Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights.

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Programming effects of intrauterine hyperthermia on adrenal gland development.

Maternal heat stress during late pregnancy can lead to intrauterine hyperthermia and affect fetal hypothalamic-pituitary-adrenal axis development and function. Herein, we investigated the effects of chronic environmental heat stress exposure of Holstein cows in the last 2 mo of gestation on their offspring's adrenal gland histomorphology and transcriptome. Cows in their last 54 ± 5 d of gestation were either heat-stressed (i.e., housed under the shade of a free stall barn) or provided heat-stress abatement via active cooling (i.e., via water soakers and fans) during a subtropical summer (Temperature-Humidity Index >68). Respiration rate (RR) and skin temperature (ST) were elevated in heat-stressed dams relative to the cows with access to heat abatement (23 bpm and 2 ◦C higher for RR and ST, respectively). Heifers born to heat-stressed cows experienced heat stress in utero (HS), while heifers born to actively cooled cows did not (CL). The adrenal gland was harvested from 6 heifers per group that were euthanized at birth (d 0; n = 12) or one week after weaning (d 63; n = 12). Circulating cortisol was measured from blood samples collected weekly throughout the pre-weaning period. At d 63, heifers that experienced HS while developing in utero had heavier adrenal glands, with a greater total tissue surface area and thickness of the zona glomerulosa (ZG), fasciculata (ZF), and reticularis (ZR), compared with CL heifers. In addition, the adrenal gland of in utero HS heifers had less cells in the ZG, more and larger cells in the ZF and larger cells in the ZR, relative to CL heifers. Although no changes in circulating cortisol were observed through the pre-weaning period, the transcriptomic profile of the adrenal tissue was altered by fetal exposure to hyperthermia. Both at birth and on d 63, approximately 30 pathways were differentially expressed in the adrenal glands of in utero HS heifers relative to CL. These pathways were associated with immune function, inflammation, prolactin signaling, cell function, and calcium transport. Upstream regulators significantly activated or inhibited in the adrenal glands of heifers exposed to intrauterine hyperthermia were identified. Maternal exposure to heat stress during late gestation caused an enlargement of their offspring's adrenal glands by inducing ZG and ZF cell hypertrophy, and caused gene expression changes. These phenotypic, histological, and molecular changes in the adrenal gland might lead to alterations in stress, immune, and metabolic responses later in life.

Genetic parameters and trends for Johne's disease in US Holsteins: An updated study.

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, IDEXX Paratuberculosis Screening Ab Test (IDX) and Parachek 2 (PCK), were used to analyze milk samples with the ELISA technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of EBVs of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant. The correlations of bulls' PTA with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency.

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.

Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite.

Large data sets allow estimating feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and body weight change during 28- and 42-d feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the energy-corrected milk formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, while the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kg DMI / kg body weight/lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by NASEM (2021) based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with body weight and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows.

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling.

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Host and rumen microbiome contributions to feed efficiency traits in Holstein cows.

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

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.

Hormonal profile prior to luteolysis modulates the uterine luminal transcriptome in the subsequent cycle in beef cross-bred cows†.

Sex steroid concentrations modulate endometrial function and fertility in cattle. Our objective was to compare the post-estrus luminal transcriptome of cows that were exposed to contrasting concentrations of progesterone (P4) before luteolysis that displayed estrus and ovulated spontaneously. Cross-bred beef cows received either (1) a new CIDR and GnRH (day -9; high progesterone treatment; HP4; n = 16) or (2) a previously used CIDR, PGF2α, and GnRH (low progesterone treatment; LP4; n = 24). All cows received PGF2α at CIDR removal (day -2). Ovarian ultrasonography and blood collections were performed on days -9, -2, -0.5, and 0 (day of observed estrus), and days 4, 7, and 14 for measurement of ovarian structures, P4, and estradiol (E2). Luminal epithelial cells were collected using a cytology brush on days 4, 7, and 14 for RNAseq. On day -2, CL area and concentrations of P4 were greater, while on day -0.5, concentrations of E2 were decreased in HP4. Ovarian structures and hormonal concentrations were similar on days 4, 7, or 14 (P > 0.05). There were enriched pathways in HP4 related to activation and signaling of the innate immune system at day 4, downregulation in the network involved in the extracellular matrix remodeling at day 7, and exacerbated inflammatory response as well as differentiation and activation of macrophages at day 14 (Benjamini-Hochberg P-value ≤ 0.05). In conclusion, manipulation of pre-luteolysis sex steroid concentrations altered the post-estrus luminal transcriptome even though all cows showed estrus and ovulated spontaneously.

