Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs

Carmelo, Victor Adriano Okstoft; Banerjee, Priyanka; Diniz, Wellison J. S.; Kadarmideen, Haja N.

doi:10.1038/s41598-019-57182-4

Cited by 29 publications

(45 citation statements)

References 59 publications

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“…This entails a clear advantage over other methods in terms of translatability. 73 Recently, studies have shown that the metabolomic signatures of blood from cattle 74 and pigs 75 can be used to accurately separate animals by their feed efficiency which is one of the most economically important traits in livestock production. Studies of the human metabolome and its relevance to human health has also clearly increased the last five years, 76,77 and is very likely to be one of the cornerstones in an implementation of personalised medicine.…”

Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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Understanding the genotype -phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins, metabolites. This facilitates our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We do this by performing NMR metabolomics on four replicate pools of male flies from each of 170 isogenic lines. We show first that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, we show that for four of five traits related to behaviour and stress resistance the metabolome predicts phenotypes more accurately than genomic data, and that the metabolites driving the increased accuracy was trait specific. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding has strong applied importance e.g. in human medicine and animal and plant breeding.1 is a design matrix linking the genomic ( 1 ) and metabolomic ( 1 ) effects to the phenotypes. The random genomic effects are 1~( , [1,1] ! " ), and the metabolomic effects are 1~( , [1,1] 5 " ), where is the additive genomic relationship matrix as specified previously, and is the metabolomic relationship matrix.The metabolomic relationship matrix was computed as = ′ NMR ⁄ , where is a × NMR matrix of *77 18* T 6 , N 6 ), and was compared (using paired t-test corrected for multiple testing by a false discovery rate (FDR) of <0.05) within and across 9/ clusters to identify the NMR features resulting in the largest prediction accuracy. We only considered Drosophila melanogaster Genetic Reference Panel lines.

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Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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“…As the end products of cellular regulatory processes, metabolites represent the ultimate response of biological systems associated with genetic changes, so metabolomics is considered the link between genotypes and phenotypes [4]. Metabolomics refers to the measurements of all endogenous metabolites, intermediates, and products of metabolism and has been applied to measure the dynamic metabolic responses in pigs [5,6] and dairy cows [7,8]. Additionally, metabolites could provide details of physiological state, so genetic variant-associated metabolites are expected to display larger effect sizes [9].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we aimed to identify genetic variants (SNP markers) affecting concentrations of metabolites and to reveal the biochemical mechanisms underlying genetic variation for pigs' feed efficiency. Our study is based on two of our previously published papers and datasets used therein [6,11]. Briefly, the experiment consisted of 59 Duroc and 49 Landrace pigs with data on feed efficiency (RFI), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from liquid chromatography-mass spectrometry (LC-MS) system).…”

Section: Introductionmentioning

confidence: 99%

“…Briefly, the experiment consisted of 59 Duroc and 49 Landrace pigs with data on feed efficiency (RFI), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from liquid chromatography-mass spectrometry (LC-MS) system). While our previous studies only looked at metabolome-phenotype associations [6], we report an integrated systems genomics approach to identify quantitative trait loci (QTLs) or SNPs affecting metabolite concentration via metabolite GWAS methods (mGWAS), where each metabolite is itself a phenotype. To the best of our knowledge, this is the first study to link the genomics with metabolomics to identify significant genetic variants associated with known metabolites that differ in pigs with different levels of feed efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Metabolite Genome-Wide Association Study (mGWAS) and Gene-Metabolite Interaction Network Analysis Reveal Potential Biomarkers for Feed Efficiency in Pigs

Wang

Kadarmideen

2020

Metabolites

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Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake (RFI)), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs’ feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (p-value < 1.06 × 10−6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4, and CCNC consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine, and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.

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“…To cope with increasing market demand and breeding costs, improving the feed e ciency (FE) of pigs has always been a concern of breeders. Since FE cannot be measured directly for its complex pleiotropy, the feed conversion ratio (FCR) and residual feed intake (RFI) are often used as alternative selection conditions for FE [1][2][3][4]. However, although RFI traits have a strong genetic correlation with FCR (0.76 to 0.99) [5], a uni ed indicator for evaluating feed e ciency has not yet been determined.…”

Section: Introductionmentioning

confidence: 99%

Using nontargeted LC-MS metabolomics to identify the association of biomarkers in pig feces with feed efficiency

Quan

et al. 2020

Preprint

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Background: Improving feed efficiency is economically and environmentally beneficial in the pig industry. A deeper understanding of feed efficiency is essential on many levels for its highly complex nature. The aim of this project is to explore the relationship between fecal metabolites and feed efficiency-related traits, thereby identifying metabolites that may assist in the screening of the feed efficiency of pigs.Result: We performed fecal metabolomics analysis on 50 individuals selected from 225 Duroc x (Landrace x Yorkshire) (DLY) commercial pigs, 25 with an extremely high feed efficiency and 25 with an extremely low feed efficiency. A total of 6,749 and 5,644 m/z features were detected in positive and negative ionization modes by liquid chromatography-mass spectrometry (LC/MS). Through weighted coexpression network analysis (WGCNA), we found that one module in each positive and negative mode was related to residual feed intake (RFI), and six and three metabolites were further identified. The nine metabolites were found to be involved in multiple metabolic pathways, including lipid metabolism (primary bile acid synthesis, linoleic acid metabolism), vitamin D, glucose metabolism, and others. In parallel, the expression patterns of seven genes involved in lipid metabolism were illuminated by real-time qPCR. Then, Lasso regression analysis was used to evaluate the importance of nine metabolites obtained by the annotation process.Conclusions: Altogether, this study can not only provide new insights for the subsequent evaluation of commercial pig feed efficiency through small molecule metabolites, but also provide a reference for the development of new feed additives.

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Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs

Cited by 29 publications

References 59 publications

Prediction of complex phenotypes using theDrosophilametabolome

Prediction of complex phenotypes using theDrosophilametabolome

Metabolite Genome-Wide Association Study (mGWAS) and Gene-Metabolite Interaction Network Analysis Reveal Potential Biomarkers for Feed Efficiency in Pigs

Using nontargeted LC-MS metabolomics to identify the association of biomarkers in pig feces with feed efficiency

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