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Poultry Genetics and Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Animal Genetics and Genomics".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 33155

Special Issue Editors

College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
Interests: poultry; genetics and breeding; gene function; epigenetics; molecular-marker-assisted breeding; adipogenesis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
Interests: poultry; quantitative genetics; myogenesis; gene function; molecular mechanisms; non-coding RNA
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Poultry meat and eggs are among the most common animal sources of food consumed at the global level. With continuous genetic selection, modern poultry has become the most efficient domestic animal, producing cheap and high-quality protein. The rapid development of poultry genetics and genomics breeding has brought huge economic benefits to the global poultry industry. However, numerous problems in poultry genetics and genomics remain to be addressed. For example, the complex genetic basis of important economic traits is still poorly understood, and the functional genes responsible for target traits require further mining. Genomic selection technology is immature, and its application in poultry breeding is still relatively limited.

This Special Issue aims to collect high-quality original research articles and comprehensive reviews to address emerging challenges in poultry genetics and genomics. Topics welcomed include, but are not limited to, genetic diversity and evolution, genome annotation, function and molecular mechanisms, omics studies, genomic selection, gene editing, and other new progress related to poultry genetics and genomics.

Dr. Tao Zhang
Dr. Genxi Zhang
Guest Editors

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Keywords

  • poultry
  • gene function
  • genome annotation
  • omics
  • gene editing
  • genomic selection
  • molecular mechanisms
  • molecular markers

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Published Papers (14 papers)

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Research

17 pages, 4935 KiB  
Article
Genome-Wide Association Study Revealed Putative SNPs and Candidate Genes Associated with Growth and Meat Traits in Japanese Quail
by Natalia A. Volkova, Michael N. Romanov, Alexandra S. Abdelmanova, Polina V. Larionova, Nadezhda Yu. German, Anastasia N. Vetokh, Alexey V. Shakhin, Ludmila A. Volkova, Alexander A. Sermyagin, Dmitry V. Anshakov, Vladimir I. Fisinin, Darren K. Griffin, Johann Sölkner, Gottfried Brem, John C. McEwan, Rudiger Brauning and Natalia A. Zinovieva
Genes 2024, 15(3), 294; https://doi.org/10.3390/genes15030294 - 25 Feb 2024
Cited by 4 | Viewed by 1805
Abstract
The search for SNPs and candidate genes that determine the manifestation of major selected traits is one crucial objective for genomic selection aimed at increasing poultry production efficiency. Here, we report a genome-wide association study (GWAS) for traits characterizing meat performance in the [...] Read more.
The search for SNPs and candidate genes that determine the manifestation of major selected traits is one crucial objective for genomic selection aimed at increasing poultry production efficiency. Here, we report a genome-wide association study (GWAS) for traits characterizing meat performance in the domestic quail. A total of 146 males from an F2 reference population resulting from crossing a fast (Japanese) and a slow (Texas White) growing breed were examined. Using the genotyping-by-sequencing technique, genomic data were obtained for 115,743 SNPs (92,618 SNPs after quality control) that were employed in this GWAS. The results identified significant SNPs associated with the following traits at 8 weeks of age: body weight (nine SNPs), daily body weight gain (eight SNPs), dressed weight (33 SNPs), and weights of breast (18 SNPs), thigh (eight SNPs), and drumstick (three SNPs). Also, 12 SNPs and five candidate genes (GNAL, DNAJC6, LEPR, SPAG9, and SLC27A4) shared associations with three or more traits. These findings are consistent with the understanding of the genetic complexity of body weight-related traits in quail. The identified SNPs and genes can be used in effective quail breeding as molecular genetic markers for growth and meat characteristics for the purpose of genetic improvement. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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Figure 1
<p>Assortment of quail products: wholesale commercial egg tray and a pack of seasoned smoked hard-boiled eggs (in the foreground); whole egg mayonnaise, quail egg nutrient noodles, and frozen quail carcasses (in the background). Credit: authors’ own photo.</p>
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<p>Quails of the F<sub>2</sub> reference population: (<b>A</b>) at 3 days of age and (<b>B</b>) at 7 weeks of age.</p>
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<p>Overview of the experimental design. The reference population consisting of 146 F<sub>2</sub> male quails was created and extensively phenotyped using reciprocal crosses between a slow-growing Japanese and a fast-growing Texas White breed. <span class="html-italic">n</span>, number of progenies (of both sexes) from each F<sub>2</sub> family.</p>
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<p>Principal component analysis (PCA) of the F<sub>2</sub> quail reference population based on GBS data. (<b>A</b>) PCA performed in the plane of the first (PC1, <span class="html-italic">X</span>-axis) and second (PC2, <span class="html-italic">Y</span>-axis) components. (<b>B</b>) PCA performed in the plane of the first (PC1, <span class="html-italic">X</span>-axis) and second (PC3, <span class="html-italic">Y</span>-axis) components. Individuals from different groups are indicated by different colors.</p>
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<p>Manhattan plots of the GWAS results for the studied meat production traits: (<b>A</b>) body weight at 1 day of age, (<b>B</b>) body weight at 56 days of age, (<b>C</b>) average daily body weight gain, (<b>D</b>) dressed carcass weight, (<b>E</b>) breast weight, (<b>F</b>) thigh weight, and (<b>G</b>) drumstick weight. Distribution of SNPs in quail chromosomes for single traits are shown relative to the thresholds for the genome-wide nominal significance level (−log<sub>10</sub> (<span class="html-italic">p</span>)) according to the estimated probability values of <span class="html-italic">p</span> &lt; 1.0 × 10<sup>−5</sup> (lower line) and <span class="html-italic">p</span> &lt; 5.4 × 10<sup>−7</sup> (upper line). Points are color-coded only to visualize chromosome separation.</p>
Full article ">Figure 5 Cont.