Pre-estrus progesterone does not affect post-estrus luminal metabolome in cross-bred beef cows.

In brief: The concentration of progesterone through the estrous cycle modulates uterine function to affect the luminal metabolome. This paper reports that the dynamic changes in the bovine uterine luminal metabolome during diestrus are independent of the concentration of progesterone in the previous cycle. Abstract: In cattle, the concentration of sex steroids modulates uterine function, which is reflected in the composition of the luminal metabolome. Ultimately, the uterine luminal metabolome influences embryonic growth and development. Our objectives were (i) to compare the luminal metabolome 4, 7, and 14 days after estrus of cows that were exposed to greater (HP4; n = 16) vs lower (LP4; n = 24) concentrations of progesterone before displaying estrus and ovulating spontaneously and (ii) to identify changes in the luminal concentration of metabolites across these time points. Luminal epithelial cells and fluid were collected using a cytology brush, and gene expression and metabolite concentrations were assessed by RNAseq and targeted mass spectrometry, respectively. Metabolome profile was similar between treatments within each of days 4, 7, and 14 (false discovery rate (FDR): ≥ 0.1). Concentrations of 53 metabolites changed, independent of treatment, across the diestrus. Metabolites were mostly lipids (40 out 53) and the greatest concentrations were at day 14 (FDR: ≤ 0.1). On day 7, the concentration of putrescine and the gene expression of ODC1, PAOX, SLC3A2, and SAT1 increased (P ≤ 0.05). On day 14, the concentration of 3 ceramides, 4 glucosylceramides, and 12 sphingomyelins and the expression of SGMS2 were increased, in addition to the concentration of choline and 20 phosphatidylcholines. Collectively, the post-estrus concentration of luminal metabolites changed dynamically, independent of the concentration of sex steroids on the previous cycle, and the greatest magnitude changes were on day 14 when lipid metabolism was the most enriched pathway.

Effects of type traits, inbreeding, and production on survival in US Jersey cattle.

Greater longevity is associated with lower replacement costs, higher average milk production, and fewer replacement heifers needed. Longevity data are obtained late in life, and for this reason stayability, defined as the probability of survival from birth until a certain age, can be used as an alternative measure. The objective of this study was to evaluate the effects of different type traits, inbreeding, and production level on the stayability of Jersey cows to various ages, and to assess trends over time. Data consisted of 460,172 to 204,658 stayability records, depending on length of the opportunity period, for survival from birth until 36, 48, 60, 72, or 84 mo of age. Threshold models were used to analyze the stayability traits, including different type traits, inbreeding coefficient, and within-herd production level as explanatory variables. Heritability estimates for stayability traits ranged from 0.05 (36 mo) to 0.22 (84 mo). As expected, the probability of survival decreased as age increased. Highly productive cows were more likely to survive than their poor-producing contemporaries regardless of age and the type trait evaluated. Our data indicate that farmers' selection decisions tend to punish poor production at early ages and reward high production at later stages. Inbreeding negatively affected the probability of survival, especially when inbreeding coefficients exceeded 10%, and this impact was most noticeable at 48 mo of age or later. Some type traits, such as stature and foot angle, had little effect on the probability of survival. Other type traits, such as strength, dairy form, rump width, and rear legs, showed higher probability of survival at intermediate scores, whereas other type traits, such as fore udder attachment, rear udder height, udder depth, and final score, showed higher probability of survival at higher scores. Finally, our results indicate that the probability of survival has decreased in the last decade, probably due to a greater number of heifers available and, therefore, higher culling rates.

Transcriptomic analysis reveals gene expression changes in peripheral white blood cells of cows after embryo transfer: Implications for pregnancy tolerance.

Most embryo losses occur in the first trimester of pregnancy in cows and include losses following embryo transfer. There is a resulting negative economic impact on cattle production systems when this occurs. Cellular and molecular mechanisms behind the maternal immune response to the growing embryo have not been fully characterized. The objective of this study was to examine the gene expression profiles of peripheral white blood cells (PWBCs) from pregnant cows 21 days after an embryo was transferred, and cows that were treated equally but lost the embryo. Specifically, we obtained and compared the transcriptome of PWBC from heifers that became pregnant at day 21 (N = 5) or failed to become pregnant after the embryo transfer (N = 5). Sequencing data can be accessed by Gene Expression Omnibus (GEO) with the accession number GSE210665. A total of 13,167 genes were evaluated for differential expression between groups. A total of 682 genes showed differential expression (p-value <.01), 302 genes were up-regulated while 380 were down-regulated due to pregnancy. The most significant genes were COL1A2, H2AC18, HTRA1, MMP14, CD5L, ADAMDEC1, MYO1A and RPL39, among others. Most of the significant genes are related to the up-regulation of inflammatory chemokine activity and immune defence response. Our findings extend the current knowledge that pregnancy alters the PWBC by promoting immune tolerance, cell chemotaxis, blood coagulation, angiogenesis, inflammatory response, cell adhesion and cytokine secretion. Our data suggest that pregnancy and ectoparasites could trigger poorly described genes in PWBC of cows, and a few previously escribed genes, such as IFI44. These results could shed light on the genes and mechanisms that promote tolerance to pregnancy and allow survival of the developing embryo.

