Chicken Genome Research Articles

Genome of Russian Snow-White Chicken Reveals Genetic Features Associated with Adaptations to Cold and Diseases.

Russian Snow White (RSW) chickens are characterized by high egg production, extreme resistance to low temperatures, disease resistance, and by the snow-white color of the day-old chicks. Studying the genome of this unique chicken breed will reveal its evolutionary history and help to understand the molecular genetic mechanisms underlying the unique characteristics of this breed, which will open new breeding opportunities and support future studies. We have sequenced and made a de novo assembly of the whole RSW genome using deep sequencing (250×) by the short reads. The genome consists of 40 chromosomes with a total length of 1.1 billion nucleotide pairs. Phylogenetic analysis placed the RSW near the White Leghorn, Fayoumi, and Houdan breeds. Comparison with other chicken breeds revealed a wide pool of mutations unique to the RSW. The functional annotation of these mutations showed the adaptation of genes associated with the development of the nervous system, thermoreceptors, purine receptors, and the TGF-beta pathway, probably caused by selection for low temperatures. We also found adaptation of the immune system genes, likely driven by selection for resistance to viral diseases. Integration with previous genome-wide association studies (GWAS) suggested several causal single nucleotide polymorphisms (SNPs). Specifically, we identified an RSW-specific missense mutation in the RALYL gene, presumably causing the snow-white color of the day-old chicks, and an RSW-specific missense mutation in the TLL1 gene, presumably affecting the egg weight.

Mining of Novel Simple Sequence Repeat (SSR) Markers Derived from Chicken (Gallus gallus) Genome Assembly

Mining of Novel Simple Sequence Repeat (SSR) Markers Derived from Chicken (Gallus gallus) Genome Assembly

Comprehensive Annotation and Expression Profiling of C2H2 Zinc Finger Transcription Factors across Chicken Tissues.

As the most abundant class of transcription factors in eukaryotes, C2H2-type zinc finger proteins (C2H2-ZFPs) play critical roles in various biological processes. Despite being extensively studied in mammals, C2H2-ZFPs remain poorly characterized in birds. Recent accumulation of multi-omics data for chicken enables the genome-wide investigation of C2H2-ZFPs in birds. The purpose of this study is to reveal the genomic occurrence and evolutionary signature of chicken C2H2-ZFPs, and further depict their expression profiles across diverse chicken tissues. Here, we annotated 301 C2H2-ZFPs in chicken genome, which are associated with different effector domains, including KRAB, BTB, HOMEO, PHD, SCAN, and SET. Among them, most KRAB-ZFPs lack orthologues in mammals and tend to form clusters by duplication, supporting their fast evolution in chicken. We also annotated a unique and previously unidentified SCAN-ZFP, which is lineage-specific and highly expressed in ovary and testis. By integrating 101 RNA-seq datasets for 32 tissues, we found that most C2H2-ZFPs have tissue-specific expression. Particularly, 74 C2H2-ZFPs-including 27 KRAB-ZFPs-show blastoderm-enriched expression, indicating their association with early embryo development. Overall, this study performs comprehensive annotation and expression profiling of C2H2 ZFPs in diverse chicken tissues, which gives new insights into the evolution and potential function of C2H2-ZFPs in avian species.

Study of extended homozygous chromosome regions in Italian partridge breed of chickens

Selection and accompanying inbreeding are the basis for breeding chicken breeds. With the advent of modern molecular methods for assessing inbreeding, it has become possible to identify genes and their functions in the homozygous regions (ROH) of chicken chromosomes. In this study, a genome-wide analysis of homozygous regions of chromosomes in Italian partridge chickens was carried out using an SNP chip Illumina Chicken 60KSNPiSelectBeadChipchip. An average of 177 ± 5 ROH-sequences per chicken were identified in the chicken chromosomes, and the average inbreeding coefficient was 0.30 ± 0.01. It was determined that the chicken genome is dominated by short ROH-sequences of 0.25–2 Mbp. ROH-sequences longer than 8 Mbp indicating recent inbreeding account for no more than 2.2%. It was found that, in general, ROH-sequences are more densely distributed in chicken microchromosomes, with the exception of chromosome 16, which does not have ROH-sequences. This fact is due to the heterozygosity of the alleles of the genes responsible for immunity, located in microchromosome 16. ROH-islands were found in the chicken chromosomes 1, 5, and 14. The genes in ROH-islands are responsible for feather pecking in chickens (DMD gene), immune status (TAB3, EIF2S3 genes), body weight (IL1RAPL1 gene), pH of meat (EIF2S3, APOO, KLHL15 genes), egg production (APOO gene), feed uptake (SAT1 gene), aggressiveness of roosters (SLITRK6 gene), transport of intracellular components in developing neurons and protection of heterochromatin in the cell nuclei of neurons (NDE1 gene), adaptation of chickens to tropical living conditions (CDIN1 gene). Thus, ROH analysis allowed us to identify genes potentially selectable as a result of breeding Italian partridge breed chickens.

