A comparative analysis of the transcriptome profiles of liver and muscle tissue in pigs divergent for feed efficiency

Identification of consensus homozygous regions and their associations with growth and feed efficiency traits in American mink

The recent chromosome-based genome assembly and the newly developed 70K single nucleotide polymorphism (SNP) array for American mink (Neogale vison) facilitate the identification of genetic variants underlying complex traits in this species. The objective of this study was to evaluate the association between consensus runs of homozygosity (ROH) with growth and feed efficiency traits in American mink. A subsample of two mink populations (n = 2,986) were genotyped using the Affymetrix Mink 70K SNP array. The identified ROH segments were included simultaneously, concatenated into consensus regions, and the ROH-based association studies were carried out with linear mixed models considering a genomic relationship matrix for 11 growth and feed efficiency traits implemented in ASReml-R version 4. In total, 298,313 ROH were identified across all individuals, with an average length and coverage of 4.16 Mb and 414.8 Mb, respectively. After merging ROH segments, 196 consensus ROH regions were detected and used for genome-wide ROH-based association analysis. Thirteen consensus ROH regions were significantly (P < 0.01) associated with growth and feed efficiency traits. Several candidate genes within the significant regions are known for their involvement in growth and body size development, including MEF2A, ADAMTS17, POU3F2, and TYRO3. In addition, we found ten consensus ROH regions, defined as ROH islands, with frequencies over 80% of the population. These islands harbored 12 annotated genes, some of which were related to immune system processes such as DTX3L, PARP9, PARP14, CD86, and HCLS1. This is the first study to explore the associations between homozygous regions with growth and feed efficiency traits in American mink. Our findings shed the light on the effects of homozygosity in the mink genome on growth and feed efficiency traits, that can be utilized in developing a sustainable breeding program for mink.

Transcriptome analysis of divergent residual feed intake phenotypes in the M. longissimus thoracis et lumborum of Wannan Yellow rabbits

Introduction: Feed efficiency is an important economic trait in rabbit meat production. The identification of molecular mechanisms and candidate genes for feed efficiency may improve the economic and environmental benefits of the rabbit meat industry. As an alternative to the conventional feed conversion ratio, residual feed intake (RFI) can be used as an accurate indicator of feed efficiency. Methods: RNA sequencing was used to identify the differentially expressed genes (DEGs) in the M. longissimus thoracis et lumborum of eight Wannan Yellow rabbits with excessively high or low RFIs (HRFI or LRFI, respectively). Thereafter, Gene Ontology (GO) analysis, enrichment using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) network analysis was conducted. Results: In total, 445 DEGs were identified in the M. longissimus thoracis et lumborum of rabbits with high and low RFIs. The significantly enriched GO terms identified in these two groups were primarily involved in energy and mitochondrial metabolism and oxidation-reduction processes. KEGG analysis identified 11 significantly enriched pathways, including oxidative phosphorylation, PI3K-Akt signaling, and extracellular matrix-receptor interaction pathways. According to GSEA, the expressions of genes and pathways related to mitochondrial function were upregulated in HRFI rabbits, whereas genes with upregulated expressions in LRFI rabbits were related to immune response and energy metabolism. Additionally, PPI network analysis revealed five potential candidate genetic markers. Conclusion: Comparative analysis of the M. longissimus thoracis et lumborum transcriptomes in HRFI and LRFI rabbits revealed FOS, MYC, PRKACB, ITGA2, and FN1 as potential candidate genes that affect feed efficiency in rabbits. In addition, key signaling pathways involved in oxidative phosphorylation and PI3K-Akt and ECM-receptor interaction signaling impact rabbit feed efficiency. These findings will aid in breeding programs to improve feed efficiency and optimize RFI selection of rabbits for meat production.

