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Tiezzi F, Schwab C, Shull C, Maltecca C. Multiple-trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait. J Anim Breed Genet 2024. [PMID: 38985010 DOI: 10.1111/jbg.12887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/11/2024]
Abstract
Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information.
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Affiliation(s)
- Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
| | | | | | - Christian Maltecca
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
- Department of Animal Science, North Carolina State University, Raleigh, North Carolina, USA
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Devailly G, Fève K, Saci S, Sarry J, Valière S, Lluch J, Bouchez O, Ravon L, Billon Y, Gilbert H, Riquet J, Beaumont M, Demars J. Divergent selection for feed efficiency in pigs altered the duodenum transcriptomic response to feed intake and its DNA methylation profiles. Physiol Genomics 2024; 56:397-408. [PMID: 38497119 DOI: 10.1152/physiolgenomics.00123.2023] [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: 10/20/2023] [Revised: 03/01/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
Feed efficiency is a trait of interest in pigs as it contributes to lowering the ecological and economical costs of pig production. A divergent genetic selection experiment from a Large White pig population was performed for 10 generations, leading to pig lines with relatively low- (LRFI) and high- (HRFI) residual feed intake (RFI). Feeding behavior and metabolic differences have been previously reported between the two lines. We hypothesized that part of these differences could be related to differential sensing and absorption of nutrients in the proximal intestine. We investigated the duodenum transcriptome and DNA methylation profiles comparing overnight fasting with ad libitum feeding in LRFI and HRFI pigs (n = 24). We identified 1,106 differentially expressed genes between the two lines, notably affecting pathways of the transmembrane transport activity and related to mitosis or chromosome separation. The LRFI line showed a greater transcriptomic response to feed intake than the HRFI line. Feed intake affected genes from both anabolic and catabolic pathways in the pig duodenum, such as rRNA production and autophagy. Several nutrient transporter and tight junction genes were differentially expressed between lines and/or by short-term feed intake. We also identified 409 differentially methylated regions in the duodenum mucosa between the two lines, while this epigenetic mark was less affected by feeding. Our findings highlighted that the genetic selection for feed efficiency in pigs changed the transcriptome profiles of the duodenum, and notably its response to feed intake, suggesting key roles for this proximal gut segment in mechanisms underlying feed efficiency.NEW & NOTEWORTHY The duodenum is a key organ for the hunger/satiety loop and nutrient sensing. We investigated how the duodenum transcriptome and DNA methylation profiles are affected by feed intakes in pigs. We observed thousands of changes in gene expression levels between overnight-fasted and fed pigs in high-feed efficiency pig lines, but almost none in the related low-feed efficiency pig line.
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Affiliation(s)
| | - Katia Fève
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Safia Saci
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Julien Sarry
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Sophie Valière
- INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France
| | - Jérôme Lluch
- INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France
| | - Olivier Bouchez
- INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France
| | - Laure Ravon
- Pig Phenotyping and Innovative Breeding Facility, GenESI, UE1372, INRAE, Surgères, France
| | - Yvon Billon
- Pig Phenotyping and Innovative Breeding Facility, GenESI, UE1372, INRAE, Surgères, France
| | - Hélène Gilbert
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Juliette Riquet
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Martin Beaumont
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - Julie Demars
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
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Khasanah H, Kusbianto DE, Purnamasari L, Cruz JFD, Widianingrum DC, Hwang SG. Modulation of chicken gut microbiota for enhanced productivity and health: A review. Vet World 2024; 17:1073-1083. [PMID: 38911084 PMCID: PMC11188898 DOI: 10.14202/vetworld.2024.1073-1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/22/2024] [Indexed: 06/25/2024] Open
Abstract
Microbiota in the digestive tract has become an interesting topic for researchers in recent years. The profile of chicken digestive tract microbiota and its relationship with health and production efficiency have become basic data for modulating the diversity and abundance of the digestive tract microbiota. This article reviews the techniques used to analyze the diversity, role, and function of the gastrointestinal microbiota and the mechanisms by which they are modulated. The gut microbiota plays an important role in animal production, especially during feed digestion and animal health, because it interacts with the host against pathogens. Feed modulation can be a strategy to modulate gut composition and diversity to increase production efficiency by improving growth conditions.
