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Siberski-Cooper CJ, Mayes MS, Gorden PJ, Kramer L, Bhatia V, Koltes JE. The genetic architecture of complete blood counts in lactating Holstein dairy cows. Front Genet 2024; 15:1360295. [PMID: 38601075 PMCID: PMC11004310 DOI: 10.3389/fgene.2024.1360295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/04/2024] [Indexed: 04/12/2024] Open
Abstract
Complete blood counts (CBCs) measure the abundance of individual immune cells, red blood cells, and related measures such as platelets in circulating blood. These measures can indicate the health status of an animal; thus, baseline circulating levels in a healthy animal may be related to the productive life, resilience, and production efficiency of cattle. The objective of this study is to determine the heritability of CBC traits and identify genomic regions that are associated with CBC measurements in lactating Holstein dairy cattle. The heritability of CBCs was estimated using a Bayes C0 model. The study population consisted of 388 cows with genotypes at roughly 75,000 markers and 16 different CBC phenotypes taken at one to three time points (n = 33, 131, and 224 for 1, 2, and 3 time points, respectively). Heritabilities ranged from 0.00 ± 0.00 (red cell distribution width) to 0.68 ± 0.06 (lymphocytes). A total of 96 different 1-Mb windows were identified that explained more than 1% of the genetic variance for at least one CBC trait, with 10 windows explaining more than 1% of the genetic variance for two or more traits. Multiple genes in the identified regions have functions related to immune response, cell differentiation, anemia, and disease. Positional candidate genes include RAD52 motif-containing protein 1 (RDM1), which is correlated with the degree of immune infiltration of immune cells, and C-X-C motif chemokine ligand 12 (CXCL12), which is critically involved in neutrophil bone marrow storage and release regulation and enhances neutrophil migration. Since animal health directly impacts feed intake, understanding the genetics of CBCs may be useful in identifying more disease-resilient and feed-efficient dairy cattle. Identification of genes responsible for variation in CBCs will also help identify the variability in how dairy cattle defend against illness and injury.
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Affiliation(s)
| | - Mary S. Mayes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Patrick J. Gorden
- Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United States
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Vishesh Bhatia
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
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Houlahan K, Schenkel FS, Miglior F, Jamrozik J, Stephansen RB, González-Recio O, Charfeddine N, Segelke D, Butty AM, Stratz P, VandeHaar MJ, Tempelman RJ, Weigel K, White H, Peñagaricano F, Koltes JE, Santos JEP, Baldwin RL, Baes CF. Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle. J Dairy Sci 2024; 107:1523-1534. [PMID: 37690722 DOI: 10.3168/jds.2022-23124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/05/2023] [Indexed: 09/12/2023]
Abstract
Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.
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Affiliation(s)
- K Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1
| | - F S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1
| | - F Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Lactanet, Guelph, ON, Canada, N1K 1E5
| | - J Jamrozik
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Lactanet, Guelph, ON, Canada, N1K 1E5
| | - R B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - O González-Recio
- Departamento de Producción Animal, ETSI Agrónomos, Universidad Politécnica, Ciudad Universitaria s/n, 28040 Madrid, Spain
| | | | - D Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. 27283 Verden/Aller
| | | | - P Stratz
- Qualitas AG, 6300 Zug, Switzerland
| | - M J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - K Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - H White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - F Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - J E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL 32611
| | - R L Baldwin
- Animal Genomics and Improvement Laboratory, USDA, Beltsville, MD 20705
| | - C F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Institute of Genetics, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
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3
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van Staaveren N, Rojas de Oliveira H, Houlahan K, Chud TCS, Oliveira GA, Hailemariam D, Kistemaker G, Miglior F, Plastow G, Schenkel FS, Cerri R, Sirard MA, Stothard P, Pryce J, Butty A, Stratz P, Abdalla EAE, Segelke D, Stamer E, Thaller G, Lassen J, Manzanilla-Pech CIV, Stephansen RB, Charfeddine N, García-Rodríguez A, González-Recio O, López-Paredes J, Baldwin R, Burchard J, Parker Gaddis KL, Koltes JE, Peñagaricano F, Santos JEP, Tempelman RJ, VandeHaar M, Weigel K, White H, Baes CF. The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions. J Dairy Sci 2024; 107:1510-1522. [PMID: 37690718 DOI: 10.3168/jds.2022-22951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
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.
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Affiliation(s)
- Nienke van Staaveren
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Hinayah Rojas de Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Kerry Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Tatiane C S Chud
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Gerson A Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dagnachew Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | | | - Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Ronaldo Cerri
- Applied Animal Biology, Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
| | - Marc Andre Sirard
- Department of Animal Sciences, Laval University, Qubec G1V 0A6, QC, Canada
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jennie Pryce
- School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia; Agriculture Victoria Research, LaTrobe University, Bundoora, Victoria 3083, Australia
| | | | | | - Emhimad A E Abdalla
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schröder-Weg 1, 27283, Verden, Germany
| | - Dierck Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schröder-Weg 1, 27283, Verden, Germany; Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, 24098, Kiel, Germany
| | | | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, 24098, Kiel, Germany
| | - Jan Lassen
- Viking Genetics, Ebeltoftvej 16, 8960 Assentoft, Denmark
| | | | - Rasmus B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark
| | - Noureddine Charfeddine
- Spanish Holstein Association (CONAFE), Ctra. Andalucía km 23600 Valdemoro, 28340 Madrid, Spain
| | - Aser García-Rodríguez
- Department of Animal Production, NEIKER-Basque Institute for Agricultural Research and Development, 01192 Arkaute, Spain
| | - Oscar González-Recio
- Department of Animal Breeding, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA-CSIC), 28040 Madrid, Spain
| | - Javier López-Paredes
- Federación Española de Criadores de Limusín, C/Infanta Mercedes, 31, 28020 Madrid, Spain
| | - Ransom Baldwin
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
| | | | | | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | | | - Robert J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Michael VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Kent Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Heather White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Vetsuisse Faculty, Institute of Genetics, University of Bern, 3012 Bern, Switzerland.
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Durbin HJ, Yampara-Iquise H, Rowan TN, Schnabel RD, Koltes JE, Powell JG, Decker JE. Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle. G3 (Bethesda) 2024; 14:jkad279. [PMID: 38092373 PMCID: PMC10849337 DOI: 10.1093/g3journal/jkad279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/17/2023] [Indexed: 02/09/2024]
Abstract
Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.
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Affiliation(s)
- Harly J Durbin
- Genetics Area Program, University of Missouri, Columbia, MO 65211, USA
- Syngenta, Research Triangle Park, NC 27709, USA
| | | | - Troy N Rowan
- Genetics Area Program, University of Missouri, Columbia, MO 65211, USA
- University of Tennessee Institute of Agriculture, Knoxville, TN 37996, USA
| | - Robert D Schnabel
- Genetics Area Program, University of Missouri, Columbia, MO 65211, USA
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50010, USA
- Department of Animal Science, University of Arkansas, Fayetteville, AR 72701, USA
| | - Jeremy G Powell
- Department of Animal Science, University of Arkansas, Fayetteville, AR 72701, USA
| | - Jared E Decker
- Genetics Area Program, University of Missouri, Columbia, MO 65211, USA
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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5
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Cavani L, Parker Gaddis KL, Baldwin RL, Santos JEP, Koltes JE, Tempelman RJ, VandeHaar MJ, White HM, Peñagaricano F, Weigel KA. Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency. J Dairy Sci 2024; 107:1054-1067. [PMID: 37769947 DOI: 10.3168/jds.2023-23774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023]
Abstract
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.
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Affiliation(s)
- Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
| | | | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705
| | - José E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL 32608
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Robert J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Michael J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Heather M White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Kent A Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
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Beiki H, Murdoch BM, Park CA, Kern C, Kontechy D, Becker G, Rincon G, Jiang H, Zhou H, Thorne J, Koltes JE, Michal JJ, Davenport K, Rijnkels M, Ross PJ, Hu R, Corum S, McKay S, Smith TPL, Liu W, Ma W, Zhang X, Xu X, Han X, Jiang Z, Hu ZL, Reecy JM. Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology. Gigascience 2024; 13:giae019. [PMID: 38626724 PMCID: PMC11020238 DOI: 10.1093/gigascience/giae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 07/29/2023] [Accepted: 03/27/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. RESULTS A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. CONCLUSIONS These validated results show significant improvement over current bovine genome annotations.
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Affiliation(s)
- Hamid Beiki
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Brenda M Murdoch
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Carissa A Park
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Chandlar Kern
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Denise Kontechy
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Gabrielle Becker
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | | | - Honglin Jiang
- Department of Animal and Poultry Sciences, Virginia Tech, VA 24060, USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Jacob Thorne
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Jennifer J Michal
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Kimberly Davenport
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Monique Rijnkels
- Department of Veterinary Integrative Biosciences, Texas A&M University, TX 77843, USA
| | - Pablo J Ross
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Rui Hu
- Department of Animal and Poultry Sciences, Virginia Tech, VA 24060, USA
| | - Sarah Corum
- Zoetis, Parsippany-Troy Hills, NJ 07054, USA
| | | | | | - Wansheng Liu
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Wenzhi Ma
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Xiaohui Zhang
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Xiaoqing Xu
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Xuelei Han
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Zhihua Jiang
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Zhi-Liang Hu
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
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7
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Stephansen RB, Martin P, Manzanilla-Pech CIV, Gredler-Grandl B, Sahana G, Madsen P, Weigel K, Tempelman RJ, Peñagaricano F, Parker Gaddis KL, White HM, Santos JEP, Koltes JE, Schenkel F, Hailemariam D, Plastow G, Abdalla E, VandeHaar M, Veerkamp RF, Baes C, Lassen J. Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe. J Dairy Sci 2023; 106:9078-9094. [PMID: 37678762 DOI: 10.3168/jds.2023-23330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/06/2023] [Indexed: 09/09/2023]
Abstract
Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.
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Affiliation(s)
- R B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark.
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France
| | - C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - B Gredler-Grandl
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - G Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - P Madsen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - K Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824-1226
| | - F Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | | | - H M White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | - J E P Santos
- Department of Animal Science, University of Florida, Gainesville, FL 32608
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - F Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - D Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - G Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - E Abdalla
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heideweg 1, 27283, Verden, Germany
| | - M VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824-1226
| | - R F Veerkamp
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - C Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Department of Clinical Research and Veterinary Public Health, University of Bern, Bern, 3001, Switzerland
| | - J Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark; Viking Genetics, Ebeltoftvej 16, Assentoft, 8960 Randers, Denmark
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8
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Triant DA, Walsh AT, Hartley GA, Petry B, Stegemiller MR, Nelson BM, McKendrick MM, Fuller EP, Cockett NE, Koltes JE, McKay SD, Green JA, Murdoch BM, Hagen DE, Elsik CG. AgAnimalGenomes: browsers for viewing and manually annotating farm animal genomes. Mamm Genome 2023; 34:418-436. [PMID: 37460664 PMCID: PMC10382368 DOI: 10.1007/s00335-023-10008-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
Current genome sequencing technologies have made it possible to generate highly contiguous genome assemblies for non-model animal species. Despite advances in genome assembly methods, there is still room for improvement in the delineation of specific gene features in the genomes. Here we present genome visualization and annotation tools to support seven livestock species (bovine, chicken, goat, horse, pig, sheep, and water buffalo), available in a new resource called AgAnimalGenomes. In addition to supporting the manual refinement of gene models, these browsers provide visualization tracks for hundreds of RNAseq experiments, as well as data generated by the Functional Annotation of Animal Genomes (FAANG) Consortium. For species with predicted gene sets from both Ensembl and RefSeq, the browsers provide special tracks showing the thousands of protein-coding genes that disagree across the two gene sources, serving as a valuable resource to alert researchers to gene model issues that may affect data interpretation. We describe the data and search methods available in the new genome browsers and how to use the provided tools to edit and create new gene models.
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Affiliation(s)
- Deborah A Triant
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Amy T Walsh
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Gabrielle A Hartley
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, 06269, USA
| | - Bruna Petry
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Morgan R Stegemiller
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Benjamin M Nelson
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Makenna M McKendrick
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Emily P Fuller
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, 06269, USA
| | - Noelle E Cockett
- Department of Animal, Dairy, and Veterinary Sciences, Utah State University, Logan, UT, 84322, USA
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Stephanie D McKay
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, 05405, USA
| | - Jonathan A Green
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Brenda M Murdoch
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Darren E Hagen
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Christine G Elsik
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
- Division of Plant Science & Technology, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO, 65211, USA.
