Hasil untuk "Microbial ecology"

R. Bardgett, C. Freeman, N. Ostle

M. Riley, J. Wertz

K. Cummins, M. Klug

Jizhong Zhou, Ye Deng, Feng Luo et al.

ABSTRACT Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research. The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

A. M. Eren, L. Maignien, W. Sul et al.

Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High‐throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets. In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches. Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance. Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open‐source software pipeline for oligotyping.

E. Zaura, B. Brandt, M. Joost Teixeira de Mattos et al.

ABSTRACT Due to the spread of resistance, antibiotic exposure receives increasing attention. Ecological consequences for the different niches of individual microbiomes are, however, largely ignored. Here, we report the effects of widely used antibiotics (clindamycin, ciprofloxacin, amoxicillin, and minocycline) with different modes of action on the ecology of both the gut and the oral microbiomes in 66 healthy adults from the United Kingdom and Sweden in a two-center randomized placebo-controlled clinical trial. Feces and saliva were collected at baseline, immediately after exposure, and 1, 2, 4, and 12 months after administration of antibiotics or placebo. Sequences of 16S rRNA gene amplicons from all samples and metagenomic shotgun sequences from selected baseline and post-antibiotic-treatment sample pairs were analyzed. Additionally, metagenomic predictions based on 16S rRNA gene amplicon data were performed using PICRUSt. The salivary microbiome was found to be significantly more robust, whereas the antibiotics negatively affected the fecal microbiome: in particular, health-associated butyrate-producing species became strongly underrepresented. Additionally, exposure to different antibiotics enriched genes associated with antibiotic resistance. In conclusion, healthy individuals, exposed to a single antibiotic treatment, undergo considerable microbial shifts and enrichment in antibiotic resistance in their feces, while their salivary microbiome composition remains unexpectedly stable. The health-related consequences for the gut microbiome should increase the awareness of the individual risks involved with antibiotic use, especially in a (diseased) population with an already dysregulated microbiome. On the other hand, understanding the mechanisms behind the resilience of the oral microbiome toward ecological collapse might prove useful in combating microbial dysbiosis elsewhere in the body. IMPORTANCE Many health care professionals use antibiotic prophylaxis strategies to prevent infection after surgery. This practice is under debate since it enhances the spread of antibiotic resistance. Another important reason to avoid nonessential use of antibiotics, the impact on our microbiome, has hardly received attention. In this study, we assessed the impact of antibiotics on the human microbial ecology at two niches. We followed the oral and gut microbiomes in 66 individuals from before, immediately after, and up to 12 months after exposure to different antibiotic classes. The salivary microbiome recovered quickly and was surprisingly robust toward antibiotic-induced disturbance. The fecal microbiome was severely affected by most antibiotics: for months, health-associated butyrate-producing species became strongly underrepresented. Additionally, there was an enrichment of genes associated with antibiotic resistance. Clearly, even a single antibiotic treatment in healthy individuals contributes to the risk of resistance development and leads to long-lasting detrimental shifts in the gut microbiome. Many health care professionals use antibiotic prophylaxis strategies to prevent infection after surgery. This practice is under debate since it enhances the spread of antibiotic resistance. Another important reason to avoid nonessential use of antibiotics, the impact on our microbiome, has hardly received attention. In this study, we assessed the impact of antibiotics on the human microbial ecology at two niches. We followed the oral and gut microbiomes in 66 individuals from before, immediately after, and up to 12 months after exposure to different antibiotic classes. The salivary microbiome recovered quickly and was surprisingly robust toward antibiotic-induced disturbance. The fecal microbiome was severely affected by most antibiotics: for months, health-associated butyrate-producing species became strongly underrepresented. Additionally, there was an enrichment of genes associated with antibiotic resistance. Clearly, even a single antibiotic treatment in healthy individuals contributes to the risk of resistance development and leads to long-lasting detrimental shifts in the gut microbiome.

