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Marková K, Kreisinger J, Vinkler M. Are there consistent effects of gut microbiota composition on performance, productivity and condition in poultry? Poult Sci 2024; 103:103752. [PMID: 38701628 PMCID: PMC11078699 DOI: 10.1016/j.psj.2024.103752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
Microbiome of the gastrointestinal tract (GIT) has been identified as one of the crucial factors influencing the health and condition of domestic animals. The global poultry industry faces the challenge of understanding the complex relationship between gut microbiota composition and performance-related traits in birds. Considerable variation exists in the results of correlational studies using either 16S rRNA profiling or metagenomics to identify bacterial taxa associated with performance, productivity, or condition in poultry (e.g., body weight, growth rate, feeding efficiency, or egg yield). In this review, we survey the existing reports, discuss variation in research approaches, and identify bacterial taxa consistently linked to improved or deteriorated performance across individual poultry-focused studies. Our survey revealed high methodological heterogeneity, which was in contrast with vastly uniform focus of the research mainly on the domestic chicken (Gallus gallus) as a model. We also show that the bacterial taxa most frequently used in manipulative experiments and commercial probiotics intended for use in poultry (e.g., species of Lactobacillus, Bacillus, Enterococcus, or Bifidobacterium) do not overlap with the bacteria consistently correlated with their improved performance (Candidatus Arthromitus, Methanobrevibacter). Our conclusions urge for increased methodological standardization of the veterinary research in this field. We highlight the need to bridge the gap between correlational results and experimental applications in animal science. To better understand causality in the observed relationships, future research should involve a broader range of host species that includes both agricultural and wild models, as well as a broader range of age groups.
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Affiliation(s)
- Kateřina Marková
- Charles University, Faculty of Science, Department of Zoology, 128 43 Prague, Czech Republic.
| | - Jakub Kreisinger
- Charles University, Faculty of Science, Department of Zoology, 128 43 Prague, Czech Republic
| | - Michal Vinkler
- Charles University, Faculty of Science, Department of Zoology, 128 43 Prague, Czech Republic
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Mancin E, Maltecca C, Huang YJ, Mantovani R, Tiezzi F. A first characterization of the microbiota-resilience link in swine. MICROBIOME 2024; 12:53. [PMID: 38486255 PMCID: PMC10941389 DOI: 10.1186/s40168-024-01771-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/30/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes. RESULTS This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience. CONCLUSION Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.
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Affiliation(s)
- Enrico Mancin
- Department of Agronomy, Animals and Environment, (DAFNAE), Food, Natural Resources, University of Padova, Viale del Università 14, 35020, Legnaro (Padova), Italy
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy
| | - Yi Jian Huang
- Smithfield Premium Genetics, Rose Hill, NC, 28458, USA
| | - Roberto Mantovani
- Department of Agronomy, Animals and Environment, (DAFNAE), Food, Natural Resources, University of Padova, Viale del Università 14, 35020, Legnaro (Padova), Italy
| | - Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy.
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Haqani MI, Nakano M, Nagano AJ, Nakamura Y, Tsudzuki M. Association analysis of production traits of Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing. Sci Rep 2023; 13:21307. [PMID: 38042890 PMCID: PMC10693557 DOI: 10.1038/s41598-023-48293-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 10/10/2023] [Accepted: 11/24/2023] [Indexed: 12/04/2023] Open
Abstract
This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1-13, 15, 17-20, 24, 26-28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail.
