Valorisation of grape stem as an alternative ingredient in rabbit feed.

Grape stem is a winery by-product that it is currently disposed as waste or at best as soil conditioner. However, it is rich in fibres and polyphenols which makes it interesting for animal feeding. In this regard, rabbit farming emerges as a target livestock farming since fibre content is essential in rabbit's diets for preventing digestive troubles and polyphenols are associated with improved performances in animals due to their antimicrobial and antioxidant activities. This study aims to assess the suitability of a grape stem-based ingredient for rabbit feeding. The stem was dried using flash drying technology to prevent rapid spoilage and stabilise the ingredient. Then, its nutritional value was evaluated resulting in a high fibre (>40%) and polyphenol (>6%) content ingredient with antioxidant and antimicrobial activity against Staphylococcus aureus. A feed efficiency trial was conducted and inclusion rates of up to 10% of grape stem-based ingredient did not affect animals' mortality, average daily feed intake, daily gain or feed conversion ratio. In conclusion, grape stem-based ingredient arises as a secondary feedstuff for cuniculture reducing the dependence on other fibre sources, such as cereals or sunflower hulls. This could also contribute to reduce the environmental footprint of the wine sector by giving a second life to an existing waste, while generating a new activity based on circular economy.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Effect of Chitosan on Ruminal Fermentation and Microbial Communities, Methane Emissions, and Productive Performance of Dairy Cattle.

This study aimed to expand the knowledge about the activity and mode of action of CHI on methanogenesis and rumen microbial populations in vivo. A total of 16 lactating dairy cows were distributed in two groups, one of them receiving 135 mg CHI/kg body weight daily. The effect on productive performance, milk composition, fermentation efficiency, methane emissions, microbial protein synthesis, and ruminal microbial communities was determined. Supplementation with CHI did not affect rumen microbial diversity but increased the relative abundance (RA) of the bacteria Anaeroplasma and decreased those of rumen ciliates and protozoa resulting in a shift towards a lower acetic to propionic ratio. However, no effect on milk yield or methane intensity was observed. In conclusion, supplementing 135 mg CHI/kg body weight increased the RA of Anaeroplasma and decreased those of rumen ciliates and protozoa, both being related to fiber degradation in the rumen in different ways and resulted in a shift of ruminal fermentation towards more propionate proportions, without affecting CH4 emissions, milk yield, or milk composition. Further research with higher doses would be necessary to assess the potential use of this additive as a methane inhibitor.

Evaluation of Valorisation Strategies to Improve Spent Coffee Grounds' Nutritional Value as an Ingredient for Ruminants' Diets.

Lignin in animal diets is a limiting factor due to its low digestibility. This study assessed the effects of thermal or mechanical pre-treatments and enzymatic hydrolysis on spent coffee grounds' (SCG) nutritional value and digestibility. A first trial studied the effect of thermal pre-treatment and hydrolysis with removal of the liquid part and a second trial studied mechanical pre-treatment and hydrolysis with and without removal of the liquid part. Autoclaving did not improve the enzymatic performance nor the nutritional value. Hydrolysis reduced the digestibility of the solid phase and impaired its ruminal fermentation efficiency. Hydrolysates without removing the liquid part improved its nutritional value, but not compared with unprocessed SCG. Grinding increased crude protein and reduced crude fibre and protein, which led to greater fermentation and in vitro digestibility. Thus, grinding emerges as the most promising valorisation strategy to improve SCG nutritional characteristics and their use for animal feed, contributing to the circular economy.

Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability.