Progesterone-dependent and progesterone-independent modulation of luminal epithelial transcription to support pregnancy in cattle.

In cattle, starting 4-5 days after estrus, preimplantation embryonic development occurs in the confinement of the uterine lumen. Cells in the endometrial epithelial layer control the molecular traffic to and from the lumen and, thereby determine luminal composition. Starting early postestrus, endometrial function is regulated by sex steroids, but the effects of progesterone on luminal cells transcription have not been measured in vivo. The first objective was to determine the extent to which progesterone controls transcription in luminal epithelial cells 4 days (D4) after estrus. The second objective was to discover luminal transcripts that predict pregnancy outcomes when the effect of progesterone is controlled. Endometrial luminal epithelial cells were collected from embryo transfer recipients on D4 using a cytological brush and their transcriptome was determined by RNASeq. Pregnancy by embryo transfer was measured on D30 (25 pregnant and 18 nonpregnant). Progesterone concentration on D4 was associated positively (n = 182) and negatively (n = 58) with gene expression. Progesterone-modulated transcription indicated an increase in oxidative phosphorylation, biosynthetic activity, and proliferation of epithelial cells. When these effects of progesterone were controlled, different genes affected positively (n = 22) and negatively (n = 292) odds of pregnancy. These set of genes indicated that a receptive uterine environment was characterized by the inhibition of phosphoinositide signaling and innate immune system responses. A panel of 25 genes predicted the pregnancy outcome with sensitivity and specificity ranging from 64%-96% and 44%-83%, respectively. In conclusion, in the early diestrus, both progesterone-dependent and progesterone-independent mechanisms regulate luminal epithelial transcription associated with pregnancy outcomes in cattle.

Genetic analysis of fetal loss in Holstein cattle.

Pregnancy loss is recognized as one of the major factors contributing to poor reproductive performance in dairy cattle. Here, we performed a comprehensive genetic analysis of fetal loss, defined as a pregnancy loss that occurs after detection of a viable embryo around 42 d of gestation. The objectives of this study were to reveal (1) whether fetal loss is heritable and, hence, whether it will respond to selection, and (2) to what extent current fertility traits, such as daughter pregnancy rate, are associated with fetal loss. Data consisted of 59,308 confirmed pregnancy or fetal loss records distributed across nulliparous heifers and primiparous and multiparous cows. We defined fetal loss as a binary trait (yes vs. no) or as an ordinal trait (pregnancy maintenance, early fetal loss ≤150 d of gestation, and late fetal loss >150 d of gestation), and we assessed both linear and threshold models. Heritability estimates for fetal loss ranged from 1 to 18%, depending upon parity, trait definition, and statistical model used. Heritability estimates were greater for lactating cows than for nonlactating nulliparous heifers. Threshold models were able to capture more additive genetic variance and, thus, yielded higher heritability estimates than linear models. Notably, fetal loss traits were highly genetically correlated with each other but only weakly correlated with current fertility traits included in the national genetic evaluation. Overall, our study provides evidence that fetal loss is heritable enough to make genetic selection for reducing fetal loss and improving pregnancy maintenance feasible. In addition, our results suggest that fetal loss is largely independent from current traits used to select for cow fertility, and thus current breeding efforts have unfortunately little effect on reducing the incidence of fetal loss.

Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows.