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Affiliation(s)
- Himmatul Khasanah
- Study Program of Animal Husbandry University of Jember, Jember 68121, Indonesia
- Applied Molecular and Microbial Biotechnology (AM2B) Research Group, University of Jember, Jawa Timur, 68121, Indonesia
| | - Dwi E. Kusbianto
- Study Program of Agricultural Science, University of Jember, Jember 68121, Indonesia
| | - Listya Purnamasari
- Study Program of Animal Husbandry University of Jember, Jember 68121, Indonesia
- School of Animal Life Convergence Science, Hankyong National University, Anseong 17579, Republic of Korea
| | - Joseph F. dela Cruz
- Department of Basic Veterinary Sciences, College of Veterinary Medicine, University of the Philippines Los Baños, Los Baños-4031, Philippines
| | - Desy C. Widianingrum
- Study Program of Animal Husbandry University of Jember, Jember 68121, Indonesia
- Applied Molecular and Microbial Biotechnology (AM2B) Research Group, University of Jember, Jawa Timur, 68121, Indonesia
| | - Seong Gu Hwang
- School of Animal Life Convergence Science, Hankyong National University, Anseong 17579, Republic of Korea
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He Z, Liu R, Wang M, Wang Q, Zheng J, Ding J, Wen J, Fahey AG, Zhao G. Combined effect of microbially derived cecal SCFA and host genetics on feed efficiency in broiler chickens. MICROBIOME 2023; 11:198. [PMID: 37653442 PMCID: PMC10472625 DOI: 10.1186/s40168-023-01627-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 07/18/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Improving feed efficiency is the most important goal for modern animal production. The regulatory mechanisms of controlling feed efficiency traits are extremely complex and include the functions related to host genetics and gut microbiota. Short-chain fatty acids (SCFAs), as significant metabolites of microbiota, could be used to refine the combined effect of host genetics and gut microbiota. However, the association of SCFAs with the gut microbiota and host genetics for regulating feed efficiency is far from understood. RESULTS In this study, 464 broilers were housed for RFI measuring and examining the host genome sequence. And 300 broilers were examined for cecal microbial data and SCFA concentration. Genome-wide association studies (GWAS) showed that four out of seven SCFAs had significant associations with genome variants. One locus (chr4: 29414391-29417189), located near or inside the genes MAML3, SETD7, and MGST2, was significantly associated with propionate and had a modest effect on feed efficiency traits and the microbiota. The genetic effect of the top SNP explained 8.43% variance of propionate. Individuals with genotype AA had significantly different propionate concentrations (0.074 vs. 0.131 μg/mg), feed efficiency (FCR: 1.658 vs. 1.685), and relative abundance of 14 taxa compared to those with the GG genotype. Christensenellaceae and Christensenellaceae_R-7_group were associated with feed efficiency, propionate concentration, the top SNP genotypes, and lipid metabolism. Individuals with a higher cecal abundance of these taxa showed better feed efficiency and lower concentrations of caecal SCFAs. CONCLUSION Our study provides strong evidence of the pathway that host genome variants affect the cecal SCFA by influencing caecal microbiota and then regulating feed efficiency. The cecal taxa Christensenellaceae and Christensenellaceae_R-7_group were identified as representative taxa contributing to the combined effect of host genetics and SCFAs on chicken feed efficiency. These findings provided strong evidence of the combined effect of host genetics and gut microbial SCFAs in regulating feed efficiency traits. Video Abstract.
