ABCD4 is associated with mammary gland development in mammals.

BACKGROUND: Mammary gland development is a critical process in mammals, crucial for their reproductive success and offspring nourishment. However, the functional roles of key candidate genes associated with teat number, including ABCD4, VRTN, PROX2, and DLST, in this developmental process remain elusive. To address this gap in knowledge, we conducted an in-depth investigation into the dynamic expression patterns, functional implications, and regulatory networks of these candidate genes during mouse mammary gland development. RESULTS: In this study, the spatial and temporal patterns of key genes were characterized in mammary gland development. Using time-series single-cell data, we uncovered differences in the expression of A bcd4, Vrtn, Prox2, and Dlst in cell population of the mammary gland during embryonic and adult stages, while Vrtn was not detected in any cells. We found that only overexpression and knockdown of Abcd4 could inhibit proliferation and promote apoptosis of HC11 mammary epithelial cells, whereas Prox2 and Dlst had no significant effect on these cells. Using RNA-seq and qPCR, further analysis revealed that Abcd4 can induce widespread changes in the expression levels of genes involved in mammary gland development, such as Igfbp3, Ccl5, Tlr2, and Prlr, which were primarily associated with the MAPK, JAK-STAT, and PI3K-AKT pathways by functional enrichment. CONCLUSIONS: These findings revealed ABCD4 as a candidate gene pivotal for regulating mammary gland development and lactation during pregnancy by influencing PRLR expression.

Mapping and Functional Dissection of the Rumpless Trait in Piao Chicken Identifies a Causal Loss of Function Mutation in the Novel Gene Rum.

Rumpless chickens exhibit an abnormality in their tail development. The genetics and biology of this trait has been studied for decades to illustrate a broad variation in both the types of inheritance and the severity in the developmental defects of the tail. In this study, we created a backcross pedigree by intercrossing Piao (rumpless) with Xianju (normal) to investigate the genetic mechanisms and molecular basis of the rumpless trait in Piao chicken. Through genome-wide association and linkage analyses, the candidate region was fine-mapped to 798.5 kb (chromosome 2: 86.9 to 87.7 Mb). Whole-genome sequencing analyses identified a single variant, a 4.2 kb deletion, which was completely associated with the rumpless phenotype. Explorations of the expression data identified a novel causative gene, Rum, that produced a long, intronless transcript across the deletion. The expression of Rum is embryo-specific, and it regulates the expression of MSGN1, a key factor in regulating T-box transcription factors required for mesoderm formation and differentiation. These results provide genetic and molecular experimental evidence for a novel mechanism regulating tail development in chicken and report the likely causal mutation for the tail abnormity in the Piao chicken. The novel regulatory gene, Rum, will, due to its role in fundamental embryo development, be of interest for further explorations of a potential role in tail and skeletal development also in other vertebrates.

Antibacterial activity of the antimicrobial peptide PMAP-36 in combination with tetracycline against porcine extraintestinal pathogenic Escherichia coli in vitro and in vivo.

The increase in the emergence of antimicrobial resistance has led to great challenges in controlling porcine extraintestinal pathogenic Escherichia coli (ExPEC) infections. Combinations of antimicrobial peptides (AMPs) and antibiotics can synergistically improve antimicrobial efficacy and reduce bacterial resistance. In this study, we investigated the antibacterial activity of porcine myeloid antimicrobial peptide 36 (PMAP-36) in combination with tetracycline against porcine ExPEC PCN033 both in vitro and in vivo. The minimum bactericidal concentrations (MBCs) of AMPs (PMAP-36 and PR-39) against the ExPEC strains PCN033 and RS218 were 10 µM and 5 µM, respectively. Results of the checkerboard assay and the time-kill assay showed that PMAP-36 and antibiotics (tetracycline and gentamicin) had synergistic bactericidal effects against PCN033. PMAP-36 and tetracycline in combination led to PCN033 cell wall shrinkage, as was shown by scanning electron microscopy. Furthermore, PMAP-36 delayed the emergence of PCN033 resistance to tetracycline by inhibiting the expression of the tetracycline resistance gene tetB. In a mouse model of systemic infection of PCN033, treatment with PMAP-36 combined with tetracycline significantly increased the survival rate, reduced the bacterial load and dampened the inflammatory response in mice. In addition, detection of immune cells in the peritoneal lavage fluid using flow cytometry revealed that the combination of PMAP-36 and tetracycline promoted the migration of monocytes/macrophages to the infection site. Our results suggest that AMPs in combination with antibiotics may provide more therapeutic options against multidrug-resistant porcine ExPEC.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