The onset of lactation results in a sudden irreversible loss of Ca for colostrum and milk synthesis. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, whereas a larger proportion of cows develop subclinical hypocalcemia that predisposes them to other peripartum diseases. The objective of this study was to perform a comprehensive genomic analysis of blood total Ca concentration in periparturient Holstein cows. We first performed a genomic scan and a subsequent gene-set analysis to identify candidate genes, biological pathways, and molecular mechanisms affecting postpartum Ca concentration. Then, we assessed the prediction of postpartum Ca concentration using genomic information. Data consisted of 7,691 records of plasma or serum concentrations of Ca measured in the first, second, and third day after parturition of 959 primiparous and 1,615 multiparous cows that calved between December 2015 and June 2020 in 2 dairy herds. All cows were genotyped with 80k SNPs. The statistical model included lactation (1 to 5+), calf category (male, females, twins), and day as fixed effects, and season-treatment-experiment, animal, and permanent environmental as random effects. Model predictive ability was evaluated using 10-fold cross-validation. Heritability and repeatability estimates were 0.083 (standard error = 0.017) and 0.444 (standard error = 0.028). The association mapping identified 2 major regions located on Bos taurus autosome (BTA)6 and BTA16 that explained 1.2% and 0.7% of additive genetic variance of Ca concentration, respectively. Interestingly, the region on BTA6 harbors the GC gene, which encodes the vitamin D binding protein, and the region on BTA16 harbors LRRC38, which is actively involved in K transport. Other sizable peaks were identified on BTA5, BTA2, BTA7, BTA14, and BTA9. These regions harbor genes associated with Ca channels (CACNA1S, CRACR2A), K channels (KCNK9), bone remodeling (LRP6), and milk production (SOCS2). The gene-set analysis revealed terms related to vitamin transport, calcium ion transport, calcium ion binding, and calcium signaling. Genomic predictions of phenotypic and genomic estimated breeding values of Ca concentration yielded predictive correlations up to 0.50 and 0.15, respectively. Overall, the present study contributes to a better understanding of the genetic basis of postpartum blood Ca concentration in Holstein cows. In addition, the findings may contribute to the development of novel selection and management strategies for reducing periparturient hypocalcemia in dairy cattle.

Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows.

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Maternal methionine supplementation during gestation alters alternative splicing and DNA methylation in bovine skeletal muscle.

BACKGROUND: The evaluation of alternative splicing, including differential isoform expression and differential exon usage, can provide some insights on the transcriptional changes that occur in response to environmental perturbations. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to assess potential changes in splicing events in the longissimus dorsi muscle of beef calves gestated under control or methionine-rich diets. RNA sequencing and whole-genome bisulfite sequencing were used to evaluate muscle transcriptome and methylome, respectively. RESULTS: Alternative splicing patterns were significantly altered by maternal methionine supplementation. Most of the altered genes were directly implicated in muscle development, muscle physiology, ATP activities, RNA splicing and DNA methylation, among other functions. Interestingly, there was a significant association between DNA methylation and differential exon usage. Indeed, among the set of genes that showed differential exon usage, significant differences in methylation level were detected between significant and non-significant exons, and between contiguous and non-contiguous introns to significant exons. CONCLUSIONS: Overall, our findings provide evidence that a prenatal diet rich in methyl donors can significantly alter the offspring transcriptome, including changes in isoform expression and exon usage, and some of these changes are mediated by changes in DNA methylation.

Effect of natural pre-luteolytic prostaglandin F2α pulses on the bovine luteal transcriptome during spontaneous luteal regression†.

The pulsatile pattern of prostaglandin F2alpha (PGF) secretion during spontaneous luteolysis is well documented, with multiple pulses of exogenous PGF necessary to induce regression using physiologic concentrations of PGF. However, during spontaneous regression, the earliest pulses of PGF are small and not associated with detectable changes in circulating progesterone (P4), bringing into question what, if any, role these early, subluteolytic PGF pulses have during physiologic regression. To investigate the effect of small PGF pulses, luteal biopsies were collected throughout natural luteolysis in conjunction with bihourly blood samples to determine circulating P4 and PGF metabolite to retrospectively assign biopsies to early and later regression. Whole transcriptome analysis was conducted on CL biopsies. Early PGF pulses altered the luteal transcriptome, inducing differential expression of 210 genes (Q < 0.05) during early regression, compared with 4615 differentially expressed genes during later regression. In early regression, few of these differentially expressed genes were directly associated with luteolysis, rather there were changes in local steroid and glutathione metabolism. Most (94%) differentially expressed genes from early regression were also differentially expressed during later regression, with 98% of these continuing to be altered in the same direction compared with CL at a similar stage of the cycle that had not yet been exposed to PGF. Thus, early, subluteolytic PGF pulses impact the luteal transcriptome, though not by altering steroidogenesis or causing direct inhibition of cellular function. Rather, small pulses alter pathways resulting in the removal of cellular support systems, which may sensitize the CL to later pulses of PGF.

Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle.

BACKGROUND: Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV). RESULTS: Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV. CONCLUSIONS: Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.