Individual Microbial Species Research Articles

Fluorescently probing anaerobic digester sludge: Measuring single-cell anabolic activity in methanogens (Methanosarcina and Methanothermobacter) with deuterium-labeled Raman analysis

This study addresses the challenge of obtaining in situ information on substrate utilization rates for individual microbial species in complex microbial communities such as anaerobic digester sludge. To overcome this hurdle, a novel approach combining doubly-labelled deuterium, fluorescence in situ hybridization (FISH) and Raman microspectroscopy was developed. The method enables quantitative determination of anabolic heavy hydrogen incorporation into FISH-targeted, exemplified by methanogenic cells from the genera Methanosarcina and Methanothermobacter.The deuterium incorporation rates ascertained by Raman red-shifting of C-Hx vibrational region to C-Dx vibrations, quantified through Raman peak area ratios, were compared for different carbon sources. Methanosarcina exhibited highest kinetic rates with acetate and propionate, while Methanothermobacter demonstrated faster incorporation under acetate and methanol supplementation.This groundbreaking study demonstrates the feasibility of obtaining quantitative metabolic rate information at a single-cell level using deuterium, FISH probes, and Raman microspectroscopy.

Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence

Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD. By using local explanation embeddings and unsupervised clustering, the research identifies distinct ASD subgroups, underscoring the disorder's heterogeneity. Specific microbial biomarkers associated with ASD are revealed, and the best classifiers achieved an AU-ROC of 0.965± 0.005 and an AU-PRC of 0.967± 0.008. The findings support the notion that gut microbiome composition varies significantly among individuals with ASD. This work's broader significance lies in its potential to inform personalized interventions, enhancing precision in ASD management and classification. These insights highlight the importance of individualized microbiome profiles for developing tailored therapeutic strategies for ASD.

Accuracy of odor-based microorganism identification by microbiological technologists with different years of experience: A cross-sectional study

IntroductionElucidating the characteristic odors of microbes can facilitate microorganism identification. This study aimed to evaluate the accuracy of microbial identification based on odor and its association with years of experience among microbiological technologists. MethodsA cross-sectional study was conducted on February 19, 2023, in Osaka, Japan, in a laboratory capable of handling microorganisms that were rated at or below biosafety level 2. This study included 70 microbiological technologists (including 45 women) with a mean experience of 7.1 years (standard deviation, 5.7). Ten bacterial strains with distinct odors were selected. Participants were blindfolded and asked to identify the bacterial strains based on odor of cultured microbes alone. Linear and logistic regression analyses were used for data analysis. The primary outcome was the number of accurately identified bacterial strains per year of experience. ResultsThe number of years of experience was not significantly associated with the accuracy of odor identification (regression coefficient = 0.037 [95 % confidence interval: 0.038 to 0.113]). Additionally, generally low accuracy was noted in the identification of individual microbial species. ConclusionsOur findings indicate that microorganism identification based solely on odor is challenging. Incorporating additional information, such as visual cues, may enhance the identification accuracy.

House dust metagenome and pulmonary function in a US farming population

BackgroundChronic exposure to microorganisms inside homes can impact respiratory health. Few studies have used advanced sequencing methods to examine adult respiratory outcomes, especially continuous measures. We aimed to identify metagenomic profiles in house dust related to the quantitative traits of pulmonary function and airway inflammation in adults. Microbial communities, 1264 species (389 genera), in vacuumed bedroom dust from 779 homes in a US cohort were characterized by whole metagenome shotgun sequencing. We examined two overall microbial diversity measures: richness (the number of individual microbial species) and Shannon index (reflecting both richness and relative abundance). To identify specific differentially abundant genera, we applied the Lasso estimator with high-dimensional inference methods, a novel framework for analyzing microbiome data in relation to continuous traits after accounting for all taxa examined together.ResultsPulmonary function measures (forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio) were not associated with overall dust microbial diversity. However, many individual microbial genera were differentially abundant (p-value < 0.05 controlling for all other microbial taxa examined) in relation to FEV1, FVC, or FEV1/FVC. Similarly, fractional exhaled nitric oxide (FeNO), a marker of airway inflammation, was unrelated to overall microbial diversity but associated with differential abundance for many individual genera. Several genera, including Limosilactobacillus, were associated with a pulmonary function measure and FeNO, while others, including Moraxella to FEV1/FVC and Stenotrophomonas to FeNO, were associated with a single trait.ConclusionsUsing state-of-the-art metagenomic sequencing, we identified specific microorganisms in indoor dust related to pulmonary function and airway inflammation. Some were previously associated with respiratory conditions; others were novel, suggesting specific environmental microbial components contribute to various respiratory outcomes. The methods used are applicable to studying microbiome in relation to other continuous outcomes.3hzq1LfdnyTZVYvBm7QT8RVideo

