Cold stress initiates catecholaminergic and serotonergic responses in the chicken gut that are associated with functional shifts in the microbiome.

Climate change is one of the most significant challenges facing the sustainability of global poultry production. Stress resulting from extreme temperature swings, including cold snaps, is a major concern for food production birds. Despite being well-documented in mammals, the effect of environmental stress on enteric neurophysiology and concomitant impact on host-microbiome interactions remains poorly understood in birds. As early life stressors may imprint long-term adaptive changes in the host, the present study sought to determine whether cold temperature stress, a prominent form of early life stress in chickens, elicits changes in enteric stress-related neurochemical concentrations that coincide with compositional and functional changes in the microbiome that persist into the later life of the bird. Chicks were, or were not, subjected to cold ambient temperature stress during the first week post-hatch and then remained at normal temperature for the remainder of the study. 16S rRNA gene and shallow shotgun metagenomic analyses demonstrated taxonomic and functional divergence between the cecal microbiomes of control and cold stressed chickens that persisted for weeks following cessation of the stressor. Enteric concentrations of serotonin, norepinephrine, and other monoamine neurochemicals were elevated (P < 0.05) in both cecal tissue and luminal content of cold stressed chickens. Significant (P < 0.05) associations were identified between cecal neurochemical concentrations and microbial taxa, suggesting host enteric neurochemical responses to environmental stress may shape the cecal microbiome. These findings demonstrate for the first time that early life exposure to environmental temperature stress can change the developmental trajectory of both the chicken cecal microbiome and host neuroendocrine enteric physiology. As many neurochemicals serve as interkingdom signaling molecules, the relationships identified here could be exploited to control the impact of climate change-driven stress on avian enteric host-microbe interactions.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Interactions between Medications and the Gut Microbiome in Inflammatory Bowel Disease.

In view of the increasing evidence that commonly prescribed, non-antibiotic drugs interact with the gut microbiome, we re-examined the microbiota variance in inflammatory bowel disease (IBD) to determine the degree to which medication and supplement intake might account for compositional differences between disease subtypes and geographic location. We assessed the confounding effects of various treatments on the faecal microbiota composition (16S rRNA gene sequencing) in persons with Crohn's disease (CD; n = 188) or ulcerative colitis (UC; n = 161) from either Cork (Ireland) or Manitoba (Canada) sampled at three time points. The medication profiles between persons with UC and CD and from different countries varied in number and type of drugs taken. Among Canadian participants with CD, surgical resection and overall medication and supplement usage is significantly more common than for their Irish counterparts. Treatments explained more microbiota variance (3.5%) than all other factors combined (2.4%) and 40 of the 78 tested medications and supplements showed significant associations with at least one taxon in the gut microbiota. However, while treatments accounted for a relatively small proportion of the geographic contribution to microbiome variance between Irish and Canadian participants, additive effects from multiple medications contributed significantly to microbiome differences between UC and CD.

Improved gut microbiome recovery following drug therapy is linked to abundance and replication of probiotic strains.

Probiotics have been used for decades to alleviate the negative side-effects of oral antibiotics, but our mechanistic understanding on how they work is so far incomplete. Here, we performed a metagenomic analysis of the fecal microbiota in participants who underwent a 14-d Helicobacter pylori eradication therapy with or without consumption of a multi-strain probiotic intervention (L. paracasei CNCM I-1518, L. paracasei CNCM I-3689, L. rhamnosus CNCM I-3690, and four yogurt strains) in a randomized, double-blinded, controlled clinical trial. Using a strain-level analysis for detection and metagenomic determination of replication rate, ingested strains were detected and replicated transiently in fecal samples and in the gut during and following antibiotic administration. Consumption of the fermented milk product led to a significant, although modest, improvement in the recovery of microbiota composition. Stratification of participants into two groups based on the degree to which their microbiome recovered showed i) a higher fecal abundance of the probiotic L. paracasei and L. rhamnosus strains and ii) an elevated replication rate of one strain (L. paracasei CNCMI-1518) in the recovery group. Collectively, our findings show a small but measurable benefit of a fermented milk product on microbiome recovery after antibiotics, which was linked to the detection and replication of specific probiotic strains. Such functional insight can form the basis for the development of probiotic-based intervention aimed to protect gut microbiome from drug treatments.

Low-Molecular-Weight Seaweed-Derived Polysaccharides Lead to Increased Faecal Bulk but Do Not Alter Human Gut Health Markers.