De Novo Assembly of 20 Chicken Genomes Reveals the Undetectable Phenomenon for Thousands of Core Genes on Microchromosomes and Subtelomeric Regions.

The gene numbers and evolutionary rates of birds were assumed to be much lower than those of mammals, which is in sharp contrast to the huge species number and morphological diversity of birds. It is, therefore, necessary to construct a complete avian genome and analyze its evolution. We constructed a chicken pan-genome from 20 de novo assembled genomes with high sequencing depth, and identified 1,335 protein-coding genes and 3,011 long noncoding RNAs not found in GRCg6a. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were seldomly measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal subtelomeric regions and microchromosomes, surrounded by extremely high proportions of tandem repeats, which strongly blocks DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had 3-fold elevated substitution rates than known ones, updating the knowledge about evolutionary rates in birds. Our study provides a framework for constructing a better chicken genome, which will contribute toward the understanding of avian evolution and the improvement of poultry breeding.

Loss of MuRF1 in Duroc pigs promotes skeletal muscle hypertrophy.

Muscle mass development depends on increased protein synthesis and reduced muscle protein degradation. Muscle ring-finger protein-1 (MuRF1) plays a key role in controlling muscle atrophy. Its E3 ubiquitin ligase activity recognizes and degrades skeletal muscle proteins through the ubiquitin-proteasome system. The loss of Murf1, which encodes MuRF1, in mice leads to the accumulation of skeletal muscle proteins and alleviation of muscle atrophy. However, the function of Murf1 in agricultural animals remains unclear. Herein, we bred F1 generation Murf1+/- and F2 generation Murf1-/- Duroc pigs from F0 Murf1-/- pigs to investigate the effect of Murf1 knockout on skeletal muscle development. We found that the Murf1+/- pigs retained normal levels of muscle growth and reproduction, and their percentage of lean meat increased by 6% compared to that of the wild type (WT) pigs. Furthermore, the meat color, pH, water-holding capacity, and tenderness of the Murf1+/- pigs were similar to those of the WT pigs. The drip loss rate and intramuscular fat decreased slightly in the Murf1+/- pigs. However, the cross-sectional area of the myofibers in the longissimus dorsi increased in the adult Murf1+/- pigs. The skeletal muscle proteins MYBPC3 and actin, which are targeted by MuRF1, accumulated in the Murf1+/- and Murf1-/- pigs. Our findings show that inhibiting muscle protein degradation in MuRF1-deficient Duroc pigs increases the size of their myofibers and their percentage of lean meat without influencing their growth or pork quality. Our study demonstrates that Murf1 is a target gene for promoting skeletal muscle hypertrophy in pig breeding.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population.

BACKGROUND: Although the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data. RESULTS: We used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN. CONCLUSIONS: The SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Two cis-regulatory SNPs upstream of ABCG2 synergistically cause the blue eggshell phenotype in the duck.

