Genetic relationships between feed efficiency and gut microbiome in pig lines selected for residual feed intake

Aliakbari, Amir; Zemb, Olivier; Billon, Yvon; Barilly, Céline; Ahn, Ingrid; Riquet, Juliette; Gilbert, Hélène

doi:10.1111/jbg.12539

Cited by 25 publications

(30 citation statements)

References 58 publications

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“…In chapter 6 it was found that some OTU are highly heritable, with values up to 0.47. This is in line with literature, where values up to 0.50 and 0.57 were found for individual genera (Camarinha-Silva et al, 2017;Aliakbari et al, 2021). However, the microbiota described in chapter 6 that had a high contribution to the prediction of feed efficiency traits, had much lower heritability values.…”

Section: Microbiotasupporting

confidence: 91%

“…Metabolites that are not used in the body are excreted via the urine, the gastro-intestinal tract, or to the air as CO 2 . Thus, we may hypothesize that variation in individual phenotypes for feed efficiency traits is associated with individual differences in blood metabolite profiles, in addition to being associated with differences in faecal microbiota composition as recently observed (Camarinha-Silva et al, 2017;Aliakbari et al, 2021).…”

Section: Introductionmentioning

confidence: 75%

“…Genetic correlations for OTUs with feed efficiency in chapter 6 are unknown, but the OTU with the highest contribution to FCR contributed only 0.44% to the phenotypic variation. However, Aliakbari et al (2021) showed genetic correlations of bacterial genera with FCR as high as 0.32. The metabolite with the highest contribution to FCR in chapter 6 contributes only 1.03% to the phenotypic variation.…”

Section: Future Prospectsmentioning

confidence: 94%

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Improving feed efficiency in pigs : bridging genetics and nutrition

Verschuren¹

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1 -General introduction 2 -Variation in faecal digestibility values related to feed efficiency traits of grower-finisher pigs 3 -Fecal microbial composition associated with variation in feed efficiency in pigs depends on diet and sex 4 -Prediction of nutrient digestibility in grower-finisher pigs based on faecal microbiota composition 5 -Between animal variation in dietary nitrogen and amino acid efficiency in grower-finisher pigs 6 -Combining faecal microbiome, serum metabolome, and host genome for phenotypic prediction of feed efficiency in grower-finisher pigs 7 -General discussion Summary Résumé Curriculum Vitae Acknowledgements General introduction 2 Variation in faecal digestibility values related to feed efficiency traits of grower-finisher pigs

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Section: Microbiotasupporting

confidence: 91%

Section: Introductionmentioning

confidence: 75%

Section: Future Prospectsmentioning

confidence: 94%

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Improving feed efficiency in pigs : bridging genetics and nutrition

Verschuren¹

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“…Phenotypic records and faecal samples (604 pigs) were collected from the two last generations (G9 and G10) of two experimental French Large White pig lines that were developed over 11 generations of divergent selection for RFI during 18 years at INRAE (UE GenESI, Surgères, France, https://doi.org/10.15454/1.5572415481185847E12 ), as described in Aliakbari et al [ 12 ]. The structure of the lines and the selection process are described in Aliakbari et al [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

et al. 2022

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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.

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“…The only ASV associated with H-RFI in the stool environment was classi ed as a Ruminococcaceae UCG-005. Members of this family were correlated with FCR of low e cient pigs (McCormack et al, 2017;Aliakbari et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

Andrade

Afli

Bressani

et al. 2021

Preprint

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Background: The impact of extreme changes in weather patterns in the economy and human welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since it is responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiome components of 52 Brazilian Nelore bulls, belonging to two treatment groups based on the feed intervention, conventional and by-products based diet, could be used in the future as biomarkers for methane emission and feed efficiency in bovine.Results: We identified a total of 5,693 Amplicon Sequence Variants (ASVs) in the Nelore bulls microbiomes. Differential abundance (DA) analysis with the ANCOM approach identified 30 bacterial and 15 archaea ASVs as DA among treatment groups. Association analysis using Maaslin2 and Mixed Linear Models indicated that bacterial ASVs are linked to the residual methane emission (RCH4) and Residual Feed Intake (RFI) phenotypes, contributing to the host’s phenotypic variation, suggesting their potential as targets for interventions and/or biomarkers.Conclusion: Feed composition induced significant differences in abundance and richness of ruminal and fecal microbial populations. The diet based on industrial byproducts applied to our treatment groups influenced the microbiome diversity of bacteria and archaea, but not of protozoa. Different ASVs were associated with RCH4 emission and RFI in both ruminal and fecal microbiomes. While ruminal ASVs are expected to directly influence RCH4 emission and RFI, the relation of fecal taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits might also be associated with host health due to their link to anti-inflammatory compounds, and these have the potential to be used as accessible biomarkers for these complex phenotypes.

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Genetic relationships between feed efficiency and gut microbiome in pig lines selected for residual feed intake

Cited by 25 publications

References 58 publications

Improving feed efficiency in pigs : bridging genetics and nutrition

Improving feed efficiency in pigs : bridging genetics and nutrition

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

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