Alterations of rumen and fecal microbiome in growing beef and dairy steers fed rumen-protected Capsicum oleoresin.

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicum genus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicum oleoresin at 0 (Control), 5, 10, or 15 mg kg-1 diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P > 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P > 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P < 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.

The microbiome is the collection of microbes present in an animal's body and has been discovered to be directly connected to animal health and productivity. In production animals, such as feedlot cattle, the microbiome can be modulated by antimicrobials to promote growth, but increasing consumer pressure to reduce antimicrobial use has producers seeking alternatives. Capsaicin is a phytotherapeutic derived from chili peppers that can be used to modulate the microbiome due to its antimicrobial properties. Eight steers were fed rumen-protected Capsicum oleoresin to determine its effect on average daily gain. In addition, rumen and fecal samples were collected for microbiome testing. No differences were detected in the rumen microbiomes between cattle fed capsaicin (treatment) or those that received no capsaicin (control). While no overall effect was observed on the fecal microbiome of cattle fed different doses of capsaicin or control, we did observe changes in fecal beta diversity due to capsaicin treatment in Holstein steers fed greater doses. The fecal microbiome structure of Holsteins fed greater dosages of capsaicin differed from those fed control or low doses, as observed by the presence of two distinct clusters. This observation suggests an impact of greater doses of capsaicin treatment on microbiome structure.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Does modeling causal relationships improve the accuracy of predicting lactation milk yields?

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

Estimating test-day milk yields by modeling proportional daily yields: Going beyond linearity.

In the United States, lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans model is a de facto method for estimating test-day milk yields in the United States, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., locally estimated scatterplot smoothing) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including nonparametric models.

Improving national fertility evaluations by accounting for the rapid rise of embryo transfer in US dairy cattle.

Dairy producers have improved fertility of their herds by selecting bulls with higher conception rate evaluations. This research was motivated by the rapid increase in embryo transfer (ET) use to 11% of recent births and >1 million total births, with >5 times as many ET calves born in the United States in 2021 compared with just 5 yr earlier. Historical data used in genetic evaluations are stored in the National Cooperator Database. Recent records in the national pedigree database revealed that only 1% of ET calves have corresponding ET records in the breeding event database, 2% are incorrectly reported as artificial inseminations, and 97% have no associated breeding event. Embryo donation events are also rarely reported. Herd years reporting >10% of calves born by ET but less than half of the expected number of ET breeding events were removed to avoid potential biases. Heifer, cow, and sire conception rate evaluations were recalculated with this new data set according to the methods used for the official national evaluations. The edits removed about 1% of fertility records in the most recent 4 yr. Subsequent analysis showed that censoring herd years with inconsistent ET reporting had little effect on most bulls except for the highest ranking, younger bulls popular for ET use, and with largest effects on genomic selection. Improved ET reporting will be critical for providing accurate fertility evaluations, especially as the popularity of these advanced reproductive technologies continues to rise.

Daily milk yield correction factors: What are they?

Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient of 2.0. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.

The association of hyperketonemia with fecal and rumen microbiota at time of diagnosis in a case-control cohort of early lactation cows.

BACKGROUND: Many dairy cows experience a state of energy deficit as they transition from late gestation to early lactation. The aims of this study were to 1) determine if the development of hyperketonemia in early lactation dairy cows is indicated by their gut microbiome, and 2) to identify microbial features which may inform health status. We conducted a prospective nested case-control study in which cows were enrolled 14 to 7 days before calving and followed through their first 14 days in milk (DIM). Hyperketonemic cows (HYK, n = 10) were classified based on a blood ß-hydroxybutyrate (BHB) concentration 1.2 mmol/L within their first 14 DIM. For each HYK cow, two non-HYK (CON, n = 20) cows were matched by parity and 3 DIM, with BHB < 1.2 mmol/L. Daily blood BHB measures were used to confirm CON cows maintained their healthy status; some CON cows displayed BHB 1.2 mmol/L after matching and these cows were reclassified as control-HYK (C-HYK, n = 9). Rumen and fecal samples were collected on the day of diagnosis or matching and subjected to 16S rRNA profiling. RESULTS: No differences in taxa abundance, or alpha and beta diversity, were observed among CON, C-HYK, and HYK health groups for fecal microbiomes. Similar microbiome composition based on beta diversity analysis was detected for all health statuses, however the rumen microbiome of CON and HYK cows were found to be significantly different. Interestingly, highly similar microbiome composition was observed among C-HYK cow rumen and fecal microbiomes, suggesting that these individual animals which initially appear healthy with late onset of hyperketonemia were highly similar to each other. These C-HYK cows had significantly lower abundance of Ruminococcus 2 in their rumen microbiome compared to CON and HYK groups. Multinomial regressions used to compute log-fold changes in microbial abundance relative to health status were not found to have predictive value, therefore were not useful to identify the role of certain microbial features in predicting health status. CONCLUSIONS: Lower relative abundance of Ruminococcus 2 in C-HYK cow rumens was observed, suggesting these cows may be less efficient at degrading cellulose although the mechanistic role of Ruminococcus spp. in rumen metabolism is not completely understood. Substantial differences in fecal or rumen microbiomes among cows experiencing different levels of energy deficit were not observed, suggesting that hyperketonemia may not be greatly influenced by gut microbial composition, and vice versa. Further studies using higher resolution -omics approaches like meta-transcriptomics or meta-proteomics are needed to decipher the exact mechanisms at play.

