Transcriptomic profiling of gastrointestinal tracts in dairy cattle during lactation reveals molecular adaptations for milk synthesis.

During lactation, dairy cattle's digestive tract requires significant adaptations to meet the increased nutrient demands for milk production. As we attempt to improve milk-related traits through selective pressure, it is crucial to understand the biological functions of the epithelia of the rumen, small intestine, and colonic tissues in response to changes in physiological state driven by changes in nutrient demands for milk synthesis. In this study, we obtained a total of 108 transcriptome profiles from three tissues (epithelia of the colon, duodenum, and rumen) of five Holstein cows, spanning eight time points from the early, mid, late lactation periods to the dry period. On average 97.06% of reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We analyzed 27,607 gene expression patterns at multiple periods, enabling direct comparisons within and among tissues during different lactation stages, including early and peak lactation. We identified 1645, 813, and 2187 stage-specific genes in the colon, duodenum, and rumen, respectively, which were enriched for common or specific biological functions among different tissues. Time series analysis categorized the expressed genes within each tissue into four clusters. Furthermore, when the three tissues were analyzed collectively, 36 clusters of similarly expressed genes were identified. By integrating other comprehensive approaches such as gene co-expression analyses, functional enrichment, and cell type deconvolution, we gained profound insights into cattle lactation, revealing tissue-specific characteristics of the gastrointestinal tract and shedding light on the intricate molecular adaptations involved in nutrient absorption, immune regulation, and cellular processes for milk synthesis during lactation.

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.

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.

Unraveling the Genetic Basis of Feed Efficiency in Cattle through Integrated DNA Methylation and CattleGTEx Analysis.

Feed costs can amount to 75 percent of the total overhead cost of raising cows for milk production. Meanwhile, the livestock industry is considered a significant contributor to global climate change due to the production of greenhouse gas emissions, such as methane. Indeed, the genetic basis of feed efficiency (FE) is of great interest to the animal research community. Here, we explore the epigenetic basis of FE to provide base knowledge for the development of genomic tools to improve FE in cattle. The methylation level of 37,554 CpG sites was quantified using a mammalian methylation array (HorvathMammalMethylChip40) for 48 Holstein cows with extreme residual feed intake (RFI). We identified 421 CpG sites related to 287 genes that were associated with RFI, several of which were previously associated with feeding or digestion issues. Activator of transcription and developmental regulation (AUTS2) is associated with digestive disorders in humans, while glycerol-3-phosphate dehydrogenase 2 (GPD2) encodes a protein on the inner mitochondrial membrane, which can regulate glucose utilization and fatty acid and triglyceride synthesis. The extensive expression and co-expression of these genes across diverse tissues indicate the complex regulation of FE in cattle. Our study provides insight into the epigenetic basis of RFI and gene targets to improve FE in dairy cattle.

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.

Genome-Wide Acetylation Modification of H3K27ac in Bovine Rumen Cell Following Butyrate Exposure.

Butyrate contributes epigenetically to the changes in cellular function and tissue development of the rumen in ruminant animals, which might be achieved by its genetic or epigenetic regulation of gene expression. To explore the role of butyrate on bovine rumen epithelial function and development, this study characterized genome-wide H3K27ac modification changes and super-enhancer profiles in rumen epithelial primary cells (REPC) induced with butyrate by ChIP-seq, and analyzed its effects on gene expression and functional pathways by integrating RNA-seq data. The results showed that genome-wide acetylation modification was observed in the REPC with 94,675 and 48,688 peaks in the butyrate treatment and control group, respectively. A total of 9750 and 5020 genes with increased modification (H3K27ac-gain) and decreased modification (H3K27ac-loss) were detected in the treatment group. The super-enhancer associated genes in the butyrate-induction group were involved in the AMPK signaling pathway, MAPK signaling pathway, and ECM-receptor interaction. Finally, the up-regulated genes (PLCG1, CLEC3B, IGSF23, OTOP3, ADTRP) with H3K27ac gain modification by butyrate were involved in cholesterol metabolism, lysosome, cell adhesion molecules, and the PI3K-Akt signaling pathway. Butyrate treatment has the role of genome-wide H3K27ac acetylation on bovine REPC, and affects the changes in gene expression. The effect of butyrate on gene expression correlates with the acetylation of the H3K27ac level. Identifying genome-wide acetylation modifications and expressed genes of butyrate in bovine REPC cells will expand the understanding of the biological role of butyrate and its acetylation.

