Suppression of ovarian progesterone production in dairy cows using an implant of GnRH-agonist (deslorelin) for the purpose of evaluating progesterone metabolism

OBJECTIVE: To evaluate the potential of an implant of a GnRH-agonist (deslorelin) to create a progesterone free animal suitable for studying progesterone (P4) metabolism in intact cows by measuring blood P4 and faecal P4 metabolites. METHODS: Experiment 1: Eighteen non-lactating cycling Holstein-Friesian cows, 4 to 7 years old, were allocated to one of three groups to study plasma P4 concentrations preceding an intravaginal insert. These groups comprised: i) a deslorelin group (GnRH-agonist implanted); ii) a PGF group receiving two injections of prostaglandin (PGF2alpha) 12 days apart; and, iii) an ovariectomised (OVX) group. An intravaginal device (CIDR) was inserted into the vagina of each animal and left in place for 11 days. Plasma P4 concentrations were measured during the study period. Experiment 2: Twelve non-lactating cycling Holstein-Friesian cows, 4 to 7 years old, were allocated to two groups: i) a deslorelin group (GnRH-agonist implanted); and ii) an ovariectomised group. Plasma P4 and faecal P4 metabolites (20-oxo-pregnanes, 20alpha-OH and 20beta-OH) were monitored for a period of 5 weeks. RESULTS: Experiment 1: Average plasma P4 concentration did not differ between the three groups (1.28, 1.43 and 1.55 ng/mL for deslorelin, OVX and PGF cows, respectively, P = 0.8) during the period of supplementation. Experiment 2: There was no difference in plasma P4 (mean plasma P4 < 0.02 ng/mL, P = 0.9) and faecal P4 metabolites between deslorelin and OVX cows 2 weeks after the implantation (P = 0.7). CONCLUSIONS: These data showed that a GnRH-agonist (deslorelin) implant may be used as an alternative to ovariectomy to create a progesterone free animal suitable for studying the metabolism of administered P4.     

Distribution of alpha‐neoendorphin,ACTH (18–39) and beta‐endorphin (1–27) in the alpaca brainstem

Using an immunocytochemical technique, we have studied in the alpaca brainstem the distribution of immunoreactive structures containing prodynorphin (alpha‐neoendorphin)‐ and pro‐opiomelanocortin (adrenocorticotrophin hormone (18–39) (ACTH), beta‐endorphin (1–27))‐derived peptides. No peptidergic‐immunoreactive cell body was observed. Immunoreactive fibres were widely distributed, although in most of the brainstem nuclei the density of the peptidergic fibres was low or very low. In general, the distribution of the immunoreactive fibres containing the peptides studied was very similar. A close anatomical relationship occurred among the fibres containing alpha‐neoendorphin, ACTH or beta‐endorphin (1–27), suggesting a functional interaction among the three peptides in many of the brainstem nuclei. The number of fibres belonging to the prodynorphin system was higher than that of the pro‐opiomelanocortin system. A moderate/low density of immunoreactive fibres was observed in 65.11% (for alpha‐neoendorphin (1–27)), 18.18% (for ACTH) and 13.95% (for beta‐endorphin) of the brainstem nuclei/tracts. In the alpaca brainstem, a high density of immunoreactive fibres was not observed. The neuroanatomical distribution of the immunoreactive fibres suggests that the peptides studied are involved in auditory, motor, gastric, feeding, vigilance, stress, respiratory and cardiovascular mechanisms, taste response, sleep‐waking cycle and the control of pain transmission.     

Detection and genetic diversity of porcine rotavirus A,B and C in eastern Australian piggeries

Rotaviruses (RV) have a high prevalence in piggeries worldwide and are one of the major pathogens causing severe diarrhoea in young pigs. RV species A, B, and C have been linked to piglet diarrhoea in Australian pig herds, but their genetic diversity has not been studied in detail. Based on sequencing of the structural viral protein 7 (VP7) RVA G genotypes G3, G4 and G5, and RVC types G1, G3, G5, and G6 have been identified in Australian piggeries in previous studies. Although occurrence of RVB was reported in Australia in 1988, no further genetic analysis has been conducted. To improve health management decisions in Australian pig herds, more information on RV prevalence and genetic diversity is needed. Here, 243 enteric samples collected from 20 pig farms within Eastern Australia were analysed for the presence of RV in different age groups using a novel PCR-based multiplex assay (Pork MultiPath™ enteric panel). RVA, RVB, and RVC were detected in 10, 14, and 14 farms, respectively. Further sequencing of VP7 in selected RV-positive samples revealed G genotypes G2, G5, G9 (RVA), G6, G8, G14, G16, G20 (RVB), and G1, G3, G5, G6 (RVC) present. RVA was only detected in young (<10 weeks old) pigs whereas RVB and RVC were also detected in older animals (>11 weeks old). Interestingly, RVB and RVC G-type occurrence differed between age groups. In conclusion, this study provides new insights on the prevalence and diversity of different RV species in pig herds of Eastern Australia whilst demonstrating the ability of the Pork MultiPath™ technology to accurately differentiate between these RV species.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

Early harvest and ensilage of forage sorghum infected with ergot (Claviceps africana) reduces the risk of livestock poisoning

Sorghum ergot produces dihydroergosine (DHES) and related alkaloids, which cause hyperthermia in cattle. Proportions of infected panicles (grain heads), leaves and stems were determined in two forage sorghum crops extensively infected 2 to 4 weeks prior to sampling and the panicles were assayed for DHES. Composite samples from each crop, plus a third grain variety crop, were coarsely chopped and half of each sealed in plastic buckets for 6 weeks to simulate ensilation. The worst-infected panicles contained up to 55 mg DHES/kg, but dilution reduced average concentrations of DHES in crops to approximately 1 mg/kg, a relatively safe level for cattle. Ensilation significantly (P = 0.043) reduced mean DHES concentrations from 0.85 to 0.46 mg/kg.     

人员在岗培训在实施兽药GMP现场管理中的作用

论述了对兽药生产企业进行人员在岗培训的必要性,对实施在岗培训的基本程序和应注意的事项进行了总结,旨在为兽药企业进行GMP现场管理提供参考。