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Affiliation(s)
- Zhengxiao He
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Mengjie Wang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Qiao Wang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Jumei Zheng
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Jiqiang Ding
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Jie Wen
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
| | - Alan G. Fahey
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193 China
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Tams KW, Larsen I, Hansen JE, Spiegelhauer H, Strøm-Hansen AD, Rasmussen S, Ingham AC, Kalmar L, Kean IRL, Angen Ø, Holmes MA, Pedersen K, Jelsbak L, Folkesson A, Larsen AR, Strube ML. The effects of antibiotic use on the dynamics of the microbiome and resistome in pigs. Anim Microbiome 2023; 5:39. [PMID: 37605221 PMCID: PMC10440943 DOI: 10.1186/s42523-023-00258-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/25/2023] [Indexed: 08/23/2023] Open
Abstract
Antibiotics are widely used in pig farming across the world which has led to concerns about the potential impact on human health through the selection of antibiotic resistant pathogenic bacteria. This worry has resulted in the development of a production scheme known as pigs Raised Without Antibiotics (RWA), in which pigs are produced in commercial farms, but are ear-tagged as RWA until slaughter unless they receive treatment, thus allowing the farmer to sell the pigs either as premium priced RWA or as conventional meat. Development of antibiotic resistance in pig farming has been studied in national surveys of antibiotic usage and resistance, as well as in experimental studies of groups of pigs, but not in individual pigs followed longitudinally in a commercial pig farm. In this study, a cohort of RWA designated pigs were sampled at 10 time points from birth until slaughter along with pen-mates treated with antibiotics at the same farm. From these samples, the microbiome, determined using 16S sequencing, and the resistome, as determined using qPCR for 82 resistance genes, was investigated, allowing us to examine the difference between RWA pigs and antibiotic treated pigs. We furthermore included 176 additional pigs from six different RWA farms which were sampled at the slaughterhouse as an endpoint to substantiate the cohort as well as for evaluation of intra-farm variability. The results showed a clear effect of age in both the microbiome and resistome composition from early life up until slaughter. As a function of antibiotic treatment, however, we observed a small but significant divergence between treated and untreated animals in their microbiome composition immediately following treatment, which disappeared before 8 weeks of age. The effect on the resistome was evident and an effect of treatment could still be detected at week 8. In animals sampled at the slaughterhouse, we observed no difference in the microbiome or the resistome as a result of treatment status but did see a strong effect of farm origin. Network analysis of co-occurrence of microbiome and resistome data suggested that some resistance genes may be transferred through mobile genetic elements, so we used Hi-C metagenomics on a subset of samples to investigate this. We conclude that antibiotic treatment has a differential effect on the microbiome vs. the resistome and that although resistance gene load is increased by antibiotic treatment load, this effect disappears before slaughter. More studies are needed to elucidate the optimal way to rear pigs without antibiotics.
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Affiliation(s)
- Katrine Wegener Tams
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Inge Larsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, 1871, Copenhagen, Denmark
| | - Julie Elvekjær Hansen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Henrik Spiegelhauer
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | | | - Sophia Rasmussen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Anna Cäcilia Ingham
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut (SSI), 2300, Copenhagen, Denmark
| | - Lajos Kalmar
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | | | - Øystein Angen
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut (SSI), 2300, Copenhagen, Denmark
| | - Mark A Holmes
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Karl Pedersen
- Department of Animal Health and Antimicrobial Strategies, National Veterinary Institute, 751 89, Uppsala, Sweden
| | - Lars Jelsbak
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Anders Folkesson
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Anders Rhod Larsen
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut (SSI), 2300, Copenhagen, Denmark
| | - Mikael Lenz Strube
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
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Le Graverand Q, Marie-Etancelin C, Meynadier A, Weisbecker JL, Marcon D, Tortereau F. Predicting feed efficiency traits in growing lambs from their ruminal microbiota. Animal 2023; 17:100824. [PMID: 37224614 DOI: 10.1016/j.animal.2023.100824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/26/2023] Open
Abstract
Selecting feed-efficient sheep could improve the sustainability of this livestock production. However, most sheep breeding companies cannot afford to record feed intake to select feed-efficient animals. Past studies underlined the potential of omics data, including microbiota metabarcoding data, as proxies for feed efficiency. The study involved 277 Romane lambs from two lines divergently selected for residual feed intake (RFI). There were two objectives: check the consequences of selecting for feed efficiency over the rumen microbiota, and assess the predictive ability of the rumen microbiota for host traits. The study assessed two contrasting diets (concentrate diet and mixed diet) and two microbial groups (prokaryotes and eukaryotes). Discriminant analyses did not highlight any significant effect of sheep selection for residual feed intake on the rumen microbiota composition. Indeed, prokaryotic and eukaryotic microbiota compositions poorly discriminated the RFI lines, with averaged balanced error rates ranging from 45% to 55%. Correlations between host traits (feed efficiency and production traits) and their predictions from microbiota data varied between -0.07 and 0.56, depending on the trait, diet and sequencing. Feed intake was the most accurately predicted trait. However, predictions from fixed effects and BW were more accurate than or as accurate as predictions from the microbiota. Environmental effects can greatly affect the variability of microbiota compositions. Considering batch and environmental effects should be paramount when the predictive ability of the microbiota is assessed. This study argues why metabarcoding the rumen microbiota is not the best way to predict meat sheep production traits: fixed effects and BW were more cost-effective proxies and they led to similar or better predictive accuracies than microbiota metabarcoding (16S and 18S sequencing).
