BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.
RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).
CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, β-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

This study aimed to evaluate the relationship between prepartum subclinical hypomagnesemia (pre-SHMg) and the occurrence of dystocia, metritis, clinical mastitis, lameness, and subclinical hypomagnesemia postpartum (post-SHMg) in pasture-based dairy cows. Also, the difference in means of prepartum magnesium (Mg) concentration by postpartum health events was evaluated. A total of 890 dairy cows from 32 commercial farms located in southern Chile were enrolled. Cows were examined twice, once between 30 and 3 days before and once between 3 and 30 days after calving. Blood samples were collected on both assessments, and cows were considered as having SHMg if serum total Mg < 0.65 mmol/L. On the postpartum visit, cows were evaluated for metritis and lameness. Information about clinical mastitis and dystocia was collected from on-farm records. Data were analyzed using multivariable mixed linear models and multivariable mixed logistic regression models. The overall prevalence of pre-SHMg was 9.9%, and its presence was associated with the occurrence of post-SHMg (odd ratio [OR] = 5.7; P < 0.0001) and metritis (OR = 3.1; P = 0.04). However, we did not detect an association between pre-SHMg and dystocia, clinical mastitis, or lameness after calving. Prepartum serum Mg concentrations were lower in cows that developed post-SHMg than those that did not (LSM ± SE = 0.75 ± 0.02 mmol/L vs. 0.83 ± 0.02 mmol/L; P < 0.0001). In conclusion, pre-SHMg was associated with a higher risk of post-SHMg and metritis in grazing dairy cows but not other postpartum health events.

Metabolic disorders pose significant challenges in transition dairy cows. Numerous parameters have been investigated in this context, and apelin has recently emerged as a potential metabolic indicator. Accordingly, this study aimed to assess the associations between this hormone and other metabolic parameters. Twenty-two adult Holstein-Friesian dairy cows, 21 days before their expected calving date, were selected for blood sampling and serum separation at four time points: 21 and 10 days before calving and 10 and 21 days after parturition. Serum concentrations of apelin, leptin, insulin, cortisol, T3, T4, non-esterified fatty acids, glucose, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, triglycerides, cholesterol, high, low and very low-density lipoproteins, total, direct and indirect bilirubin were measured in these samples. Surrogate indices for insulin resistance, body condition score, and milk production were also evaluated. Throughout the transition period, a significant increase in apelin levels was observed. Various models were employed to identify associations between apelin and the studied metabolic parameters. Notably, significant correlations between apelin and Leptin, Insulin, Cortisol, T3, T4, NEFA, Cholesterol, LDL, VLDL, Total Protein, Albumin, Globulin, Total Bilirubin, Direct Bilirubin and Indirect Bilirubin were observed, with some being immediate while others developed over time. These findings indicate a mutual influence between apelin and specific metabolic indices. Changes in any component of the metabolic profile at one stage can lead to alterations in apelin levels in subsequent stages. The correlations uncovered between apelin and other components of the metabolic profile in transitioning dairy cows offer valuable insights, contributing to a better understanding of the potential effects of apelin on the studied indicators and vice versa.

Bovine coronavirus (BCoV) is one of the important causes of diarrhoea in cattle. The virus is responsible for the high fatality rate associated with acute diarrhoea in calves. Rapid and accurate tests need to be conducted to detect the virus and minimise economic losses associated with the disease. Nucleic acid-based detection assays including PCR is an accurate test for detecting pathogens. However, these tests need skilled personnel, time and expensive devices. In this study, we developed a novel assay for the detection of BCoV in clinical cases. This novel assay combined reverse transcription-recombinase polymerase amplification with CRISPR/Cas13 and conducted a rapid visualisation of cleavage activity using a Lateral Flow Device. A conserved sequence of the BCV M gene was used as a target gene and the assays were tested in terms of specificity, sensitivity and time consumption. The result showed the specificity of the assay as 100% with no false positives being detected. Ten copies of the input RNA were enough to detect the virus and perform the assay. It took up to forty minutes for reading the results. Conducted together, the assay should be used as a rapid test to clinically diagnose infectious pathogens including bovine coronavirus. However, the assay needed the RNA to be extracted from the clinical sample in order to detect the virus. Therefore, more studies are needed to optimise the assay to be able to detect the virus in the clinical sample without extracting the RNA.