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9
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Herrera-Uribe J, Lim KS, Byrne KA, Daharsh L, Liu H, Corbett RJ, Marco G, Schroyen M, Koltes JE, Loving CL, Tuggle CK. Integrative profiling of gene expression and chromatin accessibility elucidates specific transcriptional networks in porcine neutrophils. Front Genet 2023; 14:1107462. [PMID: 37287538 PMCID: PMC10242145 DOI: 10.3389/fgene.2023.1107462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 04/27/2023] [Indexed: 06/09/2023] Open
Abstract
Neutrophils are vital components of the immune system for limiting the invasion and proliferation of pathogens in the body. Surprisingly, the functional annotation of porcine neutrophils is still limited. The transcriptomic and epigenetic assessment of porcine neutrophils from healthy pigs was performed by bulk RNA sequencing and transposase accessible chromatin sequencing (ATAC-seq). First, we sequenced and compared the transcriptome of porcine neutrophils with eight other immune cell transcriptomes to identify a neutrophil-enriched gene list within a detected neutrophil co-expression module. Second, we used ATAC-seq analysis to report for the first time the genome-wide chromatin accessible regions of porcine neutrophils. A combined analysis using both transcriptomic and chromatin accessibility data further defined the neutrophil co-expression network controlled by transcription factors likely important for neutrophil lineage commitment and function. We identified chromatin accessible regions around promoters of neutrophil-specific genes that were predicted to be bound by neutrophil-specific transcription factors. Additionally, published DNA methylation data from porcine immune cells including neutrophils were used to link low DNA methylation patterns to accessible chromatin regions and genes with highly enriched expression in porcine neutrophils. In summary, our data provides the first integrative analysis of the accessible chromatin regions and transcriptional status of porcine neutrophils, contributing to the Functional Annotation of Animal Genomes (FAANG) project, and demonstrates the utility of chromatin accessible regions to identify and enrich our understanding of transcriptional networks in a cell type such as neutrophils.
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Affiliation(s)
- Juber Herrera-Uribe
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Kyu-Sang Lim
- Department of Animal Science, Iowa State University, Ames, IA, United States
- Department of Animal Resource Science, Kongju National University, Yesan, Republic of Korea
| | - Kristen A. Byrne
- USDA-Agriculture Research Service, National Animal Disease Center, Food Safety and Enteric Pathogens Research Unit, Ames, IA, United States
| | - Lance Daharsh
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Haibo Liu
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Ryan J. Corbett
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Gianna Marco
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Martine Schroyen
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Crystal L. Loving
- USDA-Agriculture Research Service, National Animal Disease Center, Food Safety and Enteric Pathogens Research Unit, Ames, IA, United States
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10
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Cavani L, Parker Gaddis KL, Baldwin RL, Santos JE, Koltes JE, Tempelman RJ, VandeHaar MJ, Caputo MJ, White HM, Peñagaricano F, Weigel KA. Impact of parity differences on residual feed intake estimation in Holstein cows. JDS Commun 2023; 4:201-204. [PMID: 37360126 PMCID: PMC10285233 DOI: 10.3168/jdsc.2022-0307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 06/28/2023]
Abstract
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.
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Affiliation(s)
- Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Ransom L. Baldwin
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
| | - José E.P. Santos
- Department of Animal Sciences, University of Florida, Gainesville 32608
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames 50011
| | | | | | - Malia J.M. Caputo
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | - Heather M. White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Kent A. Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
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11
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Krishna S, Spaulding HR, Koltes JE, Quindry JC, Valentine RJ, Selsby JT. Indicators of increased ER stress and UPR in aged D2-mdx and human dystrophic skeletal muscles. Front Physiol 2023; 14:1152576. [PMID: 37179835 PMCID: PMC10166835 DOI: 10.3389/fphys.2023.1152576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/10/2023] [Indexed: 05/15/2023] Open
Abstract
Duchenne muscular dystrophy (DMD) is a progressive muscle disease that results in muscle wasting, wheelchair dependence, and eventual death due to cardiac and respiratory complications. In addition to muscle fragility, dystrophin deficiency also results in multiple secondary dysfunctions, which may lead to the accumulation of unfolded proteins causing endoplasmic reticulum (ER) stress and the unfolded protein response (UPR). The purpose of this investigation was to understand how ER stress and the UPR are modified in muscle from D2-mdx mice, an emerging DMD model, and from humans with DMD. We hypothesized that markers of ER stress and the UPR are upregulated in D2-mdx and human dystrophic muscles compared to their healthy counterparts. Immunoblotting in diaphragms from 11-month-old D2-mdx and DBA mice indicated increased ER stress and UPR in dystrophic diaphragms compared to healthy, including increased relative abundance of ER stress chaperone CHOP, canonical ER stress transducers ATF6 and pIRE1α S724, and transcription factors that regulate the UPR such as ATF4, XBP1s, and peIF2α S51. The publicly available Affymetrix dataset (GSE38417) was used to analyze the expression of ER stress and UPR-related transcripts and processes. Fifty-eight upregulated genes related to ER stress and the UPR in human dystrophic muscles suggest pathway activation. Further, based on analyses using iRegulon, putative transcription factors that regulate this upregulation profile were identified, including ATF6, XBP1, ATF4, CREB3L2, and EIF2AK3. This study adds to and extends the emerging knowledge of ER stress and the UPR in dystrophin deficiency and identifies transcriptional regulators that may be responsible for these changes and be of therapeutic interest.
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Affiliation(s)
- Swathy Krishna
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Hannah R. Spaulding
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - John C. Quindry
- School of Integrative Physiology and Athletic Training, University of Montana, Missoula, MT, United States
| | - Rudy J. Valentine
- Department of Kinesiology, Iowa State University, Ames, IA, United States
| | - Joshua T. Selsby
- Department of Animal Science, Iowa State University, Ames, IA, United States
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12
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Siberski-Cooper CJ, Mayes MS, Gorden PJ, Hayman K, Hardie L, Shonka-Martin BN, Koltes DA, Healey M, Goetz BM, Baumgard LH, Koltes JE. The impact of health disorders on automated sensor measures and feed intake in lactating Holstein dairy cattle. Front Anim Sci 2023. [DOI: 10.3389/fanim.2022.1064205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Animal health and feed intake are closely interrelated, with the latter being an important indicator of an animal’s health status. Automated sensors for dairy cattle have been developed to detect changes in indicators of health, such as decreased rumination or activity. Previous studies have identified associations between sensor measurements and feed intake. Thus, the objective of this study was to determine if health disorders impact the associations identified between sensors and dry matter intake (DMI), and to measure the impact of health disorders on DMI. A total of 934 cows with health disorders (lameness, mastitis, and other), of which 57, 94, and 333 cows had observations for a rumen bolus and one of two ear tags, were analyzed to determine how health disorders impact the association of sensors with DMI. Eleven sensor measurements were collected across the three sensors, including total and point-in-time activity, rumination time, inner-ear temperature, rumen pH and rumen temperature. Associations of health disorders and sensor measures with DMI were evaluated when accounting for systematic effects (i.e., contemporary group, parity, and days in milk) and energy sinks accounted for in determination of feed efficiency (e.g., milk production, body weight and composition). In order to determine if inclusion of health disorders or sensor measures improved model fit, model AICs were assessed. Health disorders were significantly associated with all sensor measurements (P< 0.0001), with the direction of association dependent on sensor measure and health disorder. Moreover, DMI decreased with all health disorders, with larger impacts observed in animals in third and higher lactations. Numerous sensor measurements were associated with DMI, including when DMI was adjusted for energy sink variables and health. Inclusion of rumen bolus temperature, rumination or activity with health data reduced model AIC when evaluating DMI as the dependent variable. Some sensor measures, including measurements of activity, temperature and rumination, accounted for additional variation in feed intake when adjusted for health disorders. Results from the study indicate that feed intake and sensor measures are impacted by health disorders. These findings may have implications for use of sensors in genetic evaluations and precision feeding of dairy cattle.
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13
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Liang Z, Prakapenka D, Parker Gaddis KL, VandeHaar MJ, Weigel KA, Tempelman RJ, Koltes JE, Santos JEP, White HM, Peñagaricano F, Baldwin VI RL, Da Y. Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows. Front Genet 2022; 13:1017490. [PMID: 36386803 PMCID: PMC9664219 DOI: 10.3389/fgene.2022.1017490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
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.
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Affiliation(s)
- Zuoxiang Liang
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | - Dzianis Prakapenka
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | | | - Michael J. VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - Kent A. Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Robert J. Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | | | - Heather M. White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Ransom L. Baldwin VI
- Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD, United States
| | - Yang Da
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States,*Correspondence: Yang Da,
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14
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Andrade BGN, Bressani FA, Cuadrat RRC, Cardoso TF, Malheiros JM, de Oliveira PSN, Petrini J, Mourão GB, Coutinho LL, Reecy JM, Koltes JE, Neto AZ, R de Medeiros S, Berndt A, Palhares JCP, Afli H, Regitano LCA. Stool and Ruminal Microbiome Components Associated With Methane Emission and Feed Efficiency in Nelore Beef Cattle. Front Genet 2022; 13:812828. [PMID: 35656319 PMCID: PMC9152269 DOI: 10.3389/fgene.2022.812828] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/02/2022] [Indexed: 12/27/2022] Open
Abstract
Background: The impact of extreme changes in weather patterns on the economy and human welfare is one of the biggest challenges our civilization faces. From anthropogenic contributions to climate change, reducing the impact of farming activities is a priority since it is responsible for up to 18% of global greenhouse gas emissions. To this end, we tested whether ruminal and stool microbiome components could be used as biomarkers for methane emission and feed efficiency in bovine by studying 52 Brazilian Nelore bulls belonging to two feed intervention treatment groups, that is, conventional and by-product-based diets. Results: We identified a total of 5,693 amplicon sequence variants (ASVs) in the Nelore bulls’ microbiomes. A Differential abundance analysis with the ANCOM approach identified 30 bacterial and 15 archaeal ASVs as differentially abundant (DA) among treatment groups. An association analysis using Maaslin2 software and a linear mixed model indicated that bacterial ASVs are linked to the host’s residual methane emission (RCH4) and residual feed intake (RFI) phenotype variation, suggesting their potential as targets for interventions or biomarkers. Conclusion: The feed composition induced significant differences in both abundance and richness of ruminal and stool microbial populations in ruminants of the Nelore breed. The industrial by-product-based dietary treatment applied to our experimental groups influenced the microbiome diversity of bacteria and archaea but not of protozoa. ASVs were associated with RCH4 emission and RFI in ruminal and stool microbiomes. While ruminal ASVs were expected to influence CH4 emission and RFI, the relationship of stool taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits was not reported before and might be associated with host health due to their link to anti-inflammatory compounds. Overall, the ASVs associated here have the potential to be used as biomarkers for these complex phenotypes.
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Affiliation(s)
- Bruno G N Andrade
- Embrapa Southeast Livestock, São Carlos, Brazil.,Department of Computer Science, Munster Technological University, MTU/ADAPT, Cork, Ireland
| | | | - Rafael R C Cuadrat
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | | | | | | | - Juliana Petrini
- Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil
| | - Gerson B Mourão
- Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil
| | - Luiz L Coutinho
- Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | | | | | | | | | - Haithem Afli
- Department of Computer Science, Munster Technological University, MTU/ADAPT, Cork, Ireland
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15
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de Souza MM, Niciura SCM, Rocha MIP, Pan Z, Zhou H, Bruscadin JJ, da Silva Diniz WJ, Afonso J, de Oliveira PSN, Mourão GB, Zerlotini A, Coutinho LL, Koltes JE, de Almeida Regitano LC. DNA methylation may affect beef tenderness through signal transduction in Bos indicus. Epigenetics Chromatin 2022; 15:15. [PMID: 35562812 PMCID: PMC9107245 DOI: 10.1186/s13072-022-00449-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/12/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Beef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus. RESULTS Comparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity. CONCLUSIONS We present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.