Statistical Methods Revisited for Estimating Daily Milk Yields: How Well do They Work?

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of "2.0" for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies.

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics-in addition to the traditional considerations when conducting an animal science study-makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type.

Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association.

Our objectives were to robustly characterize a cohort of Holstein cows for udder and teat type traits and perform high-density genome-wide association studies for those traits within the same group of animals, thereby improving the accuracy of the phenotypic measurements and genomic association study. Additionally, we sought to identify a novel udder and teat trait composite risk index to determine loci with potential pleiotropic effects related to mastitis. This approach was aimed at improving the biological understanding of the genetic factors influencing mastitis. Cows (N = 471) were genotyped on the Illumina BovineHD777k beadchip and scored for front and rear teat length, width, end shape, and placement; fore udder attachment; udder cleft; udder depth; rear udder height; and rear udder width. We used principal component analysis to create a single composite measure describing type traits previously linked to high odds of developing mastitis within our cohort of cows. Genome-wide associations were performed, and 28 genomic regions were significantly associated (Bonferroni-corrected p < 0.05). Interrogation of these genomic regions revealed a number of biologically plausible genes whicht may contribute to the development of mastitis and whose functions range from regulating cell proliferation to immune system signaling, including ZNF683, DHX9, CUX1, TNNT1, and SPRY1. Genetic investigation of the risk composite trait implicated a novel locus and candidate genes that have potentially pleiotropic effects related to mastitis.

Graduate Student Literature Review: Understanding the genetic mechanisms underlying mastitis.

Mastitis is the costliest disease facing dairy producers today; consequently, it has been the subject of substantial research focus. Efforts have evolved from an initial focus on understanding the etiology of intramammary infections to the application of preventative measures, including attempts to breed cows that are resistant to infection. However, breeding for resistance to infection has proven difficult, given the complexity of the disease and the high expense associated with assembling high-quality genotypes and phenotypes. This review provides a brief background on mastitis; illustrates current understanding of the genetics influencing mastitis and the application of this knowledge; and discusses challenges and limitations in understanding these mechanisms and applying these findings to genetic improvement strategies.

Time- and population-dependent genetic patterns underlie bovine milk somatic cell count.

The objective of this study was to determine whether genetic regulation of bovine milk somatic cell count (SCC) varied throughout the course of an individual lactation and to identify quantitative trait loci (QTL) that may differentiate populations of chronically mastitic and robustly healthy cows. Milk SCC has long been a proxy for clinical mastitis diagnosis in management and genetic improvement strategies to control the disease. Cows (n = 471) were genotyped on the Illumina BovineHD 777K BeadChip (Illumina Inc., San Diego, CA), and composite milk samples were collected for SCC at 0-1 d in milk (DIM), 3-5 DIM, 10-14 DIM, 90-110 DIM, and 210-230 DIM, with each time span representing key physiological transitions for the cow. Median lactation somatic cell score (SCS) and area under the SCS curve were calculated from farm test data. A total of 8 genome-wide associations were performed and 167 SNP spanning the genome were significantly associated (false discovery rate <0.05). Of these associated regions, 27 of 48 associated QTL were novel for clinical mastitis or SCC. The linkage disequilibrium block surrounding the associated QTL or a 1-Mb window in the absence of linkage disequilibrium was interrogated for candidate genes, and many of those identified were related to multiple arms of the immune system, including toll-like receptor signaling, macrophage activation, B-cell maturation, T-cell recruitment, and the complement pathway. These genes included EXOC4, BAMBI, ITSN2, IL34, FCN3, CD8A, and CD8B. In addition, we identified populations of robustly healthy (SCS ≤4 from 10-14 DIM until study end), chronically mastitic (SCS >4 from 10-14 DIM until study end), and average cows with fluctuating SCS, and calculated fixation indices to identify regions of the genome differentiating these 3 populations. A total of 12 SNP were identified that showed moderate allelic differentiation (Wright's F statistic, FST ≥ 0.4) between the "chronic," "healthy," and "average" populations of cows. Candidate genes in the region surrounding differentiated QTL were related to cell signaling and immune response, such as JAKMIP1 and MADCAM1. The wide range of significantly associated QTL spanning the genome and the diversity of gene functions reinforces that mastitis is a complex trait and suggests that selection based on lactation stage-specific SCS rather than a generalized score may lead to greater success in breeding mastitis-resistant cows.