Impact of parity differences on residual feed intake estimation in Holstein cows.

Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. In lactating dairy cattle, RFI is typically obtained as the difference between dry matter intake observations and predictions from regression on known energy sinks, and effects of parity, days in milk, and cohort. The impact of parity (lactation number) on the estimation of RFI is not well understood, so the objectives of this study were to (1) evaluate alternative RFI models in which the energy sinks (metabolic body weight, body weight change, and secreted milk energy) were nested or not nested within parity, and (2) estimate variance components and genetic correlations for RFI across parities. Data consisted of 72,474 weekly RFI records of 5,813 lactating Holstein cows collected from 2007 to 2022 in 5 research stations across the United States. Estimates of heritability, repeatability, and genetic correlations between weekly RFI for parities 1, 2, and 3 were obtained using bivariate repeatability animal models. The nested RFI model showed better goodness of fit than the nonnested model, and some partial regression coefficients of dry matter intake on energy sinks were heterogeneous between parities. However, the Spearman's rank correlation between RFI values calculated from nested and nonnested models was equal to 0.99. Similarly, Spearman's rank correlation between the RFI breeding values from these 2 models was equal to 0.98. Heritability estimates for RFI were equal to 0.16 for parity 1, 0.19 for parity 2, and 0.22 for parity 3. Repeatability estimates for RFI across weeks within parities were high, ranging from 0.51 to 0.57. Spearman's rank correlations of sires' breeding values were 0.99 between parities 1 and 2, 0.91 between parities 1 and 3, and 0.92 between parities 2 and 3. We conclude that nesting energy sinks within parity when computing RFI improves model goodness of fit, but the impact on the estimated breading values appears to be minimal.

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.

Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows.

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197-0.333) under A + A×A model and was 0.231 (with range of 0.188-0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02-14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.

Butyrate Induces Modifications of the CTCF-Binding Landscape in Cattle Cells.

Butyrate is produced in the rumen from microbial fermentation and is related to several functions, including cell differentiation and proliferation. Butyrate supplementation in calves can accelerate rumen development. DNA-protein interactions, such as the CCCTC-binding factor (CTCF), play essential roles in chromatin organization and gene expression regulation. Although CTCF-binding sites have been identified recently in cattle, a deeper characterization, including differentially CTCF-binding sites (DCBS), is vital for a better understanding of butyrate's role in the chromatin landscape. This study aimed to identify CTCF-binding regions and DCBS under a butyrate-induced condition using ChIP-seq in bovine cells; 61,915 CTCF peaks were identified in the butyrate and 51,347 in the control. From these regions, 2265 DCBS were obtained for the butyrate vs. control comparison, comprising ~90% of induced sites. Most of the butyrate DCBS were in distal intergenic regions, showing a potential role as insulators. Gene ontology enrichment showed crucial terms for the induced DCBS, mainly related to cellular proliferation, cell adhesion, and growth regulation. Interestingly, the ECM-receptor interaction pathway was observed for the induced DCBS. Motif enrichment analysis further identified transcription factors, including CTCF, BORIS, TGIF2, and ZIC3. When DCBS was integrated with RNA-seq data, putative genes were identified for the repressed DCBS, including GATA4. Our study revealed promising candidate genes in bovine cells by a butyrate-induced condition that might be related to the regulation of rumen development, such as integrins, keratins, and collagens. These results provide a better understanding of the function of butyrate in cattle rumen development and chromatin landscape regulation.

Differentially CTCF-Binding Sites in Cattle Rumen Tissue during Weaning.