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Affiliation(s)
- Mohammad Ibrahim Haqani
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
| | - Michiharu Nakano
- Faculty of Agriculture and Marine Sciences, Kochi University, Nankoku, Kochi, 783-8502, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Otsu, Shiga, 520-2194, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, 997-0017, Japan
| | - Yoshiaki Nakamura
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
| | - Masaoki Tsudzuki
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
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Omotoso AO, Reyer H, Oster M, Ponsuksili S, Wimmers K. Jejunal microbiota of broilers fed varying levels of mineral phosphorus. Poult Sci 2023; 102:103096. [PMID: 37797492 PMCID: PMC10562922 DOI: 10.1016/j.psj.2023.103096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/30/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023] Open
Abstract
Efforts to achieve sustainable phosphorus (P) inputs in broiler farming which meet the physiological demand of animals include nutritional intervention strategies that have the potential to modulate and utilize endogenous and microbiota-associated capacities. A temporal P conditioning strategy in broiler nutrition is promising as it induces endocrinal and transcriptional responses to maintain mineral homeostasis. In this context, the current study aims to evaluate the composition of the jejunal microbiota as a functional entity located at the main absorption site involved in nutrient metabolism. Starting from a medium or high P supply in the first weeks of life of broilers, a depletion strategy was applied at growth intervals from d 17 to 24 and d 25 to 37 to investigate the consequences on the composition of the jejunal microbiota. The results on fecal mineral P, calcium (Ca), and phytate contents showed that the diets applied to the depleted and non-depleted cohorts were effective. Microbial diversity in jejunum was represented by alpha diversity indices which appeared unaffected between dietary groups. However, chickens assigned to the dietary P depletion groups showed significantly higher abundances of Facklamia, Lachnospiraceae, and Ruminococcaceae compared to non-depleted control groups. Based on current knowledge of microbial function, these microorganisms make only a minor contribution to the birds' adaptive mechanism in the jejunum following P depletion. Microbial taxa such as Brevibacterium, Brachybacterium, and genera of the Staphylococcaceae family proliferated in a P-enriched environment and might be considered biomarkers for excessive P supply in commercial broiler chickens.
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Affiliation(s)
- Adewunmi O Omotoso
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Henry Reyer
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Michael Oster
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Siriluck Ponsuksili
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany; Faculty of Agricultural and Environmental Sciences, Justus-von-Liebig-Weg 6b, University of Rostock, 18059 Rostock, Germany.
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Xiong X, Rao Y, Ma J, Wang Z, He Q, Gong J, Sheng W, Xu J, Zhu X, Tan Y, Yang Y. A catalog of microbial genes and metagenome-assembled genomes from the quail gut microbiome. Poult Sci 2023; 102:102931. [PMID: 37499616 PMCID: PMC10393819 DOI: 10.1016/j.psj.2023.102931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
The gut microbiome plays an important role in quail feed efficiency, immunity, production, and even behavior. Gut microbial gene catalogs and reference genomes are important for understanding the quail gut microbiome. However, quail gut microbes are lacked sequenced genomes and functional information to date. In this study, we report the first catalog of the microbial genes and metagenome-assembled genomes (MAGs) in fecal and cecum luminal content samples from 3 quail breeds using deep metagenomic sequencing. We identified a total of 2,419,425 nonredundant genes in the quail genome catalog, and a total of 473 MAGs were reconstructed through binning analysis. At 95% average nucleotide identity, the 473 MAGs were clustered into 283 species-level genome bins (SGBs), of which 225 SGBs belonged to species without any available genomes in the current database. Based on the quail gene catalog and MAGs, we identified 142 discriminative bacterial species and 244 discriminative MAGs between Chinese yellow quails and Japanese quails. The discriminative MAGs suggested a strain-level difference in the gut microbial composition. Additionally, a total of 25 Kyoto Encyclopedia of Genes and Genomes functional terms and 88 carbohydrate-active enzymes were distinctly enriched between Chinese yellow quails and Japanese quails. Most of the different species and MAGs were significantly interrelated with the shifts in the functional capacities of the quail gut microbiome. Taken together, we constructed a quail gut microbial gene catalog and enlarged the reference of quail gut microbial genomes. The results of this study provide a powerful and invaluable resource for quail gut microbiome-related research.
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Affiliation(s)
- Xinwei Xiong
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China.