<p>Manhattan plots of the GWAS results for the studied meat production traits: (<b>A</b>) body weight at 1 day of age, (<b>B</b>) body weight at 56 days of age, (<b>C</b>) average daily body weight gain, (<b>D</b>) dressed carcass weight, (<b>E</b>) breast weight, (<b>F</b>) thigh weight, and (<b>G</b>) drumstick weight. Distribution of SNPs in quail chromosomes for single traits are shown relative to the thresholds for the genome-wide nominal significance level (−log<sub>10</sub> (<span class="html-italic">p</span>)) according to the estimated probability values of <span class="html-italic">p</span> &lt; 1.0 × 10<sup>−5</sup> (lower line) and <span class="html-italic">p</span> &lt; 5.4 × 10<sup>−7</sup> (upper line). Points are color-coded only to visualize chromosome separation.</p>
Full article ">Figure 5 Cont.
<p>Manhattan plots of the GWAS results for the studied meat production traits: (<b>A</b>) body weight at 1 day of age, (<b>B</b>) body weight at 56 days of age, (<b>C</b>) average daily body weight gain, (<b>D</b>) dressed carcass weight, (<b>E</b>) breast weight, (<b>F</b>) thigh weight, and (<b>G</b>) drumstick weight. Distribution of SNPs in quail chromosomes for single traits are shown relative to the thresholds for the genome-wide nominal significance level (−log<sub>10</sub> (<span class="html-italic">p</span>)) according to the estimated probability values of <span class="html-italic">p</span> &lt; 1.0 × 10<sup>−5</sup> (lower line) and <span class="html-italic">p</span> &lt; 5.4 × 10<sup>−7</sup> (upper line). Points are color-coded only to visualize chromosome separation.</p>
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15 pages, 4097 KiB  
Article
Analysis of Transcriptomic Differences in the Ovaries of High- and Low-Laying Ducks
by Yuguang Chang, Rongbing Guo, Tao Zeng, Hanxue Sun, Yong Tian, Xue Han, Yongqing Cao, Ligen Xu, Mingcai Duan, Lizhi Lu and Li Chen
Genes 2024, 15(2), 181; https://doi.org/10.3390/genes15020181 - 29 Jan 2024
Cited by 3 | Viewed by 1912
Abstract
The egg-laying performance of Shan Ma ducks (Anas Platyrhynchos) is a crucial economic trait. Nevertheless, limited research has been conducted on the egg-laying performance of this species. We examined routine blood indicators and observed higher levels of metabolic and immune-related factors in the [...] Read more.
The egg-laying performance of Shan Ma ducks (Anas Platyrhynchos) is a crucial economic trait. Nevertheless, limited research has been conducted on the egg-laying performance of this species. We examined routine blood indicators and observed higher levels of metabolic and immune-related factors in the high-egg-production group compared with the low-egg-production group. Furthermore, we explored the ovarian transcriptome of both high- and low-egg-production groups of Shan Ma ducks using Illumina NovaSeq 6000 sequencing. A total of 1357 differentially expressed genes (DEGs) were identified, with 686 down-regulated and 671 up-regulated in the high-egg-production (HEP) ducks and low-egg-production (LEP) ducks. Several genes involved in the regulation of ovarian development, including neuropeptide Y (NPY), cell cycle protein-dependent kinase 1 (CDK1), and transcription factor 1 (E2F1), exhibited significant differential expressions at varying stages of egg production. Pathway functional analysis revealed that the DEGs were primarily associated with the steroid biosynthesis pathway, and the neuroactive ligand–receptor interaction pathway exhibited higher activity in the HEP group compared to the LEP group. This study offers valuable information about and novel insights into high egg production. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Ovarian gene expression differed between the HEP and LEP groups. (<b>A</b>) Principal component analysis graph. Each point in the diagram represents a sample, and the position of the sample in space is determined by the differences in expression of the genes contained within it. (<b>B</b>) Volcano plots of differential gene expression. Each point in the graph represents a specific gene or transcript, with red points indicating significantly up-regulated genes, blue points indicating significantly down-regulated genes, and black points indicating non-significantly different genes. (<b>C</b>) Heat map of DEGs. The color represents the level of expression: the redder the color, the higher the gene expression. The heat map in the analysis results is a plot of the top 100 genes with the smallest <span class="html-italic">p</span>-values for display.</p>
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<p>Functional annotations of differentially expressed genes in HEP and LEP groups. (<b>A</b>) Histogram of differential gene GO enrichment. The horizontal coordinate is the number of genes, and the vertical coordinate is the enrichment of genes in GO. (<b>B</b>) Scatter plot of KEGG enrichment for differentially expressed genes. The top 30 enrichment classifications of the KEGG pathway of the DEGs are listed in the figure. The horizontal axis indicates the enrichment factor, and the vertical axis indicates the name of the pathway. The point size indicates the number of enriched DEGs in the pathway, and the point color corresponds to a different range of <span class="html-italic">p</span>-values.</p>
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<p>Protein–protein interaction (PPI) network for the cutoff differentially expressed genes (DEGs) based on the KEGG pathway. A total of 170 nodes and 450 edges were identified. The line color indicates the type of interaction evidence.</p>
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<p>The three protein–protein interaction (PPI) hub network modules. The three significant modules, including (<b>A</b>) module 1 (MCODE score = 3.3), (<b>B</b>) module 2 (score = 10), and (<b>C</b>) module 3 (score = 3.7), were constructed from the PPI network of differentially expressed genes using MCODE. The seed node of each module was shaped, as highlighted by the red gene symbols.</p>
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<p>Comparative analysis of qRT-PCR versus RNA-seq. Selected DEGs were validated by qRT-PCR for comparison.</p>
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13 pages, 2735 KiB  
Article
Genome-Wide Association Study of Egg Production Traits in Shuanglian Chickens Using Whole Genome Sequencing
by Ming Fu, Yan Wu, Jie Shen, Ailuan Pan, Hao Zhang, Jing Sun, Zhenhua Liang, Tao Huang, Jinping Du and Jinsong Pi
Genes 2023, 14(12), 2129; https://doi.org/10.3390/genes14122129 - 25 Nov 2023
Cited by 7 | Viewed by 1904
Abstract
Egg production is the most important economic trait in laying hens. To identify molecular markers and candidate genes associated with egg production traits, such as age at first egg (AFE), weight at first egg (WFE), egg weight (EW), egg number (EN), and maximum [...] Read more.
