Location: Nutrition, Growth and Physiology
2019 Annual Report
Objectives
Objective 1: Determine the effects of dietary changes on efficiency of growth and nutrient utilization of beef cattle and swine.
Sub-objective 1A: Prediction of dry matter intake from neutral detergent fiber concentration.
Sub-objective 1B: Determine the effects of feed additives on feed efficiency.
Sub-objective 1C: Evaluate the use of an antibiotic alternative in swine.
Objective 2: Improve determination of dynamic changes in nutrient requirements as the animal's physiological status changes to allow for timed nutrient delivery.
Objective 3: Use novel forage systems for growing and maintaining beef cattle.
Objective 4: Determine metabolic and physiological mechanisms responsible for variation in feed efficiency that is under genetic control.
Sub-objective 4A: Evaluate genetic relationships with feed efficiency.
Sub-objective 4B: Effects of metabolites and hormones on feed efficiency.
Sub-objective 4C: Relationships between mitochondrial function and feed efficiency.
Objective 5: Determine the environmental factors that contribute to the variation in feeding behavior, growth, and well-being of livestock.
Sub-objective 5A: Novel methods for early detection of illness.
Sub-objective 5B: Relationships between swine feeding behavior with feeder size and placement.
Sub-objective 5C: Effects of weather on cattle well-being and feeding behavior.
Approach
Feed costs represent the single largest input in both beef and swine production; however, less than 20% of the energy from feed is converted to edible product. Improving the efficiency that feed is converted to animal products has the potential to improve the economic efficiency of animal production while also improving the sustainability of animal agriculture. To maximize feed efficiency the correct profile of nutrients are matched to meet an animal’s needs for its current biological status (growth, pregnancy, lactation, previous nutrient history, and disease). In order to provide the correct profile of nutrients, the nutrient composition of feeds and the dynamic nutrient requirements of the animal must both be identified and then synchronized. There is genetic variation among animals in their ability to utilize feed. Multiple genes are associated with the regulation of feed intake, weight gain, and the utilization of ingested nutrients. Differential expression of these genes results in variation of feed efficiency among animals within populations, and these genetic differences potentially change the nutrient requirements of the animal. Identifying the role of nutrition in regulating gene expression and the mechanisms by which efficient animals utilize nutrients is needed to develop nutrition management strategies. In addition to variation in physiological responses, there is a need to understand genetic and environmental variation in animal feeding behavior that lead to variation in nutrient utilization.
Progress Report
Finishing cattle (N=768) were used to determine if feeding an essential oil would decrease the presence of liver abscesses at harvest. Libraries for RNA-sequencing were prepared from the total RNA isolated from rumen papillae of 62 cattle with severe liver abscesses and from control animals with no liver abscesses. (Objective 1)
One-hundred and sixty beef heifers were placed on a study to evaluate how daily dry matter intake changes among diets with different concentrations of neutral detergent fiber. This experiment will allow the development of dry matter intake prediction equations for growing replacement heifers while grazing forages. (Objective 1)
Whole blood samples for plasma and RNA were collected on 320 beef cattle to evaluate circulating cytokines and the white blood cell transcriptome of animals with and without severe liver abscesses. (Objective 1)
Lactobacillus acidophilus fermentation products (LAFP) and lysozyme, alone and in combination, were evaluated as an alternative to antibiotics in nursery pig diets. Pigs (N=1,200) were weaned from the sow and allowed to consume one of 4 diets during the nursery phase of production (control diets, control diets + lysozyme, control diets + LAFP, or control diets + lysozyme + LAFP). Pigs were evaluated for growth performance, gut health, pathogen shedding, and antibiotic resistance genes. (Objective 1)
Growth data and reproductive tract data was collected on 395 heifers grazing corn stalks and cover crops. Performance data was collected on 600 cows that had been developed in the corn stalk and cover crop systems. (Objective 3)
Individual feed intake and body weight gain data was collected on 212 five-year-old cows. Feed efficiency phenotypes were previously collected on these same animals as heifers. These data will be used to determine the genetic correlation between feed intake and gain at these two production stages. (Objective 4)
Colostrum and milk were collected from 160 sows for metabolomic analysis. One median weight pig per litter was euthanized to correlate milk composition with piglet gut health. In addition, 196 pigs from these litters were phenotyped for individual feed intake and growth from 14 to 20 weeks of age. Twenty-four of these pigs from 10 litters with divergent feed efficiency phenotypes were euthanized and tissues (muscle, liver, gastrointestinal) were collected for transcriptomic analysis. Data will be analyzed to correlate milk composition and gene expression with the divergent feed efficiency phenotypes. (Objective 4)