Avian eggshell color is an interesting genetic trait. Here, we report that the blue eggshell color of the domestic duck is caused by two cis-regulatory G to A transitions upstream of ABCG2, which encodes an efflux transporter. The juxtaposed blue eggshell allele A-A exhibited higher promoter activity and stronger nuclear protein binding capacity than the white eggshell allele G-G. Transcription factor analysis suggested differential binding capability of CTCF between blue eggshell and white eggshell alleles. Knockdown of CTCF expression significantly decreased the promoter activity of the blue eggshell but not the white eggshell allele. DNA methylation analysis revealed similar high methylation of the region upstream of the CTCF binding sites in both blue-eggshelled and white-eggshelled ducks. However, DNA methylation levels downstream of the binding sites were decreased and 35% lower in blue-eggshelled ducks than in white-eggshelled ducks. Consistent with the in vitro regulatory pattern of causative sites, ABCG2 exhibited higher expression in uteruses of blue-eggshelled ducks and also showed polarized distribution in their endometrial epithelial cells, distributing at the apical surface of endometrial epithelial cells and with orientation toward the uterine cavity, where the eggshell is pigmented. In conclusion, our results suggest that two cis-regulatory SNPs upstream of ABCG2 are the causative mutations for blue eggshells in ducks. The blue eggshell variant up-regulated ABCG2 expression through recruiting CTCF binding, which may function as a barrier element to shield the downstream region from high methylation levels present upstream. ABCG2 was identified as the only candidate causative gene for blue eggshells; it may function as an efflux transporter of biliverdin to the uterine cavity.

Genomic Analysis Revealed a Convergent Evolution of LINE-1 in Coat Color: A Case Study in Water Buffaloes (Bubalus bubalis).

Visible pigmentation phenotypes can be used to explore the regulation of gene expression and the evolution of coat color patterns in animals. Here, we performed whole-genome and RNA sequencing and applied genome-wide association study, comparative population genomics and biological experiments to show that the 2,809-bp-long LINE-1 insertion in the ASIP (agouti signaling protein) gene is the causative mutation for the white coat phenotype in swamp buffalo (Bubalus bubalis). This LINE-1 insertion (3' truncated and containing only 5' UTR) functions as a strong proximal promoter that leads to a 10-fold increase in the transcription of ASIP in white buffalo skin. The 165 bp of 5' UTR transcribed from the LINE-1 is spliced into the first coding exon of ASIP, resulting in a chimeric transcript. The increased expression of ASIP prevents melanocyte maturation, leading to the absence of pigment in white buffalo skin and hairs. Phylogenetic analyses indicate that the white buffalo-specific ASIP allele originated from a recent genetic transposition event in swamp buffalo. Interestingly, as a similar LINE-1 insertion has been identified in the cattle ASIP gene, we discuss the convergent mechanism of coat color evolution in the Bovini tribe.

PKD1 deficiency induces Bronchiectasis in a porcine ADPKD model.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a prevalent genetic disorder, mainly characterized by the development of renal cysts, as well as various extrarenal manifestations. Previous studies have shown that ADPKD is related to bronchiectasis, while its pathogenic mechanism is unclear. In previous studies, we have generated the PKD1+/- pigs to simulate the progression of cyst formation and physiological alterations similar to those seen in ADPKD patients. METHODS: Phenotypic changes to airway epithelial cell and mesenchymal cell in PKD1+/- pigs were assessed by histological analysis. The molecular mechanisms driving these processes were investigated by using PKD1+/- pig lungs, human mesenchymal cells, and generating PKD1 deficient human epithelial cells. RESULTS: We identified bronchiectasis in PKD1+/- pigs, which is consistent with the clinical symptoms in ADPKD patients. The deficiency of PKD1 suppressed E-cadherin expression in the airway epithelial barrier, which aggravated invasion and leaded to a perpetuated inflammatory response. During this process, extracellular matrix (ECM) components were altered, which contributed to airway smooth muscle cell phenotype switch from a contractile phenotype to a proliferative phenotype. The effects on smooth muscle cells resulted in airway remodeling and establishment of bronchiectasis. CONCLUSION: To our knowledge, the PKD1+/- pig provides the first model recapitulating the pathogenesis of bronchiectasis in ADPKD. The role of PKD1 in airway epithelial suggests a potential target for development of new strategies for the diagnosis and treatment of bronchiectasis.

Revisiting the Pig IGHC Gene Locus in Different Breeds Uncovers Nine Distinct IGHG Genes.

