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Elite horse athletes that live in individual boxes and train and compete for hours experience long-term physical and mental stress that compromises animal welfare and alters the gut microbiota. We therefore assessed if a temporary period out to pasture with conspecifics could improve animal welfare and in turn, favorably affect intestinal microbiota composition. A total of 27 athletes were monitored before and after a period of 1.5 months out to pasture, and their fecal microbiota and behavior profiles were compared to those of 18 horses kept in individual boxes. The overall diversity and microbiota composition of pasture and control individuals were temporally similar, suggesting resilience to environmental challenges. However, pasture exposure induced an increase in Ruminococcus and Coprococcus that lasted 1-month after the return to individual boxes, which may have promoted beneficial effects on health and welfare. Associations between the gut microbiota composition and behavior indicating poor welfare were established. Furthermore, withdrawn behavior was associated with the relative abundances of Lachnospiraceae AC2044 group and Clostridiales family XIII. Both accommodate a large part of butyrate-producing bacterial genera. While we cannot infer causality within this study, arguably, these findings suggest that management practices maintained over a longer period of time may moderate the behavior link to the gut ecosystem beyond its resilience potential.

Colorectal cancer (CRC), also known as colon cancer, is the third most common form of cancer worldwide in men and the second in women and is characterized by several genetic alterations, among them the expression of several genes. 1,2-dimethylhydrazine (DMH) and its metabolite azoxymethane (AOM) are procarcinogens commonly used to induce colon cancer in rats (DMH/AOM rat model). This rat model has been used to study changes in mRNA expression in genes involved in this pathological condition. However, a lack of proper detailed PCR primer design in the literature limits the reproducibility of the published data. The present study aims to design, optimize and validate the qPCR, in accordance with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines, for seventeen genes commonly used in the DMH/AOM rat model of CRC ( Apc, Aurka, Bax, Bcl2, β -catenin, Ccnd1, Cdkn1a, Cox2, Gsk3beta, IL-33, iNOs, Nrf2, p53, RelA, Smad4, Tnfα and Vegfa ) and two reference genes ( Actb or β - actin and B2m ). The specificity of all primer pairs was empirically validated on agarose gel, and furthermore, the melting curve inspection was checked as was their efficiency (%) ranging from 90 to 110 with a correlation coefficient of r 2  > 0.980. Finally, a pilot study was performed to compare the robustness of two candidate reference genes.

Background Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models. Results As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra . Conclusions Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78–0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66–0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41–0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.

NAFLD has emerged as a significant public health concern, with its prevalence increasing globally. Emphasizing the complex relationship between dietary patterns and epigenetic modifications such as DNA methylation or miRNA expression can exert a positive impact on preventing and managing metabolic disorders, including NAFLD, within the 2030 Sustainable Development Goals. This review aims to evaluate the influence of dietary patterns on hepatic epigenetic gene modulation and provide dietary recommendations for the prevention and management of NAFLD in the general population. Methods: Comprehensive screening and eligibility criteria identified eleven articles focusing on epigenetic changes in NAFLD patients through dietary modifications or nutrient supplementation. Results and Discussion: Data were organized based on study types, categorizing them into evaluations of epigenetic changes in NAFLD patients through dietary pattern modifications or specific nutrient intake. Conclusions: The study highlights the importance of dietary interventions in managing and preventing NAFLD, emphasizing the potential of dietary patterns to influence hepatic epigenetic gene modulation. This study provides valuable insights and recommendations to mitigate the risk of developing NAFLD: (i) eat a primarily plant-based diet; (ii) increase consumption of high-fiber foods; (iii) consume more polyunsaturated and monounsaturated fatty acids; (iv) limit processed foods, soft drinks, added sugars, and salt; and (v) avoid alcohol.

Bioactive compounds in food contribute to reducing the risk of developing colon cancer by modulating the gut microbiota. We have recently demonstrated that garambullo (Myrtillocactus geometrizans), an endemic fruit of Mexico rich in bioactive compounds, attenuates aberrant crypt foci in an animal model. However, its potential to modulate the gut microbiota is unknown. The main objective of this study was to evaluate whether its consumption modulates colon carcinogenesis by altering the microbiota in an in vivo model induced by azoxymethane and dextran sulfate sodium (AOM/DSS). Fecal samples were collected from twelve male Sprague-Dawley rats and analyzed for microbiota composition after 0, 8, and 16 weeks of treatment with saline (control), AOM/DSS, garambullo (G), or residue of garambullo (RG) with AOM/DSS (G+AOM/DSS and RG+AOM/DSS, respectively). Characterization of the microbiome was based on the conserved region of the 16S rRNA V3-V4 gene, and analyzed by the ZymoBIOMICS’ Targeted Metagenomics Sequencing (Zymo Research) service. In an animal model induced with AOM/DSS for 8 weeks, consumption of G and its residue increased the bacterial genera Shuttleworthiia, Subdoligranulum, Lactobacillus, Faecalibacterium, and Alloprevotella (p < 0.05). Consumption of G and its residue allowed the proliferation of bacteria that produce short-chain fatty acids and are associated with protective mechanisms of the colon.

