1 Introduction

Feed efficiency (FE) is a crucial trait in poultry production, which directly affects the profit of breeding. Because in some chicken breeds, feed costs can account for more than 70% of the total cost (Patience et al., 2015; Ye et al., 2024). Improving feed efficiency not only makes breeding more profitable, but also reduces the waste of resources and the discharge of manure, and is more environmentally friendly. Whether it is large-scale commercial breeding or small-scale breeding, improving feed efficiency has always been an important goal in breeding (Yi et al., 2015; Sinpru et al., 2021; Ruban and Danshyn, 2024).

The genetic structure of feed efficiency is complex and the heritability is moderate. It is relatively difficult to improve this trait by traditional methods (Xiao et al., 2021; Ye et al., 2024). Feed efficiency is affected by many physiological processes, such as metabolism and immune response, etc. These processes involve many genes and interact with environmental factors, making the situation rather complex (Yang et al., 2020; Sinpru et al., 2021). Yi et al. (2015) and Ye et al. (2024) found that traditional breeding methods had low accuracy in discovering and utilizing these specific genes related to feed efficiency, which also slowed down the speed of genetic improvement.

This study explored how the feed efficiency of chickens is regulated by genes through the integration of transcriptomics and metabolomics analysis, expounded the gene expression and metabolic pathways of high-efficiency and low-efficiency chickens, identified some key genes, regulatory RNAs, and important metabolic processes related to high feed efficiency. This study aims to establish a relatively complete molecular framework to facilitate more precise breeding plans in the future and also promote the genetic improvement of poultry feed efficiency.

2 Feed Efficiency Traits and Their Biological Complexity

2.1 Definitions and measurement approaches

Poultry feed efficiency (FE) is mainly measured by two indicators: feed conversion rate (FCR) and residual feed intake (RFI). FCR refers to how much feed a chicken needs to consume for each additional kilogram of body weight. This indicator reflects the efficiency of a chicken in converting feed into body weight. RFI is the difference between the actual feed consumed and the predicted feed needed. This indicator has little relation to growth rate and yield and is independent (Prakash et al., 2020). Both of these traits have certain heritability. The heritability of FCR is between 0.31 and 0.49, and that of RFI is between 0.42 and 0.52. This indicates that FE can be improved through selection and breeding. To measure these indicators, it is necessary to record the feed intake, weight changes, or egg production of each chicken over a period of time, and then calculate the values of these traits and the relationships among them using genetic and statistical models (Aggrey et al., 2010; Marchesi et al., 2021; Zhou et al., 2022).

2.2 Factors affecting FE in chickens

Feed efficiency is influenced by factors such as genetics, physiology and nutrition, and the interaction among these factors is very complex. The genetic factors of the host play a significant role in the differences in feed efficiency, but the intestinal microbiota, especially the flora in the cecum, cannot be ignored either. Wen et al. (2021) and Zhou et al. (2022) found that the composition of cecal microbiota could explain up to 28% of the differences in RFI. The research by Wu et al. (2019) and Zampiga et al. (2021) indicates that the composition of the diet, such as energy and nutrient density, as well as the addition of additives like amino acids and minerals, can directly affect the absorption and utilization of feed, thereby enhancing feed efficiency. Dao et al. (2023) indicates that environmental factors, such as chicken coop conditions, management methods, and the use of kitchen waste as alternative feed, can also affect feed intake, digestibility, and health, thereby influencing feed efficiency. The interaction between genetics and diet can also regulate the intestinal flora and further affect feed efficiency (Wen et al., 2021; Bernard et al., 2024).

2.3 Physiological and molecular basis of FE variability

Physiologically, factors such as digestive efficiency, nutrient absorption, metabolic level, and body composition (such as protein deposition and fat accumulation) all affect the feed efficiency of chickens (Aggrey et al., 2010; Tallentire et al., 2016). At the molecular level, genome-wide association studies (GWAS) identified many candidate genes and gene regions related to feed efficiency, such as ATRNL1, PIK3C2A, and SORCS3, which are involved in metabolic regulatory pathways (Figure 1) (Marchesi et al., 2021). The microbiota in the intestinal tract, especially some bacteria in the cecum and duodenum, can also affect energy acquisition, short-chain fatty acid production and nutrient absorption, thereby influencing feed efficiency (Wen et al., 2021; Zhou et al., 2022; Bernard et al., 2024). The research results of Wen et al. (2021) and Zhou et al. (2022) indicate that although the interaction between host genes and microorganisms is not strong, some specific bacterial classifications remain stable in chicken flocks with high feed efficiency, suggesting that they may have potential for utilization in breeding and microbial management.

