Chicken adaptive response to low energy diet: main role of the hypothalamic lipid metabolism revealed by a phenotypic and multi-tissue transcriptomic approach

Investigating the Impact of Fasting and Refeeding on Blood Biochemical Indicators and Transcriptional Profiles in the Hypothalamus and Subcutaneous Adipose Tissue in Geese

Fasting and refeeding systems can cause significant short-term fluctuations in nutrient and energy levels, triggering adaptive physiological responses in animals. This study examines the effects of fasting and refeeding on blood biochemical indicators and transcriptional profiles in the hypothalamus and subcutaneous adipose tissue of geese. Biochemical assays reveal that fasting significantly increases levels of free fatty acids and glucagon, while reducing concentrations of triglycerides, leptin, and insulin. Transcriptomic analyses identify a complex transcriptional response in both the hypothalamus and subcutaneous adipose tissue, affecting several metabolic pathways and key genes associated with feed intake and energy metabolism. In subcutaneous adipose tissue, fasting downregulates genes involved in fatty acid synthesis (LPL, SCD, and ACSL1) and upregulates PLIN2, a gene promoting lipid droplet degradation. Fasting affects a variety of metabolic pathways and critical genes in the hypothalamus, including Apelin, insulin, and mTOR signaling pathways. After fasting, the mRNA expression of NOG, GABRD, and IGFBP-1 genes in the hypothalamus are significantly upregulated, while proopiomelanocortin (POMC) gene expression is markedly downregulated. This study highlights the intricate biological responses to nutritional changes in geese, which adds to our understanding of energy balance and metabolic regulation in avian species.

Integrated transcriptome and microbiome analyses of residual feed intake in ducks during high production period

Residual feed intake (RFI) is a crucial parameter for assessing the feeding efficiency of poultry. Minimizing RFI can enhance feed utilization and reduce costs. In this study, 315 healthy female ducks were individually housed in cages. Growth performance was monitored during the high laying period, from 290 to 325 d of age. The cecal transcriptome and microbiome of 12 ducks with high RFI and 12 with low residual feed intake (LRFI) were analyzed. Regarding growth performance, the LRFI group exhibited significantly lower RFI, feed conversion ratio (FCR), and feed intake (Fi) compared to the HRFI group (p < 0.01). However, there were no significant differences observed in body weight (BW), body weight gain (BWG), and egg mass (EML) between the groups (p > 0.05). Microbiome analysis demonstrated that RFI impacted gut microbial abundance, particularly affecting metabolism and disease-related microorganisms such as Romboutsia, Enterococcus, and Megamonas funiformis. Transcriptome analysis revealed that varying RFI changed the expression of genes related to glucose metabolism and lipid metabolism, including APOA1, G6PC1, PCK1, and PLIN1. The integrated analysis indicated that host genes were closely linked to the microbiota and primarily function in lipid metabolism, which may enhance feeding efficiency by influencing metabolism and maintaining gut homeostasis.

Comparison of growth performance, meat quality, and blood biochemical indexes of Yangzhou goose under different feeding patterns

The East China region is the main market for the breeding and consumption of meat geese in China, in order to provide data reference for small and medium-sized farms and farmers to choose breeding methods and growth performance. This study selected 300 Yangzhou geese as materials and determined the number of geese in each group according to different modes. The meat quality, blood biochemical indicators, and economic benefits of 4 common feeding methods (Group I: full concentrate feeding; Group II: concentrate feeding in the first stage + 3% fat addition in the later stage; Group III: concentrate feeding + pasture supplementation; Group IV: grazing feeding + concentrate) in East China were analyzed. The results are as follows: The average daily weight gain of Yangzhou geese in Group IV at 5 to 8 wk old was the highest, with the highest feed utilization rate. The body weight at 8 wk old was significantly higher than that of the group III (P < 0.05). The total mortality rate of Group I and II remained at a relatively low level, while the mortality rates of Group III and IV exceeded 17%. The SR, FECR, and FECW of female geese in Groups II, III, and IV were significantly higher than those in Control group I (P < 0.05). Different feeding methods have little effect on the quality of goose breast muscles, while in terms of leg muscles, Group II has the highest binding force, significantly higher than Group I (P < 0.05). The rate of chest muscle loss in group III was significantly higher than that in groups I and II (P < 0.05). However, the pH of leg muscles in groups I, II and III was significantly higher than that in group IV (P < 0.05). Group II has the highest protein and collagen content, and Group I has the highest fat content. Except for the significantly higher histidine content in Groups I And II compared to those in Groups III and IV (P < 0.05), there was almost no significant difference in amino acid content among the groups (P > 0.05). There was no significant difference in ALB/GLO content among the 3 groups of Groups II to IV, but they were all significantly higher than those of Group I (P < 0.05). There was no statistically significant difference in other indicators among the groups (P > 0.05). There was no significant difference in the content of Ca, Cu, Fe, P, Zn, and other elements in the muscles between the groups (P > 0.05). This study solved the problems of slow growth, poor meat performance, and low economic benefits in meat goose breeding, providing theoretical basis and data support for meat goose breeding enterprises and farmers to choose appropriate breeding modes.

ELOVL gene family plays a virtual role in response to breeding selection and lipid deposition in different tissues in chicken (Gallus gallus)

Background

Elongases of very long chain fatty acids (ELOVLs), a family of first rate-limiting enzymes in the synthesis of long-chain fatty acids, play an essential role in the biosynthesis of complex lipids. Disrupting any of ELOVLs affects normal growth and development in mammals. Genetic variations in ELOVLs are associated with backfat or intramuscular fatty acid composition in livestock. However, the effects of ELOVL gene family on breeding selection and lipid deposition in different tissues are still unknown in chickens.