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Affiliation(s)
- Q Le Graverand
- GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France.
| | - C Marie-Etancelin
- GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France
| | - A Meynadier
- GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France
| | - J-L Weisbecker
- GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France
| | - D Marcon
- INRAE, Unité Expérimentale P3R, Domaine de la Sapinière, F-18390 Osmoy, France
| | - F Tortereau
- GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France
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Cheng Y, Ding S, Azad MAK, Song B, Kong X. Small Intestinal Digestive Functions and Feed Efficiency Differ in Different Pig Breeds. Animals (Basel) 2023; 13:ani13071172. [PMID: 37048428 PMCID: PMC10093237 DOI: 10.3390/ani13071172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
Small intestinal growth and health affect its digestion and absorption ability, while little information exists about the small intestinal morphology and function differences among the different pig breeds. Therefore, 90 healthy 35 days of age Taoyuan black (TB), Xiangcun black (XB), and Duroc (DR) pigs (30 pigs per breed) with similar body weight (BW) of the same breed were reared to 185 days of age to evaluate the potential relationship between feed efficiency and small intestinal morphology and function at 80, 125, and 185 days of age. The results show that the TB and XB pigs had lower initial and final BW, ADG, and ADFI and plasma CHO and LDL-C levels, whereas they had higher plasma LIP levels and jejunal trypsin, invertase, lactase, and maltase activities and higher DM, ADF, Tyr, Arg, and His digestibility at 80 days of age compared with the DR pigs. At 125 days of age, TB and XB pigs had lower apparent total tract digestibility and plasma CHO, HDL-C, LDL-C, and NH3 levels; XB pigs had lower DM and NDF digestibility, and TB pigs had higher jejunal lactase and maltase activities. At 185 days of age, TB and XB pigs had lower DM, EE, ADF, and GE digestibility, while having higher plasma ALT and UN levels; TB pigs had higher plasma AST level and jejunal chymase activity. Furthermore, the plasma free amino acid contents, small intestinal VH, and nutrient transporter expression levels differed at different ages. Therefore, the different pig breeds exhibited significantly different growth performance and small intestinal growth, mainly resulting from the differences in digestive enzymes and nutrient transporters in the small intestine.
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Invited Review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: implications for methane emissions in cattle. Livest Sci 2023. [DOI: 10.1016/j.livsci.2023.105171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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9
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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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Mora M, Velasco-Galilea M, Sánchez JP, Ramayo-Caldas Y, Piles M. Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models. Genet Sel Evol 2022; 54:81. [PMID: 36536288 PMCID: PMC9762025 DOI: 10.1186/s12711-022-00770-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term "effect" carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects. These indirect effects can be estimated via structural coefficients that measure the effect of the microbiota on growth while the effects of the host genetics are kept constant. In this study, we applied the SEM approach to infer causal relationships between the cecal microbiota and growth of rabbits fed under ad libitum (ADGAL) or restricted feeding (ADGR). RESULTS We identified structural coefficients that are statistically different from 0 for 138 of the 946 operational taxonomic units (OTU) analyzed. However, only 15 and 38 of these 138 OTU had an effect greater than 0.2 phenotypic standard deviations (SD) on ADGAL and ADGR, respectively. Many of these OTU had a negative effect on both traits. The largest effects on ADGR were exerted by an OTU that is taxonomically assigned to the Desulfovibrio genus (- 1.929 g/d, CSS-normalized OTU units) and by an OTU that belongs to the Ruminococcaceae family (1.859 g/d, CSS-normalized OTU units). For ADGAL, the largest effect was from OTU that belong to the S24-7 family (- 1.907 g/d, CSS-normalized OTU units). In general, OTU that had a substantial effect had low to moderate estimates of heritability. CONCLUSIONS Disentangling how direct and indirect effects act on production traits is relevant to fully describe the processes of mediation but also to understand how these traits change before considering the application of an external intervention aimed at changing a given microbial composition by blocking/promoting the presence of a particular microorganism.