3 Transcriptomic Approaches to Study Feed Efficiency

3.1 RNA-seq and gene expression profiling

Zhou et al. (2015), Yang et al. (2020), and Xiao et al. (2021) conducted transcriptome comparisons in the mammary muscle, liver, duodenum, and adipose tissue of chickens with high and low feed efficiency, and discovered hundreds and thousands of differentially expressed genes (DEGs) related to feed efficiency. Yi et al. (2015) and Ye et al. (2024) found that quantitative RT-PCR was often used in these studies to verify the expression differences of some key genes to ensure the reliability of RNA-seq data. Some new sequencing techniques, such as 3’UTR-seq, have also discovered regulatory features like intron retention, which is helpful for understanding the transcriptional regulatory mechanisms related to feed efficiency (Wang et al., 2022).

3.2 Key biological pathways identified

The genes related to mitochondrial function, oxidative phosphorylation and tricarboxylic acid cycle (TCA) in high-efficiency chickens are often upregulated, indicating that their energy metabolism capacity is stronger (Xiao et al., 2021; Yuan et al., 2024); but, the immune and inflammatory response pathways of low-efficiency chickens are often more active, which may imply that high immune system activity consumes energy and thereby affects feed efficiency (Yang et al., 2020; Ye et al., 2024). Other pathways related to feed efficiency, such as muscle remodeling, growth hormone, IGF/PI3K/Akt signaling, fat metabolism, nitrogen cycling, etc., also involve multiple aspects such as nutrient utilization, energy balance and tissue development (Aggrey et al., 2014; Yi et al., 2015; Zhou et al., 2015; Wang et al., 2022).

3.3 Regulatory networks and non-coding RNAs

Karimi et al. (2021) found that some long non-coding RNAs (lncRNAs) showed significant expression differences between high-efficiency and low-efficiency chickens in liver tissues. These lncRNAs may regulate fat and carbohydrate metabolism, as well as genes related to growth and energy balance. Circular RNAs (circRNAs) exhibit specific tissue expression patterns in the hypothalamus and liver. This expression is often not a simple linear change. They can also co-express with mRNA or interact with miRNA and RNA-binding proteins. Affect the feeding behavior and RFI of chickens (Yuan et al., 2024). Karimi et al. (2021) and Yuan et al. (2024) both believe that these non-coding RNAs may potentially become biomarkers for predicting feed efficiency in the future, bringing new ideas to the genetic regulation research of this complex trait of feed efficiency.

4 Metabolomic Insights into Feed Efficiency

4.1 Targeted and untargeted metabolomics platforms

The research conducted by Beauclercq et al. (2018) and Metzler-Zebeli et al. (2019) found that nuclear magnetic resonance (^1H NMR) technology has been used in the sample analysis of serum, ileum and cecum to help identify some metabolite markers that can predict digestive efficiency and feed efficiency (such as AMEn and RFI). Targeted analysis mainly focuses on specific amino acid and lipid metabolites, which can precisely quantify known metabolites. Non-targeted analysis can reveal broader metabolic changes such as biogenic amines and phospholipids under different feeding conditions, which is helpful for discovering new metabolic markers.

4.2 Identified metabolic signatures

Metzler-Zebeli et al. (2019) found that in chickens with low feed efficiency (high RFI), the levels of amino acids such as isoleucine, lysine, valine, histidine and ornithine in the serum were relatively high; Some biogenic amines, such as carnosine, putrescine, spermidine, and specific diacylphospholipids, are positively correlated with feed intake and weight gain. Proline in serum, fumaric acid in ileum and glucose in cecum have a significant relationship with AMEn (apparent metabolic energy) and may become biomarkers of digestive efficiency (Beauclercq et al., 2018). The levels of uric acid and cholesterol can also respectively reflect the nutritional status and RFI level of chickens (Metzler-Zebeli et al., 2019).

4.3 Gut microbiota-host metabolite interactions

Metabolomic studies have found that the interaction between gut microbiota and the metabolism of chickens themselves can affect feed efficiency. Metabolites from microbial fermentation (such as butyric acid derivatives) were included in the AMEn prediction model, indicating that the activity of microorganisms is very important in nutrient utilization. Beauclercq et al. (2018) also found that the composition of metabolites in the ileum and cecum is greatly influenced by the microbiota, and these metabolites can explain most of the differences in digestive efficiency.

