Current Research in Animal Genetic Breeding Using Multi-Omics

The application of multi-omics approaches in animal genetic breeding predominantly encompasses studies on breed identification, growth and development traits, nutritional and meat quality traits, reproductive traits, disease resistance traits, genome associations, and environmental adaptation. The majority of research has been conducted on bovine and porcine species, with secondary emphasis on ovine, avian, and anatid species. This article elucidates the roles and applications of microbiome and metabolome analyses in animal genetic breeding.

Research Strategies Involving Microbiome and Metabolome in Animal Genetic Breeding

Microbiome Sequencing

Microbiome sequencing, including both amplicon sequencing and metagenomic sequencing, provides precise data on microbial composition, gene function, and abundance. There exists a significant correlation between these microorganisms and their hosts. In the domain of animal genetic breeding, in-depth analyses of the gut microbiome can identify microbes that enhance feed conversion efficiency and production performance, as well as key factors affecting meat quality. Modulating the gut microbiome composition is anticipated to positively influence meat quality.

Metabolomics

Metabolomics involves the comprehensive qualitative and quantitative analysis of small molecular metabolites in organisms under genetic or environmental perturbations, comparing the differences between experimental and control groups. This approach aims to elucidate the types, quantities, and variation patterns of metabolites and their association with physiological and pathological changes. In animal genetic breeding, metabolomics is invaluable for estimating genetic parameters, identifying breeds, and discerning significant economic traits. This technique facilitates dynamic tracking of overall metabolic components, providing robust support for the comprehensive analysis of livestock phenotypes and their genetic and environmental interactions. Consequently, it offers metabolic phenotypic data for improving economic traits, enabling early assessment or prediction of production traits. Currently, metabolomics predominantly relies on mass spectrometry to detect and study metabolic molecules in samples such as plasma, serum, milk, urine, feces, rumen fluid, muscle, and fat.

Convergent Applications of Microbiome and Metabolome in Animal Genetic Breeding

Although microbiome and metabolomics applications in animal genetic breeding have distinct focal points, both play pivotal roles in enhancing animal production performance, disease resistance, and product quality improvement. By integrating microbial diversity and metabolomic data, interactions between microbiota and metabolites can be thoroughly investigated, leading to the identification of key microbial strains and metabolic differences. This integration provides a solid scientific foundation and technical support for improving animal production efficiency, optimizing product quality, and controlling animal diseases.

Common Research of Integration of Microbiome Sequencing and Metabolomics of Livestock Breeding

Breed Identification

Significant differences exist in the biomolecules contained within various biological samples such as muscles, milk, blood, and sperm of livestock and poultry breeds, strains, and sexes. Based on these differences, specific microorganisms and small molecular metabolites can be identified as markers to achieve precise discrimination between different breeds and sexes. This discrimination method, based on scientific analysis and assessment, aims to ensure the accuracy and reliability of livestock classification.

Animal Breed Selection and Growth Development

The screening of biomarkers, especially those closely related to traits, has become a focal point of research. By deeply analyzing the intrinsic mechanisms of genetics and growth development, this research aims to provide scientific evidence for the selection, identification, and growth process of new breeds. In studies involving animal populations, the focus is on the correlation or differential analysis between microbiota and metabolic molecules and key economic traits, serving as a crucial method for screening biomarkers.

Molecular Mechanisms of Economic Traits

Employing rigorous research protocols, a comprehensive strategy utilizing multi-omics methods such as metabolomics, transcriptomics, proteomics, and microbiomics, supplemented by conventional molecular experimental techniques, has been adopted to elucidate the complex molecular regulatory networks of phenotypes associated with economic traits like meat quality and reproductive performance. This integrated approach aids in a more comprehensive understanding of the biological mechanisms underlying these economic traits.

Figure 1. Research protocol on metabolome and microbiome in animal genetics and breeding

Sampling and Experimental Design in Animal Genetic Breeding Studies

Sample Types

The study involves the collection of various biological samples, including feces, tissues (muscle, adipose tissue, etc.), and body fluids (milk, blood, semen, ruminal fluid, urine, etc.).

Sample Grouping

Samples are categorized based on preliminary experiments to select diverse breeds, economic trait variations (high/low), different growth and developmental stages, reproductive capacities, and gradient treatments.

Typically, multiple breeds, groupings, and factors are compared within each study to ensure comprehensive analysis.

Biological Replicates

A minimum of 10 biological replicates is recommended to mitigate variability among sample replicates. Increasing the number of biological replicates is advisable to enhance statistical reliability and robustness of the results.

Case Study

Case 1: Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats

Journal: Microbiome

Publication Date: September 2023

Sample Types

Ruminal fluid from dairy goats (15 individuals with the highest average daily gain [HADG] and 15 individuals with the lowest average daily gain [LADG]).

Omics Technologies

16S rRNA sequencing, metagenomics, and metabolomics.

Research Overview

It has been observed that juvenile goats exhibiting rapid growth rates tend to have superior lactation performance in adulthood. However, the influence of ruminal microbiota on the growth performance of juvenile goats, as well as the long-term effects of early-life ruminal microbiota on the growth and lactation performance of dairy goats, remains unclear. This study tracked 99 dairy goats from six months of age through their first lactation period. From this cohort, 15 goats with the highest average daily gain (HADG) and 15 goats with the lowest average daily gain (LADG) were selected for ruminal fluid microbiome and metabolome analyses.