Egg production is the most important economic trait in laying hens. To identify molecular markers and candidate genes associated with egg production traits, such as age at first egg (AFE), weight at first egg (WFE), egg weight (EW), egg number (EN), and maximum consecutive egg laying days (MCD), a genome-wide analysis by whole genome sequencing was performed in Shuanglian chickens. Through whole genome sequencing and quality control, a total of 11,006,178 SNPs were obtained for further analysis. Heritability estimates ranged from moderate to high for EW (0.897) and MCD (0.632), and from low to moderate (0.193~0.379) for AFE, WFE, and EN. The GWAS results showed 11 genome-wide significant SNPs and 23 suggestive significant SNPs were identified to be associated with EN, MCD, WFE, and EW. Linkage disequilibrium analysis revealed twenty-seven SNPs associated with EN were located in a 0.57 Mb region on GGA10, and clustered into five blocks. Through functional annotation, three candidate genes NEO1, ADPGK, and CYP11A1, were identified to be associated with EN, while the S1PR4, LDB2, and GRM8 genes was linked to MCD, WFE, and EW, respectively. These findings may help us to better understand the molecular mechanisms underlying egg production traits in chickens and contribute to genetic improvement of these traits. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>The density plot of SNPs.</p>
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<p>Manhattan plots and quantile-quantile (Q-Q) plots of egg number at 40 weeks (EN40), egg number at 43 weeks (EN43), and maximum consecutive egg laying days (MCD). The solid and dashed line indicates the genome-wide and suggestive significance threshold, respectively. Red points indicate the genome-wide significant SNPs, green points indicate the suggestive significant SNPs.</p>
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<p>Linkage disequilibrium analysis of genomic regions associated with EN.</p>
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<p>GO annotation analysis for candidate genes.</p>
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<p>KEGG pathway analysis for candidate genes.</p>
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15 pages, 3096 KiB  
Article
Effects of Insulin on Proliferation, Apoptosis, and Ferroptosis in Primordial Germ Cells via PI3K-AKT-mTOR Signaling Pathway
by Liu Ye, Xin Liu, Kai Jin, Yingjie Niu, Qisheng Zuo, Jiuzhou Song, Wei Han, Guohong Chen and Bichun Li
Genes 2023, 14(10), 1975; https://doi.org/10.3390/genes14101975 - 22 Oct 2023
Cited by 6 | Viewed by 2075
Abstract
Primordial germ cells (PGCs) are essential for the genetic modification, resource conservation, and recovery of endangered breeds in chickens and need to remain viable and proliferative in vitro. Therefore, there is an urgent need to elucidate the functions of the influencing factors and [...] Read more.
Primordial germ cells (PGCs) are essential for the genetic modification, resource conservation, and recovery of endangered breeds in chickens and need to remain viable and proliferative in vitro. Therefore, there is an urgent need to elucidate the functions of the influencing factors and their regulatory mechanisms. In this study, PGCs collected from Rugao yellow chicken embryonic eggs at Day 5.5 were cultured in media containing 0, 5, 10, 20, 50, and 100 μg/mL insulin. The results showed that insulin regulates cell proliferation in PGCs in a dose-dependent way, with an optimal dose of 10 μg/mL. Insulin mediates the mRNA expression of cell cycle-, apoptosis-, and ferroptosis-related genes. Insulin at 50 μg/mL and 100 μg/mL slowed down the proliferation with elevated ion content and GSH/oxidized glutathione (GSSG) in PGCs compared to 10 μg/mL. In addition, insulin activates the PI3K/AKT/mTOR pathway dose dependently. Collectively, this study demonstrates that insulin reduces apoptosis and ferroptosis and enhances cell proliferation in a dose-dependent manner via the PI3K-AKT-mTOR signaling pathway in PGCs, providing a new addition to the theory of the regulatory role of the growth and proliferation of PGC in vitro cultures. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Morphological observation and identification of PGCs. (<b>A</b>) Identification of isolated PGCs. (<b>B</b>) Cell morphology of PGCs cultured with B-27 (minus insulin). (<b>C</b>) Cell morphology of PGCs cultured with B-27.</p>
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<p>Insulin mediates PGC growth in a dose-dependent way. (<b>A</b>) Cell morphology of PGCs under insulin treatment at different concentrations. (<b>B</b>,<b>C</b>) Cell proliferation rate of PGCs using CCK-8 kit (<b>B</b>) and EDU kit (<b>C</b>). (<b>D</b>) EDU staining of PGCs. <sup>a,b,c,d,e</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>mRNA levels of <span class="html-italic">CCND1</span> (<b>A</b>), <span class="html-italic">ABL1</span> (<b>B</b>), <span class="html-italic">CCNB1</span> (<b>C</b>), <span class="html-italic">CCNF</span> (<b>D</b>). <sup>a,b,c,d,e</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Insulin reduces cell apoptosis in PGCs. (<b>A</b>–<b>E</b>) mRNA levels of apoptosis-related genes, <span class="html-italic">BAX</span> (<b>A</b>), <span class="html-italic">BCL2</span> (<b>B</b>), <span class="html-italic">CASP3</span> (<b>C</b>), <span class="html-italic">CASP9</span> (<b>D</b>), <span class="html-italic">C-myc</span> (<b>E</b>). (<b>F</b>,<b>G</b>) Apoptosis rate (<b>F</b>) and flow cytometric analysis (<b>G</b>) of PGCs under insulin treatment. <sup>a,b,c,d</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Insulin reduces ferroptosis in PGCs. (<b>A</b>–<b>C</b>) mRNA levels of ferroptosis-related genes <span class="html-italic">GPX4</span> (<b>A</b>), <span class="html-italic">NOX4</span> (<b>B</b>), and <span class="html-italic">SLC7A11</span> (<b>C</b>). (<b>D</b>) Iron content in PGCs. <sup>a,b,c,d,e</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Insulin reduces the redox levels and the degree of cellular damage in PGCs. (<b>A</b>) Total glutathione in PGCs. (<b>B</b>) Oxidative GSSG in PGCs. (<b>C</b>) GSH in total glutathione in PGCs. (<b>D</b>) MDA level in PGCs. <sup>a,b,c,d,e</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Insulin activates PI3K/AKT/mTOR pathway. (<b>A</b>–<b>C</b>) Western blot and quantification of p-PI3K/PI3K (<b>A</b>,<b>B</b>) and p-Akt/Akt (<b>A</b>,<b>C</b>). (<b>D</b>–<b>F</b>) mRNA levels of <span class="html-italic">PI3K</span>, <span class="html-italic">AKT1</span>, and <span class="html-italic">MTOR</span>. <sup>a,b,c,d,e,f</sup> Means within a row with no common superscript differ significantly (<span class="html-italic">p</span> &lt; 0.05).</p>
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13 pages, 3899 KiB  
Article
Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis
by Xuanze Ling, Qifan Wang, Pengfei Wu, Kaizhi Zhou, Jin Zhang and Genxi Zhang
Genes 2023, 14(9), 1764; https://doi.org/10.3390/genes14091764 - 5 Sep 2023
Cited by 1 | Viewed by 1686
Abstract
Broiler skeletal muscle growth is significantly influenced by miRNAs. Our earlier research demonstrated that miR-24-3p significantly suppressed the proliferation of chicken myoblasts while promoting their differentiation. The purpose of this study is to investigate miR-24-3p potential target genes in chickens. We collected myoblasts [...] Read more.