IgG subclass diversification is common in placental mammals. It has been well documented in humans and mice that different IgG subclasses, with diversified functions, synergistically regulate humoral immunity. However, our knowledge on the genomic and functional diversification of IgG subclasses in the pig, a mammalian species with high agricultural and biomedical importance, is incomplete. Using bacterial artificial chromosome sequencing and newly assembled genomes generated by the PacBio sequencing approach, we characterized and mapped the IgH C region gene locus in three indigenous Chinese breeds (Erhualian, Xiang, and Luchuan) and compared them to that of Duroc. Our data revealed that IGHG genes in Chinese pigs differ from the Duroc, whereas the IGHM, IGHD, IGHA, and IGHE genes were all single copy and highly conserved in the pig breeds examined. Most striking were differences in numbers of IGHG genes: there are seven genes in Erhualian pigs, six in the Duroc, but only five in Xiang pigs. Phylogenetic analysis suggested that all reported porcine IGHG genes could be classified into nine subclasses: IGHG1, IGHG2a, IGHG2b, IGHG2c, IGHG3, IGHG4, IGHG5a, IGHG5b, and IGHG5c. Using sequence information, we developed a mouse mAb specific for IgG3. This study offers a starting point to investigate the structure-function relationship of IgG subclasses in pigs.

Waveform Design for Multi-Target Detection Based on Two-Stage Information Criterion.

Parameter estimation accuracy and average sample number (ASN) reduction are important to improving target detection performance in sequential hypothesis tests. Multiple-input multiple-output (MIMO) radar can balance between parameter estimation accuracy and ASN reduction through waveform diversity. In this study, we propose a waveform design method based on a two-stage information criterion to improve multi-target detection performance. In the first stage, the waveform is designed to estimate the target parameters based on the criterion of single-hypothesis mutual information (MI) maximization under the constraint of the signal-to-noise ratio (SNR). In the second stage, the objective function is designed based on the criterion of MI minimization and Kullback-Leibler divergence (KLD) maximization between multi-hypothesis posterior probabilities, and the waveform is chosen from the waveform library of the first-stage parameter estimation. Furthermore, an adaptive waveform design algorithm framework for multi-target detection is proposed. The simulation results reveal that the waveform design based on the two-stage information criterion can rapidly detect the target direction. In addition, the waveform design based on the criterion of dual-hypothesis MI minimization can improve the parameter estimation performance, whereas the design based on the criterion of dual-hypothesis KLD maximization can improve the target detection performance.

FcRn is not the receptor mediating the transfer of serum IgG to colostrum in pigs.

In contrast to humans or rabbits, in which maternal IgG is transmitted to offspring prenatally via the placenta or the yolk sac, large domestic animals such as pigs, cows and sheep transmit IgG exclusively through colostrum feeding after delivery. The extremely high IgG content in colostrum is absorbed by newborns via the small intestine. Although it is widely accepted that the neonatal Fc receptor, FcRn, is the receptor mediating IgG transfer across both the placenta and small intestine, it remains unclear whether FcRn also mediates serum IgG transfer across the mammary barrier to colostrum/milk, especially in large domestic animals. In this study, using a FcRn knockout pig model generated with a CRISPR-Cas9-based approach, we clearly demonstrate that FcRn is not responsible for the IgG transfer from serum to colostrum in pigs, although like in other mammals, it is involved in IgG homeostasis and mediates IgG absorption in the small intestine of newborns.

Developing PspCas13b-based enhanced RESCUE system, eRESCUE, with efficient RNA base editing.