| | - Yousheng Rao
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Jinge Ma
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Zhangfeng Wang
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Qin He
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Jishang Gong
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Wentao Sheng
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Jiguo Xu
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Xuenong Zhu
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Yuwen Tan
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Yanbei Yang
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
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Xiong X, Xu J, Yan X, Wu S, Ma J, Wang Z, He Q, Gong J, Rao Y. Gut microbiome and serum metabolome analyses identify biomarkers associated with sexual maturity in quails. Poult Sci 2023; 102:102762. [PMID: 37209654 DOI: 10.1016/j.psj.2023.102762] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/19/2023] [Accepted: 04/25/2023] [Indexed: 05/22/2023] Open
Abstract
Increasing evidence indicates that the gut microbiome plays an important role in host aging and sexual maturity. However, the gut microbial taxa associated with sexual maturity in quails are unknown. This study used shotgun metagenomic sequencing to identify bacterial taxa associated with sexual maturity in d 20 and d 70 quails. We found that 17 bacterial species and 67 metagenome-assembled genomes (e.g., Bacteroides spp. and Enterococcus spp.) significantly differed between the d 20 and d 70 groups, including 5 bacterial species (e.g., Enterococcus faecalis) enriched in the d 20 group and 12 bacterial species (e.g., Christensenella massiliensis, Clostridium sp. CAG:217, and Bacteroides neonati) which had high abundances in the d 70 group. The bacterial species enriched in d 20 or d 70 were key biomarkers distinguishing sexual maturity and significantly correlated with the shifts in the functional capacities of the gut microbiome. Untargeted serum metabolome analysis revealed that 5 metabolites (e.g., nicotinamide riboside) were enriched in the d 20 group, and 6 metabolites (e.g., D-ribose, stevioside, and barbituric acid) were enriched in the d 70 group. Furthermore, metabolites with high abundances in the d 20 group were significantly enriched for the KEGG pathways of arginine biosynthesis, nicotinate and nicotinamide metabolism, and lysine degradation. However, glutathione metabolism and valine, leucine and isoleucine biosynthesis were enriched in high-abundance metabolites from the d 70 group. These results provide important insights into the effects of gut microbiome and host metabolism on quail sexual maturity.
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Affiliation(s)
- Xinwei Xiong
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China.
| | - Jiguo Xu
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Xiao Yan
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Shuoshuo Wu
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Jinge Ma
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Zhangfeng Wang
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Qin He
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Jishang Gong
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
| | - Yousheng Rao
- Institute of Biological Technology, Nanchang Normal University, Nanchang, 330032, China; Key Laboratory for Genetic Improvement of Indigenous Chicken Breeds of Jiangxi Province, Nanchang, 330032, China
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Haas V, Rodehutscord M, Camarinha-Silva A, Bennewitz J. Inferring causal structures of gut microbiota diversity and feed efficiency traits in poultry using Bayesian learning and genomic structural equation models. J Anim Sci 2023; 101:skad044. [PMID: 36734360 PMCID: PMC10032182 DOI: 10.1093/jas/skad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/02/2023] [Indexed: 02/04/2023] Open
Abstract
Feed and phosphorus (P) efficiency are of increasing importance in poultry breeding. It has been shown recently that these efficiency traits are influenced by the gut microbiota composition of the birds. The efficiency traits and the gut microbiota composition are partly under control of the host genome. Thus, the gut microbiota composition can be seen as a mediator trait between the host genome and the efficiency traits. The present study used data from 749 individuals of a Japanese quail F2 cross. The birds were genotyped for 4k single-nucleotide polymorphism (SNP) and trait recorded for P utilization (PU) and P retention (PR), body weight gain (BWG), and feed per gain ratio (F:G). The gut microbiota composition was characterized by targeted amplicon sequencing. The alpha diversity was calculated as the Pielou's evenness index (J'). A stable Bayesian network was established using a Hill-Climbing learning algorithm. Pielou's evenness index was placed as the most upstream trait and BWG as the most downstream trait, with direct and indirect links via PR, PU, and F:G. The direct and indirect effects between J', PU, and PR were quantified with structural equation models (SEM), which revealed a causal link from J' to PU and from PU to PR. Quantitative trait loci (QTL) linkage mapping revealed three genome-wide significant QTL regions for these traits with in total 49 trait-associated SNP within the QTL regions. SEM association mapping separated the total SNP effect for a trait into a direct effect and indirect effects mediated by upstream traits. Although the indirect effects were in general small, they contributed to the total SNP effect in some cases. This enabled us to detect some shared genetic effects. The method applied allows for the detection of shared genetic architecture of quantitative traits and microbiota compositions.
