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Bars-Cortina, D.

Publications and source records attributed to Bars-Cortina, D..

4 recordsLinked to original sources

Mucosal architecture, microbiota resilience and metabolic disruption reveal holobiont dynamics during bovine respiratory disease

The gut-airway axis is increasingly recognised as an important determinant of respiratory health in livestock. However, the interactions among respiratory infections, microbial communities, mucosal barrier function, and microbial metabolism that underpin this axis remain poorly understood. Here, we combined longitudinal immunological, mucosal, microbiological, and metabolic analyses to characterise host-microbiota interactions during antibiotic treatment and experimental bovine respiratory disease (BRD). We found that the bovine respiratory tract is organised as a hierarchically structured mucosal ecosystem, with compartment-specific mucin expression patterns associated with distinct microbial communities. Within this framework, increasing respiratory pathogen loads were associated with greater clinical severity, oxidative stress, acute inflammation, and targeted alterations in mucosal regulation and microbial composition, while serum amyloid A emerged as the host biomarker most closely reflecting infection intensity. Although infection altered host and microbial states, antimicrobial treatment represented the dominant ecological perturbation. Antibiotic exposure reshaped both respiratory and intestinal microbial communities, reducing diversity, increasing community drift, and driving coordinated ecological transitions across the gut-airway axis that exceeded the effects of infection alone. Importantly, these compositional changes were accompanied by profound reductions in acetate, propionate, butyrate, and valerate, identifying short-chain fatty acids as a major functional consequence of gut microbiota disruption. Integrative multi-omics analyses revealed that mucosal organisation, microbial diversity, microbial metabolism, and immune activity were embedded within a highly structured gut-airway network. Together, these findings support a holobiont view of BRD in which disease outcomes emerge not only from pathogen-host interactions but also from ecological disturbances that propagate across interconnected mucosal compartments. Notably, antimicrobial treatment affected ecosystem structure and function more than infection itself, highlighting potential trade-offs between pathogen control and microbiota stability. Preserving microbial resilience and its metabolic functions may therefore represent an important component of future strategies to improve respiratory health and disease resilience in cattle.

systems biology↗

Stability of Oral and Fecal Microbiome at Room Temperature: Impact on Diversity

When collecting oral and fecal samples for large epidemiological microbiome studies, optimal storage conditions such as immediate freezing, are not always feasible. It is fundamental to study the impact of temporary room temperature (RT) storage and shipping on the microbiome diversity obtained in different types of samples. We performed a pilot study aimed at validating the sampling protocol based on the viability of the 16S rRNA gene sequencing in microbiome samples. Fecal and oral samples from five participants were collected and preserved in different conditions: a) 70% ethanol; b) in a FIT tube for stool samples; and c) in a chlorhexidine solution for oral wash samples. Four aliquots were prepared per sample, which were stored at RT, and frozen at days 0, 5, 10 and 15, respectively. In terms of alpha diversity, the maximum average decrease in 5 days was 0.3%, 1.6% and 1.7% for oral, stool in ethanol and stool in FIT, respectively. Furthermore, the relative abundances of the most important phyla and orders remained stable over the two weeks. The stability of fecal and oral samples for microbiome studies preserved at RT with 70% ethanol, chlorhexidine and in FIT tubes was verified for a 15-day window, with no substantial changes in terms of alpha diversity and relative abundances.

microbiology↗

Alpha and beta-diversities performance comparison between different normalization methods and centered log-ratio transformation in a microbiome public dataset

Microbiome data obtained after ribosomal RNA or shotgun sequencing represent a challenge for their ecological and statistical interpretation. Microbiome data is compositional data, with a very different sequencing depth between sequenced samples from the same experiment and harboring many zeros. To overcome this scenario, several normalizations and transformation methods have been developed to correct the microbiome datas technical biases, statistically analyze these data more optimally, and obtain more confident biological conclusions. Most existing studies have compared the performance of different normalization methods mainly linked to microbial differential abundance analysis methods but without addressing the initial statistical task in microbiome data analysis: alpha and beta-diversities. Furthermore, most of the studies used simulated microbiome data. The present study attempted to fill this gap. A public whole shotgun metagenomic sequencing dataset from a USA cohort related to gastrointestinal diseases has been used. Moreover, the performance comparison of eleven normalization methods and the transformation method based on the centered log ratio (CLR) has been addressed. Two strategies were followed to attempt to evaluate the aptitude of the normalization methods between them: the centered residuals obtained for each normalization method and their coefficient of variation. Concerning alpha diversity, the Shannon-Weaver index has been used to compare its output to the normalization methods. Regarding beta-diversity (multivariate analysis), it has been explored three types of analysis: principal coordinate analysis (PCoA) as an exploratory method; distance-based redundancy analysis (db-RDA) as interpretative analysis; and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) as machine learning discriminatory multivariate method. Moreover, other microbiome statistical approaches were compared along the normalization and transformation methods: permutational multivariate analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), beta-dispersion and multi-level pattern analysis in order to associate specific species to each type of diagnosis group in the dataset used. The GMPR (geometric mean of pairwise ratios) normalization method presented the best results regarding the dispersion of the new matrix obtained after being scaled. For the case of diversity, no differences were detected among the normalization methods compared. In terms of {beta} diversity, the db-RDA and the sPLS-DA analysis have allowed us to detect the most meaningful differences between the normalization methods. The CLR transformation method was the most informative in biological terms, allowing us to make more predictions. Nonetheless, it is important to emphasize that the CLR method and the UQ normalization method have been the only ones that have allowed us to make predictions from the sPLS-DA analysis, so their use could be more encouraged.

microbiology↗

Design, optimization and validation of genes commonly used in expression studies on DMH/AOM rat colon carcinogenesis model

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.\n\nThe 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, {beta}-catenin, Ccnd1, Cdnk-1A, Cox2, Gsk3beta, IL-33, iNOs, Nrf2, p53, RelA, Smad4, Tnfa and Vegfa) and two reference genes (Actb or {beta}-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 r2 > 0.980. Finally, a pilot study was performed to compare the robustness of two candidate reference genes.

cancer biology↗