Tissue metabolism and energy expenditures of maintenance and production

Includes bibliographical references (pages [20-21])

Perturbation Dynamics of the Rumen Microbiota in Response to Exogenous Butyrate

The capacity of the rumen microbiota to produce volatile fatty acids (VFAs) has important implications in animal well-being and production. We investigated temporal changes of the rumen microbiota in response to butyrate infusion using pyrosequencing of the 16S rRNA gene. Twenty one phyla were identified in the rumen microbiota of dairy cows. The rumen microbiota harbored 54.5±6.1 genera (mean ± SD) and 127.3±4.4 operational taxonomic units (OTUs), respectively. However, the core microbiome comprised of 26 genera and 82 OTUs. Butyrate infusion altered molar percentages of 3 major VFAs. Butyrate perturbation had a profound impact on the rumen microbial composition. A 72 h-infusion led to a significant change in the numbers of sequence reads derived from 4 phyla, including 2 most abundant phyla, Bacteroidetes and Firmicutes. As many as 19 genera and 43 OTUs were significantly impacted by butyrate infusion. Elevated butyrate levels in the rumen seemingly had a stimulating effect on butyrate-producing bacteria populations. The resilience of the rumen microbial ecosystem was evident as the abundance of the microorganisms returned to their pre-disturbed status after infusion withdrawal. Our findings provide insight into perturbation dynamics of the rumen microbial ecosystem and should guide efforts in formulating optimal uses of probiotic bacteria treating human diseases

Quantification of Transcriptome Responses of the Rumen Epithelium to Butyrate Infusion using RNA-seq Technology

Short-chain fatty acids (SCFAs), such as butyrate, produced by gut microorganisms, play a critical role in energy metabolism and physiology of ruminants as well as in human health. In this study, the temporal effect of elevated butyrate concentrations on the transcriptome of the rumen epithelium was quantified via serial biopsy sampling using RNA-seq technology. The mean number of genes transcribed in the rumen epithelial transcriptome was 17,323.63 ± 277.20 (±SD; N = 24) while the core transcriptome consisted of 15,025 genes. Collectively, 80 genes were identified as being significantly impacted by butyrate infusion across all time points sampled. Maximal transcriptional effect of butyrate on the rumen epithelium was observed at the 72-h infusion when the abundance of 58 genes was altered. The initial reaction of the rumen epithelium to elevated exogenous butyrate may represent a stress response as Gene Ontology (GO) terms identified were predominantly related to responses to bacteria and biotic stimuli. An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10−11). Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate. Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health

Synthetic Alkaloid Treatment Influences the Intestinal Epithelium and Mesenteric Adipose Transcriptome in Holstein Steers

Holstein steers (n = 16) were used to determine if a synthetic alkaloid, bromocriptine, would alter the transcriptome of the small intestine and adjacent mesenteric adipose. On d 0, steers were assigned to one of two treatments: control (CON; saline only) or bromocriptine (BROMO; 0.1 mg/kg BW bromocriptine mesylate injected intramuscularly every 3 d for 30 d). Steers were slaughtered and midpoint sections of jejunal epithelium and associated mesenteric fat were collected for RNA isolation. Transcriptome analysis was completed via RNA-Seq to determine if BROMO differed compared with CON within intestinal epithelium or mesenteric adipose mRNA isolates. Differential expression thresholds were set at a significant P-value (P \u3c 0.05) and a fold change ≥ 1.5. Only two genes were differentially expressed within the intestinal epithelium but there were 20 differentially expressed genes in the mesenteric adipose tissue (six up regulated and 14 down regulated). Functions related to cell movement, cell development, cell growth and proliferation, cell death, and overall cellular function and maintenance were the top five functional molecular categories influenced by BROMO treatment within the intestinal epithelium. The top molecular categories within mesenteric adipose were antigen presentation, protein synthesis, cell death, cell movement, and cell to cell signaling and interaction. In conclusion, BROMO treatment influenced the intestinal epithelium and mesenteric adipose transcriptome and identified genes and pathways influential to the effects associated with alkaloid exposure which are important to beef production

The effect of helminth infection on the microbial composition and structure of the caprine abomasal microbiome