PSVII-1 Widespread allele-specific expression across tissues and cells of the chicken genome

Abstract Chickens are a valuable livestock species, providing an affordable and nutritious food source worldwide through eggs and meat. Additionally, chickens are extensively used in scientific research as model organisms in virology, immunology, epigenetics, development, and conservation biology. Considering this, there are increasing efforts to deepen our understanding of their genome complexity, such as efforts by the Functional Annotation of Animal Genomes (FAANG) Consortium. Toward this goal, we are working to contribute to the annotation of the chicken transcriptome. Allele-specific expression (ASE) is an imbalance of allelic gene expression often driven by genetic and epigenetic changes in cis-regulatory regions. We, therefore, aimed to determine allelic imbalances in gene expression across 20 tissues and cells to shed light on mechanisms regulating gene expression. Using replicate paired-end (PE) RNA-seq samples, we identified around 7 million variants across 20 tissues and cells (e.g., reproductive tissues, intestine tissues, muscle, and immune tissues and cells). We applied quality control measures and filtered out monoallelic and homozygous variants. After that, we calculated read counts per allele to determine allelic expression. We found 365,894 significant ASE SNPs and 11,530 ASE genes in at least one tissue or cell type (FDR ≤ 0.05). We discovered that ASE SNPs are widely distributed throughout the chicken genome, and ASE SNPs and ASE genes showed a varied distribution across tissues and cells. Primary macrophage exhibited the most abundant, while the pectoralis major (breast muscle) had the lowest ASE genes and ASE SNPs, ranging from 7,592 to 32,505 ASE SNPs and 773 to 2,387 ASE genes. Our findings reveal several important pathways affected by allelic imbalance of expression. These pathways included fatty acid biosynthesis and regulation, cell proliferation and differentiation, cell metabolism, adipocytokine signaling, proteolysis and autophagy, and focal adhesion. In summary, our study provides insight into relevant biological processes critical to chickens and can contribute to improving genome annotation, helping to expand the transcriptomic reference source.

Genome-Wide Identification, Evolution, and miRNA-22 Regulation of Kruppel-Like Factor (KLF) Gene Family in Chicken (Gallus gallus).

Krüppel-like factors (KLFs) are a class of fundamental transcription factors that are widely present in various eukaryotes from nematodes to humans, named after their DNA binding domain which is highly homologous to the Krüppel factor in fruit flies. To investigate the composition, organization, and evolutionary trajectory of KLF gene family members in chickens, in our study, we leveraged conserved sequences of KLF genes from representative classes across fish, amphibians, birds, and mammals as foundational sequences. Bioinformatic tools were employed to perform homology alignment on the chicken genome database, ultimately identifying the KLF family members present in chickens. The gene structure, phylogenetic analysis, conserved base sequences, physicochemical properties, collinearity analysis, and protein structure were then analyzed using bioinformatic tools. Additionally, the impact of miRNA-22, related to poultry lipid metabolism, on the expression of the KLF gene family in the liver, heart, and muscle of Qingyuan partridge chickens was explored. The results showed that: (1) compared to fish, the KLF family in birds is more closely related to mammals and amphibians; (2) KLFs within the same subgroups are likely to be derived from a common ancestral gene duplication; (3) KLF3/8/12 in the same subgroup may have some similar or overlapping functions; (4) the motif 4 of KLF5 was most likely lost during evolution; (5) KLF9 may perform a similar function in chickens and pigs; (6) there are collinear relationships between certain KLF genes, indicating that there are related biomolecular functions between these KLF genes; (7) all members of the KLF family in chickens are non-transmembrane proteins; and (8) interference and overexpression of miRNA-22 in Qingyuan partridge chickens can affect the expression levels of KLF genes in liver, heart, and muscle.