This study aimed to establish an accurate epidemiological surveillance tool for the detection of different C. perfringens types from 76 diseased and 34 healthy animals in Dakhalia Governorate, Egypt. A total of 110 intestinal content samples were randomly collected from camels, sheep, and cattle. C. perfringens was isolated and biochemically identified by the VITEK2 system. Toxinotyping and genotyping of C. perfringens isolates were specified by a multiscreen ELISA and real-time qPCR (rt-qPCR). The occurrence of C. perfringens was highest among camels (20% in healthy and 25% in diseased) and was lowest in cattle (23.1% and 14.7%). The cpa toxin was detected in all isolates by rt-qPCR and in 7 isolates by ELISA, ext toxin was detected in 7 isolates by rt-qPCR and in 6 isolates by ELISA, and cpb toxin was detected in 2 isolates by both rt-qPCR and ELISA. Four types of C. perfringens were identified by rt-qPCR, type A (65.2%), B (4.3%), C (4.3%), and D (26.1%), and three types by ELISA, type D (17.4%), A (8.7%) and C (4.3%). Our study indicated the prevalence of infection in Dakahlia by C. perfringens type A and D, particularly camels, and recommends adopting an appropriate vaccination strategy among the studied animals.

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

Selenium is an essential trace element in our diet, crucial for the composition of human selenoproteins, which include 25 genes such as glutathione peroxidases and thioredoxin reductases. The regulation of the selenoproteome primarily hinges on the bioavailability of selenium, either from dietary sources or cell culture media. This selenium-dependent control follows a specific hierarchy, with \"housekeeping\" selenoproteins maintaining constant expression while \"stress-regulated\" counterparts respond to selenium level fluctuations. This study investigates the variability in fetal bovine serum (FBS) selenium concentrations among commercial batches and its effects on the expression of specific stress-related cellular selenoproteins. Despite the limitations of our study, which exclusively used HEK293 cells and focused on a subset of selenoproteins, our findings highlight the substantial impact of serum selenium levels on selenoprotein expression, particularly for GPX1 and GPX4. The luciferase reporter assay emerged as a sensitive and precise method for evaluating selenium levels in cell culture environments. While not exhaustive, this analysis provides valuable insights into selenium-mediated selenoprotein regulation, emphasizing the importance of serum composition in cellular responses and offering guidance for researchers in the selenoprotein field.

This study is focused on fractionation of insulin-like growth factor I (IGF-I) and transforming growth factor-β2 (TGF-β2) using a new electro-based membrane process calledelectrodialysis with filtration membranes (EDFM). Before EDFM, different pretreatments were tested, and four pH conditions (4.25, 3.85, 3.45, and 3.05) were used during EDFM. It was demonstrated that a 1:1 dilution of defatted colostrum with deionized water to decrease mineral content followed by the preconcentration of GFs by UF is necessary and allow for these compounds to migrate to the recovery compartment during EDFM. MS analyses confirmed the migration, in low quantity, of only α-lactalbumin (α-la) and β-lactoglobulin (β-lg) from serocolostrum to the recovery compartment during EDFM. Consequently, the ratio of GFs to total protein in recovery compartment compared to that of feed serocolostrum solution was 60× higher at pH value 3.05, the optimal pH favoring the migration of IGF-I and TGF-β2. Finally, these optimal conditions were tested on acid whey to also demonstrate the feasibility of the proposed process on one of the main by-products of the cheese industry; the ratio of GFs to total protein was 2.7× higher in recovery compartment than in feed acid whey solution, and only α-la migrated. The technology of GF enrichment for different dairy solutions by combining ultrafiltration and electrodialysis technologies was proposed for the first time.

Beef is a major global source of protein, playing an essential role in the human diet. The worldwide production and consumption of beef continue to rise, reflecting a significant trend. However, despite the critical importance of beef cattle resources in agriculture, the diversity of cattle breeds faces severe challenges, with many breeds at risk of extinction. The initiation of the Beef Cattle Genome Project is crucial. By constructing a high-precision functional annotation map of their genome, it becomes possible to analyze the genetic mechanisms underlying important traits in beef cattle, laying a solid foundation for breeding more efficient and productive cattle breeds. This review details advances in genome sequencing and assembly technologies, iterative upgrades of the beef cattle reference genome, and its application in pan-genome research. Additionally, it summarizes relevant studies on the discovery of functional genes associated with key traits in beef cattle, such as growth, meat quality, reproduction, polled traits, disease resistance, and environmental adaptability. Finally, the review explores the potential of telomere-to-telomere (T2T) genome assembly, structural variations (SVs), and multi-omics techniques in future beef cattle genetic breeding. These advancements collectively offer promising avenues for enhancing beef cattle breeding and improving genetic traits.