Interest on methane emissions from livestock has increased in later years as it is an anthropogenic greenhouse gas with an important warming potential. The rumen microbiota has a large influence on the production of enteric methane. Animals harbour a second genome consisting of microbes, collectively referred to as the "microbiome". The rumen microbial community plays an important role in feed digestion, feed efficiency, methane emission and health status. This review recaps the current knowledge on the genetic control that the cow exerts on the rumen microbiota composition. Heritability estimates for the rumen microbiota composition range between 0.05 and 0.40 in the literature, depending on the taxonomical group or microbial gene function. Variables depicting microbial diversity or aggregating microbial information are also heritable within the same range. This study includes a genome-wide association analysis on the microbiota composition, considering the relative abundance of some microbial taxa previously associated to enteric methane in dairy cattle (Archaea, Dialister, Entodinium, Eukaryota, Lentisphaerae, Methanobrevibacter, Neocallimastix, Prevotella and Stentor). Host genomic regions associated with the relative abundance of these microbial taxa were identified after Benjamini-Hoschberg correction (Padj < 0.05). An in-silico functional analysis using FUMA and DAVID online tools revealed that these gene sets were enriched in tissues like brain cortex, brain amigdala, pituitary, salivary glands and other parts of the digestive system, and are related to appetite, satiety and digestion. These results allow us to have greater knowledge about the composition and function of the rumen microbiome in cattle. The state-of-the art strategies to include methane traits in the selection indices in dairy cattle populations is reviewed. Several strategies to include methane traits in the selection indices have been studied worldwide, using bioeconomical models or economic functions under theoretical frameworks. However, their incorporation in the breeding programmes is still scarce. Some potential strategies to include methane traits in the selection indices of dairy cattle population are presented. Future selection indices will need to increase the weight of traits related to methane emissions and sustainability. This review will serve as a compendium of the current state of the art in genetic strategies to reduce methane emissions in dairy cattle.

Vegetable By-Products as Alternative and Sustainable Raw Materials for Ruminant Feeding: Nutritive Evaluation and Their Inclusion in a Novel Ration for Calf Fattening.

This research aimed to evaluate the nutritional composition, in vitro digestibility, and gas production kinetics of 15 vegetable by-products generated by the agri-food industry compared with corn silage as a reference raw material. Nutritional characterization and in vitro ruminal fermentation tests were performed to determine in vitro organic matter digestibility and digestible energy values, short-chain fatty acids, and the gas production profile. Results indicate that vegetable by-products were more degradable, more extensively fermented, and fermented at a faster rate than corn silage. Going one step further in the valorization of these by-products in animal feed, the second part of the research aimed to compare the novel ration designed for calf fattening with a conventional one. An artificial rumen unit was used to obtain nutrient disappearance, rumen fermentation parameters, and gas production of rumen digesta. Very slight differences were observed between both experimental rations, with their composition being the main difference. Most of the unitary vegetable by-products and all mixes, as real examples of by-product generation in the agri-food industry, have higher digestibility and a greater nutritional value than corn silage. These by-products showed the potential to be used in ruminant-ensiled rations and could replace part of the ingredients in conventional diets.

Pre-Partum Supplementation with Polyunsaturated Fatty Acids on Colostrum Characteristics and Lamb Immunity and Behavior after a Mild Post-Weaning Aversive Handling Period.

We studied the effect of pre-natal supplementation with n-3 α-linolenic acid (ALA) combined with a tannin-rich forage on colostrum composition and immunological quality and whether these changes had advantageous effects on lambs' survival and stress reaction to a post-weaning stressor. Forty-eight Latxa ewes were fed during the last five weeks of pregnancy with two experimental diets: a control diet based on a neutral concentrate and forage (tall fescue hay; CO-FES), and a supplemented diet based on polyunsaturated (PUFA)-rich concentrate and tanniferous forage (sainfoin; ALA-SAIN). After parturition, twenty ewes had their blood and colostrum sampled, and their lambs were monitored until post-weaning. Lambs were afterwards subjected to (i) an aversive handling period (AHP) followed by a behavioral assessment and (ii) inflammatory and lymphocyte proliferation challenge. Feeding ALA-SAIN resulted in changes in colostrum fatty acid composition, specifically higher α-linoleic acid (p < 0.001), conjugate linoleic acid (p = 0.005), vaccenic acid (p = 0.006) and long-chain n-3 PUFA (p = 0.004). Pre-partum nutrition did not affect lamb immunoglobulin (Ig) G apparent efficacy absorption, but circulating IgG tended to be higher (p = 0.054) in ALA-SAIN lambs. ALA-SAIN lambs interacted more frequently with other lambs (p = 0.002), whereas ALA-SAIN females spent more time closer to other lambs (p < 0.001). Plasma cortisol was higher (p = 0.047) and plasma interleukin (IL)-2 lower (p = 0.003) in CO-FES lambs. This research highlights the importance of prenatal nutrition on the immune system stimulation and lambs' behavior as a strategy to improve lambs' health and welfare during early life.