Complete Chromosome Wise Identification of SSRs in the Two Published Chicken Genome Assemblies

Background: Microsatellites are short repeat motifs consisting 1-6 base pair (bp) and have a high degree of length polymorphism and are useful to study the genetic diversity. In Chicken, no micro satellite markers were demarcated to different chromosomes, viz., 29, 34, 35, 36, 37, 38 and 39 in the genome. In the present study, we carried out chromosome-wise identification of SSRs that will be of great use in diversity and gene mapping studies in chicken. Methods: The perl script of MISA tool was used to screen the polymorphic microsatellites with different thresholds level. Result: We identified a total of 397877 SSRs from the latest chicken genome assembly (GRCg7b), of which mononucleotides, dinucleotides, trinucleotides, tetranucleotides, pentanucleotides and hexanucleotides composed of 307453 (77.27%), 40991 (10.30%), 23059 (5.79%), 16723 (4.20%), 8127 (2.04%) and 1524 (0.38%), respectively.

Functional role of genes in ROH sites in <i>Czech Golden</i> chickens

Modern technologies make it possible to identify homozygous chromosome regions that have arisen as a result of animal selection. The article presents the results obtained in the genotyping of chickens using the Illumina Chicken 60KSNP iSelect Bead Chip. For the first time, a genome-wide analysis of the extended homozygous SNP sequences (ROH) was performed in the genome of Czech Golden hens. The average number of ROH segments in the chicken genome was 143 ± 8. ROH segments are usually randomly distributed in chicken chromosomes. It has been proposed to prohibit the use of heterozygous SNPs in ROH segments to prevent overestimation of ROH data. The average inbreeding coefficient in chickens calculated from ROH data was 0.34 ± 0.03. ROH islands were found on chromosomes 2, 3, 9 and 22.They contain genesassociatedwithimmunity, degradation of neurons, Fabricius bursa weight, obesity, feather pigmentation, and regulation of hit-shock genes. In the Czech Golden chicken breed, selection and the associated inbreeding have therefore influenced the genes involved in several biological processes.

Genome-wide detections for runs of homozygosity and selective signatures reveal novel candidate genes under domestication in chickens

BackgroundIndigenous chickens were developed through a combination of natural and artificial selection; essentially, changes in genomes led to the formation of these modern breeds via admixture events. However, their confusing genetic backgrounds include a genomic footprint regulating complex traits, which is not conducive to modern animal breeding.ResultsTo better evaluate the candidate regions under domestication in indigenous chickens, we considered both runs of homozygosity (ROHs) and selective signatures in 13 indigenous chickens. The genomes of Silkie feather chickens presented the highest heterozygosity, whereas the highest inbreeding status and ROH number were found in Luhua chickens. Short ROH (< 1 Mb), were the principal type in all chickens. A total of 291 ROH islands were detected, and QTLdb mapping results indicated that body weight and carcass traits were the most important traits. An ROH on chromosome 2 covering VSTM2A gene was detected in 12 populations. Combined analysis with the Tajima’s D index revealed that 18 genes (e.g., VSTM2A, BBOX1, and RYR2) were under selection and covered by ROH islands. Transcriptional analysis results showed that RYR2 and BBOX1 were specifically expressed in the heart and muscle tissue, respectively.ConclusionBased on genome-wide scanning for ROH and selective signatures, we evaluated the genomic characteristics and detected significant candidate genes covered by ROH islands and selective signatures. The findings in this study facilitated the understanding of genetic diversity and provided valuable insights for chicken breeding and conservation strategies.

Annotations of four high-quality indigenous chicken genomes identify more than one thousand missing genes in subtelomeric regions and micro-chromosomes with high G/C contents.

Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. We identified varying numbers (17,497-17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 "missing" genes in birds in general, and 36 of the 174 "missing" genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

Artificial selection footprints in indigenous and commercial chicken genomes

BackgroundAlthough many studies have been done to reveal artificial selection signatures in commercial and indigenous chickens, a limited number of genes have been linked to specific traits. To identify more trait-related artificial selection signatures and genes, we re-sequenced a total of 85 individuals of five indigenous chicken breeds with distinct traits from Yunnan Province, China.ResultsWe found 30 million non-redundant single nucleotide variants and small indels (< 50 bp) in the indigenous chickens, of which 10 million were not seen in 60 broilers, 56 layers and 35 red jungle fowls (RJFs) that we compared with. The variants in each breed are enriched in non-coding regions, while those in coding regions are largely tolerant, suggesting that most variants might affect cis-regulatory sequences. Based on 27 million bi-allelic single nucleotide polymorphisms identified in the chickens, we found numerous selective sweeps and affected genes in each indigenous chicken breed and substantially larger numbers of selective sweeps and affected genes in the broilers and layers than previously reported using a rigorous statistical model. Consistent with the locations of the variants, the vast majority (~ 98.3%) of the identified selective sweeps overlap known quantitative trait loci (QTLs). Meanwhile, 74.2% known QTLs overlap our identified selective sweeps. We confirmed most of previously identified trait-related genes and identified many novel ones, some of which might be related to body size and high egg production traits. Using RT-qPCR, we validated differential expression of eight genes (GHR, GHRHR, IGF2BP1, OVALX, ELF2, MGARP, NOCT, SLC25A15) that might be related to body size and high egg production traits in relevant tissues of relevant breeds.ConclusionWe identify 30 million single nucleotide variants and small indels in the five indigenous chicken breeds, 10 million of which are novel. We predict substantially more selective sweeps and affected genes than previously reported in both indigenous and commercial breeds. These variants and affected genes are good candidates for further experimental investigations of genotype-phenotype relationships and practical applications in chicken breeding programs.

Discrimination of distinct chicken M cell subsets based on CSF1R expression.

In mammals, a subset of follicle-associated epithelial (FAE) cells, known as M cells, conduct the transcytosis of antigens across the epithelium into the underlying lymphoid tissues. We previously revealed that M cells in the FAE of the chicken lung, bursa of Fabricius (bursa), and caecum based on the expression of CSF1R. Here, we applied RNA-seq analysis on highly enriched CSF1R-expressing bursal M cells to investigate their transcriptome and identify novel chicken M cell-associated genes. Our data show that, like mammalian M cells, those in the FAE of the chicken bursa also express SOX8, MARCKSL1, TNFAIP2 and PRNP. Immunohistochemical analysis also confirmed the expression of SOX8 in CSF1R-expressing cells in the lung, bursa, and caecum. However, we found that many other mammalian M cell-associated genes such as SPIB and GP2 were not expressed by chicken M cells or represented in the chicken genome. Instead, we show bursal M cells express high levels of related genes such as SPI1. Whereas our data show that bursal M cells expressed CSF1R-highly, the M cells in the small intestine lacked CSF1R and both expressed SOX8. This study offers insights into the transcriptome of chicken M cells, revealing the expression of CSF1R in M cells is tissue-specific.

Determining the Relationships Between Genotype and Phenotype Using Molecular Genetic Tools in Chickens