Results

Genetic variation patterns and genetic associations analysis showed that the genetic variations of ELOVL genes were contributed to breeding selection of commercial varieties in chicken, and 14 SNPs in ELOVL2-6 were associated with body weight, carcass or fat deposition traits. Especially, one SNP rs17631638T > C in the promoter of ELOVL3 was associated with intramuscular fat content (IMF), and its allele frequency was significantly higher in native and layer breeds compared to that in commercial broiler breeds. Quantitative real-time PCR (qRT-PCR) determined that the ELOVL3 expressions in pectoralis were affected by the genotypes of rs17631638T > C. In addition, the transcription levels of ELOVL genes except ELOVL5 were regulated by estrogen in chicken liver and hypothalamus with different regulatory pathways. The expression levels of ELOVL1-6 in hypothalamus, liver, abdominal fat and pectoralis were correlated with abdominal fat weight, abdominal fat percentage, liver lipid content and IMF. Noteworthily, expression of ELOVL3 in pectoralis was highly positively correlated with IMF and glycerophospholipid molecules, including phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl glycerol and phospholipids inositol, rich in ω-3 and ω-6 long-chain unsaturated fatty acids, suggesting ELOVL3 could contribute to intramuscular fat deposition by increasing the proportion of long-chain unsaturated glycerophospholipid molecules in pectoralis.

Conclusions

In summary, we demonstrated the genetic contribution of ELOVL gene family to breeding selection for specialized varieties, and revealed the expression regulation of ELOVL genes and their potential roles in regulating lipid deposition in different tissues. This study provides new insights into understanding the functions of ELOVL family on avian growth and lipid deposition in different tissues and the genetic variation in ELOVL3 may aid the marker-assisted selection of meat quality in chicken.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08932-8.

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens

BACKGROUND

Feed contributes most to livestock production costs. Improving feed efficiency is crucial to increase profitability and sustainability for animal production. Host genetics and the gut microbiota can both influence the host phenotype. However, the association between the gut microbiota and host genetics and their joint contribution to feed efficiency in chickens is largely unclear.

RESULTS

Here, we examined microbial data from the duodenum, jejunum, ileum, cecum, and feces in 206 chickens and their host genotypes and confirmed that the microbial phenotypes and co-occurrence networks exhibited dramatic spatial heterogeneity along the digestive tract. The correlations between host genetic kinship and gut microbial similarities within different sampling sites were weak, with coefficients ranging from - 0.07 to 0.08. However, microbial genome-wide analysis revealed that genetic markers near or inside the genes MTHFD1L and LARGE1 were associated with the abundances of cecal Megasphaera and Parabacteroides, respectively. The effect of host genetics on residual feed intake (RFI) was 39%. We further identified three independent genetic variations that were related to feed efficiency and had a modest effect on the gut microbiota. The contributions of the gut microbiota from the different parts of the intestinal tract on RFI were distinct. The cecal microbiota accounted for 28% of the RFI variance, a value higher than that explained by the duodenal, jejunal, ileal, and fecal microbiota. Additionally, six bacteria exhibited significant associations with RFI. Specifically, lower abundances of duodenal Akkermansia muciniphila and cecal Parabacteroides and higher abundances of cecal Lactobacillus, Corynebacterium, Coprobacillus, and Slackia were related to better feed efficiency.

CONCLUSIONS

Our findings solidified the notion that both host genetics and the gut microbiota, especially the cecal microbiota, can drive the variation in feed efficiency. Although host genetics has a limited effect on the entire microbial community, a small fraction of gut microorganisms tends to interact with host genes, jointly contributing to feed efficiency. Therefore, the gut microbiota and host genetic variations can be simultaneously targeted by favoring more-efficient taxa and selective breeding to improve feed efficiency in chickens. Video abstract.

Evolution, Expression Profile, Regulatory Mechanism, and Functional Verification of EBP-Like Gene in Cholesterol Biosynthetic Process in Chickens (Gallus Gallus)

The emopamil binding protein (EBP) is an important enzyme participating in the final steps of cholesterol biosynthesis in mammals. A predictive gene EBP-like, which encodes the protein with a high identity to human EBP, was found in chicken genome. No regulatory mechanisms and biological functions of EBP-like have been characterized in chickens. In the present study, the coding sequence of EBP-like was cloned, the phylogenetic trees of EBP/EBP-like were constructed and the genomic synteny of EBP-like was analyzed. The regulatory mechanism of EBP-like were explored with in vivo and in vitro experiments. The biological functions of EBP-like in liver cholesterol biosynthetic were examined by using gain- or loss-of-function strategies. The results showed that chicken EBP-like gene was originated from a common ancestral with Japanese quail EBP gene, and was relatively conservative with EBP gene among different species. The EBP-like gene was highly expressed in liver, its expression level was significantly increased in peak-laying stage, and was upregulated by estrogen. Inhibition of the EBP-like mRNA expression could restrain the expressions of EBP-like downstream genes (SC5D, DHCR24, and DHCR7) in the cholesterol synthetic pathway, therefore downregulate the liver intracellular T-CHO level. In conclusion, as substitute of EBP gene in chickens, EBP-like plays a vital role in the process of chicken liver cholesterol synthesis. This research provides a basis for revealing the molecular regulatory mechanism of cholesterol synthesis in birds, contributes insights into the improvement of the growth and development, laying performance and egg quality in poultry.

An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC (http://www.fragencode.org/), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.