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Affiliation(s)
- Marcela Maria de Souza
- Empresa Brasileira de Pesquisa Agropecuária, Embrapa Pecuária Sudeste, São Carlos, Brazil.,Department of Animal Science, Iowa State University, Ames, USA
| | | | - Marina Ibelli Pereira Rocha
- Empresa Brasileira de Pesquisa Agropecuária, Embrapa Pecuária Sudeste, São Carlos, Brazil.,Department of Genetics and Evolution, Federal University of São Carlos, São Carlos, Brazil
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, CA, USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA, USA
| | - Jennifer Jessica Bruscadin
- Empresa Brasileira de Pesquisa Agropecuária, Embrapa Pecuária Sudeste, São Carlos, Brazil.,Department of Genetics and Evolution, Federal University of São Carlos, São Carlos, Brazil
| | - Wellison Jarles da Silva Diniz
- Empresa Brasileira de Pesquisa Agropecuária, Embrapa Pecuária Sudeste, São Carlos, Brazil.,Department of Animal Science, Auburn University, Auburn, Alabama, USA
| | - Juliana Afonso
- Empresa Brasileira de Pesquisa Agropecuária, Embrapa Pecuária Sudeste, São Carlos, Brazil
| | | | - Gerson B Mourão
- Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Adhemar Zerlotini
- Embrapa Informática Agropecuária, Empresa Brasileira de Pesquisa Agropecuária, Campinas, Brazil
| | - Luiz Lehmann Coutinho
- Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, USA
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16
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Siberski–Cooper CJ, Mayes MS, Healey M, Goetz BM, Baumgard LH, Koltes JE. Associations of Wearable Sensor Measures With Feed Intake, Production Traits, Lactation, and Environmental Parameters Impacting Feed Efficiency in Dairy Cattle. Front Anim Sci 2022. [DOI: 10.3389/fanim.2022.841797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Feed efficiency is an important trait to dairy production because of its impact on sustainability and profitability. Measuring individual cow feed intake on commercial farms would be unfeasibly costly at present. Thus, developing cheap and portable indicators of feed intake would be highly beneficial for genetic selection and precision feeding management tools. Given the growing use of automated sensors on dairy farms, the objective of this study was to determine the relationship between measurements recorded from multiple wearable sensors and feed intake. A total of three different wearable sensors were evaluated for their association with dry mater intake (DMI). The sensors measured activity (sensors = 3), rumination (sensors = 1), ear temperature (sensors = 1), rumen pH (sensors = 1) and rumen temperature (sensors = 1). A range of 56–340 cows with assorted sensors from 24 to 313 days in milk (DIM) were modeled to evaluate associations with DIM, parity, and contemporary group (CG; comprised of pen and study cohort). Models extending upon these variables included known energy sinks (i.e., milk production, milk fat/protein and metabolic body weight), to characterize the association of sensors measures and DMI. Statistically significant (i.e., P < 0.05) regression coefficients for individual sensor measures with DMI ranged from 9.01E-07 to −3.45 kg DMI/day. When integrating all measures from a single sensor in a model, estimated regression coefficients ranged 8.83E-07 to −3.48 kg DMI/day. Significant associations were also identified for milk production traits, parity, DIM and CG. Associations tended to be highest for timepoints around the time of feeding and when multiple measurements within a sensor were integrated in a single model. The findings of this study indicate sensor measures are associated with feed intake and other energy sink traits and variables impacting feed efficiency. This information would be helpful to improve feed and feeding efficiency on commercial farms as proxy measurements for feed intake.
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17
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Stephan T, Burgess SM, Cheng H, Danko CG, Gill CA, Jarvis ED, Koepfli KP, Koltes JE, Lyons E, Ronald P, Ryder OA, Schriml LM, Soltis P, VandeWoude S, Zhou H, Ostrander EA, Karlsson EK. Darwinian genomics and diversity in the tree of life. Proc Natl Acad Sci U S A 2022; 119:e2115644119. [PMID: 35042807 PMCID: PMC8795533 DOI: 10.1073/pnas.2115644119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. This imbalance reflects a perception that human studies are paramount in disease research. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.
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Affiliation(s)
- Taylorlyn Stephan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Shawn M Burgess
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Hans Cheng
- Avian Disease and Oncology Laboratory, Agricultural Research Service, US Department of Agriculture, East Lansing, MI 48823
| | - Charles G Danko
- Department of Biomedical Sciences, Baker Institute for Animal Health, Cornell University, Ithaca, NY 14850
| | - Clare A Gill
- Department of Animal Science, Texas A&M University, College Station, TX 77843
| | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Klaus-Peter Koepfli
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA 22630
- Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 20008
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Eric Lyons
- School of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ 85721
| | - Pamela Ronald
- Department of Plant Pathology, University of California, Davis, CA 95616
- The Genome Center, University of California, Davis, CA 95616
- The Innovative Genomics Institute, University of California, Berkeley, CA 94720
- Grass Genetics, Joint Bioenergy Institute, Emeryville, CA 94608
| | - Oliver A Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior, and Ecology, University of California San Diego, La Jolla, CA 92093
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Pamela Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
| | - Sue VandeWoude
- Department of Micro-, Immuno-, and Pathology, Colorado State University, Fort Collins, CO 80532
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655;
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
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18
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Siberski-Cooper CJ, Koltes JE. Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle. Animals (Basel) 2021; 12:ani12010015. [PMID: 35011121 PMCID: PMC8749788 DOI: 10.3390/ani12010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Sensors, routinely collected on-farm tests, and other repeatable, high-throughput measurements can provide novel phenotype information on a frequent basis. Information from these sensors and high-throughput measurements could be harnessed to monitor or predict individual dairy cow feed intake. Predictive algorithms would allow for genetic selection of animals that consume less feed while producing the same amount of milk. Improved monitoring of feed intake could reduce the cost of milk production, improve animal health, and reduce the environmental impact of the dairy industry. Moreover, data from these information sources could aid in animal management (e.g., precision feeding and health detection). In order to implement tools, the relationship of measurements with feed intake needs to be established and prediction equations developed. Lastly, consideration should be given to the frequency of data collection, the need for standardization of data and other potential limitations of tools in the prediction of feed intake. This review summarizes measurements of feed efficiency, factors that may impact the efficiency and feed consumption of an animal, tools that have been researched and new traits that could be utilized for the prediction of feed intake and efficiency, and prediction equations for feed intake and efficiency presented in the literature to date. Abstract Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.
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19
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Petry B, Moreira GCM, Copola AGL, de Souza MM, da Veiga FC, Jorge EC, de Oliveira Peixoto J, Ledur MC, Koltes JE, Coutinho LL. SAP30 Gene Is a Probable Regulator of Muscle Hypertrophy in Chickens. Front Genet 2021; 12:709937. [PMID: 34646299 PMCID: PMC8502938 DOI: 10.3389/fgene.2021.709937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/20/2021] [Indexed: 11/13/2022] Open
Abstract
Animals with muscle hypertrophy phenotype are targeted by the broiler industry to increase the meat production and the quality of the final product. Studies characterizing the molecular machinery involved with these processes, such as quantitative trait loci studies, have been carried out identifying several candidate genes related to this trait; however, validation studies of these candidate genes in cell culture is scarce. The aim of this study was to evaluate SAP30 as a candidate gene for muscle development and to validate its function in cell culture in vitro. The SAP30 gene was downregulated in C2C12 muscle cell culture using siRNA technology to evaluate its impact on morphometric traits and gene expression by RNA-seq analysis. Modulation of SAP30 expression increased C2C12 myotube area, indicating a role in muscle hypertrophy. RNA-seq analysis identified several upregulated genes annotated in muscle development in treated cells (SAP30-knockdown), corroborating the role of SAP30 gene in muscle development regulation. Here, we provide experimental evidence of the involvement of SAP30 gene as a regulator of muscle cell hypertrophy.
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Affiliation(s)
- Bruna Petry
- Animal Science Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo, Piracicaba, Brazil
| | | | - Aline Gonçalves Lio Copola
- Department of Morphology, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Fernanda Cristina da Veiga
- Animal Science Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo, Piracicaba, Brazil
| | - Erika Cristina Jorge
- Department of Morphology, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | | | - James E Koltes
- Animal Science Department, Iowa State University, Ames, IA, United States
| | - Luiz Lehmann Coutinho
- Animal Science Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo, Piracicaba, Brazil
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20
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Koltes JE. 51 Opportunities to Apply and Learn from Deep Phenotyping in Dairy Cattle. J Anim Sci 2021. [DOI: 10.1093/jas/skab235.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Data from automated systems including sensors, cameras and high-throughput assays, such as milk testing, generate huge amounts of valuable data for research and on-farm applications. The potential to use data collected from automated systems in real-time for predictive analytics or as indicator traits for genetic selection is an exciting opportunity for the dairy industry to address difficult phenotypes. While high frequency recording of data provides high-resolution views of animal behavior, it also creates logistical challenges for determining what data are most informative, when or if to use repeated measures, and how to integrate these repeated measurements with static or less frequently measured traits of interest. Measuring the precision and accuracy of data from automated systems is also a continued challenge. As feed is the largest cost on commercial dairy farms and intake is difficult to measure, our lab is investigating the use of different wearable and stationary sensors to monitor cow and environmental level measurements to determine their utility as potential indicators of variation in feed intake. Associations of sensor measures with feed intake and relationships with health events have been identified. Ongoing research is evaluating the ability to predict trait phenotypes with various sensor data. Beyond the potential to learning more about trait relationships and underlaying genetics of behavior, there is also the potential to develop precision management tools from sensors for producers. Integrating automated systems measurements with research data (e.g. omics) may help to fuel new discoveries and tool development. Future advances in sensing technologies will require transdisciplinary research to develop new types of sensor measures and data analytics to identify hidden information within the data that may lead to new actionable applications on farm.
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21
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Chinchilla-Vargas J, Kramer L, Lester TD, Lester TD, Backes EA, Anschutz K, Decker JE, Stalder KJ, Rothschild MF, Koltes JE. 94 Genetic basis of blood traits in beef cattle and their relationship to production traits at weaning. J Anim Sci 2020. [DOI: 10.1093/jas/skaa054.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Disease represents one of the main factors that determine profitability in animal production. Previous research has observed significant correlations between blood cell counts and the animal’s health status. We hypothesize that blood cell traits may be an effective indicator of performance in beef cattle. Complete blood counts were recorded from approximately 500 crossbred animals at weaning (Angus background crossed with Hereford, Charolais, Sim-Angus, Brangus) born between 2015 and 2016 and raised on toxic or novel tall fescue on three different farms. The animals were genotyped at an approximate density of 50,000 SNPs and the genotypes were imputed to an approximate density of 200,000 SNPs. Heritability, genetic and phenotypic correlations were estimated for 15 blood and 4 production traits across and within environments. Finally, with the objective of identifying the genetic basis underlying the different blood traits, a genome wide association study (GWAS) was performed for all traits. Heritability estimates ranged from 0.11 to 0.60, and generally weak phenotypic correlations and strong genetic correlations were found, however these parameters varied across environments, pointing to GxE interactions. GWAS identified 90 1 Mb windows that explained 0.5% or more of the estimated genetic variance for at least 1 trait with 21 windows overlapping two or more traits. Further research efforts include identifying underlying candidate genes for traits and comparing toxic and novel fescue effects on blood traits. It appears that blood traits have weak phenotypic correlations but strong genetic correlations among themselves, as evidenced by important overlapping regions of genetic control for similar blood traits. However, blood traits have limited potential as indicator traits for productivity.
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22
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Jantzi AE, Siberski CJ, Goetz BM, Healey M, Hayman K, Gorden PJ, Baumgard LH, Koltes JE. PSXI-20 Milking collar activity data is associated with health events and feed intake in lactating Holstein cattle. J Anim Sci 2020. [DOI: 10.1093/jas/skaa278.690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Feed is the largest expense for dairy farms, thus feed efficiency is essential to the sustainability and future of the industry. Our objective was to evaluate the association of milking collar activity with feed intake and health status in lactating cows. Health status was classified for impact of three durations of time (overall, current, or post diagnosis) and as: healthy, mastitis, lame, multiple, or other. Activity data for 155 lactating cows with feed intake records were averaged across two-hour windows to obtain a daily two-hour average. A larger population (n > 1,600) was used to filter out sensor failures and normalize data. Sensor data were adjusted for parity and contemporary group creating adjusted sensor measure (ASM). Dry matter intake (DMI) was adjusted (aDMI) for metabolic body weight, days in milk, and energy sinks used to calculate residual feed intake. Associations between ASM and aDMI, DMI, or health were conducted in SAS9.4. An association of ASM with aDMI was identified (estimate = 0.1635 kg/log count of average activity in a 2-hour period; P < 0.0029). ASM was also associated with DMI (0.2329 kg/log count of average activity, P < 0.0007). ASM was associated with current and overall health timeframes (P < 0.0008 and P < 0.0001, respectively). When health, ASM, and their interaction were included in a model with the response variable aDMI, significant associations were found in the models, including current and overall health (current health: ASM and health: P < 0.0001, interaction: P < 0.0009; overall health: ASM, health, and interaction: P < 0.0001). These results indicate that milking collar data may be useful as a predictor of feed intake either directly or indirectly through detection of health events. Additional studies are needed to determine the predictive ability of collar activity data and the relationship between collar data and health, and to assess if collar activity is an environmental proxy or heritable trait.
