Biology subjects

Reverter, A.

Publications and source records attributed to Reverter, A..

6 recordsLinked to original sources

bioRxiv2026

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds

The cattle rumen microbiota represents a highly complex and dynamic ecosystem, whose organization and connection to host phenotypes are of the highest importance to food security and the environment. In this study, we analyzed the rumen microbiota, from 2,492 cattle belonging to five different breeds and production systems across five countries, categorizing them into microbial co-abundance groups referred to as Ruminosignatures. We identified twelve distinct Ruminosignatures, including two that were consistently observed across all populations and were dominated by the genus Prevotella and UBA2810. Additional Ruminosignatures showed breed-and diet-specific patterns and collectively explained 96-99% of the variance in rumen microbial composition. The abundances of several Ruminosignatures were associated with methane emissions and feed efficiency, and were influenced by host genetics, with heritability estimates ranging from 0.09 to 0.51. The Ruminosignature dominated by UAB2810 was negatively associated with methane emissions across all datasets and positively linked to feed efficiency in Holstein from Italy and crossbred from Ireland. Additionally, the type of production system affects both the occurrence of Ruminosignatures and their impact on host phenotypes, emphasizing the need for context-specific approaches to modulate the rumen microbiome. Overall, our results offer new perspectives on the assembly of ruminal microbes and underscore the potential of the Ruminosignatures framework for microbiome-informed precision agriculture and breeding initiatives aimed at enhancing feed efficiency and minimizing the environmental impact of cattle farming.

microbiology↗

bioRxiv2024

Leveraging Transcriptomics-Based Approaches to Enhance Genomic Prediction: Integrating SNP weights and gene-networks for Cotton Fibre Quality Improvement

The cotton genome (Gossypium hirsutum) contains [~] 80K protein-coding genes, making precision breeding for complex traits a challenge. This study tested biology-informed approaches to improve genomic prediction (GP) accuracy for cotton fibre traits to help accelerate precision breeding of valuable traits. The studys foundational approach was the use of RNA-seq data from key time points during fibre development, namely fibre cells undergoing primary, transition, and secondary wall development. The test approaches included using a range of summary statistics from RNA-seq analysis such as gene Differential Expression (DE). The three test approaches included DE genes overall, target pairwise DE lists informed by gene functional annotation, and finally, gene-network-clusters created based on Partial Correlation and Information Theory (PCIT) as the prior information in Bayesian GP models. The most promising improvements in GP accuracy were at the level of [~] 5% increase by using PCIT-based gene-network clusters as the prior knowledge network neighbours of key genes, and for the traits of cotton fibre Elongation and Strength. These results indicate that there is scope to help improve precision breeding of target traits by incorporating biology-based inference into GP models, and points to specific approaches to achieve this. KEY MESSAGEImproved Genomic Prediction accuracy of cotton fibre quality traits for helping accelerate precision breeding can be achieved by using biology-based prior knowledge and gene-network clusters.

genomics↗

bioRxiv2020

Integrated transcriptome, DNA methylome and chromatin state accessibility landscapes reveal regulators of Atlantic salmon maturation

Despite sexual development being ubiquitous to vertebrates, the epigenetic mechanisms controlling this fundamental transition remain largely undocumented in many organisms. Through whole-methylome, whole-transcriptome and chromatin landscape sequencing, we discovered global control mechanisms as well as specific regulators of sexual maturation in Atlantic salmon. This large integrated study was based on an experimental time course that successfully sampled the period when Atlantic salmon commence their trajectory towards sexual maturation. Co-analysis of DNA methylome and gene expression changes revealed chromatin remodelling genes arid1b and smarca2 were both significantly hypermethylated and upregulated in the ovary during the onset of maturation. We also observed changes in chromatin state landscape occurred early in the transition and were strongly correlated with fundamental remodelling of gene expression. Finally, we integrated our multiomics datasets to identify trim25 and znf423 as key regulators in the pituitary that underwent 60 fold change in connectivity during the transition to sexual maturation. The study provides a comprehensive view of the spatiotemporal changes involved in a complex trait and opens the door to future efforts aiming to manipulate puberty in an economically important aquaculture species.

genomics↗

bioRxiv2020

Genome-wide co-expression distributions as a metric to prioritize genes of functional importance