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.

Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle.

BACKGROUND: 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. RESULTS: The ß-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 (Padj < 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. CONCLUSIONS: 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.

Evaluating the Inclusion of Cold-Pressed Rapeseed Cake in the Concentrate for Dairy Cows upon Ruminal Biohydrogenation Process, Ruminal Microbial Community and Milk Production and Acceptability.

The aim of this trial was to assess the effect of feeding a concentrate including cold-pressed rapeseed cake (CPRC) on productive performance, milk quality and its sensory properties, ruminal biohydrogenation, and bacterial communities. Eighteen cows were paired, and two experimental diets (control vs. CPRC) were distributed within the pair. Concentrates were iso-energetic and iso-proteic and contained similar amounts of fat. The average days in milk, milk yield, and body weight of the animals were (mean ± SD) 172 ± 112 d, 585 ± 26 kg, and 25.4 ± 6.2 kg/d, respectively. The experiment lasted for 10 wk. Feeding CPRC resulted in lower ruminal saturated (p < 0.001) and higher monounsaturated (p = 0.002) fatty acids. Feeding CPRC increased Ruminococcus, Prevotella, and Entodinium but decreased Blautia; p-75-a5; undefined genera within orders Clostridiaceae and RF39 and within families Christensenellaceae, Lachnospiracease, and Ruminococcaceae; and fungi from the phylum neocallimastigomycota. The milk fatty acid profile was characterized by a lower n6:n3 ratio (p = 0.028). Feeding CPRC did not affect the milk yield, milk quality, or fat corrected milk (p > 0.05). Feeding CPRC improved the overall milk acceptability (p = 0.047). In conclusion, CPRC affected some microbial taxa, modified the biohydrogenation process, and improved the milk fatty acid profile and consumer acceptance without detrimental effects on milk production and composition.

Characterisation of the rumen resistome in Spanish dairy cattle.

BACKGROUND: Rumen microorganisms carry antimicrobial resistance genes which pose a threaten to animals and humans in a One Health context. In order to tackle the emergence of antimicrobial resistance it is vital to understand how they appear, their relationship with the host, how they behave as a whole in the ruminal ecosystem or how they spread to the environment or humans. We sequenced ruminal samples from 416 Holstein dairy cows in 14 Spanish farms using nanopore technology, to uncover the presence of resistance genes and their potential effect on human, animal and environmental health. RESULTS: We found 998 antimicrobial resistance genes (ARGs) in the cow rumen and studied the 25 most prevalent genes in the 14 dairy cattle farms. The most abundant ARGs were related to the use of antibiotics to treat mastitis, metritis and lameness, the most common diseases in dairy cattle. The relative abundance (RA) of bacteriophages was positively correlated to the ARGs RA. The heritability of the RA of the more abundant ARGs ranged between 0.10 (mupA) and 0.49 (tetW), similar to the heritability of the RA of microbes that carried those ARGs. Even though these genes are carried by the microorganisms, the host is partially controlling their RA by having a more suitable rumen pH, folds, or other physiological traits that promote the growth of those microorganisms. CONCLUSIONS: We were able to determine the most prevalent ARGs (macB, msbA, parY, rpoB2, tetQ and TaeA) in the ruminal bacteria ecosystem. The rumen is a reservoir of ARGs, and strategies to reduce the ARG load from livestock must be pursued.

Solid State Fermentation as a Tool to Stabilize and Improve Nutritive Value of Fruit and Vegetable Discards: Effect on Nutritional Composition, In Vitro Ruminal Fermentation and Organic Matter Digestibility.