Anas Hajbi

Modern neural networks rely on generic activation functions (ReLU, GELU, SiLU) that ignore the mathematical structure inherent in scientific data. We propose Neuro-Symbolic Activation Discovery, a framework that uses Genetic Programming to extract interpretable mathematical formulas from data and inject them as custom activation functions. Our key contribution is the discovery of a Geometric Transfer phenomenon: activation functions learned from particle physics data successfully generalize to ecological classification, outperforming standard activations (ReLU, GELU, SiLU) in both accuracy and parameter efficiency. On the Forest Cover dataset, our Hybrid Transfer model achieves 82.4% accuracy with only 5,825 parameters, compared to 83.4% accuracy requiring 31,801 parameters for a conventional heavy network -- a 5.5x parameter reduction with only 1% accuracy loss. We introduce a Parameter Efficiency Score ($E_{param} = AUC / \log_{10}(Params)$) and demonstrate that lightweight hybrid architectures consistently achieve 18-21% higher efficiency than over-parameterized baselines. Crucially, we establish boundary conditions: while Physics to Ecology transfer succeeds (both involve continuous Euclidean measurements), Physics to Text transfer fails (discrete word frequencies require different mathematical structures). Our work opens pathways toward domain-specific activation libraries for efficient scientific machine learning.

Celia Méndez-García, A. Pelaez, V. Mesa et al.

Acid mine drainage (AMD) emplacements are low-complexity natural systems. Low-pH conditions appear to be the main factor underlying the limited diversity of the microbial populations thriving in these environments, although temperature, ionic composition, total organic carbon, and dissolved oxygen are also considered to significantly influence their microbial life. This natural reduction in diversity driven by extreme conditions was reflected in several studies on the microbial populations inhabiting the various micro-environments present in such ecosystems. Early studies based on the physiology of the autochthonous microbiota and the growing success of omics-based methodologies have enabled a better understanding of microbial ecology and function in low-pH mine outflows; however, complementary omics-derived data should be included to completely describe their microbial ecology. Furthermore, recent updates on the distribution of eukaryotes and archaea recovered through sterile filtering (herein referred to as filterable fraction) in these environments demand their inclusion in the microbial characterization of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including filterable fraction), and eukaryotic diversity in these ecosystems, and include a thorough depiction of the metabolism and element cycling in AMD habitats. We also review different metabolic network structures at the organismal level, which is necessary to disentangle the role of each member of the AMD communities described thus far.

Ameni Abdi, Emna Gatri, Pasquale Filannino et al.

The aim of this study was to use different concentrations of lemon juice and honey to improve the formulation of a green tea water kefir (GTWK) beverage by applying a central composite design (CCD). Honey’s concentration was 10–50% and lemon juice concentration was 1–5%, these were used as the independent factors, whereas pH, bacteria and yeasts’ count, total phenolic content, % DPPH^. scavenging activity, and overall acceptability were used as the dependent factors. The optimal concentration of honey and lemon juice for highest microbial count, antioxidant activities and overall acceptability was 42.85% and 1.771%, respectively. The analysis of variance revealed that the model was well-fitting, with R² ranging from 87.27% to 96.95%, adj-R² ranging from 78.17% to 94.26% and a non-significant lack of fits. The optimized fermented beverage showed antibacterial potential against <i>Echerichia coli</i> ATCC11229, <i>Staphylococcus aureus</i> ATCC6538 and <i>Salmonella typhimirium</i> ATCC14028 strains. The anti-inflammatory activity was evaluated on CaCo-2 and RAW 264.7 cells. According to ELISA assay, a significant decrease (<i>p</i> < 0.05) in TNF-α concentration was found after inflammatory stimulation, from 1205.41 ± 55.87 pg/mL to 478.17 ± 69.12 pg/mL.

Gong Li, Ling Jia, Jie Li et al.