Context: Chicken meat is one of the rich sources of protein and is considered one of the most commonly consumed meats in the world. Enhancing poultry production and its products is achievable through chicken breeding. Effective breeding projects for future genetic improvement require an understanding of the genetic potential of a given population. Evidence Acquisition: Articles were selected based on novel molecular markers related to economic traits of chicken in Animal quantitative trait loci (QTL) database, Ensemble genome browser, CorrDB, ChickVD, Galbase, and Google Scholar. These databases contain QTLs, markers, and reference genomes of chicken. Galbase provides research communities with updated possibilities to conduct thorough functional genomics research on chickens. ChickVD manages high-quality sequence variation data, variation analysis in relation to chicken genes, cDNAs, genetic markers, and QTLs. CorrDB compiles all available chicken genetic and phenotypic trait correlation data to aid in the analysis of genetic networks and facilitate systems biology research. Animal Quantitative Trait Loci Database (Animal QTLdb) gathers and compiles all existing data on trait mapping, including QTL (phenotype/expression, eQTL), candidate gene, association data (GWAS), and copy number variations (CNV) for livestock animal genomes. This comprehensive database facilitates the easier identification and comparison of discoveries within and across species. Ensemble genome browser is a powerful tool for analyzing vertebrate genomes, offering valuable insights into comparative genomics, evolutionary processes, sequence variations, and transcriptional regulation. Results: Valuating the relatedness of the studied broilers and layers is important for maintaining and improving the genetic reserves of the same population. Candidate genes (CGs) are used to determine genes related to economic traits in chickens. Molecular genetic techniques such as whole-genome re-sequencing (WGRS) can identify single nucleotide polymorphisms (SNPs), insertion/deletion polymorphisms (indels), structural variations (SV), CNV, and simple sequence repeats (SSRs) and their chromosomal regions in terms of QTL. Recently, the pan-genome (PG) has become one of the most important tools to study genome variation and changes in important traits. The use of molecular markers and CGs can aid in evaluating the genetic relationships between phenotype and genotype of broilers and layers. Conclusions: This review introduces molecular genetic methods and new approaches to identify novel genes related to important biological pathways and significant production and reproductive traits.

ETV2 induces endothelial, but not hematopoietic, lineage specification in birds.

Cardiovascular system develops from the lateral plate mesoderm. Its three primary cell lineages (hematopoietic, endothelial, and muscular) are specified by the sequential actions of conserved transcriptional factors. ETV2, a master regulator of mammalian hemangioblast development, however, is absent in the chicken genome and acts downstream of NPAS4L in zebrafish. Here, we investigated the epistatic relationship between NPAS4L and ETV2 in avian hemangioblast development. We showed that ETV2 is deleted in all 363 avian genomes analyzed. Mouse ETV2 induced LMO2, but not NPAS4L or SCL, expression in chicken mesoderm. Squamate (lizards, geckos, and snakes) genomes contain both NPAS4L and ETV2 In Madagascar ground gecko, both genes were expressed in developing hemangioblasts. Gecko ETV2 induced only LMO2 in chicken mesoderm. We propose that both NPAS4L and ETV2 were present in ancestral amniote, with ETV2 acting downstream of NPAS4L in endothelial lineage specification. ETV2 may have acted as a pioneer factor by promoting chromatin accessibility of endothelial-specific genes and, in parallel with NPAS4L loss in ancestral mammals, has gained similar function in regulating blood-specific genes.

High quality assemblies of four indigenous chicken genomes and related functional data resources

Many lines of evidence indicate that red jungle fowl (RJF) is the primary ancestor of domestic chickens. Although multiple versions of RJF (galgal2-galgal5 and GRCg6a) and commercial chickens (GRCg7b/w and Huxu) genomes have been assembled since 2004, no high-quality indigenous chicken genomes have been assembled, hampering the understanding of chicken domestication and evolution. To fill the gap, we sequenced the genomes of four indigenous chickens with distinct morphological traits in southwest China, using a combination of short, long and Hi-C reads. We assembled each genome (~1.0 Gb) into 42 chromosomes with chromosome N50 90.5–90.9 Mb, amongst the highest quality of chicken genome assemblies. To provide resources for gene annotation and functional analysis, we also sequenced transcriptomes of 10 tissues for each of the four chickens. Moreover, we corrected many mis-assemblies and assembled missing micro-chromosomes 29 and 34–39 for GRCg6a. Our assemblies, sequencing data and the correction of GRCg6a can be valuable resources for studying chicken domestication and evolution.

Unraveling the chicken T cell repertoire with enhanced genome annotation.