The weaning transition in calves is characterized by major structural changes such as an increase in the rumen capacity and surface area due to diet changes. Studies evaluating rumen development in calves are vital to identify genetic mechanisms affected by weaning. This study aimed to provide a genome-wide characterization of CTCF-binding sites and differentially CTCF-binding sites (DCBS) in rumen tissue during the weaning transition of four Holstein calves to uncover regulatory elements in rumen epithelial tissue using ChIP-seq. Our study generated 67,280 CTCF peaks for the before weaning (BW) and 39,891 for after weaning (AW). Then, 7401 DCBS were identified for the AW vs. BW comparison representing 0.15% of the cattle genome, comprising ~54% of induced DCBS and ~46% of repressed DCBS. Most of the induced and repressed DCBS were in distal intergenic regions, showing a potential role as insulators. Gene ontology enrichment revealed many shared GO terms for the induced and the repressed DCBS, mainly related to cellular migration, proliferation, growth, differentiation, cellular adhesion, digestive tract morphogenesis, and response to TGFß. In addition, shared KEGG pathways were obtained for adherens junction and focal adhesion. Interestingly, other relevant KEGG pathways were observed for the induced DCBS like gastric acid secretion, salivary secretion, bacterial invasion of epithelial cells, apelin signaling, and mucin-type O-glycan biosynthesis. IPA analysis further revealed pathways with potential roles in rumen development during weaning, including TGFß, Integrin-linked kinase, and Integrin signaling. When DCBS were further integrated with RNA-seq data, 36 putative target genes were identified for the repressed DCBS, including KRT84, COL9A2, MATN3, TSPAN1, and AJM1. This study successfully identified DCBS in cattle rumen tissue after weaning on a genome-wide scale and revealed several candidate target genes that may have a role in rumen development, such as TGFß, integrins, keratins, and SMADs. The information generated in this preliminary study provides new insights into bovine genome regulation and chromatin landscape.

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.

Investigation of rumen long noncoding RNA before and after weaning in cattle.

BACKGROUND: This study aimed to identify long non-coding RNA (lncRNA) from the rumen tissue in dairy cattle, explore their features including expression and conservation levels, and reveal potential links between lncRNA and complex traits that may indicate important functional impacts of rumen lncRNA during the transition to the weaning period. RESULTS: A total of six cattle rumen samples were taken with three replicates from before and after weaning periods, respectively. Total RNAs were extracted and sequenced with lncRNA discovered based on size, coding potential, sequence homology, and known protein domains. As a result, 404 and 234 rumen lncRNAs were identified before and after weaning, respectively. However, only nine of them were shared under two conditions, with 395 lncRNAs found only in pre-weaning tissues and 225 only in post-weaning samples. Interestingly, none of the nine common lncRNAs were differentially expressed between the two weaning conditions. LncRNA averaged shorter length, lower expression, and lower conservation scores than the genome overall, which is consistent with general lncRNA characteristics. By integrating rumen lncRNA before and after weaning with large-scale GWAS results in cattle, we reported significant enrichment of both pre- and after-weaning lncRNA with traits of economic importance including production, reproduction, health, and body conformation phenotypes. CONCLUSIONS: The majority of rumen lncRNAs are uniquely expressed in one of the two weaning conditions, indicating a functional role of lncRNA in rumen development and transition of weaning. Notably, both pre- and post-weaning lncRNA showed significant enrichment with a variety of complex traits in dairy cattle, suggesting the importance of rumen lncRNA for cattle performance in the adult stage. These relationships should be further investigated to better understand the specific roles lncRNAs are playing in rumen development and cow performance.

Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide.

BACKGROUND: Gram-negative bacteria are important pathogens in cattle, causing severe infectious diseases, including mastitis. Lipopolysaccharides (LPS) are components of the outer membrane of Gram-negative bacteria and crucial mediators of chronic inflammation in cattle. LPS modulations of bovine immune responses have been studied before. However, the single-cell transcriptomic and chromatin accessibility analyses of bovine peripheral blood mononuclear cells (PBMCs) and their responses to LPS stimulation were never reported. RESULTS: We performed single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) in bovine PBMCs before and after LPS treatment and demonstrated that seven major cell types, which included CD4 T cells, CD8 T cells, and B cells, monocytes, natural killer cells, innate lymphoid cells, and dendritic cells. Bioinformatic analyses indicated that LPS could increase PBMC cell cycle progression, cellular differentiation, and chromatin accessibility. Gene analyses further showed significant changes in differential expression, transcription factor binding site, gene ontology, and regulatory interactions during the PBMC responses to LPS. Consistent with the findings of previous studies, LPS induced activation of monocytes and dendritic cells, likely through their upregulated TLR4 receptor. NF-κB was observed to be activated by LPS and an increased transcription of an array of pro-inflammatory cytokines, in agreement that NF-κB is an LPS-responsive regulator of innate immune responses. In addition, by integrating LPS-induced differentially expressed genes (DEGs) with large-scale GWAS of 45 complex traits in Holstein, we detected trait-relevant cell types. We found that selected DEGs were significantly associated with immune-relevant health, milk production, and body conformation traits. CONCLUSION: This study provided the first scRNAseq and scATAC-seq data for cattle PBMCs and their responses to the LPS stimulation to the best of our knowledge. These results should also serve as valuable resources for the future study of the bovine immune system and open the door for discoveries about immune cell roles in complex traits like mastitis at single-cell resolution.