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Affiliation(s)
| | | | | | - Mary Healey
- Department of Animal Science, Iowa State University
| | - Kristen Hayman
- Vet Diagnostic & Production Animal Medicine, Iowa State University
| | - Patrick J Gorden
- Veterinary Diagnostic & Production Animal Medicine, Iowa State University
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23
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Siberski CJ, Mayes MS, Gorden PJ, Copeland A, Healey M, Goetz BM, Beiki H, Kramer LM, Baumgard LH, Dixon P, Koltes JE. PSXI-16 Inclusion of automated sensor data as a predictor of feed intake increases the variance explained by a random forest model. J Anim Sci 2020. [DOI: 10.1093/jas/skaa278.694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Prediction of feed intake from indicators would benefit the dairy industry since on-farm feed intake data are rare. The objective of this study was to examine the ability of sensor data to improve predictions of feed intake. Dry matter intake (DMI), milk yield (MY) and components, metabolic body weight (MBW; body weight0.75), and veterinary health records were collected from two cow groups (n1=47, n2=60). Automated sensors (ear tags, rumen bolus, environmental) captured measurements of cow activity, temperature, rumination and rumen pH, and barn temperature and humidity which were used to calculate THI. Random forest (RF) models were trained in R (Caret package) by 10-fold cross validation, with DMI as the response variable. Training data originated from the full study with the exception of the final day, for which DMI was then predicted. Predictive ability was evaluated against a base model excluding automated sensor data to determine changes in accuracy and the percent of variance explained (VAR). The base model included MY and components, MBW, THI, health status and parity. Base model mean square error (MSE) was 9.86, 13.25 and 12.50 kg of DMI and VAR 44.71, 42.9 and 44.85% (n = 92, 56 and 41, respectively). The correlation between actual and predicted final day DMI (CORR) was 0.05, 0.03 and 0.02 (n = 92, 56 and 41, respectively). Adding activity and temperature (first ear tag; n = 92) reduced MSE to 9.70 kg and VAR increased to 45.62% (CORR=0.20). Independently adding bolus activity, rumen temperature and pH (n = 56) to the base model also decreased MSE to 12.53 kg (VAR=46.24% and CORR=0.26). Lastly, adding activity and rumination from the second ear tag (n = 41) to the base model decreased MSE to 12.32 kg (VAR=45.63%, CORR=0.18). Automated sensors appear to explain additional variation in DMI that is not captured in the typical energy sink variables utilized when predicting intake.
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Affiliation(s)
| | - Mary S Mayes
- Department of Animal Science, Iowa State University
| | - Patrick J Gorden
- Veterinary Diagnostic & Production Animal Medicine, Iowa State University
| | - Adam Copeland
- Veterinary Diagnostic & Production Animal Medicine, Iowa State University
| | - Mary Healey
- Department of Animal Science, Iowa State University
| | | | - Hamid Beiki
- Department of Animal Science, Iowa State University
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24
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Chinchilla-Vargas J, Kramer LM, Tucker JD, Hubbell DS, Powell JG, Lester TD, Backes EA, Anschutz K, Decker JE, Stalder KJ, Rothschild MF, Koltes JE. Genetic Basis of Blood-Based Traits and Their Relationship With Performance and Environment in Beef Cattle at Weaning. Front Genet 2020; 11:717. [PMID: 32719722 PMCID: PMC7350949 DOI: 10.3389/fgene.2020.00717] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 06/12/2020] [Indexed: 12/16/2022] Open
Abstract
The objectives of this study were to explore the usefulness of blood-based traits as indicators of health and performance in beef cattle at weaning and identify the genetic basis underlying the different blood parameters obtained from complete blood counts (CBCs). Disease costs represent one of the main factors determining profitability in animal production. Previous research has observed associations between blood cell counts and an animal’s health status in some species. CBC were recorded from approximately 570 Angus based, crossbred beef calves at weaning born between 2015 and 2016 and raised on toxic or novel tall fescue. The calves (N = ∼600) were genotyped at a density of 50k SNPs and the genotypes (N = 1160) were imputed to a density of 270k SNPs. Genetic parameters were estimated for 15 blood and 4 production. Finally, with the objective of identifying the genetic basis underlying the different blood-based traits, genome-wide association studies (GWAS) were performed for all traits. Heritability estimates ranged from 0.11 to 0.60, and generally weak phenotypic correlations and strong genetic correlations were observed among blood-based traits only. Genome-wide association study identified ninety-one 1-Mb windows that accounted for 0.5% or more of the estimated genetic variance for at least 1 trait with 21 windows overlapping across two or more traits (explaining more than 0.5% of estimated genetic variance for two or more traits). Five candidate genes have been identified in the most interesting overlapping regions related to blood-based traits. Overall, this study represents one of the first efforts represented in scientific literature to identify the genetic basis of blood cell traits in beef cattle. The results presented in this study allow us to conclude that: (1) blood-based traits have weak phenotypic correlations but strong genetic correlations among themselves. (2) Blood-based traits have moderate to high heritability. (3) There is evidence of an important overlap of genetic control among similar blood-based traits which will allow for their use in improvement programs in beef cattle.
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Affiliation(s)
| | - Luke M Kramer
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - John D Tucker
- Division of Agriculture, Livestock and Forestry Research Station, Batesville, AR, United States
| | - Donald S Hubbell
- Division of Agriculture, Livestock and Forestry Research Station, Batesville, AR, United States
| | - Jeremy G Powell
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Toby D Lester
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Elizabeth A Backes
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Karen Anschutz
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Jared E Decker
- Division of Animal Science, University of Missouri, Columbia, MO, United States
| | - Kenneth J Stalder
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Max F Rothschild
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
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25
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de Lima AO, Koltes JE, Diniz WJS, de Oliveira PSN, Cesar ASM, Tizioto PC, Afonso J, de Souza MM, Petrini J, Rocha MIP, Cardoso TF, Neto AZ, Coutinho LL, Mourão GB, Regitano LCA. Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses. Front Genet 2020; 11:189. [PMID: 32194642 PMCID: PMC7064723 DOI: 10.3389/fgene.2020.00189] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
Feed efficiency helps to reduce environmental impacts from livestock production, improving beef cattle profitability. We identified potential biomarkers (hub genes) for feed efficiency, by applying co-expression analysis in Longissimus thoracis RNA-Seq data from 180 Nelore steers. Six co-expression modules were associated with six feed efficiency-related traits (p-value ≤ 0.05). Within these modules, 391 hub genes were enriched for pathways as protein synthesis, muscle growth, and immune response. Trait-associated transcription factors (TFs) ELF1, ELK3, ETS1, FLI1, and TCF4, were identified with binding sites in at least one hub gene. Gene expression of CCDC80, FBLN5, SERPINF1, and OGN was associated with multiple feed efficiency-related traits (FDR ≤ 0.05) and were previously related to glucose homeostasis, oxidative stress, fat mass, and osteoblastogenesis, respectively. Potential regulatory elements were identified, integrating the hub genes with previous studies from our research group, such as the putative cis-regulatory elements (eQTLs) inferred as affecting the PCDH18 and SPARCL1 hub genes related to immune system and adipogenesis, respectively. Therefore, our analyses contribute to a better understanding of the biological mechanisms underlying feed efficiency in bovine and the hub genes disclosed can be used as biomarkers for feed efficiency-related traits in Nelore cattle.
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Affiliation(s)
- Andressa O de Lima
- Center for Biological and Health Sciences, Federal University of São Carlos, São Carlos, Brazil
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Wellison J S Diniz
- Center for Biological and Health Sciences, Federal University of São Carlos, São Carlos, Brazil
| | | | - Aline S M Cesar
- Department of Agroindustry, Food and Nutrition, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | | | - Juliana Afonso
- Center for Biological and Health Sciences, Federal University of São Carlos, São Carlos, Brazil
| | - Marcela M de Souza
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Juliana Petrini
- Exact Sciences Institute, Federal University of Alfenas, Alfenas, Brazil
| | - Marina I P Rocha
- Center for Biological and Health Sciences, Federal University of São Carlos, São Carlos, Brazil
| | - Tainã F Cardoso
- Embrapa Pecuária Sudeste, Empresa Brazileira de Pesquisa Agropecuária, São Carlos, Brazil
| | - Adhemar Zerlotini Neto
- Embrapa Informática Agropecuária, Empresa Brazileira de Pesquisa Agropecuária, Campinas, Brazil
| | - Luiz L Coutinho
- Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Gerson B Mourão
- Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Luciana C A Regitano
- Embrapa Pecuária Sudeste, Empresa Brazileira de Pesquisa Agropecuária, São Carlos, Brazil
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26
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Andrade BGN, Bressani FA, Cuadrat RRC, Tizioto PC, de Oliveira PSN, Mourão GB, Coutinho LL, Reecy JM, Koltes JE, Walsh P, Berndt A, Palhares JCP, Regitano LCA. The structure of microbial populations in Nelore GIT reveals inter-dependency of methanogens in feces and rumen. J Anim Sci Biotechnol 2020; 11:6. [PMID: 32123563 PMCID: PMC7038601 DOI: 10.1186/s40104-019-0422-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 12/23/2019] [Indexed: 12/18/2022] Open
Abstract
Background The success of different species of ruminants in the colonization of a diverse range of environments is due to their ability to digest and absorb nutrients from cellulose, a complex polysaccharide found in leaves and grass. Ruminants rely on a complex and diverse microbial community, or microbiota, in a unique compartment known as the rumen to break down this polysaccharide. Changes in microbial populations of the rumen can affect the host’s development, health, and productivity. However, accessing the rumen is stressful for the animal. Therefore, the development and use of alternative sampling methods are needed if this technique is to be routinely used in cattle breeding. To this end, we tested if the fecal microbiome could be used as a proxy for the rumen microbiome due to its accessibility. We investigated the taxonomic composition, diversity and inter-relations of two different GIT compartments, rumen and feces, of 26 Nelore (Bos indicus) bulls, using Next Generation Sequencing (NGS) metabarcoding of bacteria, archaea and ciliate protozoa. Results We identified 4265 Amplicon Sequence Variants (ASVs) from bacteria, 571 from archaea, and 107 from protozoa, of which 143 (96 bacteria and 47 archaea) were found common between both microbiomes. The most prominent bacterial phyla identified were Bacteroidetes (41.48%) and Firmicutes (56.86%) in the ruminal and fecal microbiomes, respectively, with Prevotella and Ruminococcaceae UCG-005 the most relatively abundant genera identified in each microbiome. The most abundant archaeal phylum identified was Euryarchaeota, of which Methanobrevibacter gottschalkii, a methanogen, was the prevalent archaeal species identified in both microbiomes. Protozoa were found exclusively identified in the rumen with Bozasella/Triplumaria being the most frequent genus identified. Co-occurrence among ruminal and fecal ASVs reinforces the relationship of microorganisms within a biological niche. Furthermore, the co-occurrence of shared archaeal ASVs between microbiomes indicates a dependency of the predominant fecal methanogen population on the rumen population. Conclusions Co-occurring microorganisms were identified within the rumen and fecal microbiomes, which revealed a strong association and inter-dependency between bacterial, archaeal and protozoan populations of the same microbiome. The archaeal ASVs identified as co-occurring between GIT compartments corresponded to the methanogenic genera Methanobrevibacter and Methanosphaera and represented 26.34% of the overall archaeal sequencesdiversity in the rumen and 42.73% in feces. Considering that these archaeal ASVs corresponded to a significant part of the overall diversity of both microbiomes, which is much higher if one includes the interactions of these co-occurring with other rumen archaea ASVs, we suggest that fecal methanogens could be used as a proxy of ruminal methanogens.
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Affiliation(s)
| | | | - Rafael R C Cuadrat
- 2Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | | | | | - Gerson B Mourão
- 4Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil
| | - Luiz L Coutinho
- 4Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil
| | - James M Reecy
- 5Department of Animal Science, Iowa State University, Ames, IA USA
| | - James E Koltes
- 5Department of Animal Science, Iowa State University, Ames, IA USA
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27
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Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM, Reuter R, Rexroad CE, Rosa GJM, Serão NVL, White SN, Woodward-Greene MJ, Worku M, Zhang H, Reecy JM. A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock. Front Genet 2019; 10:1197. [PMID: 31921279 PMCID: PMC6934059 DOI: 10.3389/fgene.2019.01197] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/29/2019] [Indexed: 01/28/2023] Open
Abstract
Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., “big data” training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population.
