The first objective of this study was to evaluate the dynamics and their potential association with animal performance of the microbiota in both the rumen and colon of dairy cows as they move from a nonlactation to a lactation ration. The second objective was to assess the potential effects on the microbiota of live yeast supplementation. Twenty-one Holstein cows were split in 2 treatments consisting of 1 × 1010 cfu/d of live yeast (LY; n = 10) or no supplementation (control; n = 11) starting 21 d before until 21 d after calving. At 14 d before and 7 and 21 d after calving, samples of rumen and colon digesta were obtained from each cow using an endoscope. Total DNA was extracted and submitted to high-throughput sequencing. Shannon diversity index, in both the rumen and colon, was unaffected by LY; however, in the rumen it was lowest 7 d after calving and returned to precalving values at 21 d in milk, whereas in the colon it was greatest 14 d before calving but decreased after calving. In the rumen, LY supplementation increased the relative abundance (RA) of Bacteroidales (group UCG-001), Lachnospiracea (groups UCG-002 and UCG-006), and Flexilinea 14 d before calving, and increased RA of Streptococcus 21 d after calving compared with control cows. However, changes in the ruminal microbiota were more drastic across days relative to calving than as influenced by the dietary treatment, and the effect of LY in the colon was milder than in the rumen. The ruminal RA of several genera was associated with postcalving DMI, and that of Gastranaerophilales was the only order positively associated with milk yield. Several genera were positively correlated with feed efficiency, with Clostridiales (unclassified) being the only genus negatively associated with feed efficiency. In the colon, Prevotellaceae (group Ga6A1) was the only genus positively associated with feed efficiency. The ruminal RA of Prevotella 7 and Ruminobacter 14 d precalving was negatively correlated with dry matter intake and milk yield postcalving. The RA of Parabacteroides in the colon 14 d before calving was negatively correlated with milk yield, whereas the RA of Eggerthellaceae (unclassified) and Erysipelotrichaceae (groups c and unclassified) were positively correlated with feed efficiency. Interestingly, LY supplementation doubled the RA of Eggerthellaceae (unclassified) in the colon. It is concluded that microbial diversity in the rumen experiences a transient reduction after calving, whereas in the colon, the reduction is maintained at least until 21 d in milk. Most of the effects of LY on rumen microbiota were observed before calving, whereas in the colon, LY effects were more moderate but consistent and independent of the stage of production. The microbial community of the rumen after calving is more associated with feed intake, milk yield, and feed efficiency than that of the colon. However, the colon microbiota before calving is more associated with feed efficiency after calving than that of the rumen.

Background: Microbiome studies need to analyze massive sequencing data, which requires the use of sophisticated bioinformatics pipelines. Up to date, several tools are available, although the literature is scarce on studies that compare the performance of different bioinformatics pipelines on rumen microbiota when 16S rRNA amplicons are analyzed. The impact of the pipeline on the outcome of the results is also unknown, mainly in terms of the output from studies using these tools as an intermediate phenotype (pseudophenotypes). This study compares two commonly used software (Quantitative Insights Into Microbial Ecology) (QIIME) and mothur, and two microbial gene data bases (GreenGenes and SILVA) for 16S rRNA gene analysis, using metagenome read data collected from rumen content of a cohort of dairy cows. Results: We compared the relative abundance (RA) of the identified OTUs at the genus level. Both tools presented a high degree of agreement at identifying the most abundant genera: Bifidobacterium, Butyrivibrio, Methanobrevibacter, Prevotella, and Succiniclasticum (RA > 1%), regardless the database. There were no statistical differences between mothur and QIIME (P > 0.05) at estimating the overall RA of the most abundant (RA > 10%) genera, either using SILVA or GreenGenes. However, differences were found at RA < 10% (P < 0.05) when using GreenGenes as database, with mothur assigning OTUs to a larger number of genera and in larger RA for these less frequent microorganisms. With this database mothur resulted in larger richness (P < 0.05), more favorable rarefaction curves and a larger analytic sensitivity. These differences caused significant and relevant differences between tools at identifying the dissimilarity of microbiotas between pairs of animals. However, these differences were attenuated, but not erased, when SILVA was used as the reference database. Conclusion: The findings showed that the SILVA database seemed a preferred reference dataset for classifying OTUs from rumen microbiota. If this database was used, both QIIME and mothur produced comparable richness and diversity, and also in the RA of most common rumen microbes. However, important differences were found for less common microorganisms which impacted on the beta diversity calculated between pipelines. This may have relevant implications at studying global rumen microbiota.

Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions. The β-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R² < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (P_adj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals. Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH₄ emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Gene expression is one of the main factors to influence meat quality by modulating fatty acid metabolism, composition, and deposition rates in muscle tissue. This study aimed to explore the transcriptomics of the Longissimus lumborum muscle in two local pig breeds with distinct genetic background using next-generation sequencing technology and Real-Time qPCR. RNA-seq yielded 49 differentially expressed genes between breeds, 34 overexpressed in the Alentejano (AL) and 15 in the Bísaro (BI) breed. Specific slow type myosin heavy chain components were associated with AL (MYH7) and BI (MYH3) pigs, while an overexpression of MAP3K14 in AL may be associated with their lower loin proportion, induced insulin resistance, and increased inflammatory response via NFkB activation. Overexpression of RUFY1 in AL pigs may explain the higher intramuscular (IMF) content via higher GLUT4 recruitment and consequently higher glucose uptake that can be stored as fat. Several candidate genes for lipid metabolism, excluded in the RNA-seq analysis due to low counts, such as ACLY, ADIPOQ, ELOVL6, LEP and ME1 were identified by qPCR as main gene factors defining the processes that influence meat composition and quality. These results agree with the fatter profile of the AL pig breed and adiponectin resistance can be postulated as responsible for the overexpression of MAP3K14′s coding product NIK, failing to restore insulin sensitivity.

When compared to modern lean-type breeds, Portuguese local Alentejano (AL) and Bísaro (BI) pig breeds present a high potential for subcutaneous and intramuscular fat (IMF) deposition which contributes for better meat quality. The aim of this work was to explore the genome function to better understand the underlying physiological mechanisms associated with body fat accretion. Dorsal subcutaneous fat samples were collected at slaughter from adult animals (n = 4 for each breed) with ~150 kg body weight. Total RNA was obtained and sequenced for transcriptome analysis using DESeq2. A total of 458 differentially expressed (DE) genes (q-value < 0.05) were identified, with 263 overexpressed in AL and 195 in BI. Key genes involved in de novo fatty acid biosynthesis, elongation and desaturation were upregulated in AL such as ACLY, FASN, ME1, ELOVL6 and SCD. A functional enrichment analysis of the DE genes was performed using Ingenuity Pathway Analysis. Cholesterol synthesis is suggested to be higher in AL via SREBF2, SCAP and PPARG, while lipolytic activity may be more active in BI through GH and AMPK signalling. Increased signalling of CD40 together with the predicted activation of INSIG1 and INSIG2 in BI suggests that this breed is more sensitive to insulin whereas the AL is less sensitive like the Iberian breed.

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.