Udder and teat conformational risk factors for elevated somatic cell count and clinical mastitis in New York Holsteins.

Our primary objective was to identify udder and teat conformational risk factors associated with the occurrence of elevated somatic cell count (SCC) and clinical mastitis using a prospective cohort study design with careful assessment of exposure and disease outcomes. Mastitis prevalence was evaluated by parity across 6 sampling periods representing key physiological transitions during lactation: 0-1 day in milk (DIM), 3-5 DIM, 10-14 DIM, 50-60 DIM, 90-110 DIM, and 210-230 DIM. Cows were scored for front and rear teat length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Two independent multivariable logistic regression models were used to generate odds ratios (OR) for elevated SCC (≥ 200,000 cells/ml) and farm-diagnosed clinical mastitis. We identified that loose fore udder attachment (reference level: strong fore udder attachment, OR = 2.1, 95% confidence interval (CI) = 1.2-3.8) and flat teat end shape (reference level: round teat end shape, OR = 1.4, 95% CI = 1.1-1.9) increased the odds of an elevated SCC event, whereas a negative California Mastitis Test score at 0-1 DIM decreased the odds of an elevated SCC event (OR = 0.6, 95% CI = 0.4 to 0.8). Loose fore udder attachment (reference level: strong fore udder attachment, OR = 3.7, 95% CI = 1.3-10.7), flat teat end shape (reference level: round teat end shape, OR = 1.5, 95% CI = 1.0-2.4), low rear udder height (reference level: intermediate rear udder height, OR = 2.8, 95% CI = 0.3-6.2), and increasing rear teat width (OR = 2.2, 95% CI = 1.2-4.4) heightened the odds of developing clinical mastitis. We identified that within our study cohort, loose fore udder attachment and flat teat ends had an important association with increased odds of both an elevated SCC event and clinical mastitis diagnosis. The identification of these udder and teat conformational risk factors for mastitis can provide farmers an effective and inexpensive tool to manage mastitis.

A longitudinal study of digital cushion thickness and its function as a predictor for compromised locomotion and hoof lesions in Holstein cows.

Lameness is a major animal welfare and economic issue for the dairy industry and is a challenge to overcome due to multifaceted causes. Digital cushion thickness (DCT) is a strong predictor of lameness and is phenotypically associated with incidence of claw horn disruption lesions (CHDL; sole ulcers and white line disease). We hypothesized that DCT varies between digits and across lactation within the cow. This variation could be characterized to predict the occurrence of CHDL or compromised locomotion. BCS, visual locomotion score (VLS), DCT, and presence or absence of lesions were collected at 4 time points: <40 d prepartum (DPP), 1 to 30 d in milk (DIM), 90 to 120 DIM, and ≥255 DIM for 183 commercial Holstein cows enrolled in the study. Cows underwent digital sonographic examination for the measurement of DCT evaluated at the typical sole ulcer site beneath the flexor tuberosity for the right front medial and lateral digits and right hind medial and lateral digits. Factors such as parity number and stage in lactation were obtained from farm management software (DairyComp 305; Valley Agricultural Software, Tulare, CA). Cows were grouped by parity: primiparous (parity = 1) or multiparous (parity ≥ 2). The prevalence of CHDL among time points ranged from 0% to 4.2% for primiparous cows vs. 2.5% to 25% for multiparous cows, whereas the prevalence of lameness based on VLS of 3 to 5 ranged from 1.7% to 8.3% for primiparous cows vs. 12.7% to 33% for multiparous cows. DCT varied within primiparous and multiparous cows based on stage of lactation and digit (P < 0.05) and was thicker for both parity groups prior to dry off (≥255 DIM) and thinnest prior to calving (<40 DPP) and after peak lactation (90 to 120 DIM). The DCT of the front medial digit was thickest for primiparous heifers, whereas the hind lateral digit was thickest for multiparous cows. The DCT of the hind medial digit was thinnest for both parity groups. Parity group and DCT of the hind lateral digit <40 DPP were important predictors of CHDL (P < 0.05), whereas parity group and DCT of the hind lateral digit and front lateral digit at 1 to 30 DIM were key predictors of VLS lameness (P < 0.05). These results may help identify animals with higher odds of developing these diseases by highlighting key time points and specific digits of importance for monitoring. In addition, it improves our biological understanding of the relationship between DCT and lameness.