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Affiliation(s)
- James E Koltes
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - John B Cole
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, United States
| | - Roxanne Clemmens
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Ryan N Dilger
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Luke M Kramer
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Joan K Lunney
- Animal Parasitic Diseases Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States
| | - Molly E McCue
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, United States
| | - Stephanie D McKay
- Department of Animal and Veterinary Sciences, College of Agriculture and Life Sciences, University of Vermont, Burlington, VT, United States
| | - Raluca G Mateescu
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States
| | - Brenda M Murdoch
- Department of Animal and Veterinary Science, University of Idaho, Moscow, ID, United States
| | - Ryan Reuter
- Department of Animal and Food Sciences, College of Agricultural Sciences and Natural Resources, Oklahoma State University, Stillwater, OK, United States
| | - Caird E Rexroad
- Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States
| | - Guilherme J M Rosa
- Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, United States
| | - Nick V L Serão
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Stephen N White
- Animal Disease Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States.,Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States.,Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | - M Jennifer Woodward-Greene
- Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States
| | - Millie Worku
- Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
| | - Hongwei Zhang
- Department of Electrical and Computer Engineering, College of Engineering, Iowa State University, Ames, IA, United States
| | - James M Reecy
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
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28
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Chinchilla-Vargas J, Kramer LM, Tucker JD, Hubbell DS, Powell JG, Lester TD, Backes EA, Anschutz K, Stalder KJ, Rothschild MF, Koltes JE. PSIII-12 Peripheral blood parameters as proxies of performance in beef cattle: Heritability and genetic correlations between peripheral blood parameters and performance phenotypes. J Anim Sci 2019. [DOI: 10.1093/jas/skz122.289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Disease incidence and feed costs are two main drivers in cattle production operations. Frequently environmental stressors such as fescue toxicosis have negative effects on livestock performance and health. Low-cost methods to measure these types of health and stress response phenotypes are needed to capture their impact on production traits. Previous research has correlated white blood cell parameters to disease resistance in beef cattle. Based on this, blood parameters at weaning may be candidates that could be a proxy for selection and identification of high-performance animals in commercial settings. To identify candidate blood parameters, blood samples were collected at weaning on approximately 500 crossbred animals (Angus background crossed with Hereford, Charolais, Sim-Angus, Brangus) born between 2015 and 2016 and raised on toxic fescue. The animals were also genotyped at an approximate density of 50,000 SNPs. Complete blood counts (CBC) were obtained the blood samples and heritabilities for 15 peripheral blood parameters were estimated. For the CBC traits that were measured, heritabilities ranged from low to moderate (0.02 to 0.35). Based on current findings, a substantial genetic component for some CBC parameters exists and selection could be effective at improving these traits. Further research will estimate genetic correlations between peripheral blood parameters, weaning weight and average daily gain (ADG) with the intention of identifying correlated traits to be used in commercial selection programs.
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29
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Siberski CJ, Goetz BM, Baumgard LH, Koltes JE. PSIII-4 Preliminary exploration of relationship of automated sensor data with feed intake and efficiency in lactating dairy cattle. J Anim Sci 2019. [DOI: 10.1093/jas/skz122.296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Feed costs represent the greatest expense on a dairy farm, making feed efficiency an important trait to consider among production traits. Current tools to measure feed intake have limited application in commercial settings, due to affordability and lack of portability of technologies. Therefore, development of automated sensor-based indicator traits for feed intake could prove to be valuable. The objective of the current study was to determine if automated eartag data was associated with feed intake. Activity and inner ear temperature were collected every 19 minutes utilizing Quantified Ag eartags (n = 48 lactating cows). Ear tags were placed 5 days prior to the start of the trial, with cows ranging from 67-192 days in milk (DIM). Daily feed intake, milk weights, milk components and body weight (BW) were also recorded. Data were analyzed using PROX GLIMMIX in SAS. Dry matter intake (DMI) was modeled including fixed effects for DIM, milk weight, component composition, metabolic body weight (BW0.75), eartag activity or temperature, as well as the random effects of parity and group. To identify informative timeframes with reduced influence of environmental noise, data were analyzed over 3-day rolling windows of time. Six windows were significantly associated with dry matter intake (P ≤ 0.05) when utilizing ear tag activity. Three windows of time of ear tag temperature were found to be significantly associated with DMI (P ≤ 0.05). These findings indicate that eartag sensor data may be useful indicators of feed intake; however, days in milk and season may impact the informativeness of sensor data. Additional studies are warranted to validate the efficacy of activity and ear temperature as indicators of feed intake and determine the impact of other variables on these potential sensor indicator traits over time.
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30
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Koltes JE, Arora I, Gupta R, Nguyen DC, Schaid M, Kim JA, Kimple ME, Bhatnagar S. A gene expression network analysis of the pancreatic islets from lean and obese mice identifies complement 1q like-3 secreted protein as a regulator of β-cell function. Sci Rep 2019; 9:10119. [PMID: 31300714 PMCID: PMC6626003 DOI: 10.1038/s41598-019-46219-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 06/24/2019] [Indexed: 12/29/2022] Open
Abstract
Secreted proteins are important metabolic regulators. Identifying and characterizing the role of secreted proteins from small tissue depots such as islets of Langerhans, which are required for the proper control of whole-body energy metabolism, remains challenging. Our objective was to identify islet-derived secreted proteins that affect islet function in obesity. Lean and obese mouse islet expression data were analyzed by weighted gene co-expression network analysis (WGCNA) to identify trait-associated modules. Subsequently, genes within these modules were filtered for transcripts that encode for secreted proteins based on intramodular connectivity, module membership, and differential expression. Complement 1q like-3 (C1ql3) secreted protein was identified as a hub gene affecting islet function in obesity. Co-expression network, hierarchal clustering, and gene-ontology based approaches identified a putative role for C1ql3 in regulating β-cell insulin secretion. Biological validation shows that C1ql3 is expressed in β-cells, it inhibits insulin secretion and key genes that are involved in β-cell function. Moreover, the increased expression of C1ql3 is correlated with the reduced insulin secretion in islets of obese mice. Herein, we demonstrate a streamlined approach to effectively screen and determine the function of secreted proteins in islets, and identified C1ql3 as a putative contributor to reduced insulin secretion in obesity, linking C1ql3 to an increased susceptibility to type 2 diabetes.
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Affiliation(s)
- James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Itika Arora
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine and Comprehensive Diabetes Center, University of Alabama, Birmingham, AL, 35294, USA
| | - Rajesh Gupta
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine and Comprehensive Diabetes Center, University of Alabama, Birmingham, AL, 35294, USA
| | - Dan C Nguyen
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine and Comprehensive Diabetes Center, University of Alabama, Birmingham, AL, 35294, USA
| | - Michael Schaid
- Interdisciplinary Graduate Program in Nutritional Sciences, University of Wisconsin-Madison College of Agriculture and Life Sciences, Madison, WI, 53706, USA.,Research Service, William S Middleton Memorial VA Hospital, Madison, WI, 53705, USA
| | - Jeong-A Kim
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine and Comprehensive Diabetes Center, University of Alabama, Birmingham, AL, 35294, USA
| | - Michelle E Kimple
- Interdisciplinary Graduate Program in Nutritional Sciences, University of Wisconsin-Madison College of Agriculture and Life Sciences, Madison, WI, 53706, USA.,Divison of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, USA.,Department of Cell and Regenerative Biology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, USA.,Department of Academic Affairs, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, USA.,Research Service, William S Middleton Memorial VA Hospital, Madison, WI, 53705, USA
| | - Sushant Bhatnagar
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine and Comprehensive Diabetes Center, University of Alabama, Birmingham, AL, 35294, USA.
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31
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de Oliveira PSN, Coutinho LL, Cesar ASM, Diniz WJDS, de Souza MM, Andrade BG, Koltes JE, Mourão GB, Zerlotini A, Reecy JM, Regitano LCA. Co-Expression Networks Reveal Potential Regulatory Roles of miRNAs in Fatty Acid Composition of Nelore Cattle. Front Genet 2019; 10:651. [PMID: 31354792 PMCID: PMC6637853 DOI: 10.3389/fgene.2019.00651] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/19/2019] [Indexed: 12/15/2022] Open
Abstract
Fatty acid (FA) content affects the sensorial and nutritional value of meat and plays a significant role in biological processes such as adipogenesis and immune response. It is well known that, in beef, the main FAs associated with these biological processes are oleic acid (C18:1 cis9, OA) and conjugated linoleic acid (CLA-c9t11), which may have beneficial effects on metabolic diseases such as type 2 diabetes and obesity. Here, we performed differential expression and co-expression analyses, weighted gene co-expression network analysis (WGCNA) and partial correlation with information theory (PCIT), to uncover the complex interactions between miRNAs and mRNAs expressed in skeletal muscle associated with FA content. miRNA and mRNA expression data were obtained from skeletal muscle of Nelore cattle that had extreme genomic breeding values for OA and CLA. Insulin and MAPK signaling pathways were identified by WGCNA as central pathways associated with both of these fatty acids. Co-expression network analysis identified bta-miR-33a/b, bta-miR-100, bta-miR-204, bta-miR-365-5p, bta-miR-660, bta-miR-411a, bta-miR-136, bta-miR-30-5p, bta-miR-146b, bta-let-7a-5p, bta-let-7f, bta-let-7, bta-miR 339, bta-miR-10b, bta-miR 486, and the genes ACTA1 and ALDOA as potential regulators of fatty acid synthesis. This study provides evidence and insights into the molecular mechanisms and potential target genes involved in fatty acid content differences in Nelore beef cattle, revealing new candidate pathways of phenotype modulation that could positively benefit beef production and human consumption.
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Affiliation(s)
| | - Luiz L Coutinho
- Department of Animal Science, University of São Paulo, Piracicaba, Brazil
| | - Aline S M Cesar
- Department of Agroindustry, Food and Nutrition, University of São Paulo, Piracicaba, Brazil
| | | | - Marcela M de Souza
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Bruno G Andrade
- Embrapa Pecuária Sudeste, Empresa Brasileira de Pesquisa Agropecuária, São Carlos, Brazil
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Gerson B Mourão
- Department of Agroindustry, Food and Nutrition, University of São Paulo, Piracicaba, Brazil
| | | | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Luciana C A Regitano
- Embrapa Pecuária Sudeste, Empresa Brasileira de Pesquisa Agropecuária, São Carlos, Brazil
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32
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Gonçalves TM, de Almeida Regitano LC, Koltes JE, Cesar ASM, da Silva Andrade SC, Mourão GB, Gasparin G, Moreira GCM, Fritz-Waters E, Reecy JM, Coutinho LL. Gene Co-expression Analysis Indicates Potential Pathways and Regulators of Beef Tenderness in Nellore Cattle. Front Genet 2018; 9:441. [PMID: 30344530 PMCID: PMC6182065 DOI: 10.3389/fgene.2018.00441] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 09/14/2018] [Indexed: 12/13/2022] Open
Abstract
Beef tenderness, a complex trait affected by many factors, is economically important to beef quality, industry, and consumer’s palatability. In this study, RNA-Seq was used in network analysis to better understand the biological processes that lead to differences in beef tenderness. Skeletal muscle transcriptional profiles from 24 Nellore steers, selected by extreme estimated breeding values (EBVs) for shear force after 14 days of aging, were analyzed and 22 differentially expressed transcripts were identified. Among these were genes encoding ribosomal proteins, glutathione transporter ATP-binding cassette, sub-family C (CFTR/MRP), member 4 (ABCC4), and synaptotagmin IV (SYT4). Complementary co-expression analyses using Partial Correlation with Information Theory (PCIT), Phenotypic Impact Factor (PIF) and the Regulatory Impact Factor (RIF) methods identified candidate regulators and related pathways. The PCIT analysis identified ubiquitin specific peptidase 2 (USP2), growth factor receptor-bound protein 10 (GBR10), anoctamin 1 (ANO1), and transmembrane BAX inhibitor motif containing 4 (TMBIM4) as the most differentially hubbed (DH) transcripts. The transcripts that had a significant correlation with USP2, GBR10, ANO1, and TMBIM4 enriched for proteasome KEGG pathway. RIF analysis identified microRNAs as candidate regulators of variation in tenderness, including bta-mir-133a-2 and bta-mir-22. Both microRNAs have target genes present in the calcium signaling pathway and apoptosis. PIF analysis identified myoglobin (MB), enolase 3 (ENO3), and carbonic anhydrase 3 (CA3) as potentially having fundamental roles in tenderness. Pathways identified in our study impacted in beef tenderness included: calcium signaling, apoptosis, and proteolysis. These findings underscore some of the complex molecular mechanisms that control beef tenderness in Nellore cattle.
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Affiliation(s)
| | | | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | | | - Sónia Cristina da Silva Andrade
- Department of Animal Science, University of São Paulo, Piracicaba, Brazil.,Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil
| | | | - Gustavo Gasparin
- Department of Animal Science, University of São Paulo, Piracicaba, Brazil
| | | | - Elyn Fritz-Waters
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, United States
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33
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Gupta R, Nguyen DC, Schaid MD, Lei X, Balamurugan AN, Wong GW, Kim JA, Koltes JE, Kimple ME, Bhatnagar S. Complement 1q-like-3 protein inhibits insulin secretion from pancreatic β-cells via the cell adhesion G protein-coupled receptor BAI3. J Biol Chem 2018; 293:18086-18098. [PMID: 30228187 DOI: 10.1074/jbc.ra118.005403] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/06/2018] [Indexed: 01/04/2023] Open
Abstract
Secreted proteins are important metabolic regulators in both healthy and disease states. Here, we sought to investigate the mechanism by which the secreted protein complement 1q-like-3 (C1ql3) regulates insulin secretion from pancreatic β-cells, a key process affecting whole-body glucose metabolism. We found that C1ql3 predominantly inhibits exendin-4- and cAMP-stimulated insulin secretion from mouse and human islets. However, to a lesser extent, C1ql3 also reduced insulin secretion in response to KCl, the potassium channel blocker tolbutamide, and high glucose. Strikingly, C1ql3 did not affect insulin secretion stimulated by fatty acids, amino acids, or mitochondrial metabolites, either at low or submaximal glucose concentrations. Additionally, C1ql3 inhibited glucose-stimulated cAMP levels, and insulin secretion stimulated by exchange protein directly activated by cAMP-2 and protein kinase A. These results suggest that C1ql3 inhibits insulin secretion primarily by regulating cAMP signaling. The cell adhesion G protein-coupled receptor, brain angiogenesis inhibitor-3 (BAI3), is a C1ql3 receptor and is expressed in β-cells and in mouse and human islets, but its function in β-cells remained unknown. We found that siRNA-mediated Bai3 knockdown in INS1(832/13) cells increased glucose-stimulated insulin secretion. Furthermore, incubating the soluble C1ql3-binding fragment of the BAI3 protein completely blocked the inhibitory effects of C1ql3 on insulin secretion in response to cAMP. This suggests that BAI3 mediates the inhibitory effects of C1ql3 on insulin secretion from pancreatic β-cells. These findings demonstrate a novel regulatory mechanism by which C1ql3/BAI3 signaling causes an impairment of insulin secretion from β-cells, possibly contributing to the progression of type 2 diabetes in obesity.