Whole blood collected into a preservative to stabilize RNA from the white blood cells was collected on 96 pigs from a feed efficiency study. These samples will be used to validate the data generated from a study that examined the relationship between the transcriptome of white blood cells and feed efficiency phenotypes from the previous year. (Objective 4)
Whole blood samples from ~800 beef calves were collected in EDTA and a RNA preservative to evaluate the hematology parameters and white blood cell transcriptome of sick and case control animals at 3 time points: pre-weaning, weaning and at detection of illness. (Objective 5)
Accomplishments
1. Identification of cellular mechanisms involved in weight gain differences in beef cattle. Metabolomics is a powerful technology that simultaneously measures numerous metabolites and molecules and provides insight into metabolic responses to a nutritional intervention or health condition in animals. Based on this approach, ARS scientists at Clay Center, Nebraska, and the University of Nebraska-Lincoln, conducted metabolic analyses from multiple tissues to provide evidence of physiological mechanisms involved in differences in weight-to-gain ratios in cattle. Individual feed intake and body weight were measured on 144 steers during 105 days on a finishing ration. At the end of the feeding study, steers were selected according to high or low average daily gain (ADG), but with similar feed consumption. Intestine, liver, adipose and muscle samples were collected at harvest, and metabolomics profiles were used to identify differential metabolites between ADG groups. Although unique profiles were identified in each tissue; overall, lipid transport and oxidation were the common metabolic mechanism involved in weight-gain differences in beef cattle. Combining analyses of multiple tissues offers a powerful approach for defining the molecular basis of differences in performance among cattle for key production attributes, which may lead to opportunities to improve both ADG and feed efficiency in feedlot cattle.
2. Development of a model to predict illness using swine feeding behavior. Feeding patterns of pigs were investigated for utility in identifying sick animals within a herd. Feeding behavior is dependent on environmental and genetic factors, including temperature, humidity, gender, breed, and time of day. All these factors complicate the accurate prediction of animal well-being based on feeding behavior. Machine learning is a type of artificial intelligence that focuses on the development of computer programs that can change and adapt when exposed to new data. ARS scientists at Clay Center, Nebraska, and collaborators at South Dakota State University used an electronic system to monitor the feeding behavior of pigs during the grow-finishing phase and utilized machine learning tools to develop a suitable model for predicting swine feeding behavior based on temperature and time of day. Large deviations between predicted and observed feeding behavior during an outbreak of pneumonia demonstrated the potential for the model to be used in the automated detection of an early onset disease outbreak and/or other stressful events. This work will be used to develop a computer-based modeling system for swine feeding behavior. Future work is expected to lead to the development of software tools that will allow swine producers to utilize real-time feeding behavior data as an early predictor of illness and stress events at the individual animal level, improving both animal well-being and productivity.
3. White blood cell counts, and inflammation biomarkers may be predictive of bovine respiratory disease. Bovine respiratory disease (BRD) is responsible for nearly $900 million annually in economic losses from death, reduced feed efficiency, and treatment. The ability to predict whether an animal could be succumbing to BRD would benefit producers by allowing them to manage animals with higher susceptibility differently. The purpose of this study was to evaluate the blood count and the expression of genes with important immune function (cytokines) for differences between healthy and sick animals at various time points. ARS scientists at Clay Center, Nebraska, identified differences in the levels of subsets of white blood cells. Neutrophils were higher and monocytes were lower in sick animals at the time of BRD diagnosis. In addition, monocytes were lower in sick animals at weaning. Three of the genes evaluated were detected in higher abundance in the animals with BRD at the time of clinical diagnosis. These changes in gene expression were suggestive of a response to pathogens. Differences were identified in complete blood count (CBC) values between animals that became sick and those that remained healthy at various time points. In addition, differences were identified among several cytokine genes and their receptors that may also be useful biomarkers of BRD. White blood cell counts and cytokine expression data may provide insight into an animal’s immediate health status which may lead to treatments and management programs that will enable producers to improve both animal well-being and productivity.