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes.

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

Terpenes modulate bacterial and fungal growth and sorghum rhizobiome communities.

Terpenes are among the oldest and largest class of plant-specialized bioproducts that are known to affect plant development, adaptation, and biological interactions. While their biosynthesis, evolution, and function in aboveground interactions with insects and individual microbial species are well studied, how different terpenes impact plant microbiomes belowground is much less understood. Here we designed an experiment to assess how belowground exogenous applications of monoterpenes (1,8-cineole and linalool) and a sesquiterpene (nerolidol) delivered through an artificial root system impacted its belowground bacterial and fungal microbiome. We found that the terpene applications had significant and variable impacts on bacterial and fungal communities, depending on terpene class and concentration; however, these impacts were localized to the artificial root system and the fungal rhizosphere. We complemented this experiment with pure culture bioassays on responsive bacteria and fungi isolated from the sorghum rhizobiome. Overall, higher concentrations (200 µM) of nerolidol were inhibitory to Ferrovibrium and tested Firmicutes. While fungal isolates of Penicillium and Periconia were also more inhibited by higher concentrations (200 µM) of nerolidol, Clonostachys was enhanced at this higher level and together with Humicola was inhibited by the lower concentration tested (100 µM). On the other hand, 1,8-cineole had an inhibitory effect on Orbilia at both tested concentrations but had a promotive effect at 100 µM on Penicillium and Periconia. Similarly, linalool at 100 µM had significant growth promotion in Mortierella, but an inhibitory effect for Orbilia. Together, these results highlight the variable direct effects of terpenes on single microbial isolates and demonstrate the complexity of microbe-terpene interactions in the rhizobiome. IMPORTANCE Terpenes represent one of the largest and oldest classes of plant-specialized metabolism, but their role in the belowground microbiome is poorly understood. Here, we used a "rhizobox" mesocosm experimental set-up to supply different concentrations and classes of terpenes into the soil compartment with growing sorghum for 1 month to assess how these terpenes affect sorghum bacterial and fungal rhizobiome communities. Changes in bacterial and fungal communities between treatments belowground were characterized, followed by bioassays screening on bacterial and fungal isolates from the sorghum rhizosphere against terpenes to validate direct microbial responses. We found that microbial growth stimulatory and inhibitory effects were localized, terpene specific, dose dependent, and transient in time. This work paves the way for engineering terpene metabolisms in plant microbiomes for improved sustainable agriculture and bioenergy crop production.

Next-generation sequencing of the athletic gut microbiota: a systematic review.

