Equivalence of variance components between standard and recursive genetic models using LDL' transformations.

BACKGROUND: Recursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL' or block-LDL' transformations. RESULTS: The procedure was employed on a dataset comprising five traits (birth weight-BW, weight at 90 days-W90, weight at 210 days-W210, cold carcass weight-CCW and conformation-CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models. CONCLUSIONS: The LDL' or block-LDL' transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle.

BACKGROUND: Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage. RESULTS: By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits. CONCLUSIONS: Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

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.

Summary-data based Mendelian randomization identifies gene expression regulatory polymorphisms associated with bovine paratuberculosis by modulation of the nuclear factor Kappa ß (NF-κß)-mediated inflammatory response.

Genome-wide association studies (GWAS) have identified host genetic variants associated with paratuberculosis (PTB) susceptibility. Most of the GWAS-identified SNPs are in non-coding regions. Connecting these non-coding variants and downstream affected genes is a challenge and, up to date, only a few functional mutations or expression quantitative loci (cis-eQTLs) associated with PTB susceptibility have been identified. In the current study, the associations between imputed whole-genome sequence genotypes and whole RNA-Sequencing data from peripheral blood (PB) and ileocecal valve (ICV) samples of Spanish Holstein cows (N = 16) were analyzed with TensorQTL. This approach allowed the identification of 88 and 37 cis-eQTLs regulating the expression levels of 90 and 37 genes in PB and ICV samples, respectively (False discorey rate, FDR ≤ 0.05). Next, we applied summary-based data Mendelian randomization (SMR) to integrate the cis-eQTL dataset with GWAS data obtained from a cohort of 813 culled cattle that were classified according to the presence or absence of PTB-associated histopathological lesions in gut tissues. After multiple testing corrections (FDR ≤ 0.05), we identified two novel cis-eQTLs affecting the expression of the early growth response factor 4 (EGR4) and the bovine neuroblastoma breakpoint family member 6-like protein isoform 2 (MGC134040) that showed pleiotropic associations with the presence of multifocal and diffuse lesions in gut tissues; P = 0.002 and P = 0.017, respectively. While EGR4 acts as a brake on T-cell proliferation and cytokine production through interaction with the nuclear factor Kappa ß (NF-κß), MGC134040 is a target gene of NF-κß. Our findings provide a better understanding of the genetic factors influencing PTB outcomes, confirm that the multifocal lesions are localized/confined lesions that have different underlying host genetics than the diffuse lesions, and highlight regulatory SNPs and regulated-gene targets to design future functional studies.

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.

Association between High Interferon-Gamma Production in Avian Tuberculin-Stimulated Blood from Mycobacterium avium subsp. paratuberculosis-Infected Cattle and Candidate Genes Implicated in Necroptosis.

The mechanisms underlying host resistance to Mycobacterium avium subsp. paratuberculosis (MAP) infection are largely unknown. In the current study, we hypothesize that cows with an ability to produce higher levels of interferon-gamma (IFNÉ£) might control MAP infection more successfully. To test this hypothesis, IFNÉ£ production was measured using a specific IFNÉ£ ELISA kit in avian purified protein derivative (aPPD)-stimulated blood samples collected from 152 Holstein cattle. DNA isolated from peripheral blood samples of the animals included in the study was genotyped with the EuroG Medium-Density Bead Chip, and the genotypes were imputed to whole-genome sequencing. A genome-wide association analysis (GWAS) revealed that high levels of IFNÉ£ in response to the aPPD were associated with a specific genetic profile (heritability = 0.64) and allowed the identification of 71 SNPs, 40 quantitative trait loci (QTL), and 104 candidate genes. A functional analysis using the 104 candidate genes revealed a significant enrichment of genes involved in the innate immune response and, more specifically, in necroptosis. Taken together, our results define a heritable and distinct immunogenetic profile associated with the production of high IFNÉ£ levels and with the capacity of the host to lyse MAP-infected macrophages by necroptosis.

Evaluating the potential of (epi)genotype-by-low pass nanopore sequencing in dairy cattle: a study on direct genomic value and methylation analysis.