4. Development of a model to improve prediction of heat stress in beef cattle. Variation in weather may generate more frequent heat waves resulting in substantial cattle production losses through increased heat stress. Understanding cattle stress and the difference between individual animals can be used to help select for heat tolerant animals. However, there are few phenotypes available to quantify heat stress of a given individual. A study by ARS scientists at Clay Center, Nebraska, and collaborators at the University of Nebraska-Lincoln, the University of Central Florida and Yogyakarta State University, evaluated a novel method to assess heat stress. The time differences between maximum ambient temperature and maximum body temperature can be defined as a lag. A second lag can be calculated as the difference between minimum ambient temperature and body temperature. The lengths of duration of these two lags were calculated using several different types of mathematical models. The evaluation of these two lags can be used to assess the stress level of feedlot cattle in warm and hot environments. The timing of these two lags could also be used to quantify an individual animal’s heat stress level. Improvements in the measurement of heat stress will allow producers to more accurately identify when animals are suffering from heat stress and better apply strategies to mitigate heat stress in beef cattle.
5. The identification of functional DNA polymorphisms in swine. One of the key aims of livestock genetics and genomics research is to discover the genetic variants underlying economically important traits such as reproductive performance, feed efficiency, disease resistance/susceptibility, and product quality. In order to understand the basis by which variation in DNA is associated with a particular phenotype, it is necessary to know whether that variant is functional and consequently may alter the structure or function of its gene product. ARS scientists at Clay Center, Nebraska, sequenced the genomes of 181 members of a heavily phenotyped experimental herd of swine and identified over 21 million variants. Approximately 275,000 of these variants were expected to alter or disrupt the protein coded by a gene. These variants that are likely to have a more significant effect on phenotypic variation and will be the focus of future analyses which will evaluate their effect on various performance traits. This work is the first step in identifying functional genetic markers that will influence economically relevant traits in swine. Continued examination of these variants is expected to lead to the development of panels of functional genetic markers that will allow swine producers and breeders to be able to make more rapid genetic progress by including them into their selection decisions.
6. A new equation to improve estimates of feed metabolizable energy. It is important to know the metabolizable energy of feed in order to accurately determine the feed requirements of animals. Measuring metabolizable energy is time consuming and expensive and requires the measurement of energy in the feed, feces, urine, and released methane. The last being technically difficult to measure. Developing mathematical relationships between digestible and metabolizable energy allows for the prediction of metabolizable energy and reduces the cost associated with getting estimates. Historically, a constant of 0.82 has been used; however, recent literature suggests that the relationship between digestible and metabolizable energy is variable depending on the type of diet used and is typically greater than 0.90 when high-concentrate diets are fed. ARS scientists at Clay Center, Nebraska, developed and evaluated a new equation for estimating metabolizable energy from digestible energy on 234 beef cattle from several studies. A maximum biological threshold for the conversion of digestible to metabolizable energy was estimated at 3.78 megacalories per kilogram of digestible energy. These data suggest the relationship between digestible energy and metabolizable energy is not static, especially in high-concentrate diets. The equation developed is an alternative that can be used for the calculation of metabolizable energy from digestible energy in current feedlot diets.
Review Publications
Judy, J.V., Bachman, G.C., Brown-Brandl, T.M., Fernando, S.C., Hales, K.E., Miller, P.S., Stowell, R.R., Kononoff, P.J. 2018. Energy balance and diurnal variation in methane production as affected by feeding frequency in Jersey cows in late lactation. Journal of Dairy Science. 101(12):10899-10910. https://doi.org/10.3168/jds.2018-14596.
Judy, J.V., Bachman, G.C., Brown-Brandl, T.M., Fernando, S.C., Hales, K.E., Harvatine, K.J., Miller, P.S., Kononoff, P.J. 2019. Increasing the concentration of linolenic acid in diets fed to Jersey cows in late lactation does not affect methane production. Journal of Dairy Science. 102(3):2085-2093. https://doi.org/10.3168/jds.2018-14608.
Judy, J.V., Bachman, G.C., Brown-Brandl, T.M., Fernando, S.C., Hales, K.E., Miller, P.S., Stowell, R.R., Kononoff, P.J. 2019. Reducing methane production with corn oil and calcium sulfate: Responses on whole-animal energy and nitrogen balance in dairy cattle. Journal of Dairy Science. 102(3):2054-2067. https://doi.org/10.3168/jds.2018.14567.
Condotta, I.C.F.S., Brown-Brandl, T.M., Stinn, J.P., Rohrer, G.A., Davis, J.D., Silva-Miranda, K.O. 2018. Dimensions of the modern pig. Transactions of the ASABE. 61(5):1729-1739. https://doi.org/10.13031/trans.12826.