Metagenomic comparisons revealed enhanced carbohydrate and amino acid metabolic functions in the ruminal microbiota of HADG goats, indicating higher feed fermentation capacity. Streptococcus, Candidatus Saccharimonans, and Succinivibrionaceae UCG-001 were positively correlated with juvenile growth rate and enriched metabolites such as propionate, butyrate, maltotriose, and amino acids in HADG goats. Conversely, certain species of Prevotella and methanogens were negatively correlated with growth rate and were associated with acetate and methane, metabolites enriched in LADG goats.

Additionally, functional keystone bacteria in the rumen of juvenile goats, such as Prevotella, showed significant correlations with similar bacterial populations in the rumens of lactating goats. Prevotella was also enriched in LADG lactating goats and negatively affected ruminal fermentation efficiency. Further analysis using Random Forest machine learning indicated that ruminal microbiota and metabolites in juvenile goats, such as Prevotellaceae UCG-003 and the acetate/propionate ratio, could serve as potential microbial biomarkers to distinguish between high and low average daily gain goats, with prediction accuracies exceeding 81.3%. Similarly, the abundance of Streptococcus in the rumen of juvenile goats accurately predicted the milk yield of lactating goats.

This study elucidates the mechanisms by which key bacterial populations influence ruminal microbiome structure and animal growth performance, providing scientific guidance for enhancing the production efficiency of ruminants.

Figure 2. Prediction analyses based on the random forest model. Classification of host ADG using rumen metabolites

Case 2: Combined urine metabolomics and 16S rDNA sequencing analyses reveals physiological mechanism underlying decline in natural mating behavior of captive giant pandas

Journal: Frontiers in Microbiology

Publication Date: September 2022

Sample Types

Urine samples from giant pandas with (n=6) and without (n=6) natural mating experience.

Omics Technologies

16S rRNA sequencing and metabolomics.

Research Overview

The decline in natural mating behavior is a primary factor impeding the population growth of captive giant pandas. However, the contributing factors and underlying mechanisms remain unclear. It is hypothesized that the reduction in natural mating behavior may be associated with psychological stress induced by captivity, which restricts the pandas' ability to freely choose mates. To test this hypothesis, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) combined with 16S rRNA sequencing was employed to conduct metabolomic and microbiome analyses of urine samples, aiming to elucidate the physiological mechanisms behind the decline in natural mating behavior in captive giant pandas.

The results indicated that the decline in mating capability might be associated with abnormalities in arginine biosynthesis and neurotransmitter synthesis. Furthermore, a significant decrease in the relative abundance of Firmicutes, Proteobacteria, Actinobacteria, as well as genera such as Acinetobacter, Weissella, and Pseudomonas, was observed in pandas with reduced natural mating behavior. These findings provide insights into the environment-induced mechanisms affecting mate selection in captive giant pandas. Based on these results, the authors propose a novel strategy for identifying sexual motivation in giant pandas using urine microbiota, which could significantly enhance the success rate of natural breeding in captive populations.

Figure 3.Correlation analysis of urine microorganisms and metabolites.

Case 3: Hybridization alters the gut microbial and metabolic profile concurrent with modifying intestinal functions in Tunchang pigs

Journal: Frontiers in Microbiology

Publication Date: April 2023

Sample Types

Nine hybrid BT piglets and nine purebred TC piglets.

Omics Technologies

Metagenomics and untargeted metabolomics.

Research Overview

Previous studies have demonstrated superior growth performance in Bama miniature pigs (BT) compared to the Tunchang pig (TC), while retaining the favorable meat quality characteristics of TC pigs. However, the differences in the gut environment between TC and BT pigs remain unclear, despite their significant impact on porcine health and production performance. This study employed metagenomics and untargeted metabolomics to analyze the gut microbial diversity and metabolic composition of hybrid BT pigs and purebred TC pigs. Additionally, various molecular experiments were conducted to elucidate how the gut microbiome influences traits in TC and BT pigs, including growth performance, nutrient digestion and absorption, and immune response.

The analysis revealed significant alterations in the gut microbiota following hybridization, particularly in the prevalence of Prevotella and Lactobacillus. Distinct differences were observed in gut metabolites, including fatty acyls, steroids, and steroid derivatives. Furthermore, hybridization was associated with a reduction in gut barrier function and an enhancement in nutrient metabolism.

These findings provide novel insights into the role of hybridization in the gut microbiome-metabolome relationship and offer a theoretical foundation for future microbiota transplantation and pig breeding programs.

Figure 4. Spearman's correlation analysis of microbiome and metabolism.

References

1. Wang D, Chen L, Tang G,et al. Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats. Microbiome. 2023 Sep 29;11(1):215.
2. Zhang MY, Wang XY, Ayala J, et al. Combined urine metabolomics and 16S rDNA sequencing analyses reveals physiological mechanism underlying decline in natural mating behavior of captive giant pandas. Front Microbiol. 2022 Sep 2;13:906737.
3. He J, Zhang Y, Li H, Xie Y, et al. Hybridization alters the gut microbial and metabolic profile concurrent with modifying intestinal functions in Tunchang pigs. Front Microbiol. 2023 Apr 20;14:1159653.
4. Yan X, Si H, Zhu Y, Li S, Han Y, Liu H, Du R, Pope PB, Qiu Q, Li Z. Integrated multi-omics of the gastrointestinal microbiome and ruminant host reveals metabolic adaptation underlying early life development. Microbiome. 2022 Dec 12;10(1):222.