BACKGROUND: Genotype-by-sequencing has been proposed as an alternative to SNP genotyping arrays in genomic selection to obtain a high density of markers along the genome. It requires a low sequencing depth to be cost effective, which may increase the error at the genotype assigment. Third generation nanopore sequencing technology offers low cost sequencing and the possibility to detect genome methylation, which provides added value to genotype-by-sequencing. The aim of this study was to evaluate the performance of genotype-by-low pass nanopore sequencing for estimating the direct genomic value in dairy cattle, and the possibility to obtain methylation marks simultaneously. RESULTS: Latest nanopore chemistry (LSK14 and Q20) achieved a modal base calling accuracy of 99.55%, whereas previous kit (LSK109) achieved slightly lower accuracy (99.1%). The direct genomic value accuracy from genotype-by-low pass sequencing ranged between 0.79 and 0.99, depending on the trait (milk, fat or protein yield), with a sequencing depth as low as 2 × and using the latest chemistry (LSK114). Lower sequencing depth led to biased estimates, yet with high rank correlations. The LSK109 and Q20 achieved lower accuracies (0.57-0.93). More than one million high reliable methylated sites were obtained, even at low sequencing depth, located mainly in distal intergenic (87%) and promoter (5%) regions. CONCLUSIONS: This study showed that the latest nanopore technology in useful in a LowPass sequencing framework to estimate direct genomic values with high reliability. It may provide advantages in populations with no available SNP chip, or when a large density of markers with a wide range of allele frequencies is needed. In addition, low pass sequencing provided nucleotide methylation status of > 1 million nucleotides at ≥ 10 × , which is an added value for epigenetic studies.

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.

Impact of oestrus synchronization devices on ewes vaginal microbiota and artificial insemination outcome.

Introduction: The low pregnancy rate by artificial insemination in sheep represents a fundamental challenge for breeding programs. In this species, oestrus synchronization is carried out by manipulating hormonal regimens through the insertion of progestogen intravaginal devices. This reproductive strategy may alter the vaginal microbiota affecting the artificial insemination outcome. Methods: In this study, we analyzed the vaginal microbiome of 94 vaginal swabs collected from 47 ewes with alternative treatments applied to the progesterone-releasing intravaginal devices (probiotic, maltodextrin, antibiotic and control), in two sample periods (before placing and after removing the devices). To our knowledge, this is the first study using nanopore-based metagenome sequencing for vaginal microbiome characterization in livestock. Results: Our results revealed a significant lower abundance of the genera Oenococcus (Firmicutes) and Neisseria (Proteobacteria) in pregnant compared to non-pregnant ewes. We also detected a significant lower abundance of Campylobacter in the group of samples treated with the probiotic. Discussion: Although the use of probiotics represents a promising practice to improve insemination results, the election of the suitable species and concentration requires further investigation. In addition, the use of progestogen in the synchronization devices seemed to increase the alpha-diversity and decrease the abundance of harmful microorganisms belonging to Gammaproteobacteria and Fusobacteriia classes, suggesting a beneficial effect of their use.

A Genome-Wide Association Study for Tolerance to Paratuberculosis Identifies Candidate Genes Involved in DNA Packaging, DNA Damage Repair, Innate Immunity, and Pathogen Persistence.

Although the genetic susceptibility to diseases has been extensively studied, the genetic loci and the primary molecular and cellular mechanisms that control disease tolerance are still largely unknown. Bovine paratuberculosis (PTB) is an enteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP). PTB affects cattle worldwide and represents a major issue on animal health. In this study, the associations between host genetic and PTB tolerance were investigated using the genotypes from 277 Spanish Holstein cows with two distinct phenotypes: cases) infected animals with positive PCR and bacteriological culture results but without lesions in gut tissues (N= 24), and controls) animals with negative PCR and culture results but with PTB-associated lesions (N= 253). DNA from peripheral blood of the study population was genotyped with the Bovine EuroG MD Bead Chip, and the corresponding genotypes were imputed to whole-genome sequencing (WGS) data. A genome-wide association study was performed using the WGS data and the defined phenotypes in a case-control approach. A total of 142 single nucleotide polymorphisms (SNPs) were associated (false discovery rate ≤ 0.05, P values between 1.5 × 10-7 and 5.7 × 10-7) with tolerance (heritability= 0.55). The 40 SNPs with P-values < 5 × 10-7 defined 9 QTLs and 98 candidate genes located on BTA4, BTA9, BTA16, BTA25, and BTA26. Some of the QTLs identified in this study overlap with QTLs previously associated with PTB, bovine tuberculosis, mastitis, somatic cell score, bovine diarrhea virus persistent infection, tick resistance, and length of productive life. Two candidate genes with important roles in DNA damage response (ERCC4 and RMI2) were identified on BTA25. Functional analysis using the 98 candidate genes revealed a significant enrichment of the DNA packaging process (TNP2/PRMI1/PRM2/PRM3). In addition, the TNF-signaling (bta04668; TRAF5/CREB5/CASP7/CHUK) and the toxoplasmosis (bta05145; TGFß2/CHUK/CIITA/SOCS1) pathways were significantly enriched. Interestingly, the nuclear Factor NF-κß Inhibitor Kinase Alpha (CHUK), a key molecule in the regulation of the NF-κB pathway, was enriched in both pathways. Taken together, our results define a distinct immunogenetic profile in the PTB-tolerant animals designed to control bacterial growth, modulate inflammation, limit tissue damage and increase repair, thus reducing the severity of the disease.

