A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding.

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.

Authentication of fattening diet of goat kid according to their fatty acid profile from perirenal fat.

Fatty acids of forty-two samples of perirenal fat of goat kids reared on three different feeding systems: goat milk (B), milk replacer (R) and milk-based starter (F) have been analyzed by Gas Chromatography flame ionization detector. The lipids were extracted by melting of perirenal fat in a microwave oven. The fat was then filtered and dissolved in hexane. This analysis was performed on a column (100 m x 0.25 mm i.d. and 0.25 microm film thickness) coated with a polar stationary phase HP-88 and flame ionization detector was used. Hydrogen (25 psi inlet constant pressure) was used as carrier gas. Programmed temperature was kept at 175 degrees C and held isothermally for 10 min, and was then raised to 205 degrees C at a rate of 3 degrees C/min and held isothermally for 10 min. By using the fatty acids as chemical descriptors, pattern recognition techniques were applied to differentiate between goat milk, milk replacer and milk-based starter fattening diet of goat kid. C18:2 and C18:3 acids were found to be the most differentiating variables.