Aim: There is growing evidence that physical activity modulates gut microbiota composition through complex interactions between diet and microbial species. On the other hand, next-generation sequencing techniques include shotgun metagenomics and 16S amplicon sequencing. These methodologies allow a comprehensive characterisation of microbial communities of athletes from different disciplines as well as non-professional players and sedentary adults exposed to training. This systematic review summarises recent applications of next-generation sequencing to characterise the athletic gut microbiome. Methods: A systematic review of microbiome research was performed to determine the association of microbiota composition profiles with sports performance. Results: Bibliographic analysis revealed the importance of a novel research trend aiming at deciphering the associations between individual microbial species and sports performance. In addition, literature review highlighted the role of butyrate-producing bacteria such as Anaerostipes hadrus, Clostridium bolteae, Faecalibacterium prausnitzii, Roseburia hominis and unidentified species belonging to Clostridiales, Lachnospiraceae and Subdoligranulum species in gut health and sports performance across several disciplines. Interestingly, metabolic activities of Prevotella copri and Veillonella atypica involved in branched amino acid and lactate metabolism may contribute to reducing muscular fatigue. Other microbial metabolic pathways of interest involved in carbohydrate metabolism showed increased proportions in athletes´ metagenomes. Conclusion: Future research will aim at developing personalised nutrition interventions to modulate key species associated with certain components of exercise.

Coral holobiont research needs spatial analyses at the microbial scale.

Coral holobiont research needs spatial analyses at the microbial scale.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Microbial determinants of effective donors in faecal microbiota transplantation for UC

ObjectiveFaecal microbiota transplantation (FMT) has variable efficacy in treating UC. Recently, oral lyophilised FMT was found to induce remission in patients with UC, with one donor having 100% efficacy compared...

Terahertz thermal curve analysis for label-free identification of pathogens

In this study, we perform a thermal curve analysis with terahertz (THz) metamaterials to develop a label-free identification tool for pathogens such as bacteria and yeasts. The resonant frequency of the metasensor coated with a bacterial layer changes as a function of temperature; this provides a unique fingerprint specific to the individual microbial species without the use of fluorescent dyes and antibodies. Differential thermal curves obtained from the temperature-dependent resonance exhibit the peaks consistent with bacterial phases, such as growth, thermal inactivation, DNA denaturation, and cell wall destruction. In addition, we can distinguish gram-negative bacteria from gram-positive bacteria which show strong peaks in the temperature range of cell wall destruction. Finally, we perform THz melting curve analysis on the mixture of bacterial species in which the pathogenic bacteria are successfully distinguished from each other, which is essential for practical clinical and environmental applications such as in blood culture.

Bacterial and fungal communities in tracheal aspirates of intubated COVID-19 patients: a pilot study

Co-infections with bacterial or fungal pathogens could be associated with severity and outcome of disease in COVID-19 patients. We, therefore, used a 16S and ITS-based sequencing approach to assess the biomass and composition of the bacterial and fungal communities in endotracheal aspirates of intubated COVID-19 patients. Our method combines information on bacterial and fungal biomass with community profiling, anticipating the likelihood of a co-infection is higher with (1) a high bacterial and/or fungal biomass combined with (2) predominance of potentially pathogenic microorganisms. We tested our methods on 42 samples from 30 patients. We observed a clear association between microbial outgrowth (high biomass) and predominance of individual microbial species. Outgrowth of pathogens was in line with the selective pressure of antibiotics received by the patient. We conclude that our approach may help to monitor the presence and predominance of pathogens and therefore the likelihood of co-infections in ventilated patients, which ultimately, may help to guide treatment.

An investigation of sewage water treatment plant and its physico-chemical analysis

In general, metropolitan sewage problems are more perplexing because the amount of wastewater is enormous, necessitating a massive Sewage Treatment Plant (STP). In this present study is to evaluate an efficacy of waste water treatment process containing to analyze the number of microbial consortiums for homegrown wastewater treatment in order to reduce or restrict the amount of slime. The experimental trial study investigation was carried out in an organic treatment settling, with strong microbial consortia and individual microbial species in distinct reactors. The physicochemical boundary of the unpredictable wastewater was identified using pH, Temperature, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Ammonical Nitrogen, Phosphorous, Ammonia and Oil & Grease (O&G). The ultimate goal of achievement is to stable natural norms for treating the sewage water. However, the most important part of the research is to confirm the function of microbial verdure in the sewage treatment in large scale. • Studied characteristics of sewage water universally. • Raw sewage differentiate due to the man-made activities. • 2.5 times COD will be higher than BOD. • Treated water can utilized for the RRR purpose.

ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses.