Colorectal cancer (CRC), the third most common cancer globally, has shown links to disturbed gut microbiota. While significant efforts have been made to establish a microbial signature indicative of CRC using shotgun metagenomic sequencing, the challenge lies in validating this signature with 16S ribosomal RNA (16S) gene sequencing. The primary obstacle is reconciling the differing outputs of these two methodologies, which often lead to divergent statistical models and conclusions. In this study, we introduce an algorithm designed to bridge this gap by mapping shotgun-derived taxa to their 16S counterparts. This mapping enables us to assess the predictive performance of a shotgun-based microbiome signature using 16S data. Our results demonstrate a reduction in performance when applying the 16S-mapped taxa in the shotgun prediction model, though it retains statistical significance. This suggests that while an exact match between shotgun and 16S data may not yet be feasible, our approach provides a viable method for comparative analysis and validation in the context of CRC-associated microbiome research.

Evasion of growth suppression is among the prominent hallmarks of cancer. Phosphatase and tensin homolog (PTEN) and p53 tumor-suppressive pathways are compromised in most human cancers, including glioblastoma (GB). Hence, these signaling pathways are an ideal point of focus for novel cancer therapeutics. Recombinant viruses can selectivity kill cancer cells and carry therapeutic genes to tumors. Specifically, oncolytic viruses (OV) have been successfully employed for gene delivery in GB animal models and showed potential to neutralize immunosuppression at the tumor site. However, the associated systemic immunogenicity, inefficient transduction of GB cells, and inadequate distribution to metastatic tumors have been the major bottlenecks in clinical studies. Mesenchymal stem cells (MSCs), with tumor-tropic properties and immune privilege, can improve OVs targeting. Remarkably, combining the two approaches can address their individual issues. Herein, we summarize findings to advocate the reactivation of tumor suppressors p53 and PTEN in GB treatment and use MSCs as a “Trojan horse” to carry oncolytic viral cargo to disseminated tumor beds. The integration of MSCs and OVs can emerge as the new paradigm in cancer treatment.

(1) Introduction: The global rise of gastrointestinal diseases, including colorectal cancer and inflammatory bowel diseases, highlights the need to understand their causes. Diet is a common risk factor and a crucial regulator of gene expression, with alterations observed in both conditions. This study aims to elucidate the specific biological mechanisms through which diet influences the risk of bowel diseases. (2) Methods: We analyzed data from 436 participants from the BarcUVa-Seq population-based cross-sectional study utilizing gene expression profiles (RNA-Seq) from frozen colonic mucosal biopsies and dietary information from a semi-quantitative food frequency questionnaire. Dietary variables were evaluated based on two dietary patterns and as individual variables. Differential expression gene (DEG) analysis was performed for each dietary factor using edgeR. Protein–protein interaction (PPI) analysis was conducted with STRINGdb v11 for food groups with more than 10 statistically significant DEGs, followed by Reactome-based enrichment analysis for the resulting networks. (3) Results: Our findings reveal that food intake, specifically the consumption of blue fish, alcohol, and potatoes, significantly influences gene expression in the colon of individuals without tumor pathology, particularly in pathways related to DNA repair, immune system function, and protein glycosylation. (4) Discussion: These results demonstrate how these dietary components may influence human metabolic processes and affect the risk of bowel diseases.

We simultaneously measured the fecal microbiota and multiple environmental and host-related variables in a cohort of 185 healthy horses reared in similar conditions during a period of eight months. The pattern of rare bacteria varied from host to host and was largely different between two time points. Among a suite of variables examined, equitation factors were highly associated with the gut microbiota variability, evoking a relationship between gut microbiota and high levels of physical and mental stressors. Behavioral indicators that pointed toward a compromised welfare state (e.g. stereotypies, hypervigilance and aggressiveness) were also associated with the gut microbiota, reinforcing the notion for the existence of the microbiota-gut-brain axis. These observations were consistent with the microbiability of behaviour traits (> 15%), illustrating the importance of gut microbial composition to animal behaviour. As more elite athletes suffer from stress, targeting the microbiota offers a new opportunity to investigate the bidirectional interactions within the brain gut microbiota axis.