The mRNA-lncRNA landscape of multiple tissues uncovers key regulators and molecular pathways that underlie heterosis for feed intake and efficiency in laying chickens

BACKGROUND

Heterosis is routinely exploited to improve animal performance. However, heterosis and its underlying molecular mechanism for feed intake and efficiency have been rarely explored in chickens. Feed efficiency continues to be an important breeding goal trait since feed accounts for 60 to 70% of the total production costs in poultry. Here, we profiled the mRNA-lncRNA landscape of 96 samples of the hypothalamus, liver and duodenum mucosa from White Leghorn (WL), Beijing-You chicken (YY), and their reciprocal crosses (WY and YW) to elucidate the regulatory mechanisms of heterosis.

RESULTS

We observed negative heterosis for both feed intake and residual feed intake (RFI) in YW during the laying period from 43 to 46 weeks of age. Analysis of the global expression pattern showed that non-additivity was a major component of the inheritance of gene expression in the three tissues for YW but not for WY. The YW-specific non-additively expressed genes (YWG) and lncRNA (YWL) dominated the total number of non-additively expressed genes and lncRNA in the hypothalamus and duodenum mucosa. Enrichment analysis of YWG showed that mitochondria components and oxidation phosphorylation (OXPHOS) pathways were shared among the three tissues. The OXPHOS pathway was enriched by target genes for YWL with non-additive inheritance of expression in the liver and duodenum mucosa. Weighted gene co-expression network analysis revealed divergent co-expression modules associated with feed intake and RFI in the three tissues from WL, YW, and YY. Among the negatively related modules, the OXPHOS pathway was enriched by hub genes in the three tissues, which supports the critical role of oxidative phosphorylation. Furthermore, protein quantification of ATP5I was highly consistent with ATP5I expression in the liver, which suggests that, in crossbred YW, non-additive gene expression is down-regulated and decreases ATP production through oxidative phosphorylation, resulting in negative heterosis for feed intake and efficiency.

CONCLUSIONS

Our results demonstrate that non-additively expressed genes and lncRNA involved in oxidative phosphorylation in the hypothalamus, liver, and duodenum mucosa are key regulators of the negative heterosis for feed intake and RFI in layer chickens. These findings should facilitate the rational choice of suitable parents for producing crossbred chickens.

Combined transcriptome and metabolome analysis reveal key regulatory genes and pathways of feed conversion efficiency of oriental river prawn Macrobrachium nipponense

BACKGROUND

Oriental river prawn Macrobrachium nipponense is an economically important aquaculture species in China, Japan, and Vietnam. In commercial prawn farming, feed cost constitutes about 50 to 65% of the actual variable cost. Improving feed conversion efficiency in prawn culture will not only increase economic benefit, but also save food and protect the environment. The common indicators used for feed conversion efficiency include feed conversion ratio (FCR), feed efficiency ratio (FER), and residual feed intake (RFI). Among these, RFI is much more suitable than FCR and FER during the genetic improvement of feed conversion efficiency for aquaculture species.

RESULTS

In this study, the transcriptome and metabolome of hepatopancreas and muscle of M. nipponense from high RFI low RFI groups, which identified after culture for 75 days, were characterized using combined transcriptomic and metabolomic analysis. A total of 4540 differentially expressed genes (DEGs) in hepatopancreas, and 3894 DEGs in muscle were identified, respectively. The DEGs in hepatopancreas were mainly enriched in KEGG pathways including the metabolism of xenobiotics by cytochrome P450 (down-regulated), fat digestion and absorption (down-regulated) and aminoacyl-tRNA biosynthesis (up-regulated), etc. The DEGs in muscle were mainly enriched in KEGG pathways including the protein digestion and absorption (down-regulated), glycolysis/gluconeogenesis (down-regulated), and glutathione metabolism (up-regulated), etc. At the transcriptome level, the RFI of M. nipponense was mainly controlled in biological pathways such as the high immune expression and the reduction of nutrients absorption capacity. A total of 445 and 247 differently expressed metabolites (DEMs) were identified in the hepatopancreas and muscle, respectively. At the metabolome level, the RFI of M. nipponense was affected considerably by amino acid and lipid metabolism.