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

BackgroundMass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research.ResultsWe optimized and employed a pipeline integrating various “guilt-by-association” (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions.ConclusionsThis work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

Annotation of Ribosomal Protein Mass Peaks in MALDI-TOF Mass Spectra of Bacterial Species and Their Phylogenetic Significance

Although MALDI-TOF mass spectrometry based microbial identification has achieved a level of accuracy that facilitate its use in classifying microbes to the species and strain level, questions remain on the identities of the mass peaks profiled from individual microbial species. Specifically, in the popular approach of comparing the mass spectrum of known and unknown microbes for identification purposes, the identities of the mass peaks were not taken into consideration. This study sought to determine if ribosomal proteins could account for some of the mass peaks profiled in MALDI-TOF mass spectra of different bacterial species. Using calculated molecular mass of ribosomal proteins for annotating mass peaks in bacterial species’ MALDITOF mass spectra downloaded from the SpectraBank database, this study revealed that ribosomal proteins could account for the low molecular weight mass peaks of <10000 Da. However, contrary to published reports, ribosomal proteins could not account for most of the mass peaks profiled. In particular, the data revealed that between 1 and 6 ribosomal protein mass peaks could be annotated in each mass spectrum. Annotated ribosomal proteins were S16, S17, S18, S20 and S21 from the small ribosome subunit, and L27, L28, L29, L30, L31, L31 Type B, L32, L33, L34, L35 and L36 from the large ribosome subunit. The ribosomal proteins with the most number of mass peak annotations were L36 and L29, with L34, L33, and L31 completing the list of ribosomal proteins with large number of annotations. Given the highly conserved nature of most ribosomal proteins, possible phylogenetic significance of the annotated ribosomal proteins were investigated through reconstruction of maximum likelihood phylogenetic trees. Results revealed that except for ribosomal protein L34, L31, L36 and S18, all annotated ribosomal proteins hold phylogenetic significance under the criteria of recapitulation of phylogenetic cluster groups present in the phylogeny of 16S rRNA. Phylogenetic significance of the annotated ribosomal proteins was further verified by the phylogenetic tree constructed based on the concatenated amino acid sequence of L29, S16, S20, S17, L27 and L35. Finally, analysis of the structure of the annotated ribosomal proteins did not reveal a high conservation of structure of the ribosomal proteins. Collectively, small low molecular weight (<10000 Da) ribosomal proteins could annotate some of the mass peaks in MALDI-TOF mass spectra of various bacterial species, and most of the ribosomal proteins hold phylogenetic significance. However, structural analysis did not identify a conserved structure for the annotated ribosomal proteins. Annotation of ribosomal protein mass peaks in MALDI-TOF mass spectra highlighted the deep biological basis inherent in the mass spectrometry-based microbial identification method.

The microbiome of deep-sea fish reveals new microbial species and a sparsity of antibiotic resistance genes

ABSTRACT Adaptation to life in the deep-sea can be dramatic, with fish displaying behaviors and appearances unlike those seen in any other aquatic habitat. However, the extent of which adaptations may have developed at a microbial scale is not as clear. Shotgun metagenomic sequencing of the intestinal microbiome of 32 species of deep-sea fish from across the Atlantic Ocean revealed that many of the associated microbes differ extensively from those previously identified in reference databases. 111 individual metagenome-assembled genomes (MAGs) were constructed representing individual microbial species from the microbiomes of these fish, many of which are potentially novel bacterial taxa and provide a window into the microbial diversity in this underexplored environment. These MAGs also demonstrate how these microbes have adapted to deep-sea life by encoding a greater capacity for several cellular processes such as protein folding and DNA replication that can be inhibited by high pressure. Another intriguing feature was the almost complete lack of genes responsible for acquired resistance to known antibiotics in many of the samples. This highlights that deep-sea fish microbiomes may represent one of few animal-associated microbiomes with little influence from human activity. The ability of the microbes in these samples to bioluminesce is lower than expected given predictions that this trait has an important role in their life cycle at these depths. The study highlights the uniqueness, complexity and adaptation of microbial communities living in one of the largest and harshest environments on Earth.