Abstract Background Carbapenem-resistant hypervirulent K. pneumoniae (CR-HvKP) is a growing public health threat due to its virulence and limited treatment options. While prevalent in hospitals, its presence in livestock, particularly pigs, is poorly understood. The gut microbiome provides colonization resistance, but how it restricts CR-HvKP remains unclear. Results To further elucidate the colonization resistance mechanisms of the gut microbiota against CR-HvKP, we analyzed stool samples from piglets (L), nursery (N), fattening (F), and sows (PS) using microbiome modeling (Micolo) and competition assays. ST290 K. pneumoniae isolated from PS inhibited CR-HvKP via carbohydrate competition, with a pronounced effect observed for sucrose. Niche-specific supplementation with methyl pyruvate was found to partially alleviate this ecological inhibitory effect. Conclusions Carbohydrate-based interventions could be explored as potential therapeutic or prophylactic strategies to combat CR-HvKP colonization, thereby potentially improving animal and public health outcomes. Video Abstract Graphical Abstract

Valentin Girard, Antoine Martin, Maud Rio et al.

The digitalization of societies raises questions about its sustainability and the socio-technical impacts it generates. Ecological redirection applied to organizations is a field of research aiming for achieving sustainability as a direction, rather than for technical means. Arbitration and renunciation to some digital usage and technologies are investigated. Ecological redirection is, however, not yet addressing concrete methodologies for its implementation in organizations. This paper therefore proposes a protocol to support stakeholders in the ecological redirection of their digital practices. This protocol is based on mapping attachments to digital tools through a multi-disciplinary survey. It then proposes increasing stakeholders' knowledge and skills to prepare a debate on the arbitration of renunciations, and finally, to operationalize the closure/transformation of targeted digital practices. This protocol will be tested in real conditions in different contexts. An empirical study is proposed to measure 1) the fluidity with which participants carry out the protocol, 2) the effectiveness of the protocol in terms of the redirection objective, 3) the socio-technical barriers to the redirection process. The paper concludes on the potential benefits for organizations to better understand both the barriers related to its ecological redirection and the transformative aim of such protocols. This will help them trigger large and radical policies towards a desirable and sustainable society.

Charles Morphy D. Santos, Luciana Campos Paulino, Michaella P. Andrade et al.

Achieving complete reproducibility in science, particularly in research fields such as biodiversity, is challenging due to analytical choices, bias and interpretation. Here, we examine examples of reproducibility in biological systematics, ecology, and molecular biology. To mitigate the impact of interpretation and analytical choices, Artificial Intelligence (AI) has provided potential tools. In the present work, while emphasizing the need for methodological rigor and transparency, we acknowledge the role of interpretation in activities such as coding biological characters and detecting morphological patterns in nature. We explore the opportunities and limitations associated with the synergy between big data and AI in molecular biology, emphasizing the need for a more comprehensive and integrated approach based on dataset quality and usefulness. We conclude by advocating for AI-based tools to assist biologists, reinforcing consilience as a criterion for scientific validity without hindering scientific progress.

Bob T. Rosier, Alex Mira

Abstract In our opinion, the ‘hang-time’ of nitrate-containing products discussed in the letter by Green and Green is an interesting variable that should be considered when nitrate-based treatment or prevention strategies are designed. However, due to direct nitrate recycling after nitrate intake, products with a long ‘hang-time’ (e.g., chewing gum) may not always have an advantage compared to products with a short ‘hang-time’ (e.g., vegetable juices). We argue that extending the ‘hang-time’ is especially relevant and potentially beneficial for different applications, such as using a low nitrate dose to stimulate the oral effects, reaching oral tissues that may otherwise not be exposed to dietary nitrate (e.g., periodontal pockets), and providing a longer nitrate exposure in individuals with an impaired salivary flow. Apart from the ‘hang-time’, other important variables are the nitrate dose and source (e.g., different salts and vegetable extracts), as well as the desired effect (e.g., an oral effect versus systemic effects). Finally, we believe that the alterations in salivary microbiota observed before and after chewing three nitrate-rich gums over a period of ~5 h, as reported by Green and Green, could be considered beneficial. However, the oral microbiota composition is affected by the circadian rhythm and the effect of gum mastication should be evaluated. These results should thus be confirmed by a placebo-controlled study, where these confounding factors can be accounted for.

Ziwei Kou, Kai Liu, Zhengtong Qiao et al.