Broiler skeletal muscle growth is significantly influenced by miRNAs. Our earlier research demonstrated that miR-24-3p significantly suppressed the proliferation of chicken myoblasts while promoting their differentiation. The purpose of this study is to investigate miR-24-3p potential target genes in chickens. We collected myoblasts of Jinghai yellow chicken and transfected four samples with mimics of miR-24-3p and another four samples with mimic NC (negative control) for RNA-seq. We obtained 54.34 Gb of raw data in total and 50.79 Gb of clean data remained after filtering. Moreover, 11,635 genes were found to be co-expressed in these two groups. The mimic vs. NC comparison group contained 189 DEGs in total, 119 of which were significantly up-regulated and 70 of which were significantly down-regulated. Important biological process (BP) terminology such as nuclear chromosomal segregation, reproduction, and nuclear division were discovered by GO enrichment analysis for DEGs in the mimic vs. NC comparison group. KEGG pathway analysis showed that focal adhesion, cytokine–cytokine receptor interaction, the TGF-β signaling pathway, and the MAPK signaling pathway were enriched in the top 20. Variation site analysis illustrated the SNP (single nucleotide polymorphisms) and INDEL (insertion–deletion) in the tested samples. By comparing the target genes predicted by miRDB (MicroRNA target prediction database) and TargetScan with the 189 DEGs found by the transcriptome sequencing, we discovered two up-regulated DEGs (NEURL1 and IQSEC3) and two down-regulated DEGs (REEP1 and ST6GAL1). Finally, we carried out qPCR experiments on eight DEGs and discovered that the qPCR results matched the sequencing outcomes. These findings will aid in identifying potential miR-24-3p target genes in chicken skeletal muscle and offer some new directions for upcoming research on broiler breeding. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Sequencing data analysis. (<b>A</b>) The principal component analysis (PCA) of these 8 samples. (<b>B</b>) The correlation scatterplot with density. The colors from yellow to purple represent the intensity of FPKM values from high to low. (<b>C</b>) Boxplot of gene expression distribution of all examples. (<b>D</b>) Venn diagram of mimic vs. NC.</p>
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<p>Statistics of DEGs in mimic vs. NC. (<b>A</b>) The Volcano plot of mimic vs. NC. (<b>B</b>) Clustering analysis of differentially expressed genes.</p>
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<p>Gene ontology enrichment analysis. (<b>A</b>) The top 30 GO terms in mimic vs. NC. (<b>B</b>) The numbers of down-regulated genes and rich factors in GO enrichment terms.</p>
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<p>KEGG pathway analysis. The figure displays the top 20 pathways and their corresponding predicted target genes in the mimic vs. NC comparison group.</p>
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<p>Variation site analysis. (<b>A</b>) INDEL (insertion–deletion) impact analysis. (<b>B</b>) INDEL region analysis. The downstream_gene_variant occurred in DOWNSTREAM. The frameshift_variant occurred in EXON. The intergenic_region occurred in INTERGENIC. The intron_variant occurred in INTRON. The splice_acceptor_variant occurred in SPLICE_SITE_ACCEPTOR. The splice_donor_variant occurred in SPLICE_SITE_DONOR. The splice_region_variant occurred in SPLICE_SITE_REGION. The non_coding_transcript_exon_variant occurred in TRANSCRIPT. The upstream_gene_variant occurred in UPSTREAM. The 3_prime_UTR_variant occurred in UTR_3_PRIME. The 5_prime_UTR_variant occurred in UTR_5_PRIME. Few variation sites were found in GENE. (<b>C</b>) SNP function analysis. (<b>D</b>) SNP impact analysis.</p>
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<p>Target gene prediction and verification of the sequencing results. (<b>A</b>) Venn diagram showed the common genes between the target genes predicted by miRDB and the DEGs detected by sequencing. (<b>B</b>) The Venn diagram showed common genes between the target genes predicted by Targetscan and DEGs detected by sequencing. (<b>C</b>) Agarose gel results of 8 DEGs primers. Character “M” represented the marker. (<b>D</b>) Expression level of 8 DEGs in RNA-seq results. The colors from red to blue represent a level from high to low. (<b>E</b>) RT-qPCR and RNA-seq results of 4 down-regulated DEGs. (<b>F</b>) RT-qPCR and RNA-seq results of 4 up-regulated DEGs.</p>
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12 pages, 2612 KiB  
Article
Combine with RNA-seq Reveals the Effect of Melatonin in the Synthesis of Melanin in Primary Melanocytes of Silky Fowls Black-Bone Chicken
by Ting Yang, Lingling Qiu, Shihao Chen, Zhixiu Wang, Yong Jiang, Hao Bai, Yulin Bi and Guobin Chang
Genes 2023, 14(8), 1648; https://doi.org/10.3390/genes14081648 - 18 Aug 2023
Cited by 4 | Viewed by 2148
Abstract
(1) Background: It was found that the melanin of black-bone chicken has various effects such as scavenging DPPH free radicals and anti-oxidation, and the synthesis of melanin is affected by various factors including hormones. In addition, several studies have found that melatonin affects [...] Read more.