5 Integration of Transcriptomics and Metabolomics

5.1 Multi-omics data integration strategies

Common methods, such as weighted gene co-expression network analysis (WGCNA) in co-expression network analysis, can link traits such as gene expression, metabolite abundance and feed efficiency. In the same year, 2024, Ye et al. and Yuan et al. established multiple co-expression modules in their research, linking certain specific transcripts (such as lncRNA and circRNA) and metabolic pathways related to feed efficiency in the liver, muscle, and intestine. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were also used to explain differentially expressed genes (DEGs) and their possible metabolic functions (Yang et al., 2020; Karimi et al., 2021; Xiao et al., 2021).

5.2 Commonly enriched biological themes

Some genes in fat metabolism, carbohydrate metabolism and energy balance pathways are often associated with differences in feed efficiency (Karimi et al., 2021; Xiao et al., 2021; Wang et al., 2022). Chickens with low RFI (high feed efficiency) usually show enhanced mitochondrial activity and more active oxidative phosphorylation pathways, indicating that their ATP synthesis is more efficient and their control ability over reactive oxygen species (ROS) is stronger (Yang et al., 2020; Ye et al., 2024). Low-efficiency chickens are often active in immune and inflammatory pathways, which may indicate that they have devoted more energy to immune activities and affected feed conversion (Yang et al., 2020; Sinpru et al., 2021). Aggrey et al. (2014) and Xiao et al. (2021) found that the differences in gene expression related to vitamin transport, amino acid metabolism, and nitrogen cycling reflected the adaptability of different chickens in terms of nutrient utilization methods. The research by Wang et al. (2022) found that extracellular matrix remodeling and peroxisome function were related to differences in fat deposition and feed efficiency.

5.3 Computational tools and challenges

In multi-omics integrated analysis, commonly used tools include RNA-Seq (for analyzing the transcriptome) and qRT-PCR (for verifying results), and some bioinformatics tools are used for functional annotation and network analysis (Yang et al., 2020; Xiao et al., 2021; Wang et al., 2022; Wang, 2024). However, omics data themselves have high dimensions and many types, and the expression differences among different tissues are also significant. Only by using robust statistical methods can the relationship between complex traits such as molecular characteristics and feed efficiency be accurately identified. The addition of non-coding RNAs such as lncRNA and circRNA and their regulatory networks will make the analysis more complex, and more advanced algorithms and models are needed for processing (Figure 2) (Karimi et al., 2021; Ye et al., 2024; Yuan et al., 2024).

6 Genotype and Breed-Specific Responses

6.1 Broiler vs. layer differences in FE mechanisms

In the abdominal adipose tissue of broilers with high FE, extracellular matrix remodeling and pathways related to fat metabolism are more active, and the activity of peroxisomes is also stronger. Moreover, the G0/G1 switch gene 2 (G0S2) is considered to be related to fat deposition and muscle growth (Wang et al., 2022). Karimi et al. (2021) also found that long non-coding RNAs (lncRNA) in commercial broilers (such as Ross) regulate fat, carbohydrate metabolism, energy balance and growth genes compared with local chicken breeds, indicating that regulatory RNAs are important in the FE differences among different chicken breeds. Broilers with low RFI exhibited stronger mitochondrial function and better control of reactive oxygen species (ROS) in skeletal muscle. Individuals with low FE in laying hens and local hens often showed upregulation of immune and inflammatory pathways (Aggrey et al., 2014; Yang et al., 2020).

6.2 Line-specific biomarkers and selection potential

Sinpru et al. (2021) found that in slow-growing Korat chickens, differentially expressed genes in the jejunum are enriched in immune response, glutathione metabolism and fat metabolism pathways, and these genes may become breeding targets for improving FE. The research results of Ye et al. (2024) demonstrated that genome-wide association studies (GWAS) identified some important SNPS and candidate genes related to FE, such as EXOC4, FBRSL1, MAT2B, and CMPK1, in Qingyuan free-form chickens. These genes support genetic improvement using molecular markers. In the multi-tissue transcriptome analysis of hybrid chickens, specific circular RNAs (circRNA) in the hypothalamus and liver were also identified. These non-additive circRNA may be used to predict the hybrid dominance of RFI and FE (Yuan et al., 2024).