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Affiliation(s)
- Valentin Haas
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | - Markus Rodehutscord
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | | | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
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Zhou Q, Lan F, Gu S, Li G, Wu G, Yan Y, Li X, Jin J, Wen C, Sun C, Yang N. Genetic and microbiome analysis of feed efficiency in laying hens. Poult Sci 2022; 102:102393. [PMID: 36805401 PMCID: PMC9958098 DOI: 10.1016/j.psj.2022.102393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/01/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Improving feed efficiency is an important target for poultry breeding. Feed efficiency is affected by host genetics and the gut microbiota, but many of the mechanisms remain elusive in laying hens, especially in the late laying period. In this study, we measured feed intake, body weight, and egg mass of 714 hens from a pedigreed line from 69 to 72 wk of age and calculated the residual feed intake (RFI) and feed conversion ratio (FCR). In addition, fecal samples were also collected for 16S ribosomal RNA gene sequencing (V4 region). Genetic analysis was then conducted in DMU packages by using AI-REML with animal model. Moderate heritability estimates for FCR (h2 = 0.31) and RFI (h2 = 0.52) were observed, suggesting that proper selection programs can directly improve feed efficiency. Genetically, RFI was less correlated with body weight and egg mass than that of FCR. The phenotypic variance explained by gut microbial variance is defined as the microbiability (m2). The microbiability estimates for FCR (m2 = 0.03) and RFI (m2 = 0.16) suggested the gut microbiota was also involved in the regulation of feed efficiency. In addition, our results showed that the effect of host genetics on fecal microbiota was minor in three aspects: 1) microbial diversity indexes had low heritability estimates, and genera with heritability estimates more than 0.1 accounted for only 1.07% of the tested fecal microbiota; 2) the genetic relationship correlations between host genetics and different microbial distance were very weak, ranging from -0.0057 to -0.0003; 3) the microbial distance between different kinships showed no significant difference. Since the RFI has the highest microbiability, we further screened out three genera, including Anaerosporobacter, Candidatus Stoquefichus, and Fournierella, which were negatively correlated with RFI and played positive roles in improving the feed efficiency. These findings contribute to a great understanding of the genetic background and microbial influences on feed efficiency.
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Affiliation(s)
- Qianqian Zhou
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Fangren Lan
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Shuang Gu
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Guangqi Li
- Beijing Huadu Yukou Poultry Industry Co. Ltd., Beijing, 101206, China
| | - Guiqin Wu
- Beijing Huadu Yukou Poultry Industry Co. Ltd., Beijing, 101206, China
| | - Yiyuan Yan
- Beijing Huadu Yukou Poultry Industry Co. Ltd., Beijing, 101206, China
| | - Xiaochang Li
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Jiaming Jin
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Chaoliang Wen
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China.
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Wang Y, Zhou P, Zhou X, Fu M, Wang T, Liu Z, Liu X, Wang Z, Liu B. Effect of host genetics and gut microbiome on fat deposition traits in pigs. Front Microbiol 2022; 13:925200. [PMID: 36204621 PMCID: PMC9530793 DOI: 10.3389/fmicb.2022.925200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Fat deposition affects meat quality, flavor, and production in pigs. Fat deposition is influenced by both genetics and environment. Symbiotic microbe with the host is an important environmental factor to influence fat deposition. In this study, the fat deposition traits were measured in 239 individuals obtained from Tongcheng pigs × Large White pigs resource population. The interactions between genetics and gut microbiome in fat deposition traits were investigated through whole-genome sequencing and cecum microbial 16S ribosomal RNA sequencing. The results showed that the percentage of leaf fat (PL) and intramuscular fat content (IMF) were significantly influenced by host genetics–gut microbiome interaction. The effects of interactions between host genetics and gut microbiome on PL and IMF were 0.13 and 0.29, respectively. The heritability of PL and IMF was estimated as 0.71 and 0.89, respectively. The microbiability of PL and IMF was 0.20 and 0.26, respectively. Microbiome-wide association analysis (MWAS) revealed Anaeroplasma, Paraprevotella, Pasteurella, and Streptococcus were significantly associated with PL, and Sharpea and Helicobacter exhibited significant association with IMF (p < 0.05). Furthermore, Paraprevotella was also identified as a critical microbe affecting PL based on the divergent Wilcoxon rank-sum test. Overall, this study reveals the effect of host genetics and gut microbiome on pig fat deposition traits and provides a new perspective on the genetic improvement of pig fat deposition traits.
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Affiliation(s)
- Yuan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Ping Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xiang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
- The Engineering Technology Research Center of Hubei Province Local Pig Breed Improvement, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Ming Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Tengfei Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Zuhong Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
- The Engineering Technology Research Center of Hubei Province Local Pig Breed Improvement, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zhiquan Wang
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Bang Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
- The Engineering Technology Research Center of Hubei Province Local Pig Breed Improvement, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
- *Correspondence: Bang Liu,
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10
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Qadri QR, Zhao Q, Lai X, Zhang Z, Zhao W, Pan Y, Wang Q. Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models. Genes (Basel) 2022; 13:genes13091580. [PMID: 36140748 PMCID: PMC9498715 DOI: 10.3390/genes13091580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 11/19/2022] Open
Abstract
Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.