(1) Background: It was found that the melanin of black-bone chicken has various effects such as scavenging DPPH free radicals and anti-oxidation, and the synthesis of melanin is affected by various factors including hormones. In addition, several studies have found that melatonin affects the melanoma cell synthesis of melanin, which has not been reported in chicken primary melanocytes; so, relevant studies were conducted. (2) Methods: In this study, chicken primary melanocytes were isolated and characterized, and then melanocytes were treated with different concentrations of melatonin to investigate the effects of melatonin on melanin synthesis in chicken melanocytes in terms of melanin synthesis-related genes, melanin content, and tyrosinase activity, and combined with RNA seq to detect the change in gene expression level of chicken melanocytes after melatonin treatment. (3) Results: We isolated and characterized primary melanocytes, and indirect immunofluorescence assay results showed positive melanocyte marker genes. RT-qPCR results showed that melatonin decreased the expression of melanin synthesis-related genes. In addition, melatonin reduced the melanin content and decreased the tyrosinase activity of melanocytes in the treated group. A total of 1703 differentially expressed genes were screened by RNA-seq, and in addition, in the KEGG results, the signaling pathway associated with melanin synthesis, and the mTOR signaling pathway were enriched. (4) Conclusions: Melatonin could decrease the synthesis of melanin in chicken primary melanocytes. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Morphological observation and identification of melanocytes. (<b>A</b>) Observation of culture of melanocytes (scale bar, 50 μm). (<b>B</b>) Melanin granules of melanocytes; the black dots in the red box and the black particles indicated by the red arrows in the image are both melanin granules (scale bar, 20 μm). (<b>C</b>) Identification of isolated melanocytes by IFA (blue fluorescence is DAPI, green fluorescence is MITF, S-1OO, PAX3, TYR; scale bar, 50 μm).</p>
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<p>Melatonin inhibits melanin synthesis in melanocytes. (<b>A</b>) and (<b>B</b>) cell viability detected by CCK8 when melanocytes were treated with different concentrations of melatonin for 24 h, 48 h, and 72 h. RT-qPCR was used to detect the expression of melanin synthesis-related genes when melanocytes were treated with different concentrations of melatonin for 24 h (<b>C</b>), 48 h (<b>D</b>) and 72 h (<b>E</b>). (<b>F</b>) Detect the change in melanin content after melatonin treatment of melanocytes for 24 h, 48 h and 72 h by NaOH. (<b>G</b>) The tyrosinase activity of melanocytes after 24 h, 48 h and 72 h of melatonin treatment was detected by 0.1% L-DOPA. Data are expressed as means ± SD (n = 3). The one-way ANOVA was used for statistical significance (* <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>RNA sequencing results’ analysis. (<b>A</b>) Violin plot of gene expression patterns for each sample, with the middle horizontal line representing the median. (<b>B</b>) The numbers of upregulated and downregulated DEGs were evaluated. (<b>C</b>) Volcano plot showing the DEGs. The upregulated DEGs are represented by red dots, downregulated DEGs are represented by green dots, and genes with no significant differences in expression are represented by blue dots. (<b>D</b>) The heat map of differentially expressed gene clustering for each sample.</p>
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<p>Enrichment analysis of differentially expressed genes. (<b>A</b>) The top 20 significantly enriched KEGG pathways. (<b>B</b>) The top 20 significantly enriched downregulated KEGG pathways. (<b>C</b>) The top 30 significantly enriched GO terms.</p>
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<p>Confirmation of the transcriptome sequencing data by RT-qPCR. Data are expressed as means ± SD (n = 3). The one-way ANOVA was used for statistical significance. (ns: <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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14 pages, 2504 KiB  
Article
Characterization of the Effects of Host p53 and Fos on Gallid Alpha Herpesvirus 1 Replication
by Zheyi Liu, Lu Cui, Xuefeng Li, Li Xu, Yu Zhang, Zongxi Han, Shengwang Liu and Hai Li
Genes 2023, 14(8), 1615; https://doi.org/10.3390/genes14081615 - 12 Aug 2023
Cited by 3 | Viewed by 1521
Abstract
Treatment options for herpesvirus infections that target the interactions between the virus and the host have been identified as promising. Our previous studies have shown that transcription factors p53 and Fos are essential host determinants of gallid alpha herpesvirus 1 (ILTV) infection. The [...] Read more.