Genome-Enabled Prediction Methods Based on Machine Learning.

Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.

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.

Identification of loci associated with pathological outcomes in Holstein cattle infected with Mycobacterium avium subsp. paratuberculosis using whole-genome sequence data.

Bovine paratuberculosis (PTB), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic granulomatous enteritis that affects cattle worldwide. According to their severity and extension, PTB-associated histological lesions have been classified into the following groups; focal, multifocal, and diffuse. It is unknown whether these lesions represent sequential stages or divergent outcomes. In the current study, the associations between host genetic and pathology were explored by genotyping 813 Spanish Holstein cows with no visible lesions (N = 373) and with focal (N = 371), multifocal (N = 33), and diffuse (N = 33) lesions in gut tissues and regional lymph nodes. DNA from peripheral blood samples of these animals was genotyped with the bovine EuroG MD Bead Chip, and the corresponding genotypes were imputed to whole-genome sequencing (WGS) data using the 1000 Bull genomes reference population. A genome-wide association study (GWAS) was performed using the WGS data and the presence or absence of each type of histological lesion in a case-control approach. A total of 192 and 92 single nucleotide polymorphisms (SNPs) defining 13 and 9 distinct quantitative trait loci (QTLs) were highly-associated (P ≤ 5 × 10-7) with the multifocal (heritability = 0.075) and the diffuse (heritability = 0.189) lesions, respectively. No overlap was seen in the SNPs controlling these distinct pathological outcomes. The identified QTLs overlapped with some QTLs previously associated with PTB susceptibility, bovine tuberculosis susceptibility, clinical mastitis, somatic cell score, bovine respiratory disease susceptibility, tick resistance, IgG level, and length of productive life. Pathway analysis with candidate genes overlapping the identified QTLs revealed a significant enrichment of the keratinization pathway and cholesterol metabolism in the animals with multifocal and diffuse lesions, respectively. To test whether the enrichment of SNP variants in candidate genes involved in the cholesterol metabolism was associated with the diffuse lesions; the levels of total cholesterol were measured in plasma samples of cattle with focal, multifocal, or diffuse lesions or with no visible lesions. Our results showed reduced levels of plasma cholesterol in cattle with diffuse lesions. Taken together, our findings suggested that the variation in MAP-associated pathological outcomes might be, in part, genetically determined and indicative of distinct host responses.

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.

Identification of loci associated with susceptibility to Mycobacterium avium subsp. paratuberculosis infection in Holstein cattle using combinations of diagnostic tests and imputed whole-genome sequence data.