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Affiliation(s)
- Qamar Raza Qadri
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qingbo Zhao
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xueshuang Lai
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhenyang Zhang
- Department of Animal Breeding and Reproduction, College of Animal Science, Zhejiang University, Hangzhou 310030, China
| | - Wei Zhao
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuchun Pan
- Department of Animal Breeding and Reproduction, College of Animal Science, Zhejiang University, Hangzhou 310030, China
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
| | - Qishan Wang
- Department of Animal Breeding and Reproduction, College of Animal Science, Zhejiang University, Hangzhou 310030, China
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
- Zhejiang Key Laboratory of Dairy Cattle Genetic Improvement and Milk Quality Research, Hangzhou 310030, China
- Correspondence:
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11
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Feng Y, Liu D, Liu Y, Yang X, Zhang M, Wei F, Li D, Hu Y, Guo Y. Host-genotype-dependent cecal microbes are linked to breast muscle metabolites in Chinese chickens. iScience 2022; 25:104469. [PMID: 35707722 PMCID: PMC9189123 DOI: 10.1016/j.isci.2022.104469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 04/08/2022] [Accepted: 05/20/2022] [Indexed: 11/18/2022] Open
Abstract
In chickens, the effect of host genetics on the gut microbiota is not fully understood, and the extent to which the heritable gut microbes affect chicken metabolism and physiology is still an open question. Here, we explored the interactions among chicken genetics, the cecal microbiota and metabolites in breast muscle from ten chicken breeds in China. We found that different chicken breeds displayed distinct cecal microbial community structures and functions, and 15 amplicon sequence variants (ASVs) were significantly associated with host genetics through different genetic loci, such as those related to the intestinal barrier function. We identified five heritable ASVs significantly associated with 53 chicken muscle metabolites, among which the Megamonas probably affected lipid metabolism through the production of propionate. Our study revealed that the chicken genetically associated cecal microbes may have the potential to affect the bird’s physiology and metabolism. The cecal microbiota are different among ten chicken breeds The chicken genetics influences the cecal microbiota structures and functions The chicken heritable cecal microbes are associated with muscle metabolites Megamonas may affect lipid metabolism by the production of propionate
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Affiliation(s)
- Yuqing Feng
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Dan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Yan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Xinyue Yang
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Meihong Zhang
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Fuxiao Wei
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Depeng Li
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
| | - Yongfei Hu
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
- Corresponding author
| | - Yuming Guo
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, PR China
- Corresponding author
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12
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Aliakbari A, Zemb O, Cauquil L, Barilly C, Billon Y, Gilbert H. Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs. Genet Sel Evol 2022; 54:29. [PMID: 35468740 PMCID: PMC9036775 DOI: 10.1186/s12711-022-00717-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 03/28/2022] [Indexed: 12/12/2022] Open
Abstract
Background The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated. Results Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect. Conclusions Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00717-7.
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13
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Haas V, Vollmar S, Preuß S, Rodehutscord M, Camarinha-Silva A, Bennewitz J. Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica). Genet Sel Evol 2022; 54:20. [PMID: 35260076 PMCID: PMC8903610 DOI: 10.1186/s12711-022-00697-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 01/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Phosphorus is an essential nutrient in all living organisms and, currently, it is the focus of much attention due to its global scarcity, the environmental impact of phosphorus from excreta, and its low digestibility due to its storage in the form of phytates in plants. In poultry, phosphorus utilization is influenced by composition of the ileum microbiota and host genetics. In our study, we analyzed the impact of host genetics on composition of the ileum microbiota and the relationship of the relative abundance of ileal bacterial genera with phosphorus utilization and related quantitative traits in Japanese quail. An F2 cross of 758 quails was genotyped with 4k genome-wide single nucleotide polymorphisms (SNPs) and composition of the ileum microbiota was characterized using target amplicon sequencing. Heritabilities of the relative abundance of bacterial genera were estimated and quantitative trait locus (QTL) linkage mapping for the host was conducted for the heritable genera. Phenotypic and genetic correlations and recursive relationships between bacterial genera and quantitative traits were estimated using structural equation models. A genomic best linear unbiased prediction (GBLUP) and microbial (M)BLUP hologenomic selection approach was applied to assess the feasibility of breeding for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota. Results Among the 59 bacterial genera examined, 24 showed a significant heritability (nominal p ≤ 0.05), ranging from 0.04 to 0.17. For these genera, six genome-wide significant QTL were mapped. Significant recursive effects were found, which support the indirect host genetic effects on the host’s quantitative traits via microbiota composition in the ileum of quail. Cross-validated microbial and genomic prediction accuracies confirmed the strong impact of microbial composition and host genetics on the host’s quantitative traits, as the GBLUP accuracies based on the heritable microbiota-mediated components of the traits were similar to the accuracies of conventional GBLUP based on genome-wide SNPs. Conclusions Our results revealed a significant effect of host genetics on composition of the ileal microbiota and confirmed that host genetics and composition of the ileum microbiota have an impact on the host’s quantitative traits. This offers the possibility to breed for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00697-8.