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Affiliation(s)
- Rajesh Gupta
- From the Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, and Comprehensive Diabetes Center, University of Alabama, Birmingham, Alabama 35294
| | - Dan C Nguyen
- From the Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, and Comprehensive Diabetes Center, University of Alabama, Birmingham, Alabama 35294
| | - Michael D Schaid
- the Interdisciplinary Graduate Program in Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706,; the William S. Middleton Memorial Veterans Hospital, Research Service, Madison, Wisconsin 53705
| | - Xia Lei
- the Department of Physiology and Center for Metabolism and Obesity Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | | | - G William Wong
- the Department of Physiology and Center for Metabolism and Obesity Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | - Jeong-A Kim
- From the Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, and Comprehensive Diabetes Center, University of Alabama, Birmingham, Alabama 35294
| | - James E Koltes
- the Department of Animal Science, Iowa State University, Ames, Iowa 50011
| | - Michelle E Kimple
- the Interdisciplinary Graduate Program in Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706,; the William S. Middleton Memorial Veterans Hospital, Research Service, Madison, Wisconsin 53705,; the Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, and the Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin 53705
| | - Sushant Bhatnagar
- From the Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, and Comprehensive Diabetes Center, University of Alabama, Birmingham, Alabama 35294,.
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Cesar ASM, Regitano LCA, Reecy JM, Poleti MD, Oliveira PSN, de Oliveira GB, Moreira GCM, Mudadu MA, Tizioto PC, Koltes JE, Fritz-Waters E, Kramer L, Garrick D, Beiki H, Geistlinger L, Mourão GB, Zerlotini A, Coutinho LL. Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits. BMC Genomics 2018; 19:499. [PMID: 29945546 PMCID: PMC6020320 DOI: 10.1186/s12864-018-4871-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 06/14/2018] [Indexed: 12/21/2022] Open
Abstract
Background Integration of high throughput DNA genotyping and RNA-sequencing data allows for the identification of genomic regions that control gene expression, known as expression quantitative trait loci (eQTL), on a whole genome scale. Intramuscular fat (IMF) content and carcass composition play important roles in metabolic and physiological processes in mammals because they influence insulin sensitivity and consequently prevalence of metabolic diseases such as obesity and type 2 diabetes. However, limited information is available on the genetic variants and mechanisms associated with IMF deposition in mammals. Thus, our hypothesis was that eQTL analyses could identify putative regulatory regions and transcription factors (TFs) associated with intramuscular fat (IMF) content traits. Results We performed an integrative eQTL study in skeletal muscle to identify putative regulatory regions and factors associated with intramuscular fat content traits. Data obtained from skeletal muscle samples of 192 animals was used for association analysis between 461,466 SNPs and the transcription level of 11,808 genes. This yielded 1268 cis- and 10,334 trans-eQTLs, among which we identified nine hotspot regions that each affected the expression of > 119 genes. These putative regulatory regions overlapped with previously identified QTLs for IMF content. Three of the hotspots respectively harbored the transcription factors USF1, EGR4 and RUNX1T1, which are known to play important roles in lipid metabolism. From co-expression network analysis, we further identified modules significantly correlated with IMF content and associated with relevant processes such as fatty acid metabolism, carbohydrate metabolism and lipid metabolism. Conclusion This study provides novel insights into the link between genotype and IMF content as evident from the expression level. It thereby identifies genomic regions of particular importance and associated regulatory factors. These new findings provide new knowledge about the biological processes associated with genetic variants and mechanisms associated with IMF deposition in mammals. Electronic supplementary material The online version of this article (10.1186/s12864-018-4871-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aline S M Cesar
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.,Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | | | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Mirele D Poleti
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | | | | | - Gabriel C M Moreira
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | | | - Polyana C Tizioto
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Elyn Fritz-Waters
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Dorian Garrick
- School of Agriculture, Massey University, Ruakura, Hamilton, New Zealand
| | - Hamid Beiki
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | | | - Gerson B Mourão
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | | | - Luiz L Coutinho
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.
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Koltes JE, Koltes DA, Mote BE, Tucker J, Hubbell DS. Automated collection of heat stress data in livestock: new technologies and opportunities. Transl Anim Sci 2018; 2:319-323. [PMID: 32704715 PMCID: PMC7200501 DOI: 10.1093/tas/txy061] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 05/16/2018] [Indexed: 12/03/2022] Open
Affiliation(s)
- James E Koltes
- Department of Animal Science, University of Arkansas, Fayetteville, AR
| | - Dawn A Koltes
- Department of Animal Science, University of Arkansas, Fayetteville, AR
| | - Benny E Mote
- Department of Animal Science, University of Nebraska, Lincoln, NE
| | - John Tucker
- Department of Animal Science, University of Arkansas, Fayetteville, AR.,Livestock and Forestry Research Station, Division of Agriculture, Batesville, AR
| | - Don S Hubbell
- Department of Animal Science, University of Arkansas, Fayetteville, AR.,Livestock and Forestry Research Station, Division of Agriculture, Batesville, AR
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Chewning S, Meyer LR, Atchley JA, Powell JG, Tucker JD, Hubbell, III DS, Zhao J, Koltes JE. 172 Analysis of Fecal Microbiome of Crossbred Beef Cows Grazing Toxic or Novel Fescue. J Anim Sci 2018. [DOI: 10.1093/jas/sky073.169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- S Chewning
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - L R Meyer
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J A Atchley
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J G Powell
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J D Tucker
- Livestock and Forestry Research Station, Division of Agriculture, University of Arkansas, Batesville, AR
| | - D S Hubbell, III
- Livestock and Forestry Research Station, Division of Agriculture, University of Arkansas, Batesville, AR
| | - J Zhao
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA
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37
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Ratton AE, Chewning S, Meyer LR, Atchley JA, Powell JG, Tucker JD, Hubbell, III DS, Zhao J, Koltes JE. 505 Toxic Fescue Exposure Alters Vaginal Microbial Communities of Crossbred Beef Cows. J Anim Sci 2018. [DOI: 10.1093/jas/sky073.502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- A E Ratton
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - S Chewning
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - L R Meyer
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J A Atchley
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J G Powell
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J D Tucker
- Livestock and Forestry Research Station, Division of Agriculture, University of Arkansas, Batesville, AR
| | - D S Hubbell, III
- Livestock and Forestry Research Station, Division of Agriculture, University of Arkansas, Batesville, AR
| | - J Zhao
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA
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Crook TS, Beck PA, Gadberry MS, Sims MB, Stewart B, Shelton C, McLean DJ, Chapman JD, Koltes JE. 108 Effects of Omnigen-AF ® on Cow Performance. J Anim Sci 2018. [DOI: 10.1093/jas/sky027.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- T S Crook
- University of Arkansas Division of Agriculture Department of Animal Science, Fayetteville, AR
| | - P A Beck
- University of Arkansas Division of Agriculture SWREC, Hope, AR
| | - M S Gadberry
- Department of Animal Science, University of Arkansas, Little Rock, AR
| | - M B Sims
- University of Arkansas Division of Agriculture SWREC, Hope, AR
| | - B Stewart
- University of Arkansas Division of Agriculture SWREC, Hope, AR
| | - C Shelton
- University of Arkansas Division of Agriculture SWREC, Hope, AR
| | - D J McLean
- Phibro Animal Health Corporation, Teaneck, NJ
| | - J D Chapman
- Phibro Animal Health Corporation, Quincy, IL
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA
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Palmer EA, Kegley EB, Beck PA, Ball JJ, Koltes JE, Chewning S, Hornsby JA, Reynolds JL, Shoulders BP, Cravey MD, Powell JG. 97 Effect of a Combination of Live Yeast and Yeast Cell Wall Products Supplemented before and after Weaning on Heifer Growth Performance and Heat Stress. J Anim Sci 2018. [DOI: 10.1093/jas/sky027.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- E A Palmer
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - E B Kegley
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - P A Beck
- University of Arkansas Division of Agriculture SWREC, Hope, AR
| | - J J Ball
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA
| | - S Chewning
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J A Hornsby
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - J L Reynolds
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - B P Shoulders
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
| | - M D Cravey
- Phileo Lesaffre Animal Care, Milwaukee, WI
| | - J G Powell
- Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR
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Oliveira GB, Regitano LCA, Cesar ASM, Reecy JM, Degaki KY, Poleti MD, Felício AM, Koltes JE, Coutinho LL. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle. BMC Genomics 2018; 19:126. [PMID: 29415651 PMCID: PMC5804041 DOI: 10.1186/s12864-018-4514-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 01/31/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The amount of intramuscular fat can influence the sensory characteristics and nutritional value of beef, thus the selection of animals with adequate fat deposition is important to the consumer. There is growing knowledge about the genes and pathways that control the biological processes involved in fat deposition in muscle. MicroRNAs (miRNAs) belong to a well-conserved class of non-coding small RNAs that modulate gene expression across a range of biological functions in animal development and physiology. The aim of this study was to identify differentially expressed (DE) miRNAs, regulatory candidate genes and co-expression networks related to intramuscular fat (IMF) deposition. To achieve this, we used mRNA and miRNA expression data from the Longissimus dorsi muscle of 30 Nelore steers with high (H) and low (L) genomic estimated breeding values (GEBV) for IMF deposition. RESULTS Differential miRNA expression analysis between animals with extreme GEBV values for IMF identified six DE miRNAs (FDR 10%). Functional annotation of the target genes for these microRNAs indicated that the PPARs signaling pathway is involved with IMF deposition. Candidate regulatory genes such as SDHAF4, FBXO17, ALDOA and PKM were identified by partial correlation with information theory (PCIT), phenotypic impact factor (PIF) and regulatory impact factor (RIF) co-expression approaches from integrated miRNA-mRNA expression data. Two DE miRNAs (FDR 10%), bta-miR-143 and bta-miR-146b, which were upregulated in the Low IMF group, were correlated with regulatory candidate genes, which were functionally enriched for fatty acid oxidation GO terms. Co-expression patterns obtained by weighted correlation network analysis (WGCNA), which showed possible interaction and regulation between mRNAs and miRNAs, identified several modules related to immune system function, protein metabolism, energy metabolism and glucose catabolism according to in silico analysis performed herein. CONCLUSION In this study, several genes and miRNAs were identified as candidate regulators of IMF by analyzing DE miRNAs using two different miRNA-mRNA co-expression network methods. This study contributes to the understanding of potential regulatory mechanisms of gene signaling networks involved in fat deposition processes measured in muscle. Glucose metabolism and inflammation processes were the main pathways found in silico to influence intramuscular fat deposition in beef cattle in the integrative mRNA-miRNA co-expression analysis.