CONCLUSIONS

M. nipponense from higher and lower RFI groups have various physiological and metabolic capability processes. The down-regulated genes, such as carboxypeptidase A1, 6-phosphofructokinase, long-chain-acyl-CoA dehydrogenase, et. al., in digestion and absorption of nutrients, and the up-regulated metabolites, such as aspirin, lysine, et. al., in response to immunity could be potential candidate factors contributed to RFI variation for M. nipponense. Overall, these results would provide new insights into the molecular mechanism of feed conversion efficiency and assist in selective breeding to improve feed conversion efficiency in M. nipponense.

Transcription Analysis of Liver and Muscle Tissues from Landrace Finishing Pigs with Different Feed Conversion Ratios

The efficiency of feed utilization determines the cost and economic benefits of pig production. In the present study, two pairs of full-sibling and two pairs of half-sibling female Landrace finishing pigs were selected, with each pair including individuals with different feed conversion rates, with liver and longissimus muscle tissue samples collected from each group for transcriptome analysis. A total of 561 differentially expressed genes (DEGs), among which 224 were up-regulated and 337 were down-regulated, were detected in the liver transcriptomes in the high-feed efficiency group compared to the low-feed efficiency group. The DEGs related to phosphorus and phosphate metabolism, arginine biosynthesis, chemical carcinogenesis, cytokine-cytokine receptor interaction, the biosynthesis of amino acids, and drug metabolism-cytochrome P450 in liver tissue were also associated with feed efficiency. In total, 215 DEGs were screened in the longissimus muscle tissue and were mainly related to disease and immune regulation, including complement and coagulation cascades, systemic lupus erythematosus, and prion diseases. The combination of gene expression and functional annotation results led to the identification of candidate feed efficiency-related biomarkers, such as ARG1, ARG2, GOT1, GPT2, ACAA2, ACADM, and ANGPTL4, members of cytochrome P450 family, and complement component family genes. Although the novel feed efficiency-related candidate genes need to be further evaluated by a larger sample size and functional studies, the present study identifies novel candidate biomarkers for the identification of functional SNPs underlying porcine feed efficiency.

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.

Protein metabolism in the liver and white muscle is associated with feed efficiency in Chinook salmon (Oncorhynchus tshawytscha) reared in seawater: Evidence from proteomic analysis

Understanding the molecular mechanisms that underlie differences in feed efficiency (FE) is an important step toward optimising growth and achieving sustainable salmonid aquaculture. In this study, the liver and white muscle proteomes of feed efficient (EFF) and inefficient (INEFF) Chinook salmon (Oncorhynchus tshawytscha) reared in seawater were investigated by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In total, 2746 liver and 702 white muscle proteins were quantified and compared between 21 EFF and 22 INEFF fish. GSEA showed that gene sets related to protein synthesis were enriched in the liver and white muscle of the EFF group, while conversely, pathways related to protein degradation (amino acid catabolism and proteolysis, respectively) were the most affected processes in the liver and white muscle of INEFF fish. Estimates of individual daily feed intake and share of the meal within tank were significantly higher in the INEFF than the EFF fish showing INEFF fish were likely more dominant during feeding and overfed. Overeating by the INEFF fish was associated with an increase in protein catabolism. This study found that fish with different FE values had expression differences in the gene sets related to protein turnover, and this result supports the hypothesis that protein metabolism plays a role in FE.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Understanding the role of myoglobin content in Iberian pigs fattened in an extensive system through analysis of the transcriptome profile