Characterization of Accessible Chromatin Regions in Cattle Rumen Epithelial Tissue during Weaning.

Weaning in ruminants is characterized by the transition from a milk-based diet to a solid diet, which drives a critical gastrointestinal tract transformation. Understanding the regulatory control of this transformation during weaning can help to identify strategies to improve rumen health. This study aimed to identify regions of accessible chromatin in rumen epithelial tissue in pre- and post-weaning calves and investigate differentially accessible regions (DARs) to uncover regulatory elements in cattle rumen development using the ATAC-seq approach. A total of 126,071 peaks were identified, covering 1.15% of the cattle genome. From these accessible regions, 2766 DARs were discovered. Gene ontology enrichment resulted in GO terms related to the cell adhesion, anchoring junction, growth, cell migration, motility, and morphogenesis. In addition, putative regulatory canonical pathways were identified (TGFß, integrin-linked kinase, integrin signaling, and regulation of the epithelial-mesenchymal transition). Canonical pathways integrated with co-expression results showed that TGFß and ILK signaling pathways play essential roles in rumen development through the regulation of cellular adhesions. In this study, DARs during weaning were identified, revealing enhancers, transcription factors, and candidate target genes that represent potential biomarkers for the bovine rumen development, which will serve as a molecular tool for rumen development studies.

Functional annotation of regulatory elements in cattle genome reveals the roles of extracellular interaction and dynamic change of chromatin states in rumen development during weaning.

We profiled landscapes of bovine regulatory elements and explored dynamic changes of chromatin states in rumen development during weaning. The regulatory elements (15 chromatin states) and their coordinated activities in cattle were defined through genome-wide profiling of four histone modifications, CTCF-binding, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial tissues. Each chromatin state presented specific enrichment for sequence ontology, methylation, trait-associated variants, transcription, gene expression-associated variants, selection signatures, and evolutionarily conserved elements. During weaning, weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states and occurred in tandem with significant variations in gene expression and DNA methylation, significantly associated with stature, production, and reproduction economic traits. By comparing with in vitro cultured epithelial cells and in vivo rumen tissues, we showed the commonness and uniqueness of these results, especially the roles of cell interactions and mitochondrial activities in tissue development.

Transcriptional Reprogramming in Rumen Epithelium during the Developmental Transition of Pre-Ruminant to the Ruminant in Cattle.

We present an analysis of transcriptomic dynamics in rumen epithelium of 18 Holstein calves during the transition from pre-rumination to rumination in cattle-fed hay or concentrated diets at weaning. Three calves each were euthanized at 14 and 42 d of age to exemplify preweaning, and six calves each were provided diets of either milk replacer and grass hay or calf starter to introduce weaning. The two distinct phases of rumen development and function in cattle are tightly regulated by a series of signaling events and clusters of effectors on critical pathways. The dietary shift from liquid to solid feeds prompted the shifting of gene activity. The number of differentially expressed genes increased significantly after weaning. Bioinformatic analysis revealed gene activity shifts underline the functional transitions in the ruminal epithelium and signify the transcriptomic reprogramming. Gene ontogeny (GO) term enrichment shows extensively activated biological functions of differentially expressed genes in the ruminal epithelium after weaning were predominant metabolic functions. The transcriptomic reprogramming signifies a correlation between gene activity and changes in metabolism and energy production in the rumen epithelium, which occur at weaning when transitioning from glucose use to VFA use by epithelium during the weaning.

Single-cell transcriptomic analyses of dairy cattle ruminal epithelial cells during weaning.

Using the 10× Genomics Chromium Controller, we obtained scRNA-seq data of 5064 and 1372 individual cells from two Holstein calf ruminal epithelial tissues before and after weaning, respectively. We detected six distinct cell clusters, designated their cell types, and reported their marker genes. We then examined these clusters' underlining cell types and relationships by performing cell cycle, pseudotime trajectory, regulatory network, weighted gene co-expression network and gene ontology analyses. By integrating these cell marker genes with Holstein GWAS signals, we found they were enriched for animal production and body conformation traits. Finally, we confirmed their cell identities by comparing them with human and mouse stomach epithelial cells. This study presents an initial effort to implement single-cell transcriptomic analysis in cattle, and demonstrates ruminal tissue epithelial cell types and their developments during weaning, opening the door for new discoveries about tissue/cell type roles in complex traits at single-cell resolution.