Bovine paratuberculosis (PTB) is a chronic inflammatory disease caused by Mycobacterium avium susbp. paratuberculosis (MAP). Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with susceptibility to bovine PTB. The main objective of this study was to identify quantitative trait loci (QTLs) associated with MAP infection in Spanish Holstein cows (N = 983) using combinations of diagnostic tests and imputed whole-genome sequence (WGS) data. The infection status of these animals was defined by three diagnostic methods including ELISA for MAP-antibodies detection, and tissue culture and PCR for MAP detection. The 983 cows included in this study were genotyped with the Bovine MD SNP50 Bead Chip, and the corresponding genotypes were imputed to WGS using the 1,000 Bull genomes reference population. In total, 33.77 million SNP variants per animal were identified across the genome. Linear mixed models were used to calculate the heritability (h2) estimates for each diagnostic test and test combinations. Next, we performed a case-control GWAS using the imputed WGS datasets and the phenotypes and combinations of phenotypes with h2 estimates > 0.080. After performing the GWAS, the test combinations that showed SNPs with a significant association (PFDR ≤ 0.05), were the ELISA-tissue PCR-tissue culture, ELISA-tissue culture, and ELISA-tissue PCR. A total of twelve quantitative trait loci (QTLs) highly associated with MAP infection status were identified on the Bos taurus autosomes (BTA) 4, BTA5, BTA11, BTA12, BTA14, BTA23, BTA24, and BTA28, and some of these QTLs were linked to immune-modulating genes. The identified QTLs on BTA23 spanning from 18.81 to 22.95 Mb of the Bos taurus genome overlapped with several QTLs previously found to be associated with PTB susceptibility, bovine tuberculosis susceptibility, and clinical mastitis. The results from this study provide more clues regarding the molecular mechanisms underlying susceptibility to PTB infection in cattle and might be used to develop national genetic evaluations for PTB in Spain.

Influence of genetic background and dietary oleic acid on gut microbiota composition in Duroc and Iberian pigs.

BACKGROUND: Phenotypic variability for productive and meat quality traits has been largely studied in Iberian pigs, especially in genetic selection and nutritional experiments. Complex interactions among genetic background, diet composition and gut microbiota hinder the correct assessment of each factor's contribution on phenotypes. In order to disentangle these interactions, we evaluated changes in gut microbiota composition comparing 48 Iberian and Duroc pigs fed diets with different energy source (standard diet with carbohydrates vs sunflower oil-enriched diet with high oleic acid content). RESULTS: A higher richness was observed for Iberian pigs (p < 0.05) and compositional analysis was applied for beta-diversity, differential abundance and pairwise log-ratio analyses. We found significant differences in overall microbiota composition between breeds, and also between diets inside breeds, to a lesser extent. Differential abundance analysis revealed that Duroc animals have more proportion of Actinobacteria and Prevotella, while Iberian replace those microorganisms with other more variable taxa. According to dietary differences, high-oleic fed animals were richer in Prevotella. We also found microbial ratios capable of separating animals by breeds and diets, mostly related to Actinobacteria. CONCLUSION: This study reveals that both genetic background and diet composition might have a relevant impact in gut microbiota composition. The application of compositional data analysis has facilitated the identification of microorganisms and ratios as possibly related to metabolic changes due to genetic background and, to a lower extent, to dietary changes. This may lead to a relevant progress in the knowledge of interactions between pig genetics, environment and gut microbiota.

Sequencing of SARS-CoV-2 genome using different nanopore chemistries.

Nanopore sequencing has emerged as a rapid and cost-efficient tool for diagnostic and epidemiological surveillance of SARS-CoV-2 during the COVID-19 pandemic. This study compared the results from sequencing the SARS-CoV-2 genome using R9 vs R10 flow cells and a Rapid Barcoding Kit (RBK) vs a Ligation Sequencing Kit (LSK). The R9 chemistry provided a lower error rate (3.5%) than R10 chemistry (7%). The SARS-CoV-2 genome includes few homopolymeric regions. Longest homopolymers were composed of 7 (TTTTTTT) and 6 (AAAAAA) nucleotides. The R10 chemistry resulted in a lower rate of deletions in thymine and adenine homopolymeric regions than the R9, at the expenses of a larger rate (~10%) of mismatches in these regions. The LSK had a larger yield than the RBK, and provided longer reads than the RBK. It also resulted in a larger percentage of aligned reads (99 vs 93%) and also in a complete consensus genome. The results from this study suggest that the LSK preparation library provided longer DNA fragments which contributed to a better assembly of the SARS-CoV-2, despite an impaired detection of variants in a R10 flow cell. Nanopore sequencing could be used in epidemiological surveillance of SARS-CoV-2. KEY POINTS: • Sequencing SARS-CoV-2 genome is of great importance for the pandemic surveillance. • Nanopore offers a low cost and accurate method to sequence SARS-CoV-2 genome. • Ligation sequencing is preferred rather than the rapid kit using transposases.

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.