Meat color is the first perceived sensory feature and one of the most important quality traits. Myoglobin is the main pigment in meat, giving meat its characteristic cherry‐red color, highly appreciated by the consumers. In the current study, we used the RNA‐seq technique to characterize the longissimus dorsi muscle transcriptome in two groups of Iberian pigs with divergent breeding values for myoglobin content. As a result, we identified 57 differentially expressed genes and transcripts (DEGs). Moreover, we have validated the RNA‐seq expression of a set of genes by quantitative PCR (qPCR). Functional analyses revealed an enrichment of DEGs in biological processes related to oxidation (HBA1), lipid metabolism (ECH1, PLA2G10, PLD2), inflammation (CHST1, CD209, PLA2G10), and immune system (CD209, MX2, LGALS3, LGALS9). The upstream analysis showed a total of five transcriptional regulatory factors and eight master regulators that could moderate the expression of some DEGs, highlighting SPI1 and MAPK1, since they regulate the expression of DEGs involved in immune defense and inflammatory processes. Iberian pigs with high myoglobin content also showed higher expression of the HBA1 gene and both molecules, myoglobin and hemoglobin, have been described as having a protective effect against oxidative and inflammatory processes. Therefore, the HBA1 gene is a very promising candidate gene to harbor polymorphisms underlying myoglobin content, whereby further studies should be carried out for its potential use in an Iberian pig selection program.

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans

Background

Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.

Results

In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.

Conclusion

Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08373-3.

Factors Influencing Proteolysis and Protein Utilization in the Intestine of Pigs: A Review

Pigs are among the most important farm animals for meat production worldwide. In order to meet the amino acid requirements of the animals, pigs rely on the regular intake of proteins and amino acids with their feed. Unfortunately, pigs excrete about two thirds of the used protein, and production of pork is currently associated with a high emission of nitrogen compounds resulting in negative impacts on the environment. Thus, improving protein efficiency in pigs is a central aim to decrease the usage of protein carriers in feed and to lower nitrogen emissions. This is necessary as the supply of plant protein sources is limited by the yield and the cultivable acreage for protein plants. Strategies to increase protein efficiency that go beyond the known feeding options have to be investigated considering the characteristics of the individual animals. This requires a deep understanding of the intestinal processes including enzymatic activities, capacities of amino acid transporters and the microbiome. This review provides an overview of these physiological factors and the respective analyses methods.

Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq

Residual feed intake (RFI) is an important measure of feed efficiency for agricultural animals. Factors associated with cattle RFI include physiology, dietary factors, and the environment. However, a precise genetic mechanism underlying cattle RFI variations in duodenal tissue is currently unavailable. The present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing (RNA-seq). Six bulls with extremely high or low RFIs were selected for detecting differentially expressed genes (DEGs) by RNA-seq, followed by conducting GO, KEGG enrichment, protein-protein interaction (PPI), and co-expression network (WGCNA, n = 10) analysis. A total of 380 differentially expressed genes was obtained from high and low RFI groups, including genes related to energy metabolism (ALDOA, HADHB, INPPL1), mitochondrial function (NDUFS1, RFN4, CUL1), and feed intake behavior (CCK). Two key sub-networks and 26 key genes were detected using GO analysis of DEGs and PPI analysis, such as TPM1 and TPM2, which are involved in mitochondrial pathways and protein synthesis. Through WGCNA, a gene network was built, and genes were sorted into 27 modules, among which the blue (r = 0.72, p = 0.03) and salmon modules (r = -0.87, p = 0.002) were most closely related with RFI. DEGs and genes from the main sub-networks and closely related modules were largely involved in metabolism; oxidative phosphorylation; glucagon, ribosome, and N-glycan biosynthesis, and the MAPK and PI3K-Akt signaling pathways. Through WGCNA, five key genes, including FN1 and TPM2, associated with the biological regulation of oxidative processes and skeletal muscle development were identified. Taken together, our data suggest that the duodenum has specific biological functions in regulating feed intake. Our findings provide broad-scale perspectives for identifying potential pathways and key genes involved in the regulation of feed efficiency in beef cattle.

Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs

Background

Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts.

Results

Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R² = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression.

Conclusion

From small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07843-4.