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14
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He Y, Tiezzi F, Howard J, Huang Y, Gray K, Maltecca C. Exploring the role of gut microbiota in host feeding behavior among breeds in swine. BMC Microbiol 2022; 22:1. [PMID: 34979903 PMCID: PMC8722167 DOI: 10.1186/s12866-021-02409-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 12/02/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing. RESULTS Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia. CONCLUSIONS This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies.
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Affiliation(s)
- Yuqing He
- Department of Animal Science, North Carolina State University, 120 W Broughton Dr, Raleigh, 27607, NC, USA.
| | - Francesco Tiezzi
- Department of Animal Science, North Carolina State University, 120 W Broughton Dr, Raleigh, 27607, NC, USA
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy
| | - Jeremy Howard
- Smithfield Premium Genetics, Rose Hill, 28458, NC, USA
| | - Yijian Huang
- Smithfield Premium Genetics, Rose Hill, 28458, NC, USA
| | - Kent Gray
- Smithfield Premium Genetics, Rose Hill, 28458, NC, USA
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, 120 W Broughton Dr, Raleigh, 27607, NC, USA
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15
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Ma JE, Xiong XW, Xu JG, Gong JS, Li J, Xu Q, Li YF, Yang YB, Zhou M, Zhu XN, Tan YW, Sheng WT, Wang ZF, Tu XT, Zeng CY, Zhang XQ, Rao YS. Metagenomic Analysis Identifies Sex-Related Cecal Microbial Gene Functions and Bacterial Taxa in the Quail. Front Vet Sci 2021; 8:693755. [PMID: 34660751 PMCID: PMC8517240 DOI: 10.3389/fvets.2021.693755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 09/06/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Japanese quail (Coturnix japonica) are important and widely distributed poultry in China. Researchers continue to pursue genetic selection for heavier quail. The intestinal microbiota plays a substantial role in growth promotion; however, the mechanisms involved in growth promotion remain unclear. Results: We generated 107.3 Gb of cecal microbiome data from ten Japanese quail, providing a series of quail gut microbial gene catalogs (1.25 million genes). We identified a total of 606 main microbial species from 1,033,311 annotated genes distributed among the ten quail. Seventeen microbial species from the genera Anaerobiospirillum, Alistipes, Barnesiella, and Butyricimonas differed significantly in their abundances between the female and male gut microbiotas. Most of the functional gut microbial genes were involved in metabolism, primarily in carbohydrate transport and metabolism, as well as some active carbohydrate-degrading enzymes. We also identified 308 antibiotic-resistance genes (ARGs) from the phyla Bacteroidetes, Firmicutes and Euryarchaeota. Studies of the differential gene functions between sexes indicated that abundances of the gut microbes that produce carbohydrate-active enzymes varied between female and male quail. Bacteroidetes was the predominant ARG-containing phylum in female quail; Euryarchaeota was the predominant ARG-containing phylum in male quail. Conclusion: This article provides the first description of the gene catalog of the cecal bacteria in Japanese quail as well as insights into the bacterial taxa and predictive metagenomic functions between male and female quail to provide a better understanding of the microbial genes in the quail ceca.