RNA base editing is potential for cellular function research and genetic diseases treating. There are two main RNA base editors, REPAIR and RESCUE, for in vitro use. REPAIR was developed by fusing inactivated Cas13 (dCas13) with the adenine deaminase domain of ADAR2, which efficiently performs adenosine-to-inosine (A-to-I) RNA editing. RESCUE, which performs both cytidine-to-uridine (C-to-U) and A-to-I RNA editing, was developed by fusing inactivated Cas13 (dCas13) with the evolved ADAR2. However, the relatively low editing efficiency of the RESCUE system limits its broad application. Here, we constructed an enhanced RESCUE (eRESCUE) system; this dPspCas13b-RESCUE-NES system was generated by fusing inactivated PspCas13b with the evolved ADAR2. We determined the endogenous mRNA A-to-I and C-to-U editing efficiency mediated by the dPspCas13b-RESCUE-NES system in HEK-293T cells. This new RNA base editor was then used to induce 177Ser/Gly conversion of inhibitor kappa B kinase ß (IKKß) by changing the genetic code from AGU to GGU. The results showed that the eRESCUE editor mediates more efficient A-to-I and C-to-U RNA editing than the RESCUE RNA editor, as was previously reported. The 177Ser/Gly conversion of IKKß, accomplished by converting the genetic code from AGU to GGU, resulted in a decrease in the phosphorylation of IKKß and downregulation of downstream IKKß-related genes. In summary, we developed a more efficient RNA base editor, eRESCUE, which may provide a useful tool for biomedical research and genetic disease treatment. Video Abstract.

ecRESCUE: a novel ecDHFR-regulated RESCUE system with reduced RNA off-targeting activity.

The currently available RESCUE RNA base editing system demonstrates considerable potential for the treatment of genetic diseases at the transcriptional level. However, the relatively high incidence of off-target events hampers the precise RNA editing, thereby limiting its use in the clinical setting. This study describes a new RNA base editing method, named ecRESCUE, which utilizes inducible stabilization of the protein ecDHFR DD fused at the C-terminal of the original RESCUE system. In vitro experiments in 293T cells showed that the ecRESCUE editor markedly reduced the incidence of off-target single nucleotide polymorphisms without affecting the RNA A-to-I and C-to-U base editing efficiency. Altogether, these results demonstrate that the inducible ecRESCUE system represents an attractive approach to regulate and improve the outcome of the available RNA base editor with reduced off-targeting activity. Video Abstract.

Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population.

Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F2 resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1-6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.

Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.

BACKGROUND: Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. METHODS: We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. RESULTS: Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. CONCLUSIONS: Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

Genome­wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing.

BACKGROUND: Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. RESULTS: We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. CONCLUSIONS: A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

Silencing of retrotransposon-derived imprinted gene RTL1 is the main cause for postimplantational failures in mammalian cloning.

Substantial rates of fetal loss plague all in vitro procedures involving embryo manipulations, including human-assisted reproduction, and are especially problematic for mammalian cloning where over 90% of reconstructed nuclear transfer embryos are typically lost during pregnancy. However, the epigenetic mechanism of these pregnancy failures has not been well described. Here we performed methylome and transcriptome analyses of pig induced pluripotent stem cells and associated cloned embryos, and revealed that aberrant silencing of imprinted genes, in particular the retrotransposon-derived RTL1 gene, is the principal epigenetic cause of pregnancy failure. Remarkably, restoration of RTL1 expression in pig induced pluripotent stem cells rescued fetal loss. Furthermore, in other mammals, including humans, low RTL1 levels appear to be the main epigenetic cause of pregnancy failure.

Food intake-related genes in chicken determined through combinatorial genome-wide association study and transcriptome analysis.

The chicken gizzard is the primary digestive and absorptive organ regulating food intake and metabolism. Body weight is a typical complex trait regulated by an interactive polygene network which is under the control of an interacting network of polygenes. To simplify these genotype-phenotype associations, the gizzard is a suitable target organ to preliminarily explore the mechanism underlying the regulation of chicken growth through controlled food intake. This study aimed to identify key food intake-related genes through combinatorial GWAS and transcriptome analysis. We performed GWAS of body weight in an F2 intercrossed population and transcriptional profiling analysis of gizzards from chickens with different body weight. We identified a major 10 Mb quantitative trait locus (QTL) on chromosome 1 and numerous minor QTL distributed among 24 chromosomes. Combining data regarding QTL and gizzard gene expression, two hub genes, MLNR and HTR2A, and a list of core genes with small effect were found to be associated with food intake. Furthermore, the neuroactive ligand-receptor interaction pathway was found to play a key role in regulating the appetite of chickens. The present results show the major-minor gene interactions in metabolic pathways and provide insights into the genetic architecture and gene regulation during food intake in chickens.