Candidate gene screening for lipid deposition using combined transcriptomic and proteomic data from Nanyang black pigs

Background

Lower selection intensities in indigenous breeds of Chinese pig have resulted in obvious genetic and phenotypic divergence. One such breed, the Nanyang black pig, is renowned for its high lipid deposition and high genetic divergence, making it an ideal model in which to investigate lipid position trait mechanisms in pigs. An understanding of lipid deposition in pigs might improve pig meat traits in future breeding and promote the selection progress of pigs through modern molecular breeding techniques. Here, transcriptome and tandem mass tag-based quantitative proteome (TMT)-based proteome analyses were carried out using longissimus dorsi (LD) tissues from individual Nanyang black pigs that showed high levels of genetic variation.

Results

A large population of Nanyang black pigs was phenotyped using multi-production trait indexes, and six pigs were selected and divided into relatively high and low lipid deposition groups. The combined transcriptomic and proteomic data identified 15 candidate genes that determine lipid deposition genetic divergence. Among them, FASN, CAT, and SLC25A20 were the main causal candidate genes. The other genes could be divided into lipid deposition-related genes (BDH2, FASN, CAT, DHCR24, ACACA, GK, SQLE, ACSL4, and SCD), PPARA-centered fat metabolism regulatory factors (PPARA, UCP3), transcription or translation regulators (SLC25A20, PDK4, CEBPA), as well as integrin, structural proteins, and signal transduction-related genes (EGFR).

Conclusions

This multi-omics data set has provided a valuable resource for future analysis of lipid deposition traits, which might improve pig meat traits in future breeding and promote the selection progress in pigs, especially in Nanyang black pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07764-2.

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

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.

Results

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 6749 and 5644 m/z features were detected in positive and negative ionization modes by liquid chromatography-mass spectrometry (LC/MS). Regrettably, the PCA could not classify the the samples accurately. To improve the classification, OPLS-DA was introduced. However, the predictive ability of the OPLS-DA model did not perform well. Then, 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. Then, Lasso regression analysis was used to evaluate the importance of nine metabolites obtained by the annotation process.

Conclusions

Altogether, this study provides 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40813-021-00219-w.

Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales

Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.

Association of residual feed intake with growth performance, carcass traits, meat quality, and blood variables in native chickens

Improving feed efficiency is a primary goal in poultry breeding strategies. Residual feed intake (RFI) in chickens typically calculated during the growing period is a measure of feed efficiency that is independent of the level of production. The objective of this study was to evaluate phenotypic correlations of growth performance, carcass traits, meat quality, and blood variables with RFI in growing native chickens. A total of 1,008 chickens were selected for the experiment to derive RFI. After the RFI measurement period of 42 d, 25 chickens with low RFI values, 25 chickens with medium RFI values, and 25 chickens with high RFI values were selected. The RFI was significantly positively correlated with feed conversion ratio and average daily feed intake, while it was not significantly correlated with initial body weight (BW), final BW, average daily body weight gain, and metabolic BW0.75. The abdominal fat weight and yield of high RFI group were significantly greater than those of medium and low RFI groups, and the abdominal fat yield was significantly positively correlated with RFI. Moreover, the plasma insulin-like growth factor 1 (IGF-1) content of low RFI group was significantly greater than those of high and medium RFI groups. The plasma concentrations of adrenocorticotropic hormone, triiodothyronine (T3), and cortisol of high RFI birds were significantly greater than that of low RFI birds. RFI was significantly positively correlated with plasma concentrations of T3 and cortisol, while it was significantly negatively correlated with plasma concentration of IGF-1. In addition, the serum levels of glucose and triglyceride of high RFI birds were significantly lower than that of low RFI birds. The serum low-density lipoprotein cholesterol (LDL-C) content of high RFI group was significantly greater than that of medium and low RFI groups, and it was significantly positively correlated with RFI. Our data suggested that selection of chickens with low RFI values may be beneficial to reduce fat deposition in native chickens without affecting the meat quality. Circulating IGF-1, T3, cortisol, and LDL-C concentrations can be used as indirect selection indicators of feed efficiency in native chickens. The effect of IGF-1, T3, cortisol, and LDL-C on feed efficiency of native chickens should be carefully examined and validated in future breeding programs.