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Affiliation(s)
- Jing-E Ma
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Xin-Wei Xiong
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Ji-Guo Xu
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Ji-Shang Gong
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Jin Li
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China
| | - Qiao Xu
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Yuan-Fei Li
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Yang-Bei Yang
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Min Zhou
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Xue-Nong Zhu
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Yu-Wen Tan
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Wen-Tao Sheng
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Zhang-Feng Wang
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Xu-Tang Tu
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Cheng-Yao Zeng
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
| | - Xi-Quan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, Guangzhou, China.,Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China
| | - You-Sheng Rao
- Institution of Biological Technology, Nanchang Normal University, Nanchang, China.,Jiang Xi Province Key Lab of Genetic Improvement of Indigenous Chicken Breeds, Nanchang, China
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16
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Velasco-Galilea M, Piles M, Ramayo-Caldas Y, Sánchez JP. The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes. Sci Rep 2021; 11:19495. [PMID: 34593949 PMCID: PMC8484599 DOI: 10.1038/s41598-021-99028-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/13/2021] [Indexed: 02/08/2023] Open
Abstract
Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efficiency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cage-average traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efficiency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efficiency traits, although specific OTUs taxonomically assigned to 5 different phyla have a higher weight. Rabbit growth and feed efficiency are influenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.
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Affiliation(s)
- María Velasco-Galilea
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Miriam Piles
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Yuliaxis Ramayo-Caldas
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Juan P. Sánchez
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
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17
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Henry LP, Bruijning M, Forsberg SKG, Ayroles JF. The microbiome extends host evolutionary potential. Nat Commun 2021; 12:5141. [PMID: 34446709 PMCID: PMC8390463 DOI: 10.1038/s41467-021-25315-x] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.
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Affiliation(s)
- Lucas P. Henry
- grid.16750.350000 0001 2097 5006Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ USA ,grid.16750.350000 0001 2097 5006Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
| | - Marjolein Bruijning
- grid.16750.350000 0001 2097 5006Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ USA
| | - Simon K. G. Forsberg
- grid.16750.350000 0001 2097 5006Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ USA ,grid.16750.350000 0001 2097 5006Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA ,grid.8993.b0000 0004 1936 9457Dept. of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Julien F. Ayroles
- grid.16750.350000 0001 2097 5006Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ USA ,grid.16750.350000 0001 2097 5006Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
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18
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Pérez-Enciso M, Zingaretti LM, Ramayo-Caldas Y, de Los Campos G. Opportunities and limits of combining microbiome and genome data for complex trait prediction. Genet Sel Evol 2021; 53:65. [PMID: 34362312 PMCID: PMC8344190 DOI: 10.1186/s12711-021-00658-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 07/20/2021] [Indexed: 12/12/2022] Open
Abstract
Background Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered? Methods Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data. Results Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype. Conclusions While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00658-7.
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Affiliation(s)
- Miguel Pérez-Enciso
- ICREA, Passeig de Lluís Companys 23, 08010, Barcelona, Spain. .,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain. .,Dept. of Epidemiology & Biostatistics, and Dept. of Statistics & Probability, Michigan State University, East Lansing, MI, 48824, USA.
| | - Laura M Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain.,Dept. of Epidemiology & Biostatistics, and Dept. of Statistics & Probability, Michigan State University, East Lansing, MI, 48824, USA
| | - Yuliaxis Ramayo-Caldas
- Animal Breeding and Genetics Program, Institute for Research and Technology in Food and Agriculture (IRTA), Torre Marimon, 08140, Caldes de Montbui, Barcelona, Spain
| | - Gustavo de Los Campos
- Dept. of Epidemiology & Biostatistics, and Dept. of Statistics & Probability, Michigan State University, East Lansing, MI, 48824, USA
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19
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Borda-Molina D, Iffland H, Schmid M, Müller R, Schad S, Seifert J, Tetens J, Bessei W, Bennewitz J, Camarinha-Silva A. Gut Microbial Composition and Predicted Functions Are Not Associated with Feather Pecking and Antagonistic Behavior in Laying Hens. Life (Basel) 2021; 11:235. [PMID: 33809351 PMCID: PMC8001194 DOI: 10.3390/life11030235] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/11/2021] [Accepted: 03/11/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Feather pecking is a well-known problem in layer flocks that causes animal welfare restrictions and contributes to economic losses. Birds' gut microbiota has been linked to feather pecking. This study aims to characterize the microbial communities of two laying hen lines divergently selected for high (HFP) and low (LFP) feather pecking and investigates if the microbiota is associated with feather pecking or agonistic behavior. METHODS Besides phenotyping for the behavioral traits, microbial communities from the digesta and mucosa of the ileum and caeca were investigated using target amplicon sequencing and functional predictions. Microbiability was estimated with a microbial mixed linear model. RESULTS Ileum digesta showed an increase in the abundance of the genus Lactobacillus in LFP, while Escherichia was abundant in HFP hens. In the caeca digesta and mucosa of the LFP line were more abundant Faecalibacterium and Blautia. Tryptophan metabolism and lysine degradation were higher in both digesta and mucosa of the HFP hens. Linear models revealed that the two lines differ significantly in all behavior traits. Microbiabilities were close to zero and not significant in both lines and for all traits. CONCLUSIONS Trait variation was not affected by the gut microbial composition in both selection lines.