6.3 Implications for precision nutrition and breeding

Understanding the differences in metabolism and regulatory pathways among different chicken breeds is helpful for formulating more targeted nutritional intervention and genetic improvement plans. In broilers, it can enhance mitochondrial function and reduce the production of reactive oxygen species (ROS). In native chickens and slow-growing breeds, it can regulate immune and metabolic pathways, thereby increasing FE and reducing costs (Yang et al., 2020; Sinpru et al., 2021; Wang et al., 2022). Integrating the biomarkers discovered in the transcriptome and metabolome into the breeding system is beneficial for identifying chickens with better FE. It can not only promote the sustainable development of poultry farming but also enhance the economic benefits of multi-breed farming (Karimi et al., 2021; Ye et al., 2024; Yuan et al., 2024).

7 Environmental and Nutritional Modulation of Feed Efficiency (FE)

7.1 Effects of diet composition and feed additives

Adjusting the ratios of amino acids, energy and crude protein, especially increasing the ratio of arginine and lysine above the recommended value, is helpful for regulating energy and protein metabolism and thereby improving FE. The research conducted by Zampiga et al. (2021) demonstrated that some feed additives such as crystalline amino acids, proteases, and phytase can help chickens make better use of nutrients, reduce environmental pollution, and support more efficient intensive farming. Plant-based additives such as essential oils and plant extracts (PFAs) can also improve FE. They can regulate metabolic pathways, increase muscle protein synthesis, and inhibit liver fat formation (Pirgozliev et al., 2019; Flees et al., 2020), these additives can also regulate the immune system and improve nutrient retention, promoting the growth performance of chickens (Pirgozliev et al., 2019). Choi et al. (2023) and Bernard et al. (2024) found that adjusting the gut microbiota with prebiotics, probiotics and exogenous enzymes can also enhance nutrient absorption, improve body composition regulation and increase FE.

7.2 Thermal stress and housing conditions

High temperature is a common environmental stress in poultry farming. It can lead to slower growth of chickens, weakened immunity, and even death, reducing feed efficiency (FE). To cope with heat stress, it is necessary to adopt some nutritional strategies, such as providing feed with high digestibility and high nutritional density, increasing the fat content in the feed, balancing the proportion of amino acids, and supplementing vitamins, minerals and antioxidants. Early research by Mujahid (2011) indicated that these practices were helpful in reducing heat production, alleviating oxidative stress, avoiding energy waste, and enabling chickens to maintain good FE even at high temperatures. Improving ventilation and controlling the stocking density, these feeding conditions can be combined with nutritional measures to enhance the chicken flock's adaptability to high temperatures and help increase feed utilization.

7.3 Epigenetic modifications and long-term adaptation

Long-term environmental and nutritional regulation can affect gene expression through epigenetic mechanisms, influencing the metabolism, immunity and growth of chickens. The research by Wen et al. (2021) and Bernard et al. (2024) found that the gene expression profiles of chickens with different genotypes were also different under the same diet and environment. Some genes are concentrated in mitochondrial function and energy metabolism pathways, while others show stronger immune activation, indicating that genetic background and environmental exposure jointly affect the long-term adaptability of feed efficiency (FE) through epigenetics. As a result of the combined effects of genetics and diet, the gut microbiota is also involved in the regulation of FE. Some specific microbial groups are closely related to the improvement of nutrient absorption and metabolic efficiency.

8 Case Study: Integrated Transcriptomic and Metabolomic Analysis of Chickens with High and Low Feed Efficiency

8.1 Experimental design and individual selection strategy

In this case study, two indicators, namely residual feed intake (RFI) and feed-to-meat ratio (FCR), were used to conduct phenotypic identification of feed efficiency (FE) for the chicken flock. On this basis, the chickens with the most extreme performance were selected. In the experiment, the researchers classified those with the lowest RFI or FCR as the high-efficiency group and those with the highest as the low-efficiency group. For example, from over 1 000 chickens, the 5 with the lowest RFI and the 5 with the highest RFI were selected for in-depth analysis. Some studies also grouped chickens based on high and low FCR, or specifically selected high/low RFI strains for tissue sampling and subsequent omics analysis (Yang et al., 2020; Sinpru et al., 2021; Xiao et al., 2021).