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Affiliation(s)
- Gabriella B. Oliveira
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
| | | | - Aline S. M. Cesar
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
| | - James M. Reecy
- Department of Animal Science, Iowa State University, Ames, IA 50011 USA
| | - Karina Y. Degaki
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
| | - Mirele D. Poleti
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
| | - Andrezza M. Felício
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
| | - James E. Koltes
- Department of Animal Science, University of Arkansas, Fayetteville, AR 72701 USA
| | - Luiz L. Coutinho
- Department of Animal Science, University of São Paulo, Piracicaba, SP 13418-900 Brazil
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Iamartino D, Nicolazzi EL, Van Tassell CP, Reecy JM, Fritz-Waters ER, Koltes JE, Biffani S, Sonstegard TS, Schroeder SG, Ajmone-Marsan P, Negrini R, Pasquariello R, Ramelli P, Coletta A, Garcia JF, Ali A, Ramunno L, Cosenza G, de Oliveira DAA, Drummond MG, Bastianetto E, Davassi A, Pirani A, Brew F, Williams JL. Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis). PLoS One 2017; 12:e0185220. [PMID: 28981529 PMCID: PMC5628821 DOI: 10.1371/journal.pone.0185220] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Accepted: 09/10/2017] [Indexed: 11/28/2022] Open
Abstract
Background The availability of the bovine genome sequence and SNP panels has improved various genomic analyses, from exploring genetic diversity to aiding genetic selection. However, few of the SNP on the bovine chips are polymorphic in buffalo, therefore a panel of single nucleotide DNA markers exclusive for buffalo was necessary for molecular genetic analyses and to develop genomic selection approaches for water buffalo. The creation of a 90K SNP panel for river buffalo and testing in a genome wide association study for milk production is described here. Methods The genomes of 73 buffaloes of 4 different breeds were sequenced and aligned against the bovine genome, which facilitated the identification of 22 million of sequence variants among the buffalo genomes. Based on frequencies of variants within and among buffalo breeds, and their distribution across the genome, inferred from the bovine genome sequence, 90,000 putative single nucleotide polymorphisms were selected to create an Axiom® Buffalo Genotyping Array 90K. Results This 90K “SNP-Chip” was tested in several river buffalo populations and found to have ∼70% high quality and polymorphic SNPs. Of the 90K SNPs about 24K were also found to be polymorphic in swamp buffalo. The SNP chip was used to investigate the structure of buffalo populations, and could distinguish buffalo from different farms. A Genome Wide Association Study identified genomic regions on 5 chromosomes putatively involved in milk production. Conclusion The 90K buffalo SNP chip described here is suitable for the analysis of the genomes of river buffalo breeds, and could be used for genetic diversity studies and potentially as a starting point for genome-assisted selection programmes. This SNP Chip could also be used to analyse swamp buffalo, but many loci are not informative and creation of a revised SNP set specific for swamp buffalo would be advised.
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Affiliation(s)
- Daniela Iamartino
- AIA-LGS Associazione Italiana Allevatori–Laboratorio Genetica e Servizi, Cremona, Italy
- Fondazione Parco Tecnologico Padano, Lodi, Italy
- * E-mail:
| | | | - Curtis P. Van Tassell
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, Maryland, United States of America
| | - James M. Reecy
- Department of Animal Science, Iowa State University, Ames, IA, United States of America
| | - Eric R. Fritz-Waters
- Department of Animal Science, Iowa State University, Ames, IA, United States of America
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States of America
| | | | - Tad S. Sonstegard
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, Maryland, United States of America
| | - Steven G. Schroeder
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, Maryland, United States of America
| | - Paolo Ajmone-Marsan
- Institute of Zootechnics, Università Cattolica del S. Cuore, Piacenza, Italy
| | - Riccardo Negrini
- AIA-LGS Associazione Italiana Allevatori–Laboratorio Genetica e Servizi, Cremona, Italy
- Institute of Zootechnics, Università Cattolica del S. Cuore, Piacenza, Italy
| | | | | | - Angelo Coletta
- ANASB-Associazione Nazionale Allevatori Specie Bufalina, Centurano—Caserta, Italy
| | - José F. Garcia
- Universidade Estadual Paulista (UNESP), Câmpus de Araçatuba, Sao Paulo, Brazil
| | - Ahmad Ali
- COMSATS Institute of Information Technology, Sahiwal, Pakistan
| | - Luigi Ramunno
- Dipartimento di Scienze Zootecniche ed Ispezione degli Alimenti, Facoltà di Agraria, Università degli Studi di Napoli Federico II, Portici (NA), Italy
| | - Gianfranco Cosenza
- Dipartimento di Scienze Zootecniche ed Ispezione degli Alimenti, Facoltà di Agraria, Università degli Studi di Napoli Federico II, Portici (NA), Italy
| | | | | | | | | | - Ali Pirani
- Affymetrix UK Ltd, High Wycombe, United Kingdom
| | - Fiona Brew
- Affymetrix UK Ltd, High Wycombe, United Kingdom
| | - John L. Williams
- Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, Australia
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Kommadath A, Bao H, Choi I, Reecy JM, Koltes JE, Fritz-Waters E, Eisley CJ, Grant JR, Rowland RRR, Tuggle CK, Dekkers JCM, Lunney JK, Guan LL, Stothard P, Plastow GS. Genetic architecture of gene expression underlying variation in host response to porcine reproductive and respiratory syndrome virus infection. Sci Rep 2017; 7:46203. [PMID: 28393889 PMCID: PMC5385538 DOI: 10.1038/srep46203] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 03/13/2017] [Indexed: 01/21/2023] Open
Abstract
It has been shown that inter-individual variation in host response to porcine reproductive and respiratory syndrome (PRRS) has a heritable component, yet little is known about the underlying genetic architecture of gene expression in response to PRRS virus (PRRSV) infection. Here, we integrated genome-wide genotype, gene expression, viremia level, and weight gain data to identify genetic polymorphisms that are associated with variation in inter-individual gene expression and response to PRRSV infection in pigs. RNA-seq analysis of peripheral blood samples collected just prior to experimental challenge (day 0) and at 4, 7, 11 and 14 days post infection from 44 pigs revealed 6,430 differentially expressed genes at one or more time points post infection compared to the day 0 baseline. We mapped genetic polymorphisms that were associated with inter-individual differences in expression at each day and found evidence of cis-acting expression quantitative trait loci (cis-eQTL) for 869 expressed genes (qval < 0.05). Associations between cis-eQTL markers and host response phenotypes using 383 pigs suggest that host genotype-dependent differences in expression of GBP5, GBP6, CCHCR1 and CMPK2 affect viremia levels or weight gain in response to PRRSV infection.
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Affiliation(s)
- Arun Kommadath
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
| | - Hua Bao
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
- Department of Research and Development, Geneseeq Technology Inc., Toronto M5G 1L7, ON, Canada
| | - Igseo Choi
- USDA-ARS, BARC, APDL, Building1040, Beltsville 20705, MD, USA
| | - James M. Reecy
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
| | - James E. Koltes
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
- Department of Animal Science, University of Arkansas, AFLS B106D, Fayetteville, AR, 72703, USA
| | - Elyn Fritz-Waters
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
| | - Chris J. Eisley
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
- Department of Statistics, Iowa State University, 1121 Snedecor Hall, Ames, IA 50011, USA
| | - Jason R. Grant
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
| | - Robert R. R. Rowland
- College of Veterinary Medicine, Kansas State University, K-231 Mosier Hall, Manhattan 66506, KS, USA
| | - Christopher K. Tuggle
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
| | - Jack C. M. Dekkers
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames 50011, IA, USA
| | - Joan K. Lunney
- USDA-ARS, BARC, APDL, Building1040, Beltsville 20705, MD, USA
| | - Le Luo Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
| | - Graham S. Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2P5, AB, Canada
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Cole JB, Bormann JM, Gill CA, Khatib H, Koltes JE, Maltecca C, Miglior F. BREEDING AND GENETICS SYMPOSIUM: Resilience of livestock to changing environments. J Anim Sci 2017; 95:1777-1779. [PMID: 28464075 DOI: 10.2527/jas.2017.1402] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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44
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Cesar ASM, Regitano LCA, Poleti MD, Andrade SCS, Tizioto PC, Oliveira PSN, Felício AM, do Nascimento ML, Chaves AS, Lanna DPD, Tullio RR, Nassu RT, Koltes JE, Fritz-Waters E, Mourão GB, Zerlotini-Neto A, Reecy JM, Coutinho LL. Differences in the skeletal muscle transcriptome profile associated with extreme values of fatty acids content. BMC Genomics 2016; 17:961. [PMID: 27875996 PMCID: PMC5120530 DOI: 10.1186/s12864-016-3306-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 11/17/2016] [Indexed: 12/19/2022] Open
Abstract
Background Lipids are a class of molecules that play an important role in cellular structure and metabolism in all cell types. In the last few decades, it has been reported that long-chain fatty acids (FAs) are involved in several biological functions from transcriptional regulation to physiological processes. Several fatty acids have been both positively and negatively implicated in different biological processes in skeletal muscle and other tissues. To gain insight into biological processes associated with fatty acid content in skeletal muscle, the aim of the present study was to identify differentially expressed genes (DEGs) and functional pathways related to gene expression regulation associated with FA content in cattle. Results Skeletal muscle transcriptome analysis of 164 Nellore steers revealed no differentially expressed genes (DEGs, FDR 10%) for samples with extreme values for linoleic acid (LA) or stearic acid (SA), and only a few DEGs for eicosapentaenoic acid (EPA, 5 DEGs), docosahexaenoic acid (DHA, 4 DEGs) and palmitic acid (PA, 123 DEGs), while large numbers of DEGs were associated with oleic acid (OA, 1134 DEGs) and conjugated linoleic acid cis9 trans11 (CLA-c9t11, 872 DEGs). Functional annotation and functional enrichment from OA DEGs identified important genes, canonical pathways and upstream regulators such as SCD, PLIN5, UCP3, CPT1, CPT1B, oxidative phosphorylation mitochondrial dysfunction, PPARGC1A, and FOXO1. Two important genes associated with lipid metabolism, gene expression and cancer were identified as DEGs between animals with high and low CLA-c9t11, specifically, epidermal growth factor receptor (EGFR) and RNPS. Conclusion Only two out of seven classes of molecules of FA studied were associated with large changes in the expression profile of skeletal muscle. OA and CLA-c9t11 content had significant effects on the expression level of genes related to important biological processes associated with oxidative phosphorylation, and cell growth, survival, and migration. These results contribute to our understanding of how some FAs modulate metabolism and may have protective health function. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3306-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aline S M Cesar
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.,Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | | | - Mirele D Poleti
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | - Sónia C S Andrade
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.,Departament of Genetics and Evolutionary Biology-IB, USP, São Paulo, SP, 05508-090, Brazil
| | | | | | - Andrezza M Felício
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | | | - Amália S Chaves
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | - Dante P D Lanna
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | - Rymer R Tullio
- Embrapa Pecuária Sudeste, São Carlos, SP, 13560-970, Brazil
| | - Renata T Nassu
- Embrapa Pecuária Sudeste, São Carlos, SP, 13560-970, Brazil
| | - James E Koltes
- Department of Animal Science, University of Arkansas, Fayetteville, AR, 72701, USA
| | - Eric Fritz-Waters
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Gerson B Mourão
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
| | | | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Luiz L Coutinho
- Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.
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45
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Kramer LM, Ghaffar MAA, Koltes JE, Fritz-Waters ER, Mayes MS, Sewell AD, Weeks NT, Garrick DJ, Fernando RL, Ma L, Reecy JM. Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle. BMC Genomics 2016; 17:891. [PMID: 27821053 PMCID: PMC5100273 DOI: 10.1186/s12864-016-3235-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 11/01/2016] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Consumers are becoming increasingly conscientious about the nutritional value of their food. Consumption of some fatty acids has been associated with human health traits such as blood pressure and cardiovascular disease. Therefore, it is important to investigate genetic variation in content of fatty acids present in meat. Previously publications reported regions of the cattle genome that are additively associated with variation in fatty acid content. This study evaluated epistatic interactions, which could account for additional genetic variation in fatty acid content. RESULTS Epistatic interactions for 44 fatty acid traits in a population of Angus beef cattle were evaluated with EpiSNPmpi. False discovery rate (FDR) was controlled at 5 % and was limited to well-represented genotypic combinations. Epistatic interactions were detected for 37 triacylglyceride (TAG), 36 phospholipid (PL) fatty acid traits, and three weight traits. A total of 6,181, 7,168, and 0 significant epistatic interactions (FDR < 0.05, 50-animals per genotype combination) were associated with Triacylglyceride fatty acids, Phospholipid fatty acids, and weight traits respectively and most were additive-by-additive interactions. A large number of interactions occurred in potential regions of regulatory control along the chromosomes where genes related to fatty acid metabolism reside. CONCLUSIONS Many fatty acids were associated with epistatic interactions. Despite a large number of significant interactions, there are a limited number of genomic locations that harbored these interactions. While larger population sizes are needed to accurately validate and quantify these epistatic interactions, the current findings point towards additional genetic variance that can be accounted for within these fatty acid traits.