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Affiliation(s)
- Mónica Mora
- Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona Spain
| | - María Velasco-Galilea
- Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona Spain ,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola del Vallès, Barcelona Spain
| | - Juan Pablo Sánchez
- Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona Spain
| | - Yuliaxis Ramayo-Caldas
- Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona Spain
| | - Miriam Piles
- Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona Spain
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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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12
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Fregulia P, Campos MM, Dias RJP, Liu J, Guo W, Pereira LGR, Machado MA, Faza DRDLR, Guan LL, Garnsworthy PC, Neves ALA. Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas. Front Microbiol 2022; 13:1025173. [PMID: 36523842 PMCID: PMC9745175 DOI: 10.3389/fmicb.2022.1025173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/07/2022] [Indexed: 08/27/2023] Open
Abstract
Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395-signaling and cellular process was strongly related to HE animals, and K13643-genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith's Phylogenetic Diversity, Pielou's Evenness, Shannon's diversity, weighted UniFrac distance, Jaccard index, and Bray-Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.
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Affiliation(s)
- Priscila Fregulia
- Laboratório de Protozoologia, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
- Programa de Pós-Graduação em Biodiversidade e Conservação da Natureza, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | - Mariana Magalhães Campos
- Brazilian Agricultural Research Corporation (Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA), National Center for Research on Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil
| | - Roberto Júnio Pedroso Dias
- Laboratório de Protozoologia, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
- Programa de Pós-Graduação em Biodiversidade e Conservação da Natureza, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | - Junhong Liu
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Wei Guo
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China
| | - Luiz Gustavo Ribeiro Pereira
- Brazilian Agricultural Research Corporation (Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA), National Center for Research on Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil
| | - Marco Antônio Machado
- Brazilian Agricultural Research Corporation (Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA), National Center for Research on Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil
| | - Daniele Ribeiro de Lima Reis Faza
- Brazilian Agricultural Research Corporation (Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA), National Center for Research on Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil
| | - Le Luo Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Phil C. Garnsworthy
- School of Biosciences, University of Nottingham, Loughborough, United Kingdom
| | - André Luis Alves Neves
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
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13
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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: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [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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14
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Chakraborty D, Sharma N, Kour S, Sodhi SS, Gupta MK, Lee SJ, Son YO. Applications of Omics Technology for Livestock Selection and Improvement. Front Genet 2022; 13:774113. [PMID: 35719396 PMCID: PMC9204716 DOI: 10.3389/fgene.2022.774113] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/16/2022] [Indexed: 12/16/2022] Open
Abstract
Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.
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Affiliation(s)
- Dibyendu Chakraborty
- Division of Animal Genetics and Breeding, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
| | - Neelesh Sharma
- Division of Veterinary Medicine, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
- *Correspondence: Neelesh Sharma, ; Young Ok Son,
| | - Savleen Kour
- Division of Veterinary Medicine, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
| | - Simrinder Singh Sodhi
- Department of Animal Biotechnology, College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India
| | - Mukesh Kumar Gupta
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, India
| | - Sung Jin Lee
- Department of Animal Biotechnology, College of Animal Life Sciences, Kangwon National University, Chuncheon-si, South Korea
| | - Young Ok Son
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences and Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju, South Korea
- *Correspondence: Neelesh Sharma, ; Young Ok Son,
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15
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Déru V, Bouquet A, Zemb O, Blanchet B, De Almeida ML, Cauquil L, Carillier-Jacquin C, Gilbert H. Genetic relationships between efficiency traits and gut microbiota traits in growing pigs fed a conventional or a high fiber diet. J Anim Sci 2022; 100:6586877. [PMID: 35579995 PMCID: PMC9194801 DOI: 10.1093/jas/skac183] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 05/16/2022] [Indexed: 11/20/2022] Open
Abstract