8.2 Key transcriptomic findings: differentially expressed genes closely related to feed efficiency

Sinpru et al. (2021) and Wang et al. (2022) found significant differences in the expression of many genes between the high-efficiency group and the low-efficiency group of chickens in the transcriptome analysis of multiple tissues such as liver, muscle, intestine and fat. These differentially expressed genes (DEGs) are mainly concentrated in pathways such as energy metabolism, mitochondrial function, fat metabolism, carbohydrate metabolism, immune response and oxidative phosphorylation. Some key genes, including ND2, ND4, CYTB, RAC2, VCAM1, CTSS, TLR4, CAT, ACSL1, ECI2, ABCD2, ACOX1 and PCK1, are involved in ATP synthesis, reactive oxygen species control and fat metabolism (Yang et al., 2020; Xiao et al., 2021). Karimi et al. (2021) and Yuan et al. (2024) found that long non-coding RNAs (lncRNA) and circular RNAs (circRNA) are also involved in the regulation of FE and play significant roles in fat metabolism and energy balance.

8.3 Key metabolomic findings: differential metabolites and their functional significance

Previous studies have found that there are significant differences between high-efficiency chickens and low-efficiency chickens in fat metabolism, carbohydrate metabolism, and metabolites related to the nitrogen cycle. The purine recycling pathway of high-efficiency chickens is more active. They can synthesize nucleotides with less energy, promote protein retention and reduce nitrogen excretion (Aggrey et al., 2014; Wang et al., 2022). These metabolic differences are closely related to the improvement of energy utilization efficiency, the reduction of fat, and the enhancement of nutrient absorption and assimilation capacity.

8.4 Multi-omics integration and practical implications

The combination of transcriptome and metabolome data reveals the molecular regulatory mechanism of chicken feed efficiency. A large number of studies have found that pathways such as mitochondrial energy production, fat and carbohydrate metabolism, immune regulation, and nitrogen recovery work together in high-FE chickens, thereby improving feed efficiency (Yang et al., 2020; Sinpru et al., 2021; Xiao et al., 2021; Wang et al., 2022), these findings also provide references for breeding. The candidate genes, lncRNA and circRNA screened out through multi-omics can be used as molecular markers to help precisely select and breed chicken flocks with high feed efficiency, reduce breeding costs and improve the sustainability of the poultry industry.

9 Current Challenges and Future Opportunities

9.1 Biological and technical constraints

Biologically speaking, FE is related to multiple tissues, various metabolic pathways, and complex gene regulatory networks, including lncRNA, circRNA, and tissue-specific expression patterns, which have not been fully clarified at present (Karimi et al., 2021; Xiao et al., 2021; Yuan et al., 2024). Technically, the performance varies among different omics platforms. Wang et al. (2022) found that compared with the traditional RNA-seq, 3’UTR-seq has a larger error and a lower alignment rate, which may have an impact on data interpretation and repeatability. Zampiga (2018) suggested that the integration of multi-omics data is also quite challenging. This is because the data types are diverse, the dimensions are large, and the analysis methods are not uniform, which limits the construction of a more complete FE regulation model.

9.2 Advancing toward predictive omics models

Some recent studies have identified many key genes, pathways and regulatory elements related to FE, such as lncRNA, circRNA and protein-coding genes, laying the foundation for the construction of the model (Yang et al., 2020; Karimi et al., 2021; Xiao et al., 2021). To turn these research results into reliable tools that can be used in breeding and management, it is highly necessary to establish larger and more representative datasets and develop new algorithms that can integrate transcriptome, metabolome and phenotypic data. Some circRNA and gene expression characteristics have now shown the potential to predict FE, but for application in actual breeding, further verification and optimization are still needed (Zampiga et al., 2018; Yuan et al., 2024).

9.3 Future integration with proteomics and microbiomics

Future research should also integrate proteomic and microbiome data to gain a more comprehensive understanding of the feed efficiency (FE) mechanism in chickens. The fact that the proteome can reveal changes in post-transcriptional regulation and protein expression is a good complement to the transcriptome. The microbiome can help analyze the impact of gut microbiota on nutrient utilization and metabolic efficiency (Zampiga et al., 2018). It is promising to discover new regulatory networks and metabolic pathways through the joint analysis of proteo-genomics and the microbiome. The research conducted by Zampiga et al. (2018) demonstrated that this could provide support for the construction of a more accurate and comprehensive FE prediction model, promoting the improvement of chicken feed efficiency towards the integration of multiple omics.

Acknowledgments

AnimalSci Publisher appreciates the comments from Dr. Xie and Dr. Yang on the manuscript of this study.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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