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

Wu, Jie; Ye, Yong; Quan, Jianping; Ding, Rongrong; Wang, Xingwang; Zhuang, Zhanwei; Zhou, Shenping; Geng, Qian; Xu, Cineng; Hong, Linjun; Xu, Zheng; Zheng, Enqin; Cai, Guangyan; Wu, Zhenfang; Yang, Jie

doi:10.1186/s40813-021-00219-w

Cited by 12 publications

(10 citation statements)

References 54 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WGCNA was originally applied to transcriptomics data, but it has also been recently employed for network generation using metabolomics data from human and human microbiome ( Osterhoff et al, 2014 ; Pedersen and Sofia, 2018 ; Vernocchi et al, 2020 ; Murga-Garrido et al, 2021 ; Petersen et al, 2021 ), animal ( Wu et al, 2021 ), and plants ( DiLeo et al, 2011 ). ( Samal and Martin, 2011 ).…”

Section: Experimental Networkmentioning

confidence: 99%

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Amara

Frainay

Jourdan

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

show abstract

Section: Experimental Networkmentioning

confidence: 99%

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Amara

Frainay

Jourdan

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…Pork is one of the most consumed meats in the world and the main source of animal protein for humans. Furthermore, Duroc × (Landrace × Yorkshire) (DLY) commercial pigs dominate the pork market in China ( Wu et al, 2021 ). Among them, Duroc pigs provide half of the genetic contribution in DLY pigs.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Heterozygosity and Genome-Wide Analysis of Heterozygosity Regions in Two Duroc Pig Populations

et al. 2022

Self Cite

View full text Add to dashboard Cite

Heterozygosity can effectively reflect the diverse models of population structure and demographic history. However, the genomic distribution of heterozygotes and the correlation between regions of heterozygosity (runs of heterozygosity, ROHet) and phenotypes are largely understudied in livestock. The objective of this study was to identify ROHet in the Duroc pig genome, and investigate the relationships between ROHet and eight important economic traits. Here, we genotyped 3,770 American Duroc (S21) and 2,096 Canadian Duroc (S22) pigs using 50 K single nucleotide polymorphism array to analyze heterozygosity. A total of 145,010 and 84,396 ROHets were characterized for S21 and S22 populations, respectively. ROHet segments were mostly enriched in 1–2 Mb length classification (75.48% in S21 and 72.25% in S22). The average genome length covered by ROHet was 66.53 ± 12.20 Mb in S21 and 73.32 ± 13.77 Mb in S22 pigs. Additionally, we detected 20 and 13 ROHet islands in S21 and S22 pigs. Genes in these genomic regions were mainly involved in the biological processes of immunity and reproduction. Finally, the genome-wide ROHet-phenotypes association analysis revealed that 130 ROHets of S21 and 84 ROHets of S22 were significantly associated with eight economic traits. Among the candidate genes in the significant ROHet regions, 16 genes related to growth, metabolism, and meat quality were considered as candidate genes for important economic traits of pigs. This work preliminarily explores the effect of heterozygosity-rich regions in the pig genome on production performance and provides new insights for subsequent research on pig genetic improvement.

show abstract

“…The power of metabolomics is that it non-invasively detects subtle phenotypic changes, innate phenotypic propensities, and dietary responses in livestock research, breeding, and assessment through new varieties of bio-samples such as semen, amniotic fluid, saliva, and urine. Wu et al (2021) found that small metabolite profiling of pig feces by LC-MS metabolomics correlated with their feed efficiency and can be used as a reference for selecting animals with high feed conversion efficiency and responsiveness to new feed additives (Wu et al, 2021). Given that fecal metabolome are reflections of intestinal microbiota, cellular metabolism and digestion/absorption of nutrients in the gut (Zierer et al, 2018;Malheiros et al, 2021), the metabolites present in the feces could indicate their feed conversion efficiency (Wu et al, 2021).…”

Section: Metabolomicsmentioning

confidence: 99%

“…Wu et al (2021) found that small metabolite profiling of pig feces by LC-MS metabolomics correlated with their feed efficiency and can be used as a reference for selecting animals with high feed conversion efficiency and responsiveness to new feed additives (Wu et al, 2021). Given that fecal metabolome are reflections of intestinal microbiota, cellular metabolism and digestion/absorption of nutrients in the gut (Zierer et al, 2018;Malheiros et al, 2021), the metabolites present in the feces could indicate their feed conversion efficiency (Wu et al, 2021). Fecal metabolome was also shown to change as a function of stress in beef cattle (Valerio et al, 2020).…”

Section: Metabolomicsmentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 12 publications

References 54 publications

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Assessment of Heterozygosity and Genome-Wide Analysis of Heterozygosity Regions in Two Duroc Pig Populations

Applications of Omics Technology for Livestock Selection and Improvement

Contact Info

Product

Resources

About