Expression profiles and associations of FGF1 and FGF10 with intramuscular fat in Tibetan chicken

RNA-Seq explores the functional role of the fibroblast growth factor 10 gene in bovine adipocytes differentiation

OBJECTIVE

The present study was executed to explore the molecular mechanism of fibroblast growth factor 10 (FGF10) gene in bovine adipogenesis.

METHODS

The bovine FGF10 gene was overexpressed through Ad-FGF10 or inhibited through siFGF10 and their negative control (NC) in bovine adipocytes, and the multiplicity of infection, transfection efficiency, interference efficiency were evaluated through quantitative real-time polymerase chain reaction, western blotting and fluorescence microscopy. The lipid droplets, triglycerides (TG) content and the expression levels of adipogenic marker genes were measured during preadipocytes differentiation. The differentially expressed genes were explored through deep RNA sequencing.

RESULTS

The highest mRNA level was found in omasum, subcutaneous fat, and intramuscular fat. Moreover, the highest mRNA level was found in adipocytes at day 4 of differentiation. The results of red-oil o staining showed that overexpression (Ad-FGF10) of the FGF10 gene significantly (p<0.05) reduced the lipid droplets and TG content, and their downregulation (siFGF10) increased the measurement of lipid droplets and TG in differentiated bovine adipocytes. Furthermore, the overexpression of the FGF10 gene down regulated the mRNA levels of adipogenic marker genes such as CCAAT enhancer binding protein alpha (C/EBPα), fatty acid binding protein (FABP4), peroxisome proliferator-activated receptor-γ (PPARγ), lipoprotein lipase (LPL), and Fas cell surface death receptor (FAS), similarly, down-regulation of the FGF10 gene enriched the mRNA levels of C/EBPα, PPARγ, FABP4, and LPL genes (p<0.01). Additionally, the protein levels of PPARγ and FABP4 were reduced (p<0.05) in adipocytes infected with Ad-FGF10 gene and enriched in adipocytes transfected with siFGF10. Moreover, a total of 1,774 differentially expressed genes (DEGs) including 157 up regulated and 1,617 down regulated genes were explored in adipocytes infected with Ad-FGF10 or Ad-NC through deep RNA-sequencing. The top Kyoto encyclopedia of genes and genomes pathways regulated through DEGs were the PPAR signaling pathway, cell cycle, base excision repair, DNA replication, apoptosis, and regulation of lipolysis in adipocytes.

CONCLUSION

Therefore, we can conclude that the FGF10 gene is a negative regulator of bovine adipogenesis and could be used as a candidate gene in marker-assisted selection.

Identification of central regulators related to abdominal fat deposition in chickens based on weighted gene co-expression network analysis

Abdominal fat (AF) is one of the most important economic traits in chickens. Excessive AF in chickens will reduce feed utilization efficiency and negatively affect reproductive performance and disease resistance. However, the regulatory network of AF deposition needs to be further elucidated. In the present study, 300 one-day-old female Wannan chickens were reared to 17 wk of age, and 200 Wannan hens were selected to determine the abdominal fat percentage (AFP). Twenty AF tissue samples with the lowest AFP were selected as the low abdominal fat group (L-AFG), and 20 AF tissue samples with the highest AFP were selected as the high abdominal fat group (H-AFG). Eleven samples from L-AFG and 14 samples from H-AFG were selected for RNA-seq and used for weighted gene co-expression network analysis (WGCNA). Among the 25 RNA-seq samples, 5 samples with the lowest and highest AFP values were selected for differential expression gene analysis. Compared with the L-AFG, 225 and 101 genes were upregulated and downregulated in the H-AFG, respectively. A total of 20,503 genes were used to construct the WGCNA, and 44 co-expression gene modules were identified. Among these modules, 3 modules including turquoise, darkorange2, and floralwhite were identified as significantly associated with AFP traits. Furthermore, several genes including acyl-CoA oxidase 1 (ACOX1), stearoyl-CoA desaturase (SCD), aldehyde dehydrogenase 6 family member A1 (ALDH6A1), jun proto-oncogene, AP-1 transcription factor subunit (JUN), and fos proto-oncogene, AP-1 transcription factor subunit (FOS) involved in the "PPAR signaling pathway," "fatty acid metabolism," and "MAPK signaling pathway" were identified as central regulators that contribute to AF deposition. These results provide valuable information for further understanding of the gene expression and regulation of AF traits and contribute to future molecular breeding for AF in chickens.

Genomic Insights into Molecular Regulation Mechanisms of Intramuscular Fat Deposition in Chicken

Intramuscular fat (IMF) plays an important role in the tenderness, water-holding capacity, and flavor of chicken meat, which directly affect meat quality. In recent years, regulatory mechanisms underlying IMF deposition and the development of effective molecular markers have been hot topics in poultry genetic breeding. Therefore, this review focuses on the current understanding of regulatory mechanisms underlying IMF deposition in chickens, which were identified by multiple genomic approaches, including genome-wide association studies, whole transcriptome sequencing, proteome sequencing, single-cell RNA sequencing (scRNA-seq), high-throughput chromosome conformation capture (HiC), DNA methylation sequencing, and m6A methylation sequencing. This review comprehensively and systematically describes genetic and epigenetic factors associated with IMF deposition, which provides a fundamental resource for biomarkers of IMF deposition and provides promising applications for genetic improvement of meat quality in chicken.