Harnessing Tissue Engineering Tools to Interrogate Host-Microbiota Crosstalk in Cancer.

SummaryRecent studies have begun to highlight the diverse and tumor-specific microbiomes across multiple cancer types. We believe this work raises the important question of whether the classical “Hallmarks of Cancer” should be expanded to include tumor microbiomes. To answer this question, the causal relationships and co-evolution of these microbiotic tumor ecosystems must be better understood. Because host-microbe interactions should be studied in a physiologically relevant context, animal models have been preferred. Yet these models are often poor mimics of human tumors and are difficult to interrogate at high spatiotemporal resolution. We believe that in vitro tissue engineered platforms could provide a powerful alternative approach that combines the high-resolution of in vitro studies with a high degree of physiological relevance. This review will focus on tissue engineered approaches to study host-microbe interactions and to establish their role as an emerging hallmark of cancer with potential as a therapeutic target.

Microbiota and Lung Cancer. Opportunities and Challenges for Improving Immunotherapy Efficacy.

The advances in molecular biology and the emergence of Next Generation Sequencing (NGS) have revealed that microbiome composition is closely related with health and disease, including cancer. This relationship affects different levels of cancer such as development, progression, and response to treatment including immunotherapy. The efficacy of immune checkpoint inhibitors (ICIs) may be influenced by the concomitant use of antibiotics before, during or shortly after treatment with ICIs. Nevertheless, the linking mechanism between microbiote, host immunity and cancer is not clear and the role of microbiota manipulation and analyses in cancer management has not been clinically validated yet. Regarding the use of microbiome as biomarker to predict ICI efficacy it has been recently shown that the use of biochemical serum markers to monitor intestinal permeability and loss of barrier integrity, like citrulline, could be useful to monitor microbiota changes and predict ICI efficacy. There are still many unknowns about the role of these components, their relationship with the microbiota, with the use of antibiotics and the response to immunotherapy. The next challenge in microbiome research will be to identify individual microbial species that causally affect lung cancer phenotypes and response to ICI and disentangle the underlying mechanisms. Thus, further analyses in patients with lung cancer receiving treatment with ICIs and its correlation with the composition of the microbiota in different organs including the respiratory tract, peripheral blood and intestinal tract could be useful to predict the efficacy of ICIs and its modulation with antibiotic use.

Lactobacilli and other gastrointestinal microbiota of Peromyscus leucopus, reservoir host for agents of Lyme disease and other zoonoses in North America.

The cricetine rodent Peromyscus leucopus is an important reservoir for several human zoonoses, including Lyme disease, in North America. Akin to hamsters, the white-footed deermouse has been unevenly characterized in comparison to the murid Mus musculus. To further understanding of P. leucopus’ total genomic content, we investigated gut microbiomes of an outbred colony of P. leucopus, inbred M. musculus, and a natural population of P. leucopus. Metagenome and whole genome sequencing were combined with microbiology and microscopy approaches. A focus was the genus Lactobacillus, four diverse species of which were isolated from forestomach and feces of colony P. leucopus. Three of the species—L. animalis, L. reuteri, and provisionally-named species “L. peromysci”—were identified in fecal metagenomes of wild P. leucopus but not discernibly in samples from M. musculus. L. johnsonii, the fourth species, was common in M. musculus but absent or sparse in wild P. leucopus. Also identified in both colony and natural populations were a Helicobacter sp. in feces but not stomach, and a Tritrichomonas sp. protozoan in cecum or feces. The gut metagenomes of colony P. leucopus were similar to those of colony M. musculus at the family or higher level and for major subsystems. But there were multiple differences between species and sexes within each species in their gut metagenomes at orthologous gene level. These findings provide a foundation for hypothesis-testing of functions of individual microbial species and for interventions, such as bait vaccines based on an autochthonous bacterium and targeting P. leucopus for transmission-blocking.