T cell receptor (TCR) repertoire sequencing has emerged as a powerful tool for understanding the diversity and functionality of T cells within the host immune system. Yet, the chicken TCR repertoire remains poorly understood due to incomplete genome annotation of the TCR loci, despite the importance of chickens in agriculture and as an immunological model. Here, we addressed this critical issue by employing 5' rapid amplification of complementary DNA ends (5'RACE) TCR repertoire sequencing with molecular barcoding of complementary DNA (cDNA) molecules. Simultaneously, we enhanced the genome annotation of TCR Variable (V), Diversity (D, only present in β and δ loci) and Joining (J) genes in the chicken genome. To enhance the efficiency of TCR annotations, we developed VJ-gene-finder, an algorithm designed to extract VJ gene candidates from deoxyribonucleic acid (DNA) sequences. Using this tool, we achieved a comprehensive annotation of all known chicken TCR loci, including the α/δ locus on chromosome 27. Evolutionary analysis revealed that each locus evolved separately by duplication of long homology units. To define the baseline TCR diversity in healthy chickens and to demonstrate the feasibility of the approach, we characterized the splenic α/β/γ/δ TCR repertoire. Analysis of the repertoires revealed preferential usage of specific V and J combinations in all chains, while the overall features were characteristic of unbiased repertoires. We observed moderate levels of shared complementarity-determining region 3 (CDR3) clonotypes among individual birds within the α and γ chain repertoires, including the most frequently occurring clonotypes. However, the β and δ repertoires were predominantly unique to each bird. Taken together, our TCR repertoire analysis allowed us to decipher the composition, diversity, and functionality of T cells in chickens. This work not only represents a significant step towards understanding avian T cell biology, but will also shed light on host-pathogen interactions, vaccine development, and the evolutionary history of avian immunology.

Comparative peculiarities of genomic diversity in Gallus gallus domesticus chickens with decorative plumage: the muffs and beard phenotype.

Throughout history, humans have been attempting to develop the ornamental features of domestic animals in addition to their productive qualities. Many chicken breeds have developed tufts of elongated feathers that jut out from the sides and bottom of the beak, leading to the phenotype known as muffs and beard. It is an incomplete autosomal dominant phenotype determined by the Mb locus localised on chromosome GGA27. This project aimed to analyse the genetic diversity of chicken breeds using full genomic genotyping with the Chicken 60K BeadChip. A total of 53,313 Single Nucleotide Polymorphisms were analysed. DNA was obtained from breeds with the muffs and beard as a marker phenotype: Faverolles (n = 20), Ukrainian Muffed (n = 18), Orloff (n = 20), Novopavlov White (n = 20), and Novopavlov Coloured (n = 15). The Russian White (n = 20) was selected as an alternative breed without the muffs and beard phenotype. The chickens are owned by the Centre of Collective Use "Genetic Collection of Rare and Endangered Breeds of Chickens" (St. Petersburg region, Pushkin), and are also included in the Core Shared Research Facility (CSRF) and/or Large-Scale Research Facility (LSRF). Multidimensional scaling revealed that the Novopavlov White and the Novopavlov Coloured populations formed a separate group. The Ukrainian Muffed and the Orloff have also been combined into a separate group. Based on cluster analysis, with the cross-validation error and the most probable number of clusters K = 4 taken into account, the Orloff was singled out as a separate group. The Ukrainian Muffed exhibited a notable similarity with the Orloff under the same conditions. At K = 5, the populations of the Novopavlov White and the Novopavlov Coloured diverged. Only at K = 6, a distinct and separate cluster was formed by the Ukrainian Muffed. The Russian White had the greatest number of short (1-2 Mb) homozygous regions. If the HOXB8 gene is located between 3.402 and 3.404 Mb on chromosome GGA27, homozygous regions are rarely found in the chickens with the muffs and beard phenotype. Scanning the chicken genome with the Chicken 60K BeadChip provided enough information about the genetic diversity of the chicken breeds for the peculiarities of the development of the ornamental muffs and beard phenotypes in them to be understood. For example, Phoenix bantams, whose tail feathers grow throughout their lives, require greater consideration of husbandry conditions.

RNA sequencing identifies key genes involved in intramuscular fat deposition in chickens at different developmental stages