Reynolds, M.A., Brown-Brandl, T.M., Judy, J.V., Herrick, K.J., Hales, K.E., Watson, A.K., Kononoff, P.J. 2019. Use of indirect calorimetry to evaluate utilization of energy in lactating Jersey dairy cattle consuming common coproducts. Journal of Dairy Science. 102(1):320-333. https://doi.org/10.3168/jds.2018-15471.
Artegoitia, V.M., Foote, A.P., Lewis, R.M., Freetly, H.C. 2019. Metabolomics profile and targeted lipidomics in multiple tissues associated with feed efficiency in beef steers. ACS Omega. 4:3973-3982. http://doi.org/10.1021/acsomega.8b02494.
Lindholm-Perry, A.K., Kuehn, L.A., McDaneld, T.G., Miles, J.R., Workman, A.M., Chitko-McKown, C.G., Keele, J.W. 2018. Complete blood count data and leukocyte expression of cytokine genes and cytokine receptor genes associated with bovine respiratory disease in calves. BMC Research Notes. 11:786. https://doi.org/10.1186/s13104-018-3900-x.
Cunningham, H.C., Cammack, K.M., Hales, K.E., Freetly, H.C., Lindholm-Perry, A.K. 2018. Differential transcript abundance in the adipose tissue of mature beef cows during feed restriction and realimentation. PLoS One. 13(3):e0194104. https://doi.org/10.1371/journal.pone.0194104.
Cunningham, H.C., Cammack, K.M., Hales, K.E., Freetly, H.C., Lindholm-Perry, A.K. 2018. Microarray analysis of subcutaneous adipose tissue from mature cows with divergent body weight gain after feed restriction and realimentation. Data in Brief. 16:303-311. https://doi.org/10.1016/j.dib.2017.10.016.
Drewnoski, M., Parsons, J., Blanco, H., Redfearn, D., Hales, K.E., MacDonald, J. 2018. Forages and pastures symposium: cover crops in livestock production: whole-system approach. Can cover crops pull double duty: conservation and profitable forage production in the Midwestern United States? Journal of Animal Science. 96(8):3503-3512. https://doi.org/10.1093/jas/sky026.
Drehmel, O.R., Brown-Brandl, T.M., Judy, J.V., Fernando, S.C., Miller, P.S., Hales, K.E., Kononoff, P.J. 2018. The influence of fat and hemicellulose on methane production and energy utilization in lactating Jersey cattle. Journal of Dairy Science. 101(9):7892-7906. https://doi.org/10.3168/jds.2017-13822.
Keel, B.N., Nonneman, D.J., Lindholm-Perry, A.K., Oliver, W.T., Rohrer, G.A. 2018. Porcine single nucleotide polymorphisms and their functional effect: an update. BMC Research Notes. 11:860. https://doi.org/10.1186/s13104-018-3973-6.
Kismiantini, Zhang, S., Eskridge, K.M., Kachman, S.D., Qiu, Y., Brown-Brandl, T. 2019. Comparing piecewise regression and hysteresis models in assessing beef cattle heat stress. Transactions of the ASABE. 62(2):549-559. https://doi.org/10.13031/trans.12910.
Cross, A.J., Rohrer, G.A., Brown-Brandl, T.M., Cassady, J.P., Keel, B.N. 2018. Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase. Biosystems Engineering. 173:124-133. https://doi.org/10.1016/j.biosystemseng.2018.02.005.
Hales, K.E. 2019. Relationships between digestible energy and metabolizable energy in current feedlot diets. Translational Animal Science. 3(3):945-952. https://doi.org/10.1093/tas/txz073.
Freetly, H.C. 2019. Fiftieth Anniversary of the California Net Energy System Symposium: What are the energy coefficients for cows? Translational Animal Science. 3(3):969-975. https://doi.org/10.1093/tas/txz024.
Petzel, E.A., Titgemeyer, E.C., Smart, A.J., Hales, K.E., Foote, A.P., Acharya, S., Bailey, E.A., Held, J.E., Brake, D.W. 2019. What is the digestibility and caloric value of different botanical parts in corn residue to cattle? Journal of Animal Science. 97(7):3056-3070. http://doi.org/10.1093/jas/skz137.
Keel, B.N., Nonneman, D.J., Lindholm-Perry, A.K., Oliver, W.T., Rohrer, G.A. 2019. A survey of copy number variation in the porcine genome detected from whole-genome sequence. Frontiers in Genetics. 10:737. https://doi.org/10.3389/fgene.2019.00737.