Colonic microbiome profiles for improved feed efficiency can be identified despite major effects of farm of origin and contemporary group in pigs

While feed efficiency (FE) is a trait of great economic importance to the pig industry, the influence of the intestinal microbiome in determining FE is not well understood. The objective of this experiment was to determine the relative influence of FE and farm of birth on the pig colonic microbiome. Animals divergent in residual feed intake (RFI) were sourced from two geographically distinct locations (farms A + B) in Ireland. The 8 most efficient (low RFI (LRFI)) and 8 least efficient (high RFI, (HRFI)) pigs from farm A and 12 LRFI and 12 HRFI pigs from farm B were sacrificed. Colonic digesta was collected for microbial analysis using 16S ribosomal RNA gene sequencing and also for volatile fatty acid analysis. The α-diversity differed between the farms in this study, with pigs from farm A having greater diversity based on Shannon and InvSimpson measures compared to pigs from farm B (P < 0.05), with no difference identified in either Chao1 or observed measures of diversity (P > 0.05). In the analysis of β-diversity, pigs clustered based on farm of birth rather than RFI. Variation in the management of piglets, weight of the piglets, season of the year, sanitary status and dam dietary influence could potentially be causative factors in this large variation between farms. However, despite significant variation in the microbial profile between farms, consistent taxonomic differences were identified between RFI groups. Within the phylum Bacteroidetes, the LRFI pigs had increased abundance of BS11 (P < 0.05) and a tendency toward increased Bacteroidaceae (P < 0.10) relative to the HRFI group. At genus level, the LRFI pigs had increased abundance of Colinsella (P < 0.05), a tendency toward increased Bacteroides and CF231 (P < 0.10). At species level, Ruminococcus flavefaciens had increased abundance in the LRFI compared to the HRFI animals. In conclusion, while farm of birth has a substantial influence on microbial diversity in the pig colon, a microbial signature indicative of FE status was apparent.

Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency in Bos taurus Bulls

Long non-coding RNAs (lncRNAs) can influence transcriptional and translational processes in mammalian cells and are associated with various developmental, physiological and phenotypic conditions. However, they remain poorly understood and annotated in livestock species. We combined phenotypic, metabolomics and liver transcriptomic data of bulls divergent for residual feed intake (RFI) and fat accretion. Based on a project-specific transcriptome annotation for the bovine reference genome ARS-UCD.1.2 and multiple-tissue total RNA sequencing data, we predicted 3590 loci to be lncRNAs. To identify lncRNAs with potential regulatory influence on phenotype and gene expression, we applied the regulatory impact factor algorithm on a functionally prioritized set of loci (n = 4666). Applying the algorithm of partial correlation and information theory, significant and independent pairwise correlations were calculated and co-expression networks were established, including plasma metabolites correlated with lncRNAs. The network hub lncRNAs were assessed for potential cis-actions and subjected to biological pathway enrichment analyses. Our results reveal a prevalence of antisense lncRNAs positively correlated with adjacent protein-coding genes and suggest their participation in mitochondrial function, acute phase response signalling, TCA-cycle, fatty acid β-oxidation and presumably gluconeogenesis. These antisense lncRNAs indicate a stabilizing function for their cis-correlated genes and a putative regulatory role in gene expression.

Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis

BACKGROUND

Improving feed efficiency is one of the important breeding targets for poultry industry. The aim of current study was to investigate the breast muscle transcriptome data of native chickens divergent for feed efficiency. Residual feed intake (RFI) value was calculated for 1008 closely related chickens. The 5 most efficient (LRFI) and 5 least efficient (HRFI) birds were selected for further analysis. Transcriptomic data were generated from breast muscle collected post-slaughter.