BackgroundIncreasing evidence indicates the microbial ecology of chronic obstructive pulmonary disease (COPD) is intricately associated with the disease’s status and severity, and distinct microbial ecological variations exist between COPD and healthy control (HC). This systematic review and meta-analysis aimed to summarize microbial diversity indices and taxa relative abundance of oral, airway, and intestine microbiota of different stages of COPD and HC to make comparisons.MethodsA comprehensive systematic literature search was conducted in PubMed, Embase, the Web of Science, and the Cochrane Library databases to identify relevant English articles on the oral, airway, and intestine microbiota in COPD published between 2003 and 8 May 2023. Information on microbial diversity indices and taxa relative abundance of oral, airway, and intestine microbiota was collected for comparison between different stages of COPD and HC.ResultsA total of 20 studies were included in this review, involving a total of 337 HC participants, 511 COPD patients, and 154 AECOPD patients. We observed that no significant differences in alpha diversity between the participant groups, but beta diversity was significantly different in half of the included studies. Compared to HC, Prevotella, Streptococcus, Actinomyces, and Veillonella of oral microbiota in SCOPD were reduced at the genus level. Most studies supported that Haemophilus, Lactobacillus, and Pseudomonas were increased, but Veillonella, Prevotella, Actinomyces, Porphyromonas, and Atopobium were decreased at the genus level in the airway microbiota of SCOPD. However, the abundance of Haemophilus, Lactobacillus and Pseudomonas genera exhibited an increase, whereas Actinomyces and Porphyromonas showed a decrease in the airway microbiota of AECOPD compared to HC. And Lachnospira of intestine microbiota in SCOPD was reduced at the genus level.ConclusionThe majority of published research findings supported that COPD exhibited decreased alpha diversity compared to HC. However, our meta-analysis does not confirm it. In order to further investigate the characteristics and mechanisms of microbiome in the oral-airway- intestine axis of COPD patients, larger-scale and more rigorous studies are needed.Systematic review registrationPROSPERO (https://www.crd.york.ac.uk/prospero/), identifier CRD42023418726.

Tom Coenye, Merja Ahonen, Skip Anderson et al.

Priority question exercises are increasingly used to frame and set future research, innovation and development agendas. They can provide an important bridge between the discoveries, data and outputs generated by researchers, and the information required by policy makers and funders. Microbial biofilms present huge scientific, societal and economic opportunities and challenges. In order to identify key priorities that will help to advance the field, here we review questions from a pool submitted by the international biofilm research community and from practitioners working across industry, the environment and medicine. To avoid bias we used computational approaches to group questions and manage a voting and selection process. The outcome of the exercise is a set of 78 unique questions, categorized in six themes: (i) Biofilm control, disruption, prevention, management, treatment (13 questions); (ii) Resistance, persistence, tolerance, role of aggregation, immune interaction, relevance to infection (10 questions); (iii) Model systems, standards, regulatory, policy education, interdisciplinary approaches (15 questions); (iv) Polymicrobial, interactions, ecology, microbiome, phage (13 questions); (v) Clinical focus, chronic infection, detection, diagnostics (13 questions); and (vi) Matrix, lipids, capsule, metabolism, development, physiology, ecology, evolution environment, microbiome, community engineering (14 questions). The questions presented are intended to highlight opportunities, stimulate discussion and provide focus for researchers, funders and policy makers, informing future research, innovation and development strategy for biofilms and microbial communities.

Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington et al.

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the 'forecast limit'. While verification in weather forecasting follows strict criteria with established metrics and forecast limits, assessments of ecological forecasting models still remain experiment-specific, and forecast limits are rarely reported. As such, users of ecological forecasts remain uninformed of how far into the future statements can be trusted. In this work, we synthesise existing approaches to define empirical forecast limits in a unified framework for assessing ecological predictability and offer recipes for their computation. We distinguish the model's potential and absolute forecast limit, and show how a benchmark model can help determine its relative forecast limit. The approaches are demonstrated with three case studies from population, ecosystem, and Earth system research. We found that forecast limits can be computed with three requirements: A verification reference, a scoring function, and a predictive error tolerance. Within our framework, forecast limits are defined for practically any ecological forecast and support research on ecological predictability analysis.

Shengling Gao, Zhikun She, Quanyi Liang et al.

