therapy classified as an urgent antibiotic resistance threat. This pathogen interacts with host and gut microbial communities during infection, but the mechanisms of these interactions remain largely to be uncovered. Noncoding RNAs contribute to bacterial virulence and host responses, but their expression has not been explored during C. difficile infection. We took advantage of the conventional mouse model of C. difficile infection to look simultaneously to the dynamics of gene expression in pathogen, its host, and gut microbiota composition, providing valuable resources for future studies. We identified a number of ncRNAs that could mediate the adaptation of C. difficile inside the host and the crosstalk with the host immune response. Promising inflammation markers and potential therapeutic targets emerged from this work open new directions for RNA-based and microbiota-modulatory strategies to improve the efficiency of C. difficile infection treatments.

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 Ruminococcus_gnavus_group (R. gnavus), and the levels of R. gnavus 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.

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.

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.

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 (Phaseolus vulgaris 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.

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.

Importance: 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.

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.