Haemonchus contortus is arguably the most injurious helminth parasite for small ruminants. We characterized the impact of H. contortus infection on the caprine abomasal microbiome. Fourteen parasite naive goats were inoculated with 5,000 H. contortus infective larvae and followed for 50 days. Six age-matched naïve goats served as uninfected controls. Reduced bodyweight gain and a significant increase in the abosamal pH was observed in infected goats compared to uninfected controls. Infection also increased the bacterial load while reducing the abundance of the Archaea in the abomasum but did not appear to affect microbial diversity. Nevertheless, the infection altered the abundance of approximately 19% of the 432 species-level operational taxonomic units (OTU) detected per sample. A total of 30 taxa displayed a significantly different abundance between control and infected goats. Furthermore, the infection resulted in a distinct difference in the microbiome structure. As many as 8 KEGG pathways were predicted to be significantly affected by infection. In addition, H. contortus-induced changes in butyrate producing bacteria could regulate mucosal inflammation and tissue repair. Our results provided insight into physiological consequences of helminth infection in small ruminants and could facilitate the development of novel control strategies to improve animal and human health

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm

Effect of consuming endophyte-infected fescue seed on transcript abundance in the mammary gland of lactating and dry cows, as assessed by RNA sequencing

Ergot alkaloids in endophyte-infected grasses inhibit prolactin secretion and reduce milk production in lactating cows. However, we previously showed that prepartum consumption of infected seed throughout the dry period did not inhibit subsequent milk production and prior exposure to bromocriptine (ergot peptide) actually increased production in the next lactation. To identify changes in the transcriptome and molecular pathways mediating the mammary gland's response to ergot alkaloids in the diet, RNA sequencing (RNA-seq) was performed on mammary tissues obtained from 22 multiparous Holstein cows exposed to 1 of 3 treatments. Starting at 90 ± 4 d prepartum, cows were fed endophyte-free fescue seed (control; CON), endophyte-free fescue seed plus 3×/wk subcutaneous injections of bromocriptine (BROMO; 0.1 mg/kg of BW), or endophyte-infected fescue seed (INF) as 10% of the diet. Cows were dried off 60 ± 2 d prepartum. Mammary biopsies from 4 (BROMO, INF) or 5 (CON) cows/treatment at each of the 3 phases were obtained: 7 d before dry off during the initial lactation (L1), mid-dry period (D), and 10 d postpartum (L2). Although tissue from the same cow was preferentially used at 3 phases (L1, D, L2), tissue from additional cows were used to as necessary to provide RNA of sufficient quality. Individual samples were used to generate individual RNA-seq libraries. Normalized reads of the RNA-seq data were organized into technical and biological replicates before processing with the RSEM software package. Each lactation phase was processed separately and genes that differed between any of 3 treatments were identified. A large proportion of genes differentially expressed in at least 1 treatment (n = 866) were found to be similarly expressed in BROMO and INF treatments, but differentially expressed from CON (n = 575, total for 3 phases). Of genes differentially expressed compared with CON, 104 genes were common to the L1 and L2 phases. Consistent with the production findings, networks most affected by treatments in L1 and L2 included lipid metabolism, small molecule biochemistry, and molecular transport, whereas networks related more to developmental and cellular functions and maintenance were evident during D phase. Similar patterns of expression in BROMO and INF during late and early lactation suggest involvement of similar cell signaling pathways or mechanisms of action for BROMO and INF and the importance of prolactin messaging pathways

Consumption of endophyte-infected fescue seed during the dry period does not decrease milk production in the following lactation.

Ergot alkaloids in endophyte-infected grasses inhibit prolactin (PRL) secretion and may reduce milk production of cows consuming these grasses. We investigated the effects of consuming endophyte-infected fescue seed during late lactation and the dry period on mammary growth, differentiation, and milk production. Twenty-four multiparous Holstein cows were randomly assigned to 3 treatment groups. Starting at 90±4 d prepartum, cows were fed endophyte-free fescue seed (control; CON), endophyte-free fescue seed plus 3×/wk subcutaneous injections of bromocriptine (0.1mg/kg of body weight, positive control; BROMO), or endophyte-infected fescue seed (INF) as 10% of the diet on an as fed basis. Although milk yield of groups did not differ before treatment, at dry off (-60 d prepartum) INF and BROMO cows produced less milk than CON. Throughout the treatment period, basal concentrations of PRL and the prepartum increase in plasma PRL were reduced in INF and BROMO cows compared with CON cows. Three weeks after the end of treatment, circulating concentrations of PRL were equivalent across groups. In the subsequent lactation milk yield was not decreased; in fact, BROMO cows exhibited a 9% increase in milk yield relative to CON. Evaluation of mammary tissue during the dry period and the subsequent lactation, by quantitative histology and immunohistochemical analysis of proliferation markers and putative mammary stem or progenitor cell markers, indicated that feeding endophyte-infected fescue seed did not significantly affect mammary growth and development. Feeding endophyte-infected grasses during the dry period may permit effective utilization of feed resources without compromising milk production in the next lactation

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