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Affiliation(s)
- Daniel Borda-Molina
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Hanna Iffland
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Markus Schmid
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Regina Müller
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Svenja Schad
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Jana Seifert
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, 37073 Göttingen, Germany;
- Center for Integrated Breeding Research, University of Göttingen, 37075 Göttingen, Germany
| | - Werner Bessei
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
| | - Amélia Camarinha-Silva
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany; (D.B.-M.); (H.I.); (M.S.); (R.M.); (S.S.); (J.S.); (W.B.); (J.B.)
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Ponsuksili S, Oster M, Reyer H, Hadlich F, Trakooljul N, Rodehutscord M, Camarinha-Silva A, Bennewitz J, Wimmers K. Genetic regulation and heritability of miRNA and mRNA expression link to phosphorus utilization and gut microbiome. Open Biol 2021; 11:200182. [PMID: 33593158 PMCID: PMC8061690 DOI: 10.1098/rsob.200182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Improved utilization of phytates and mineral phosphorus (P) in monogastric animals contributes significantly to preserving the finite resource of mineral P and mitigating environmental pollution. In order to identify pathways and to prioritize candidate genes related to P utilization (PU), the genomic heritability of 77 and 80 trait-dependent expressed miRNAs and mRNAs in 482 Japanese quail were estimated and eQTL (expression quantitative trait loci) were detected. In total, 104 miR-eQTL (microRNA expression quantitative traits loci) were associated with SNP markers (false discovery rate less than 10%) including 41 eQTL of eight miRNAs. Similarly, 944 mRNA-eQTL were identified at the 5% False discovery rate threshold, with 573 being cis-eQTL of 36 mRNAs. High heritabilities of miRNA and mRNA expression coincide with highly significant eQTL. Integration of phenotypic data with transcriptome and microbiome data of the same animals revealed genetic regulated mRNA and miRNA transcripts (SMAD3, CAV1, ENNPP6, ATP2B4, miR-148a-3p, miR-146b-5p, miR-16-5p, miR-194, miR-215-5p, miR-199-3p, miR-1388a-3p) and microbes (Candidatus Arthromitus, Enterococcus) that are associated with PU. The results reveal novel insights into the role of mRNAs and miRNAs in host gut tissue functions, which are involved in PU and other related traits, in terms of the genetic regulation and inheritance of their expression and in association with microbiota components.
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Affiliation(s)
- Siriluck Ponsuksili
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Michael Oster
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Henry Reyer
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Frieder Hadlich
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Nares Trakooljul
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Markus Rodehutscord
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | | | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | - Klaus Wimmers
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany.,Faculty of Agricultural and Environmental Sciences, University Rostock, 18059 Rostock, Germany
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Khanal P, Maltecca C, Schwab C, Fix J, Bergamaschi M, Tiezzi F. Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine. Genet Sel Evol 2020; 52:41. [PMID: 32727371 PMCID: PMC7388461 DOI: 10.1186/s12711-020-00561-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 07/17/2020] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. 'Full' included information from all genetic markers and all operational taxonomic units (OTU), while 'informatively reduced' and 'randomly reduced' represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction. RESULTS Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-by-microbiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly. CONCLUSIONS This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.
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Affiliation(s)
- Piush Khanal
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695 USA
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695 USA
| | | | - Justin Fix
- The Maschhoffs LLC, Carlyle, IL 62231 USA
| | - Matteo Bergamaschi
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695 USA
| | - Francesco Tiezzi
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695 USA
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