Treatment options for herpesvirus infections that target the interactions between the virus and the host have been identified as promising. Our previous studies have shown that transcription factors p53 and Fos are essential host determinants of gallid alpha herpesvirus 1 (ILTV) infection. The impact of p53 and Fos on ILTV replication has ‘not been fully understood yet. Using the sole ILTV-permissive chicken cell line LMH as a model, we examined the effects of hosts p53 and Fos on all phases of ILTV replication, including viral gene transcription, viral genome replication, and infectious virion generation. We achieved this by manipulating the expression of p53 and Fos in LMH cells. Our results demonstrate that the overexpression of either p53 or Fos can promote viral gene transcription at all stages of the temporal cascade of ILTV gene expression, viral genome replication, and infectious virion production, as assessed through absolute quantitative real-time PCR, ILTV-specific RT-qPCR assays, and TCID50 assays. These findings are consistent with our previous analyses of the effects of Fos and p53 knockdowns on virus production and also suggest that both p53 and Fos may be dispensable for ILTV replication. Based on the synergistic effect of regulating ILTV, we further found that there is an interaction between p53 and Fos. Interestingly, we found that p53 also has targeted sites upstream of ICP4, and these sites are very close to the Fos sites. In conclusion, our research offers an in-depth understanding of how hosts p53 and Fos affect ILTV replication. Understanding the processes by which p53 and Fos regulate ILTV infection will be improved by this knowledge, potentially paving the way for the development of novel therapeutics targeting virus–host interactions as a means of treating herpesvirus infections. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Manipulating the expression of p53 and Fos in LMH cells. For the overexpression of chicken p53 or Fos, LMH cells were transiently transfected with pCAG-Fos-HA or pCAG-p53-Flag for 24 h. (<b>A</b>,<b>B</b>) The transcription of <span class="html-italic">Tp53</span> or <span class="html-italic">Fos</span> was analyzed by RT-qPCR. (<b>C</b>,<b>D</b>) The induction of p53 or Fos protein was detected by Western blotting or (<b>E</b>,<b>F</b>) indirect immunofluorescence using antibodies targeting HA or Flag, respectively. Empty pCAG was used as negative control in all overexpression experiments. The scale bar indicates 150 µm. Tublin was used as inner control in Western blotting. Data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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<p>Detection of viral gene transcription upon p53 overexpression in LMH cells. The mRNA levels of four ILTV genes covering all stages of ILTV transcription, namely <span class="html-italic">ICP4</span> (<b>A</b>), <span class="html-italic">ICP27</span> (<b>B</b>), <span class="html-italic">gI</span> (<b>C</b>), and <span class="html-italic">gG</span> (<b>D</b>), in LMH cells with p53 overexpression were detected by absolute qRT–PCR at 6 h post ILTV infection. Data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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<p>Effects of p53 overexpression on the replication of ILTV in LMH cells. (<b>A</b>,<b>B</b>) The replication of ILTV in LMH cells was determined by detecting the levels of viral genome replication and infectious virion production using ILTV-specific RT–qPCR assays (<b>A</b>) and TCID<sub>50</sub> assays (48 h post infection) (<b>B</b>), respectively. The results are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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<p>Detection of viral gene transcription upon overexpression of Fos in LMH cells. The mRNA levels of four ILTV genes covering all stages of ILTV transcription, namely <span class="html-italic">ICP4</span> (<b>A</b>), <span class="html-italic">ICP27</span> (<b>B</b>), <span class="html-italic">gI</span> (<b>C</b>), and <span class="html-italic">gG</span> (<b>D</b>), in LMH cells with Fos overexpression were detected by absolute qRT–PCR at 6 h post ILTV infection. Data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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<p>Effects of Fos overexpression on the replication of ILTV in LMH cells. (<b>A</b>,<b>B</b>) The replication of ILTV in LMH cells was determined by detecting the levels of viral genome replication and infectious virion production using ILTV-specific RT–qPCR assays (<b>A</b>) and TCID<sub>50</sub> assays (48 h post infection) (<b>B</b>), respectively. The results are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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<p>Co-Immunoprecipitation IP experiment revealed direct physical interaction between p53 and Fos proteins in LMH cells. (<b>A</b>,<b>B</b>) Input and IP samples are detected using HA antibody (<b>A</b>) and Flag antibody (<b>B</b>), respectively. IP: immunoprecipitation, IB: immunoblotting.</p>
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<p><span class="html-italic">ICP4</span> is a bona fide target gene of both p53 and Fos. (<b>A</b>) Schematic representation of the putative binding sites of Fos (F-1, F-2, F-3, F-4) in the promoter region of <span class="html-italic">ICP4</span>. (<b>B</b>,<b>C</b>) LMH cells were transfected with a pCAGGS-HA (vector), pCAG-Fos-HA, or pCAGGS-p53-Flag plasmid, respectively, and harvested 12 h after infection with ILTV. ChIP assays were performed with an anti-HA antibody or anti-Flag antibody. DNA input was used as a positive control and IgG1 was used as a negative control. The amount of these four putative binding sites (F-1, F-2, F-3, F-4) bound by p53 and Fos was determined by ChIP-qPCR analysis. Data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span> &lt; 0.05 indicates the levels of significance.</p>
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11 pages, 1975 KiB  
Article
Genetic Diversity Analysis and Breeding of Geese Based on the Mitochondrial ND6 Gene
by Yang Zhang, Shangzong Qi, Linyu Liu, Qiang Bao, Teng Wu, Wei Liu, Yu Zhang, Wenming Zhao, Qi Xu and Guohong Chen
Genes 2023, 14(8), 1605; https://doi.org/10.3390/genes14081605 - 10 Aug 2023
Cited by 2 | Viewed by 1594
Abstract
To explore the differences in body-weight traits of five goose breeds and analyze their genetic diversity and historical dynamics, we collected body-weight data statistics and used Sanger sequencing to determine the mitochondrial DNA of 100 samples of five typical goose breeds in China [...] Read more.
To explore the differences in body-weight traits of five goose breeds and analyze their genetic diversity and historical dynamics, we collected body-weight data statistics and used Sanger sequencing to determine the mitochondrial DNA of 100 samples of five typical goose breeds in China and abroad. The results indicated that Lion-Head, Hortobagy, and Yangzhou geese have great breeding potential for body weight. Thirteen polymorphic sites were detected in the corrected 505 bp sequence of the mitochondrial DNA (mtDNA) ND6 gene, accounting for approximately 2.57% of the total number of sites. The guanine-cytosine (GC) content (51.7%) of the whole sequence was higher than the adenine-thymine (AT) content (48.3%), showing a certain GC base preference. There were 11 haplotypes among the five breeds, including one shared haplotype. We analyzed the differences in the distribution of base mismatches among the five breeds and conducted Tajima’s D and Fu’s Fs neutral tests on the historical dynamics of the populations. The distribution of the mismatch difference presented an unsmooth single peak and the Tajima’s D value of the neutral test was negative (D < 0) and reached a significant level, which proves that the population of the three species had expanded; the Lion-Head goose population tends to be stable. The genetic diversity of Lion-Head, Zhedong White, Yangzhou, and Taihu geese was equal to the average diversity of Chinese goose breeds. The Hortobagy goose is a foreign breed with differences in mating line breeding and hybrid advantage utilization. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Images of goose population. Images were captured using a digital camera (Olympus).</p>
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<p>The site of primers according to the goose sequence (GenBank accession number NC_012920.1).</p>
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<p>The partial electrophoresis results of PCR amplification for 2 mtDNA fragments (Olympus, Japan). Note: M: DL1500 marker; 1–10: PCR products of <span class="html-italic">ND6</span>.</p>
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<p>Variable sites in the mtDNA control region for haplotypes from five breeds of geese. “·” indicate identical nucleotides. Number on far-right indicates the number of individual geese with the haplotype. HT: haplotype type.</p>
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<p>The median-joining networks of mtDNA <span class="html-italic">ND6</span> haplotypes. The colors represent different geese populations.</p>
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<p>Distribution of base differential mismatches of goose mitochondrial <span class="html-italic">ND6</span> gene sequences. Freq. Obs. represents the actual value; Freq. Exp. represents the expected value.</p>
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15 pages, 12632 KiB  
Article
Molecular Regulation of Differential Lipid Molecule Accumulation in the Intramuscular Fat and Abdominal Fat of Chickens
by Jingjing Li, Qinke Huang, Chaowu Yang, Chunlin Yu, Zengrong Zhang, Meiying Chen, Peng Ren and Mohan Qiu
Genes 2023, 14(7), 1457; https://doi.org/10.3390/genes14071457 - 17 Jul 2023
Cited by 6 | Viewed by 1877
Abstract
Reducing abdominal fat (AF) accumulation and increasing the level of intramuscular fat (IMF) simultaneously is a major breeding goal in the poultry industry. To explore the different molecular mechanisms underlying AF and IMF, gene expression profiles in the breast muscle (BM) and AF [...] Read more.