Seaweeds are potentially sustainable crops and are receiving significant interest because of their rich bioactive compound content; including fatty acids, polyphenols, carotenoids, and complex polysaccharides. However, there is little information on the in vivo effects on gut health of the polysaccharides and their low-molecular-weight derivatives. Herein, we describe the first investigation into the prebiotic potential of low-molecular-weight polysaccharides (LMWPs) derived from alginate and agar in order to validate their in vivo efficacy. We conducted a randomized; placebo-controlled trial testing the impact of alginate and agar LWMPs on faecal weight and other markers of gut health and on composition of gut microbiota. We show that these LMWPs led to significantly increased faecal bulk (20-30%). Analysis of gut microbiome composition by sequencing indicated no significant changes attributable to treatment at the phylum and family level, although FISH analysis showed an increase in Faecalibacterium prausnitzii in subjects consuming agar LMWP. Sequence analysis of gut bacteria corroborated with the FISH data, indicating that alginate and agar LWMPs do not alter human gut microbiome health markers. Crucially, our findings suggest an urgent need for robust and rigorous human in vivo testing-in particular, using refined seaweed extracts.

Biopsy bacterial signature can predict patient tissue malignancy.

Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.

Japanese quail (Coturnix japonica) as a novel model to study the relationship between the avian microbiome and microbial endocrinology-based host-microbe interactions.

BACKGROUND: Microbial endocrinology, which is the study of neuroendocrine-based interkingdom signaling, provides a causal mechanistic framework for understanding the bi-directional crosstalk between the host and microbiome, especially as regards the effect of stress on health and disease. The importance of the cecal microbiome in avian health is well-recognized, yet little is understood regarding the mechanisms underpinning the avian host-microbiome relationship. Neuroendocrine plasticity of avian tissues that are focal points of host-microbiome interaction, such as the gut and lung, has likewise received limited attention. Avian in vivo models that enable the study of the neuroendocrine dynamic between host and microbiome are needed. As such, we utilized Japanese quail (Coturnix japonica) that diverge in corticosterone response to stress to examine the relationship between stress-related neurochemical concentrations at sites of host-microbe interaction, such as the gut, and the cecal microbiome. RESULTS: Our results demonstrate that birds which contrast in corticosterone response to stress show profound separation in cecal microbial community structure as well as exhibit differences in tissue neurochemical concentrations and structural morphologies of the gut. Changes in neurochemicals known to be affected by the microbiome were also identified in tissues outside of the gut, suggesting a potential relationship in birds between the cecal microbiome and overall avian physiology. CONCLUSIONS: The present study provides the first evidence that the structure of the avian cecal microbial community is shaped by selection pressure on the bird for neuroendocrine response to stress. Identification of unique region-dependent neurochemical changes in the intestinal tract following stress highlights environmental stressors as potential drivers of microbial endocrinology-based mechanisms of avian host-microbiome dialogue. Together, these results demonstrate that tissue neurochemical concentrations in the avian gut may be related to the cecal microbiome and reveal the Japanese quail as a novel avian model in which to further examine the mechanisms underpinning these relationships. Video abstract.

Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study.

OBJECTIVE: The microbiome contributes to the pathogenesis of inflammatory bowel disease (IBD) but the relative contribution of different lifestyle and environmental factors to the compositional variability of the gut microbiota is unclear. DESIGN: Here, we rank the size effect of disease activity, medications, diet and geographic location of the faecal microbiota composition (16S rRNA gene sequencing) in patients with Crohn's disease (CD; n=303), ulcerative colitis (UC; n = 228) and controls (n=161), followed longitudinally (at three time points with 16 weeks intervals). RESULTS: Reduced microbiota diversity but increased variability was confirmed in CD and UC compared with controls. Significant compositional differences between diseases, particularly CD, and controls were evident. Longitudinal analyses revealed reduced temporal microbiota stability in IBD, particularly in patients with changes in disease activity. Machine learning separated disease from controls, and active from inactive disease, when consecutive time points were modelled. Geographic location accounted for most of the microbiota variance, second to the presence or absence of CD, followed by history of surgical resection, alcohol consumption and UC diagnosis, medications and diet with most (90.3%) of the compositional variance stochastic or unexplained. CONCLUSION: The popular concept of precision medicine and rational design of any therapeutic manipulation of the microbiota will have to contend not only with the heterogeneity of the host response, but also with widely differing lifestyles and with much variance still unaccounted for.