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Affiliation(s)
- L M Kramer
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
| | - M A Abdel Ghaffar
- Department of Animal & Poultry Production/Faculty of Environmental Agricultural Science, Arish University, North Sinai, 45516, Egypt
| | - J E Koltes
- Department of Animal Science, University of Arkansas, Fayetteville, AR, 72701, USA
| | - E R Fritz-Waters
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
| | - M S Mayes
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
| | | | - N T Weeks
- Department of Mathematics, Iowa State University, Ames, IA, 50011, USA
| | - D J Garrick
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
| | - R L Fernando
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
| | - L Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, USA
| | - J M Reecy
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
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46
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Fleming DS, Koltes JE, Fritz-Waters ER, Rothschild MF, Schmidt CJ, Ashwell CM, Persia ME, Reecy JM, Lamont SJ. Single nucleotide variant discovery of highly inbred Leghorn and Fayoumi chicken breeds using pooled whole genome resequencing data reveals insights into phenotype differences. BMC Genomics 2016; 17:812. [PMID: 27760519 PMCID: PMC5070165 DOI: 10.1186/s12864-016-3147-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 10/05/2016] [Indexed: 11/22/2022] Open
Abstract
Background Analyses of sequence variants of two distinct and highly inbred chicken lines allowed characterization of genomic variation that may be associated with phenotypic differences between breeds. These lines were the Leghorn, the major contributing breed to commercial white-egg production lines, and the Fayoumi, representative of an outbred indigenous and robust breed. Unique within- and between-line genetic diversity was used to define the genetic differences of the two breeds through the use of variant discovery and functional annotation. Results Downstream fixation test (FST) analysis and subsequent gene ontology (GO) enrichment analysis elucidated major differences between the two lines. The genes with high FST values for both breeds were used to identify enriched gene ontology terms. Over-enriched GO annotations were uncovered for functions indicative of breed-related traits of pathogen resistance and reproductive ability for Fayoumi and Leghorn, respectively. Conclusions Variant analysis elucidated GO functions indicative of breed-predominant phenotypes related to genomic variation in the lines, showing a possible link between the genetic variants and breed traits. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3147-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - J E Koltes
- Iowa State University, Ames, IA, USA.,Department of Animal Science, University of Arkansas, Fayetteville, AR, 72701, USA
| | | | | | | | - C M Ashwell
- North Carolina State University, Raleigh, NC, USA
| | - M E Persia
- Virginia Polytechnic and State University, Blacksburg, VA, USA
| | - J M Reecy
- Iowa State University, Ames, IA, USA
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47
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Buchanan JW, Reecy JM, Garrick DJ, Duan Q, Beitz DC, Koltes JE, Saatchi M, Koesterke L, Mateescu RG. Deriving Gene Networks from SNP Associated with Triacylglycerol and Phospholipid Fatty Acid Fractions from Ribeyes of Angus Cattle. Front Genet 2016; 7:116. [PMID: 27379164 PMCID: PMC4913692 DOI: 10.3389/fgene.2016.00116] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 06/06/2016] [Indexed: 11/20/2022] Open
Abstract
The fatty acid profile of beef is a complex trait that can benefit from gene-interaction network analysis to understand relationships among loci that contribute to phenotypic variation. Phenotypic measures of fatty acid profile from triacylglycerol and phospholipid fractions of longissimus muscle, pedigree information, and Illumina 54 k bovine SNP genotypes were utilized to derive an annotated gene network associated with fatty acid composition in 1,833 Angus beef cattle. The Bayes-B statistical model was utilized to perform a genome wide association study to estimate associations between 54 k SNP genotypes and 39 individual fatty acid phenotypes within each fraction. Posterior means of the effects were estimated for each of the 54 k SNP and for the collective effects of all the SNP in every 1-Mb genomic window in terms of the proportion of genetic variance explained by the window. Windows that explained the largest proportions of genetic variance for individual lipids were found in the triacylglycerol fraction. There was almost no overlap in the genomic regions explaining variance between the triacylglycerol and phospholipid fractions. Partial correlations were used to identify correlated regions of the genome for the set of largest 1 Mb windows that explained up to 35% genetic variation in either fatty acid fraction. SNP were allocated to windows based on the bovine UMD3.1 assembly. Gene network clusters were generated utilizing a partial correlation and information theory algorithm. Results were used in conjunction with network scoring and visualization software to analyze correlated SNP across 39 fatty acid phenotypes to identify SNP of significance. Significant pathways implicated in fatty acid metabolism through GO term enrichment analysis included homeostasis of number of cells, homeostatic process, coenzyme/cofactor activity, and immunoglobulin. These results suggest different metabolic pathways regulate the development of different types of lipids found in bovine muscle tissues. Network analysis using partial correlations and annotation of significant SNPs can yield information about the genetic architecture of complex traits.
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Affiliation(s)
- Justin W Buchanan
- Department of Animal Science, University of California, Davis, Davis CA, USA
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames IA, USA
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames IA, USA
| | - Qing Duan
- Department of Animal Science, Iowa State University, Ames IA, USA
| | - Don C Beitz
- Department of Animal Science, Iowa State University, Ames IA, USA
| | - James E Koltes
- Department of Animal Science, University of Arkansas, Fayetteville AR, USA
| | - Mahdi Saatchi
- Department of Animal Science, Iowa State University, Ames IA, USA
| | - Lars Koesterke
- Texas Advanced Computing Center, University of Texas at Austin Austin, TX, USA
| | - Raluca G Mateescu
- Department of Animal Sciences, University of Florida, Gainesville FL, USA
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48
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Fleming DS, Koltes JE, Markey AD, Schmidt CJ, Ashwell CM, Rothschild MF, Persia ME, Reecy JM, Lamont SJ. Genomic analysis of Ugandan and Rwandan chicken ecotypes using a 600 k genotyping array. BMC Genomics 2016; 17:407. [PMID: 27230772 PMCID: PMC4882793 DOI: 10.1186/s12864-016-2711-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Accepted: 05/06/2016] [Indexed: 02/07/2023] Open
Abstract
Background Indigenous populations of animals have developed unique adaptations to their local environments, which may include factors such as response to thermal stress, drought, pathogens and suboptimal nutrition. The survival and subsequent evolution within these local environments can be the result of both natural and artificial selection driving the acquisition of favorable traits, which over time leave genomic signatures in a population. This study’s goals are to characterize genomic diversity and identify selection signatures in chickens from equatorial Africa to identify genomic regions that may confer adaptive advantages of these ecotypes to their environments. Results Indigenous chickens from Uganda (n = 72) and Rwanda (n = 100), plus Kuroilers (n = 24, an Indian breed imported to Africa), were genotyped using the Axiom® 600 k Chicken Genotyping Array. Indigenous ecotypes were defined based upon location of sampling within Africa. The results revealed the presence of admixture among the Ugandan, Rwandan, and Kuroiler populations. Genes within runs of homozygosity consensus regions are linked to gene ontology (GO) terms related to lipid metabolism, immune functions and stress-mediated responses (FDR < 0.15). The genes within regions of signatures of selection are enriched for GO terms related to health and oxidative stress processes. Key genes in these regions had anti-oxidant, apoptosis, and inflammation functions. Conclusions The study suggests that these populations have alleles under selective pressure from their environment, which may aid in adaptation to harsh environments. The correspondence in gene ontology terms connected to stress-mediated processes across the populations could be related to the similarity of environments or an artifact of the detected admixture. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2711-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - J E Koltes
- Iowa State University, Ames, IA, USA.,University of Arkansas, Fayetteville, AR, USA
| | | | | | - C M Ashwell
- North Carolina State University, Raleigh, NC, USA
| | | | - M E Persia
- Virginia Polytechnic University, Blacksburg, VA, USA
| | - J M Reecy
- Iowa State University, Ames, IA, USA
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49
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Schroyen M, Eisley C, Koltes JE, Fritz-Waters E, Choi I, Plastow GS, Guan L, Stothard P, Bao H, Kommadath A, Reecy JM, Lunney JK, Rowland RRR, Dekkers JCM, Tuggle CK. Bioinformatic analyses in early host response to Porcine Reproductive and Respiratory Syndrome virus (PRRSV) reveals pathway differences between pigs with alternate genotypes for a major host response QTL. BMC Genomics 2016; 17:196. [PMID: 26951612 PMCID: PMC4782518 DOI: 10.1186/s12864-016-2547-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 02/26/2016] [Indexed: 01/01/2023] Open
Abstract
Background A region on Sus scrofa chromosome 4 (SSC4) surrounding single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) has been reported to be strongly associated with both weight gain and serum viremia in pigs after infection with PRRS virus (PRRSV). A proposed causal mutation in the guanylate binding protein 5 gene (GBP5) is predicted to truncate the encoded protein. To investigate transcriptional differences between WUR genotypes in early host response to PRRSV infection, an RNA-seq experiment was performed on globin depleted whole blood RNA collected on 0, 4, 7, 10 and 14 days post-infection (dpi) from eight littermate pairs with one AB (favorable) and one AA (unfavorable) WUR genotype animal per litter. Results Gene Ontology (GO) enrichment analysis of transcripts that were differentially expressed (DE) between dpi across both genotypes revealed an inflammatory response for all dpi when compared to day 0. However, at the early time points of 4 and 7dpi, several GO terms had higher enrichment scores compared to later dpi, including inflammatory response (p < 10-7), specifically regulation of NFkappaB (p < 0.01), cytokine, and chemokine activity (p < 0.01). At 10 and 14dpi, GO term enrichment indicated a switch to DNA damage response, cell cycle checkpoints, and DNA replication. Few transcripts were DE between WUR genotypes on individual dpi or averaged over all dpi, and little enrichment of any GO term was found. However, there were differences in expression patterns over time between AA and AB animals, which was confirmed by genotype-specific expression patterns of several modules that were identified in weighted gene co-expression network analyses (WGCNA). Minor differences between AA and AB animals were observed in immune response and DNA damage response (p = 0.64 and p = 0.11, respectively), but a significant effect between genotypes pointed to a difference in ion transport/homeostasis and the participation of G-coupled protein receptors (p = 8e-4), which was reinforced by results from regulatory and phenotypic impact factor analyses between genotypes. Conclusion We propose these pathway differences between WUR genotypes are the result of the inability of the truncated GBP5 of the AA genotyped pigs to inhibit viral entry and replication as quickly as the intact GBP5 protein of the AB genotyped pigs. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2547-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Martine Schroyen
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
| | - Christopher Eisley
- Department of Statistics, Iowa State University, 1121 Snedecor Hall, Ames, IA, 50011, USA.
| | - James E Koltes
- Department of Animal Science, University of Arkansas, AFLS B106D, Fayetteville, AR, 72701, USA.
| | - Eric Fritz-Waters
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
| | - Igseo Choi
- USDA-ARS, BARC, APDL, Bldg.1040, Beltsville, MD, 20705, USA.
| | - Graham S Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - Leluo Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - Hua Bao
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - Arun Kommadath
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - James M Reecy
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
| | - Joan K Lunney
- USDA-ARS, BARC, APDL, Bldg.1040, Beltsville, MD, 20705, USA.
| | - Robert R R Rowland
- College of Veterinary Medicine, Kansas State University, K-231 Mosier Hall, Manhattan, KS, 66506, USA.
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
| | - Christopher K Tuggle
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA.
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50
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Pilcher CM, Jones CK, Schroyen M, Severin AJ, Patience JF, Tuggle CK, Koltes JE. Transcript profiles in longissimus dorsi muscle and subcutaneous adipose tissue: a comparison of pigs with different postweaning growth rates. J Anim Sci 2016; 93:2134-43. [PMID: 26020309 DOI: 10.2527/jas.2014-8593] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Although most pigs recover rapidly from stresses associated with the transition of weaning, a portion of the population lags behind their contemporaries in growth performance. The underlying biological and molecular mechanisms involved in postweaning differences in growth performance are poorly understood. The objective of this experiment was to use transcriptional profiling of skeletal muscle and adipose tissue to develop a better understanding of the metabolic basis for poor weaned-pig transition. A total of 1,054 pigs was reared in commercial conditions and weighed at birth, weaning, and 3 wk postweaning. Transition ADG (tADG) was calculated as the ADG for the 3-wk period postweaning. Nine pigs from both the lowest 10th percentile (low tADG) and the 60th to 70th percentile (high tADG) were harvested at 3 wk postweaning. Differential expression analysis was conducted in longissimus dorsi muscle (LM) and subcutaneous adipose tissue using RNA-Seq methodology. In LM, 768 transcripts were differentially expressed (DE), 327 with higher expression in low tADG and 441 with higher expression in high tADG pigs (q < 0.10). Expression patterns measured in LM by RNA-Seq were verified in 30 of 32 transcripts using quantitative PCR. No DE transcripts were identified in adipose tissue. To identify biological functions potentially underlying the effects of tADG on skeletal muscle metabolism and physiology, functional annotation analysis of the DE transcripts was conducted using DAVID and Pathway Studio analytic tools. The group of DE genes with lower expression in LM of low tADG pigs was enriched in genes with functions related to muscle contraction, glucose metabolism, cytoskeleton organization, muscle development, and response to hormone stimulus (enrichment score > 1.3). The list of DE genes with higher expression in low tADG LM was enriched in genes with functions related to protein catabolism (enrichment score > 1.3). Analysis of known gene-gene interactions identified possible regulators of these differences in gene expression in LM of high and low tADG pigs; these include forkhead box O1 (FOXO1), growth hormone (GH1), and the glucocorticoid receptor (NR3C1). Differences in gene expression between poor transitioning pigs and their contemporaries indicate a shift to decreased protein synthesis, increased protein degradation, and reduced glucose metabolism in the LM of low tADG pigs.
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