This research aimed to evaluate in vitro organic matter digestibility, fermentation characteristics and methane production of fruit and vegetable discards processed by solid state fermentation (SSF) by Rhizopus sp. Mixtures were composed of approximately 28% citric fruits, 35% other fruits and 37% vegetables. Fruit and vegetables were processed and fermented to obtain a stabilized product. Nutritional characterization and in vitro ruminal fermentation tests were performed to determine the effect of fungal bioconversion on digestibility, end products and gas production kinetics. Results indicate that SSF reduced organic matter and reducing sugars, while it increased crude protein and neutral detergent fiber, acid detergent fiber and neutral detergent insoluble protein. The in vitro gas production showed that SSF led to a reduction of the organic matter digestibility (p < 0.001), short chain fatty acids (SCFA; p = 0.003) and CH4 (p = 0.002). SSF reduced the gas production from the insoluble fraction (p = 0.001), without modifying the production rate (p = 0.676) or the lag time (p = 0.574). Regarding SCFA profile, SSF increased acetic (p = 0.020) and decreased propionic (p = 0.004) and butyric (p = 0.006) acids proportions, increasing acetic to propionic (p = 0.008) and acetic plus butyric to propionic (p = 0.011) ratios. SSF succeeded in obtaining a stabilized material enriched in protein, but at the expense of a reduction of protein availability and organic matter digestibility. These changes should be considered before including them in a ruminant's rations.

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.

Spent Coffee Grounds Alter Bacterial Communities in Latxa Dairy Ewes.

Antimicrobial and antioxidant properties of spent coffee grounds (SCG) make them a potential ingredient in a diet for ruminants. This study investigated the effects of SCG on rumen microbiota. For 51 days, 36 dairy ewes were assigned to the experimental treatments (0, 30, 50, and 100 g SCG/kg). Ruminal samples were collected on day 50. DNA was extracted and subjected to paired-end Illumina sequencing of the V3-V4 hypervariable region of the 16S rRNA genes. Bioinformatic analyses were performed using QIIME (v.1.9.0). SCG increased dose-dependently bacterial diversity and altered bacterial structure. Further, 60, 78, and 449 operational taxonomic unit (OUT) were different between control and 30, 50 and 100 g/kg SCG groups, respectively. Higher differences were observed between the control and 100 g/kg SCG group, where OTU of the genera Treponema, CF231, Butyrivibrio, BF331, Anaeroplasma, Blautia, Fibrobacter, and Clostridium were enriched with SCG. Correlations between volatile fatty acids (VFA) and bacterial taxa were sparser in the SCG groups and had little overlap. Certain bacterial taxa presented different signs of the correlation with VFA in SCG and control groups, but Butyrivibrio and Blautia consistently correlated with branched-chain VFA in all groups. SCG induced shifts in the ruminal bacterial community and altered the correlation networks among bacterial taxa and ruminal VFA.

Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study.

The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH4 ) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH4 . Heritability of CH4 was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate model and from -0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH4 and the microbiota compositions are partially controlled by the host genotype.

Effect of Feeding Cold-Pressed Sunflower Cake on Ruminal Fermentation, Lipid Metabolism and Bacterial Community in Dairy Cows.

Cold-pressed sunflower cake (CPSC), by-product of oil-manufacturing, has high crude fat and linoleic acid concentrations, being a promising supplement to modulate rumen fatty acid (FA) profile. This trial studied CPSC effects on ruminal fermentation, biohydrogenation and the bacterial community in dairy cows. Ten cows were used in a crossover design with two experimental diets and fed during two 63-day periods. The cows were group fed forage ad libitum and the concentrate individually. The concentrates, control and CPSC, were isoenergetic, isoproteic and isofat. The ruminal samples collected at the end of each experimental period were analyzed for short-chain fatty acid, FA and DNA sequencing. CPSC decreased butyrate molar proportion (4%, p = 0.005). CPSC decreased C16:0 (28%, p < 0.001) and increased C18:0 (14%, p < 0.001) and total monounsaturated FA, especially C18:1 trans-11 (13%, p = 0.023). The total purine derivative excretion tended to be greater (5%, p = 0.05) with CPSC, resulting in a 6% greater daily microbial N flow. CPSC did not affect the diversity indices but increased the relative abundances of Treponema and Coprococcus, and decreased Enterococcus, Ruminococcus and Succinivibrio. In conclusion, the changes in ruminal fermentation and the FA profile were not associated with changes in microbial diversity or abundance of dominant populations, however, they might be associated with less abundant genera.