In pigs, the gut microbiota composition plays a major role in the process of digestion, but is influenced by many external factors, especially diet. To be used in breeding applications, genotype by diet interactions on microbiota composition have to be quantified, as well as their impact on genetic covariances with feed efficiency (FE) and digestive efficiency (DE) traits. This study aimed at determining the impact of an alternative diet on variance components of microbiota traits (genera and alpha diversity indices) and estimating genetic correlations between microbiota and efficiency traits for pigs fed a conventional (CO) or a high-fiber (HF) diet. Fecal microbes of 812 full-siblings fed a CO diet and 752 pigs fed the HF diet were characterized at 16 weeks of age by sequencing the V3-V4 region of the 16S rRNA gene. A total of 231 genera were identified. Digestibility coefficients of nitrogen, organic matter, and energy were predicted analyzing the same fecal samples with near infrared spectrometry. Daily feed intake, feed conversion ratio, residual feed intake and average daily gain (ADG) were also recorded. The 71 genera present in more than 20% of individuals were retained for genetic analyses. Heritability (h²) of microbiota traits were similar between diets (from null to 0.38 ± 0.12 in the CO diet and to 0.39 ± 0.12 in the HF diet). Only three out of the 24 genera and two alpha diversity indices with significant h² in both diets had genetic correlations across diets significantly different from 0.99 (P < 0.05), indicating limited genetic by diet interactions for these traits. When both diets were analyzed jointly, 59 genera had h² significantly different from zero. Based on the genetic correlations between these genera and ADG, FE, and DE traits, three groups of genera could be identified. A group of 29 genera had abundances favorably correlated with DE and FE traits, 14 genera were unfavorably correlated with DE traits, and the last group of 16 genera had abundances with correlations close to zero with production traits. However, genera abundances favorably correlated with DE and FE traits were unfavorably correlated with ADG, and vice versa. Alpha diversity indices had correlation patterns similar to the first group. In the end, genetic by diet interactions on gut microbiota composition of growing pigs were limited in this study. Based on this study, microbiota-based traits could be used as proxies to improve FE and DE in growing pigs.
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Affiliation(s)
- V Déru
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France.,France Génétique Porc, 35651 Le Rheu Cedex, France
| | - A Bouquet
- IFIP-Institut du Porc, 35651 Le Rheu Cedex, France
| | - O Zemb
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France
| | - B Blanchet
- UE3P, INRAE, Domaine de la Prise, 35590, Saint-Gilles, France
| | - M L De Almeida
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France
| | - L Cauquil
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France
| | - C Carillier-Jacquin
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France
| | - H Gilbert
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31320 Castanet Tolosan, France
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16
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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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Aliakbari A, Zemb O, Billon Y, Barilly C, Ahn I, Riquet J, Gilbert H. Genetic relationships between feed efficiency and gut microbiome in pig lines selected for residual feed intake. J Anim Breed Genet 2021; 138:491-507. [PMID: 33634901 PMCID: PMC8248129 DOI: 10.1111/jbg.12539] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/10/2021] [Accepted: 01/24/2021] [Indexed: 12/21/2022]
Abstract
This study aimed to evaluate the genetic relationship between faecal microbial composition and five feed efficiency (FE) and production traits, residual feed intake (RFI), feed conversion ratio (FCR), daily feed intake (DFI), average daily gain (ADG) and backfat thickness (BFT). A total of 588 samples from two experimental pig lines developed by divergent selection for RFI were sequenced for the 16 rRNA hypervariable V3‐V4 region. The 75 genera with less than 20% zero values (97% of the counts) and two α‐diversity indexes were analysed. Line comparison of the microbiota traits and estimations of heritability (h2) and genetic correlations (rg) were analysed. A non‐metric multidimensional scaling showed line differences between genera. The α‐diversity indexes were higher in the LRFI line than in the HRFI line (p < .01), with h2 estimates of 0.19 ± 0.08 (Shannon) and 0.12 ± 0.06 (Simpson). Forty‐eight genera had a significant h2 (>0.125). The rg of the α‐diversities indexes with production traits were negative. Some rg of genera belonging to the Lachnospiraceae, Ruminococcaceae, Prevotellaceae, Lactobacillaceae, Streptococcaceae, Rikenellaceae and Desulfovibrionaceae families significantly differed from zero (p < .05) with FE traits, RFI (3), DFI (7) and BFT (11). These results suggest that a sizable part of the variability of the gut microbial community is under genetic control and has genetic relationships with FE, including diversity indicators. It offers promising perspectives for selection for feed efficiency using gut microbiome composition in pigs.
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Affiliation(s)
- Amir Aliakbari
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
| | - Olivier Zemb
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
| | | | - Céline Barilly
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
| | - Ingrid Ahn
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
| | - Juliette Riquet
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
| | - Hélène Gilbert
- GenPhySE, Université de Toulouse, INRAE, Castanet-Tolosan, France
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