Role of central Adiponectin and its interactions with NPY and GABAergic systems on food intake in neonatal layer chicken

BACKGROUND & AIM

Adiponectin is a member of the adipokine family and contributes to regulating energy homeostasis, reproduction, and various biological functions, such as insulin receptor signaling pathway sensitivity, mitochondrial biogenesis, oxidative metabolism, neurogenesis, and suppression of inflammation. This study aimed to investigate the effects of intracerebroventricular (ICV) injection of adiponectin and its interaction with the neuropeptide Y (NPY) and GABAergic systems on central appetite regulation in neonatal layer-type chickens.

MATERIALS & METHODS

In this study, 6 experiments were conducted, each of which included 4 experimental groups. In the first experiment, the chickens were injected with saline and adiponectin (20.73, 41.45, and 62.18 nmol). In the second experiment, saline, adiponectin (62.18 nmol), B5063 (NPY1 receptor antagonist, 2.12 nmol), and simultaneous injections of adiponectin and B5063 were performed. Experiments 3 to 6 were done in the same way to experiment 1, but the chickens were injected with SF22 (NPY2 receptor antagonist, 2.66 nmol), SML0891 (NPY5 receptor antagonist, 2.89 nmol), picrotoxin (GABAA receptor antagonist, 0.89 nmol), CGP54626 (GABAB receptor antagonist, 0.047 nmol) instead of B5063. Feed consumption was measured 120 min after the injection.

RESULTS

A dose-dependent increase in appetite was observed after the injection of adiponectin (20.73, 41.45, and 62.18 nmol) (P<0.05). The injection of B5063 + adiponectin attenuated the hyperphagic effect of adiponectin (P< 0.05). In addition, co-injection of picrotoxin and adiponectin significantly decreased adiponectin-induced hyperphagia (P<0.05). In addition, adiponectin significantly increased the number of steps, jumps, exploratory food, pecks, and standing time, while decreasing sitting time and rest time (P<0.05).

CONCLUSION

These results suggest that the hyperphagic effects of adiponectin are probably mediated through NPY1 and GABAA receptors in neonatal layer-type chickens.

Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R² , concordance rate, and dosage R² were 0.959, 0.952, and 0.933, respectively. The heritability values of a*_45 min , b*_45 min , L*_45 min , C*, and H⁰ were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Co-expression analysis of long non-coding RNAs and mRNAs involved in intramuscular fat deposition in Muchuan black-bone chicken

The intramuscular fat (IMF) content in meat products is positively correlated with meat quality, making it an important consumer trait. Long non-coding RNAs (lncRNAs) play central roles in regulating various biological processes, but little is currently known about the mechanisms by which they regulate IMF deposition in chickens. This study sampled the breast muscles of chickens with high (H) and low (L) IMF content and constructed six small RNA libraries. High-throughput sequencing technology was used to profile the breast muscle transcriptome (lncRNA and mRNA) and to identify the differentially expressed lncRNAs (DELs) and mRNAs (DEGs) between the H and L groups. In total, 263 DELs (118 up-regulated and 145 down-regulated lncRNAs) and 443 DEGs (203 up-regulated and 240 down-regulated genes) were identified between the two groups. To analyse the DELs-DEGs interaction network, co-expression analysis was conducted to identify lncRNA-mRNA pairs. In total, 19,270 lncRNA/mRNA pairs were identified, including 16,398 significant correlation pairs that presented as positive and 2872 pairs that presented as negative. The lncRNA-mRNA network comprised 263 lncRNA nodes and 440 mRNA nodes. Pathway analysis, using the Kyoto Encyclopedia of Genes and Genomes, indicated that pathways associated with fat deposition and lipid metabolism such as the MAPK, PPAR, GnRH, ErbB and calcium signalling pathways, fatty acid elongation and fatty acid metabolism. Overall, the study identified potential candidate lncRNAs, genes and regulatory networks associated with chicken IMF deposition. These findings provide new insights to help clarify the regulatory mechanisms of IMF deposition in chickens which can be used to improve the IMF content in poultry.

Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.

Whole-genome resequencing of Dulong Chicken reveal signatures of selection

1. Dulong Chickens (DLCs) live at high altitude (~3000 m) and humidity (~90%), are endemic to the Yunnan province, and have gradually developed unique physiological characteristics, but their genetic basis is still unclear. Using the fixation index (F_ST ) approach, based on whole-genome resequencing, DLCs were analysed to uncover the genomic architecture of the population and candidate genes involved in selection during domestication. 2. A total of 469 candidate genes were obtained to be putatively under selection in DLCs. Further investigations revealed the genic footprint for local adaptation (high-altitude and high-humidity) as the genic signatures that are involved in economic traits (related to egg production). 3. Candidate genes were identified that may be associated with disease resistance, aggressiveness, small body size and positive selection of vision in DLCs. 4. These data revealed loci of selective signals that operate during selection for production at high altitude and humidity.