Reducing abdominal fat (AF) accumulation and increasing the level of intramuscular fat (IMF) simultaneously is a major breeding goal in the poultry industry. To explore the different molecular mechanisms underlying AF and IMF, gene expression profiles in the breast muscle (BM) and AF from three chicken breeds were analyzed. A total of 4737 shared DEGs were identified between BM and AF, of which 2602 DEGs were upregulated and 2135 DEGs were downregulated in the BM groups compared with the AF groups. DEGs involved in glycerophospholipid metabolism and glycerolipid metabolism were potential regulators, resulting in the difference in lipid metabolite accumulation between IMF and AF. The PPAR signaling pathway was the most important pathway involved in tissue-specific lipid deposition. Correlation analysis showed that most representative DEGs enriched in the PPAR signaling pathway, such as FABP5, PPARG, ACOX1, and GK2, were negatively correlated with PUFA-enriched glycerophospholipid molecules. Most DEGs related to glycerophospholipid metabolism, such as GPD2, GPD1, PEMT, CRLS1, and GBGT1, were positively correlated with glycerophospholipid molecules, especially DHA- and arachidonic acid (ARA)-containing glycerophospholipid molecules. This study elucidated the molecular mechanism underlying tissue-specific lipid deposition and poultry meat quality. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>(<b>A</b>–<b>C</b>) Volcano plot showing DEGs in GYBM vs. GYAF, JYBM vs. JYAF, TCBM vs. TCAF. Red dots represent significantly up-regulated genes and green dots represent significantly down-regulated genes. (<b>D</b>) Venn diagrams showing differentially expressed genes in the BM and AF of Guangyuan grey chickens, Jiuyuan black chickens, and Tibetan chickens.</p>
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<p>KEGG pathway enrichment analysis of the DEGs between BM and AF in Guangyuan grey chickens (<b>A</b>), Jiuyuan black chickens (<b>B</b>), and Tibetan chickens (<b>C</b>). (<b>D</b>) PPAR signaling pathway plot. The node color indicates the expression of genes: (red) up-regulated and (green) down-regulated in the BM groups relative to the AF groups. GeneRatio: the ratio of the number of differential genes annotated to the KEGG pathway number to the total number of differential genes.</p>
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<p>KEGG pathway enrichment analysis of the DEGs between BM and AF in Guangyuan grey chickens (<b>A</b>), Jiuyuan black chickens (<b>B</b>), and Tibetan chickens (<b>C</b>). (<b>D</b>) PPAR signaling pathway plot. The node color indicates the expression of genes: (red) up-regulated and (green) down-regulated in the BM groups relative to the AF groups. GeneRatio: the ratio of the number of differential genes annotated to the KEGG pathway number to the total number of differential genes.</p>
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<p>Results of the qRT-PCR validation in (<b>A</b>) GYBM vs. GYAF (<b>B</b>) JYBM vs. JYAF and (<b>C</b>) TCBM vs. TCAF.</p>
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<p>Integration analysis of lipidomics and transcriptome profiles for (<b>A</b>) GYBM vs. GYAF, (<b>B</b>) JYBM vs. JYAF, and (<b>C</b>) TCBM vs. TCAF. Each row represents a gene, and each line represents a lipid molecule. * <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Integration analysis of lipidomics and transcriptome profiles for (<b>A</b>) GYBM vs. GYAF, (<b>B</b>) JYBM vs. JYAF, and (<b>C</b>) TCBM vs. TCAF. Each row represents a gene, and each line represents a lipid molecule. * <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Integration analysis of lipidomics and transcriptome profiles for (<b>A</b>) GYBM vs. GYAF, (<b>B</b>) JYBM vs. JYAF, and (<b>C</b>) TCBM vs. TCAF. Each row represents a gene, and each line represents a lipid molecule. * <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Significant shared correlations between important PUFA-enriched glycerophospholipid molecules and genes among the three chicken breeds. * <span class="html-italic">p</span> &lt; 0.01. The y-axis is the gene id.</p>
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14 pages, 2992 KiB  
Article
Circ_PIAS1 Promotes the Apoptosis of ALV-J Infected DF1 Cells by Up-Regulating miR-183
by Ting Yang, Lingling Qiu, Shihao Chen, Zhixiu Wang, Yong Jiang, Hao Bai, Yulin Bi, Guohong Chen and Guobin Chang
Genes 2023, 14(6), 1260; https://doi.org/10.3390/genes14061260 - 14 Jun 2023
Cited by 4 | Viewed by 1638
Abstract
(1) Background: circRNAs are closed circular molecules with covalent bonds generated by reverse shearing, which have high stability and have different manifestations in different tissues, cells, or physiological conditions and play important roles in various disease processes and physiological processes. In addition, circ_PIAS1 [...] Read more.