Genome-wide gene expression is routinely used as a tool to gain a systems-level understanding of complex, biological processes. Numerical approaches that have been used to highlight influential genes include abundance, differential expression, differential variation, network connectivity and differential connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we aimed to assess a complementary strategy, namely whether the entire shape of the distribution of genome-wide co-expression values contains a meaningful biological signal that has hitherto remained hidden from view. We have developed a computational pipeline to assign one of 8 distributions (including normal, skewed, bimodal, kurtotic, inverted) to every gene. We then used a hypergeometric enrichment process to determine if particular genes (regulators versus non-regulators) and properties (differentially expressed or not) tend to be associated with particular distributions greater than would be expected by chance. Examination of several distinct data sets spanning 4 species indicates that there is indeed an additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Author summaryHigh-throughput technologies, such as RNA-Seq, enables access to a vast amount of data. Here, we describe a new approach to interrogate these data and extract further information to help researchers to understand complex phenotypes. Our method is based on gene-level co-expression distributions which were compared to eight possible template shapes to group genes with similar behaviours. The method was tested using five different datasets and the consistency of the results indicate it can be used as a complementary strategy to analyse transcriptomic data.

bioinformatics↗

bioRxiv2019

Association analysis of loci implied in “buffering” epistasis

The existence of buffering mechanisms is an emerging property of biological networks, and this results in the possible existence of \"buffering\" loci, that would allow buildup of robustness through evolution. So far, there are no explicit methods to find loci implied in buffering mechanisms. However, buffering can be seen as interaction with genetic background. Here we develop this idea into a tractable model for quantitative genetics, in which the buffering effect of one locus with many other loci is condensed into a single (statistical) effect, multiplicative on the total (statistical) additive genetic effect. This allows easier interpretation of the results, and it also simplifies the problem of detecting epistasis from quadratic to linear in the number of loci. Armed with this formulation, we construct a linear model for genome-wide association studies that estimates, and declares significance, of multiplicative epistatic effects at single loci. The model has the form of a variance components, norm reaction model and likelihood ratio tests are used for significance. This model is a generalization and explanation of previous ones. We then test our model using bovine data: Brahman and Tropical Composite animals, phenotyped for body weight at yearling and genotyped up to [~]770,000 Single Nucleotide Polymorphisms (SNP). After association analysis and based on False Discovery Rate rules, we find a number of loci with buffering action in one, the other, or both breeds; these loci do not have significant statistical additive effect. Most of these loci have been reported in previous studies, either with an additive effect, or as footprints of selection. We identify epistatic SNPs present in or near genes encoding for proteins that are functionally enriched for peptide activity and transcription factors reported in the context of signatures of selection in multi-breed cattle population studies. These include loci known to be associated with coat color, fertility and adaptation to tropical environments. In these populations we found loci that have a non-significant statistical additive effect but a significant epistatic effect. We argue that the discovery and study of loci associated with buffering effects allows attacking the difficult problems, among others, of release of maintenance variance in artificial and natural selection, of quick adaptation to the environment, and of opposite signs of marker effects in different backgrounds. We conclude that our method and our results generate promising new perspectives for research in evolutionary and quantitative genetics based on the study of loci that buffer effect of other loci.

genetics↗

bioRxiv2018

Systems biology reveals NR2F6 and TGFB1 as key regulators of feed efficiency in beef cattle

Systems biology approaches are used as strategy to uncover tissue-specific perturbations and regulatory genes related to complex phenotypes. We applied this approach to study feed efficiency (FE) in beef cattle, an important trait both economically and environmentally. Poly-A selected RNA of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle and pituitary) of eighteen young bulls, selected for high and low FE, were sequenced (100bp, pared-end). From the 17,354 expressed genes, 1,317 were prioritized by five selection categories (differentially expressed, harbouring SNPs associated with FE, tissue-specific, secreted in plasma and key regulators) and used for network construction. NR2F6 and TGFB were identified and validated by motif discovery as key regulators of hepatic inflammatory response and muscle tissue development, respectively, two biological processes demonstrated to be associated to FE. Moreover, we indicated potential biomarkers of FE which are related to hormonal control of metabolism and sexual maturity. By using robust methodologies and validation strategies, we confirmed main biological processes related to FE in Bos indicus and indicated candidate genes as regulators or biomarkers of superior animals.

systems biology↗