Urban traffic resilience has gained increased attention, with most studies adopting an engineering perspective that assumes a single optimal equilibrium and prioritizes local recovery. On the other hand, systems may possess multiple metastable states, and ecological resilience is the ability to switch between these states according to perturbations. Control strategies from these two resilience perspectives yield distinct outcomes. In fact, ecological resilience oriented control has rarely been viewed in urban traffic, despite the fact that traffic system is a complex system in highly uncertain environment with possible multiple metastable states. This absence highlights the necessity for urban traffic ecological resilience definition. To bridge this gap, we defines urban traffic ecological resilience as the ability to absorb uncertain perturbations by shifting to alternative states. The goal is to generate a system with greater adaptability, without necessarily returning to the original equilibrium. Our control framework comprises three aspects: portraying the recoverable scopes; designing alternative steady states; and controlling system to shift to alternative steady states for adapting large disturbances. Among them, the recoverable scopes are portrayed by attraction region; the alternative steady states are set close to the optimal state and outside the attraction region of the original equilibrium; the controller needs to ensure the local stability of the alternative steady states, without changing the trajectories inside the attraction region of the original equilibrium. Comparisons with classical engineering resilience oriented urban traffic resilience control schemes show that, proposed ecological resilience oriented control schemes can generate greater resilience. These results will contribute to the fundamental theory of future resilient intelligent transportation system.

Ingrid Richter, Zerrin Uzum, Philipp Wein et al.

ABSTRACTThe phytopathogenic fungus Rhizopus microsporus harbors a bacterial endosymbiont (Mycetohabitans rhizoxinica) that produces a crucial virulence factor responsible for the characteristic symptoms of rice seedling blight. The persistence of this unique microbial alliance is ensured by evasion of fungal defense mechanisms by the resident endosymbionts and their strict control of fungal reproduction. A functional bacterial Type 3 secretion system (T3SS) is essential for the establishment of the symbiosis. Yet, the nature of the effector(s) secreted through the T3SS, and their effects on fungal physiology, has remained elusive. Using bioinformatic analysis, we identified genes encoding potential T3SS effectors, namely an alanine-tryptophan-arginine triad (AWR) peptide and a set of transcription activator-like (TAL) effectors, in the genomes of eight endofungal Mycetohabitans strains. Co-culture experiments demonstrated that the sporulating phenotype of apo-symbiotic R. microsporus is unaffected upon reinfection with M. rhizoxinica Δawr compared to with wild-type M. rhizoxinica. In contrast, the ability of apo-symbiotic R. microsporus to produce mature sporangiophores when reinfected with M. rhizoxinica strains deficient in individual MTALs (M. rhizoxinica Δmtal1, M. rhizoxinica Δmtal2, and M. rhizoxinica Δmtal3) is significantly reduced. Trans-complementation experiments showed restoration of fungal sporulation, thus confirming that TAL effectors produced by M. rhizoxinica (MTAL1, MTAL2, and MTAL3) are needed for fungal sporulation. Using fluorescence microscopy, we show that AWR- and MTAL-deficient M. rhizoxinica strains successfully colonize apo-symbiotic R. microsporus, revealing the importance of bacterial MTALs in establishing a stable symbiosis after fungal colonization. Our findings attribute a new function to members of the MTAL family of T3SS-associated effectors and provide deeper insights into host control by prokaryotic symbionts.IMPORTANCEInteractions between fungi and bacteria are critically important in ecology, medicine, and biotechnology. In this study, we shed light on factors that promote the persistence of a toxin-producing, phytopathogenic Rhizopus-Mycetohabitans symbiosis that causes severe crop losses in Asia. We present an unprecedented case where bacterially produced transcription activator-like (TAL) effectors are key to maintaining a stable endosymbiosis. In their absence, fungal sporulation is abrogated, leading to collapse of the phytopathogenic alliance. The Mycetohabitans TAL (MTAL)-mediated mechanism of host control illustrates a unique role of bacterial effector molecules that has broader implications, potentially serving as a model to understand how prokaryotic symbionts interact with their eukaryotic hosts.

M. C. Yates, E. Furlan, B. Thalinger et al.

Hillary A. Craddock, Anastasia Godneva, Daphna Rothschild et al.

Abstract Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.