The mechanism whereby the inherently high variation in ovary size and the total number of high-quality oocytes in ovaries (ovarian reserve) impact on ovarian function and fertility, diagnostics to measure the size of the ovarian reserve and the factors that cause variation in the ovarian reserve are unknown. Our results show that cattle can be phenotyped reliably based on the number of antral follicles growing during follicular waves (antral follicle count, AFC). Young adult cattle with a consistently low v. a high AFC have smaller gonads, a markedly diminished ovarian reserve and many other phenotypic characteristics usually associated with ovarian aging and infertility. A powerful new approach based on a single measurement of serum concentration of anti-Mullerian hormone (AMH) is described to test the longstanding hypothesis that the size of the ovarian reserve is positively associated with fertility. Also, new evidence shows that maternal environment has a critical role in regulation of the high variation in the ovarian reserve and perhaps fertility in offspring. These results support the conclusion that the inherently high variation in the ovarian reserve, potentially caused by alterations in the maternal environment, has a negative impact on ovarian function that may result in suboptimal fertility in young adult cattle, and a single AMH measurement can be used reliably in future studies to determine if fertility is suboptimal in young adult cattle with low circulating AMH concentrations and a correspondingly diminished ovarian reserve.

Does size matter in females? An overview of the impact of the high variation in the ovarian reserve on ovarian function and fertility, utility of anti-Müllerian hormone as a diagnostic marker for fertility and causes of variation in the ovarian reserve in cattle

The objective of this study was to use pyrosequencing of the 16S rRNA genes to describe the microbial diversity of bovine milk samples derived from clinically unaffected quarters across a range of somatic cell counts (SCC) values or from clinical mastitis, culture negative quarters. The obtained microbiota profiles were used to distinguish healthy, subclinically and clinically affected quarters. Two dairy farms were used for the collection of milk samples. A total of 177 samples were used. Fifty samples derived from healthy, culture negative quarters with a SCC of less than 20,000 cells/ml (group 1); 34 samples derived from healthy, culture negative quarters, with a SCC ranging from 21,000 to 50,000 cells/ml (group 2); 26 samples derived from healthy, culture negative quarters with a SCC greater than 50,000 cells/ml (group 3); 34 samples derived from healthy, culture positive quarters, with a SCC greater than 400,000 (group 4, subclinical); and 33 samples derived from clinical mastitis, culture negative quarters (group 5, clinical). Bacterial DNA was isolated from these samples and the 16S rRNA genes were individually amplified and pyrosequenced. All samples analyzed revealed great microbial diversity. Four bacterial genera were present in every sample obtained from healthy quarters (Faecalibacterium spp., unclassified Lachnospiraceae, Propionibacterium spp. and Aeribacillus spp.). Discriminant analysis models showed that samples derived from healthy quarters were easily discriminated based on their microbiota profiles from samples derived from clinical mastitis, culture negative quarters; that was also the case for samples obtained from different farms. Staphylococcus spp. and Streptococcus spp. were among the most prevalent genera in all groups while a general multivariable linear model revealed that Sphingobacterium and Streptococcus prevalences were associated with increased 10 log SCC. Conversely, Nocardiodes and Paenibacillus were negatively correlated, and a higher percentage of the genera was associated with a lower 10 log SCC.

/pdf/microbiota-of-cow-s-milk-distinguishing-healthy-sub-3gu2gmzzww.pdf

Microbiota of cow's milk; distinguishing healthy, sub-clinically and clinically diseased quarters.

Factors affecting the lactoferrin concentration in bovine milk.

The objective of this study was to extract novel phenotypes related to disease resilience using daily feed intake data from growing pigs under a multifactorial natural disease challenge that was designed to mimic a commercial environment with high disease pressure to maximize expression of resilience. Data used were the first 1,341 crossbred wean-to-finish pigs from a research facility in Quebec, Canada. The natural challenge was established under careful veterinary oversight by seeding the facility with diseased pigs from local health-challenged farms, targeting various viral and bacterial diseases, and maintaining disease pressure by entering batches of 60-75 pigs in a continuous flow system. Feed intake (FI) is sensitive to disease, as pigs tend to eat less when they become ill. Four phenotypes were extracted from the individual daily FI data during finishing as novel measures of resilience. The first two were daily variability in FI or FI duration, quantified by the root mean square error (RMSE) from the within individual regressions of FI or duration at the feeder (DUR) on age (RMSEFI and RMSEDUR). The other two were the proportion of off-feed days, classified based on negative residuals from a 5% quantile regression (QR) of daily feed intake or duration data on age across all pigs (QRFI and QRDUR). Mortality and treatment rate had a heritability of 0.13 (±0.05) and 0.29 (±0.07), respectively. Heritability estimates for RMSEFI, RMSEDUR, QRFI, and QRDUR were 0.21 (±0.07) 0.26 (±0.07), 0.15 (±0.06), and 0.23 (±0.07), respectively. Genetic correlations of RMSE and QR measures with mortality and treatment rate ranged from 0.37 to 0.85, with QR measures having stronger correlations with both. Estimates of genetic correlations of RMSE measures with production traits were typically low, but often favorable (e.g., -0.31 between RMSEFI and finishing ADG). Although disease resilience was our target, fluctuations in FI and duration can be caused by many factors other than disease and should be viewed as overall indicators of general resilience to a variety of stressors. In conclusion, daily variation in FI or duration at the feeder can be used as heritable measures of resilience.

https://era.library.ualberta.ca/items/10814af5-4f4f-4ff7-a9e6-b715e825cdc0/view/88635183-3a11-457e-a232-66ce17fe56a5/fgene-09-00660.pdf

Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs

Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle

Somatic cell count distributions during lactation predict clinical mastitis.

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Somatic cell count distributions during lactation predict clinical mastitis.