BackgroundIntramuscular fat (IMF) is an important factor in meat quality, and triglyceride (TG) and Phospholipids (PLIP), as the main components of IMF, are of great significance to the improvement of meat quality.ResultsIn this study, we used 30 RNA sequences generated from the transcriptome of chicken breast muscle tissues at different developmental stages to construct a gene expression matrix to map RNA sequence reads to the chicken genome and identify the transcript of origin. We used weighted gene co-expression network analysis (WGCNA) and identified 27 co-expression modules, 10 of which were related to TG and PLIP. We identified 150 highly-connected hub genes related to TG and PLIP, respectively, which were found to be mainly enriched in the adipocytokine signaling pathway, MAPK signaling pathway, mTOR signaling pathway, FoxO signaling pathway, and TGF-beta signaling pathway. Additionally, using the BioMart database, we identified 134 and 145 candidate genes related to fat development in the TG-related module and PLIP-related module, respectively. Among them, RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 were identified as candidate genes related to fat development and highly-connected hub genes in the module, suggesting that these ten genes may be important candidate genes affecting IMF deposition.ConclusionsRPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 may be important candidate genes affecting IMF deposition. The purpose of this study was to identify the co-expressed gene modules related to chicken IMF deposition using WGCNA and determine key genes related to IMF deposition, so as to lay a foundation for further research on the molecular regulation mechanism underlying chicken fat deposition.

Identify Candidate Genes Associated with the Weight and Egg Quality Traits in Wenshui Green Shell-Laying Chickens by the Copy Number Variation-Based Genome-Wide Association Study.

Copy number variation (CNV), as an essential source of genetic variation, can have an impact on gene expression, genetic diversity, disease susceptibility, and species evolution in animals. To better understand the weight and egg quality traits of chickens, this paper aimed to detect CNVs in Wenshui green shell-laying chickens and conduct a copy number variation regions (CNVRs)-based genome-wide association study (GWAS) to identify variants and candidate genes associated with their weight and egg quality traits to support related breeding efforts. In our paper, we identified 11,035 CNVRs in Wenshui green shell-laying chickens, which collectively spanned a length of 13.1 Mb, representing approximately 1.4% of its autosomal genome. Out of these CNVRs, there were 10,446 loss types, 491 gain types, and 98 mixed types. Notably, two CNVRs showed significant correlations with egg quality, while four CNVRs exhibited significant associations with body weight. These significant CNVRs are located on chromosome 4. Further analysis identified potential candidate genes that influence weight and egg quality traits, including FAM184B, MED28, LAP3, ATOH8, ST3GAL5, LDB2, and SORCS2. In this paper, the CNV map of the Wenshui green shell-laying chicken genome was constructed for the first time through population genotyping. Additionally, CNVRs can be employed as molecular markers to genetically improve chickens' weight and egg quality traits.

Single-cell RNA-seq mapping of chicken peripheral blood leukocytes

BackgroundSingle-cell transcriptomics provides means to study cell populations at the level of individual cells. In leukocyte biology this approach could potentially aid the identification of subpopulations and functions without the need to develop species-specific reagents. The present study aimed to evaluate single-cell RNA-seq as a tool for identification of chicken peripheral blood leukocytes. For this purpose, purified and thrombocyte depleted leukocytes from 4 clinically healthy hens were subjected to single-cell 3′ RNA-seq. Bioinformatic analysis of data comprised unsupervised clustering of the cells, and annotation of clusters based on expression profiles. Immunofluorescence phenotyping of the cell preparations used was also performed.ResultsComputational analysis identified 31 initial cell clusters and based on expression of defined marker genes 28 cluster were identified as comprising mainly B-cells, T-cells, monocytes, thrombocytes and red blood cells. Of the remaining clusters, two were putatively identified as basophils and eosinophils, and one as proliferating cells of mixed origin. In depth analysis on gene expression profiles within and between the initial cell clusters allowed further identification of cell identity and possible functions for some of them. For example, analysis of the group of monocyte clusters revealed subclusters comprising heterophils, as well as putative monocyte subtypes. Also, novel aspects of TCRγ/δ + T-cell subpopulations could be inferred such as evidence of at least two subtypes based on e.g., different expression of transcription factors MAF, SOX13 and GATA3. Moreover, a novel subpopulation of chicken peripheral B-cells with high SOX5 expression was identified. An overall good correlation between mRNA and cell surface phenotypic cell identification was shown.ConclusionsTaken together, we were able to identify and infer functional aspects of both previously well known as well as novel chicken leukocyte populations although some cell types. e.g., T-cell subtypes, proved more challenging to decipher. Although this methodology to some extent is limited by incomplete annotation of the chicken genome, it definitively has benefits in chicken immunology by expanding the options to distinguish identity and functions of immune cells also without access to species specific reagents.