RESULTS

The differently expressed genes (DEGs) analysis showed that 24 and 325 known genes were significantly up- and down-regulated in LRFI birds. An enrichment analysis of DEGs showed that the genes and pathways related to inflammatory response and immune response were up-regulated in HRFI chickens. Moreover, Gene Set Enrichment Analysis (GSEA) was also employed, which indicated that LRFI chickens increased expression of genes related to mitochondrial function. Furthermore, protein network interaction and function analyses revealed ND2, ND4, CYTB, RAC2, VCAM1, CTSS and TLR4 were key genes for feed efficiency. And the 'phagosome', 'cell adhesion molecules (CAMs)', 'citrate cycle (TCA cycle)' and 'oxidative phosphorylation' were key pathways contributing to the difference in feed efficiency.

CONCLUSIONS

In summary, a series of key genes and pathways were identified via bioinformatics analysis. These key genes may influence feed efficiency through deep involvement in ROS production and inflammatory response. Our results suggested that LRFI chickens may synthesize ATP more efficiently and control reactive oxygen species (ROS) production more strictly by enhancing the mitochondrial function in skeletal muscle compared with HRFI chickens. These findings provide some clues for understanding the molecular mechanism of feed efficiency in birds and will be a useful reference data for native chicken breeding.

Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses

Feed efficiency helps to reduce environmental impacts from livestock production, improving beef cattle profitability. We identified potential biomarkers (hub genes) for feed efficiency, by applying co-expression analysis in Longissimus thoracis RNA-Seq data from 180 Nelore steers. Six co-expression modules were associated with six feed efficiency-related traits (p-value ≤ 0.05). Within these modules, 391 hub genes were enriched for pathways as protein synthesis, muscle growth, and immune response. Trait-associated transcription factors (TFs) ELF1, ELK3, ETS1, FLI1, and TCF4, were identified with binding sites in at least one hub gene. Gene expression of CCDC80, FBLN5, SERPINF1, and OGN was associated with multiple feed efficiency-related traits (FDR ≤ 0.05) and were previously related to glucose homeostasis, oxidative stress, fat mass, and osteoblastogenesis, respectively. Potential regulatory elements were identified, integrating the hub genes with previous studies from our research group, such as the putative cis-regulatory elements (eQTLs) inferred as affecting the PCDH18 and SPARCL1 hub genes related to immune system and adipogenesis, respectively. Therefore, our analyses contribute to a better understanding of the biological mechanisms underlying feed efficiency in bovine and the hub genes disclosed can be used as biomarkers for feed efficiency-related traits in Nelore cattle.

Analysis of the basal colonic innate immune response of pigs divergent in feed efficiency and following an ex vivo lipopolysaccharide challenge

While feed efficiency is influenced by multiple physiological processes, it is not known how efficient and inefficient pigs differ in relation to their basal immune response, and particularly their innate immune response to a microbial challenge. Hence, the objective was to examine the expression of genes encoding innate immune response markers in basal colonic tissue and colonic tissue following an ex vivo lipopolysaccharide (LPS) challenge from pigs divergent in residual feed intake (RFI). Pigs that differed in RFI were selected from two different farms of origin. Colonic tissue was harvested from high RFI (HRFI) and low (LRFI) pigs, and two experimental conditions were explored: the first was basal unchallenged tissue and the second was colonic tissue following an ex vivo LPS challenge. RNA was extracted and tested on a Nanostring panel of 72 genes coding for barrier defense proteins, transmembrane receptors, kinases, transcription regulators, cytokines, and cytokine regulators. In the basal unchallenged tissue, the LRFI pigs had increased expression of AOAH, AP1, and TRAM and the cytokines TNF, IL10, and CXCL8, compared with the HRFI pigs (P < 0.05), with a significant effect of farm of origin on 31 genes (P < 0.05). In the LPS-challenged tissues, the LRFI group had higher expression of TLR1, TLR7, TLR8, GPR43/FFAR2, JAK2, and NFAM1 compared with the HRFI group (P < 0.05). In conclusion, these data suggest that LRFI pigs have an upregulated basal colonic inflammatory state and a heightened response to an LPS challenge compared with the inefficient HRFI pigs. This immune profile potentially enhances their capacity to respond to an infectious challenge.