Use of Cold-Pressed Sunflower Cake in the Concentrate as a Low-Input Local Strategy to Modify the Milk Fatty Acid Profile of Dairy Cows.

Cold-pressed sunflower cake (CPSC) is a cheap by-product of oil-manufacturing. Supplementing diets with CPSC, rich in fat and linoleic acid, could be an effective tool for increasing healthy fatty acids (FA) in milk. To test this hypothesis, 10 cows were used in a crossover design with two experimental diets fed during two 63-day periods. Cows' milk production was recorded and samples were taken for fat, protein, lactose, and for FA composition analysis. Dry matter intake (DMI) and dry matter apparent digestibility (DMD) were estimated using two markers. Milk acceptance test was carried out. CPSC decreased milk C12:0 (10%, p = 0.023) and C16:0 (5%, p = 0.035) and increased C18:1 cis-12 (37%, p = 0.006), C18:1 trans-11 (32%, p = 0.005), C18:2 cis-9 cis-12 (13%, p = 0.004), and cis-9 trans-11 CLA (35%, p = 0.004). CPSC increased total trans-monounsaturated FA (21%, p = 0.003), total CLA (31%, p = 0.007), and PUFA:SFA ratio (18%, p = 0.006). CPSC did not affect milk production, DMD, DMI and milk composition, but reduced fat yield (9%, p = 0.013) and FCM (7%, p = 0.013). CPSC improved milk overall acceptability. In conclusion, CPSC could modify milk FA profile without a detrimental effect on digestibility, production performance, or milk acceptance.

Comparison Between Non-Invasive Methane Measurement Techniques in Cattle.

The aim of this trial was to study the agreement between the non-dispersive infrared methane analyzer (NDIR) method and the hand held laser methane detector (LMD). Methane (CH4) was measured simultaneously with the two devices totaling 164 paired measurements. The repeatability of the CH4 concentration was greater with the NDIR (0.42) than for the LMD (0.23). However, for the number of peaks, repeatability of the LMD was greater (0.20 vs. 0.14, respectively). Correlation was moderately high and positive for CH4 concentration (0.73 and 0.74, respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A moderate concordance correlation coefficient was observed for the CH4 concentration (0.62) and for the number of peaks (0.66). A moderate-high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed. However, CH4 concentrations population means and all variance components differed between instruments. In conclusion, methane concentration measurements obtained by means of NDIR and LMD cannot be used interchangeably. The joint use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between-subject and within-subject variabilities, are identified and corrected for.

Microbial and Functional Profile of the Ceca from Laying Hens Affected by Feeding Prebiotics, Probiotics, and Synbiotics.

Diet has an essential influence in the establishment of the cecum microbial communities in poultry, so its supplementation with safe additives, such as probiotics, prebiotics, and synbiotics might improve animal health and performance. This study showed the ceca microbiome modulations of laying hens, after feeding with dry whey powder as prebiotics, Pediococcus acidilactici as probiotics, and the combination of both as synbiotics. A clear grouping of the samples induced per diet was observed (p < 0.05). Operational taxonomic units (OTUs) identified as Olsenella spp., and Lactobacillus crispatus increased their abundance in prebiotic and synbiotic treatments. A core of the main functions was shared between all metagenomes (45.5%), although the genes encoding for the metabolism of butanoate, propanoate, inositol phosphate, and galactose were more abundant in the prebiotic diet. The results indicated that dietary induced-changes in microbial composition did not imply a disturbance in the principal biological roles, while the specific functions were affected.

Comparison of Mothur and QIIME for the Analysis of Rumen Microbiota Composition Based on 16S rRNA Amplicon Sequences.

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.