(1) Background: circRNAs are closed circular molecules with covalent bonds generated by reverse shearing, which have high stability and have different manifestations in different tissues, cells, or physiological conditions and play important roles in various disease processes and physiological processes. In addition, circ_PIAS1 has been screened out and verified, and the bioinformatics analyzed in previous studies. In this study, we investigated the function of circ_PIAS1 and studied its role in ALV-J infection to provide a basis for the role of circRNA in ALV-J infection. (2) Methods: the effect of circ_PIAS1 on apoptosis during ALV-J infection was studied by flow cytometry and detection of apoptotic gene expression, and miR-183 was screened by a biotin-labeled RNA pull-down technique. After overexpression and inhibition of miR-183, the effect of miR-183 on apoptosis in the process of ALV-J infection was studied by flow cytometry and detection of apoptotic gene expression. (3) Results: after overexpression of circ_PIAS1, flow cytometry and apoptotic gene expression showed that circ_PIAS1 promoted apoptosis. The results of RNA pull-down showed that 173 miRNAs could bind to circ_PIAS1, and circ_PIAS1 up-regulated the expression of miR-183. On the other hand, the same results were obtained whether miR-183 was overexpressed or inhibited that miR-183 affected ALV-J infection by promoting cell apoptosis. (4) Conclusions: circ_PIAS1 up-regulated the expression of miR-183 and influenced ALV-J infection by promoting cell apoptosis. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Circ_PIAS1 promotes cell apoptosis. (<b>a</b>) After the construction of the ALV-J infection model, gp85 protein expression was detected by western blot; (<b>b</b>) Cell apoptosis was detected by flow cytometry; (<b>c</b>) Circ_PIAS1 overexpression efficiency was detected by qRT-PCR; (<b>d</b>) Cell apoptosis rate was measured; (<b>e</b>) The expression of apoptosis-related genes was detected by qRT-PCR (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Bioinformatic analysis of biotin-labeled RNA pull-down analysis. (<b>a</b>) Statistical analysis of miRNAs that can bind to circ_PIAS1; (<b>b</b>) GO enrichment analysis of the predicted gene of 34 miRNAs with fold &gt; 1.5; (<b>c</b>) KEGG enrichment analysis of the predicted gene of 34 miRNAs with fold &gt; 1.5; (<b>d</b>) CeRNA regulatory network diagram of the p53 signaling pathway. (* <span class="html-italic">p</span> &lt; 0.05, “ns” no significant difference).</p>
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<p>GO enrichment analysis of the predicted gene of 34 miRNAs with fold &gt; 1.5.</p>
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<p>KEGG enrichment analysis of the predicted gene of 34 miRNAs with fold &gt; 1.5.</p>
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<p>Overexpression of miR-183 increases the apoptosis of ALV-J infection DF1 cells. (<b>a</b>) The expression of miR-183 was detected by qRT-PCR; (<b>b</b>) Cell apoptosis was detected by flow cytometry; (<b>c</b>) Cell apoptosis rate was measured; (<b>d</b>) The expression of apoptosis-related genes was detected by qRT-PCR. (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Inhibition of miR-183 decreases the apoptosis of ALV-J infection DF1 cells. (<b>a</b>) The expression of miR-183 was detected by qRT-PCR; (<b>b</b>) Cell apoptosis was detected by flow cytometry; (<b>c</b>) Cell apoptosis rate was measured; (<b>d</b>) The expression of apoptosis-related genes was detected by qRT-PCR. (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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11 pages, 2305 KiB  
Article
Genome-Wide Association Study Reveals the Genetic Basis of Duck Plumage Colors
by Xinye Zhang, Tao Zhu, Liang Wang, Xueze Lv, Weifang Yang, Changqing Qu, Haiying Li, Huie Wang, Zhonghua Ning and Lujiang Qu
Genes 2023, 14(4), 856; https://doi.org/10.3390/genes14040856 - 31 Mar 2023
Cited by 7 | Viewed by 4696
Abstract
Plumage color is an artificially and naturally selected trait in domestic ducks. Black, white, and spotty are the main feather colors in domestic ducks. Previous studies have shown that black plumage color is caused by MC1R, and white plumage color is caused [...] Read more.
Plumage color is an artificially and naturally selected trait in domestic ducks. Black, white, and spotty are the main feather colors in domestic ducks. Previous studies have shown that black plumage color is caused by MC1R, and white plumage color is caused by MITF. We performed a genome-wide association study (GWAS) to identify candidate genes associated with white, black, and spotty plumage in ducks. Two non-synonymous SNPs in MC1R (c.52G>A and c.376G>A) were significantly related to duck black plumage, and three SNPs in MITF (chr13:15411658A>G, chr13:15412570T>C and chr13:15412592C>G) were associated with white plumage. Additionally, we also identified the epistatic interactions between causing loci. Some ducks with white plumage carry the c.52G>A and c.376G>A in MC1R, which also compensated for black and spotty plumage color phenotypes, suggesting that MC1R and MITF have an epistatic effect. The MITF locus was supposed to be an upstream gene to MC1R underlying the white, black, and spotty colors. Although the specific mechanism remains to be further clarified, these findings support the importance of epistasis in plumage color variation in ducks. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Representative images of color in duck plumage. (<b>a</b>) Duck with white plumage (Peking duck). (<b>b</b>) Duck with spotty plumage (Shanma duck). (<b>c</b>) Duck with black plumage (Wendeng black duck).</p>
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<p>Genome-wide association study results for black plumage. (<b>a</b>) Manhattan plot of GWAS results. The <span class="html-italic">Y</span>-axis shows −log10 (<span class="html-italic">p</span>-values). The red dashed horizontal line indicates the genome-wide significance threshold (4.583099 × 10<sup>−9</sup>). (<b>b</b>) Q-Q plot. Lambda (λ) is the genomic expansion factor. A total of 16 ducks showed black plumage, and 56 controls were spotty.</p>
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<p>Genome-wide association study results for white plumage. (<b>a</b>) Manhattan plot of GWAS results. The <span class="html-italic">Y</span>-axis shows −log10 (<span class="html-italic">p</span>-values). The two red dashed horizontal line indicates genome- wide significance thresholds (6.042978 × 10<sup>−9</sup>). (<b>b</b>) Q-Q plot. Lambda (λ) is the genomic expansion factor. A total of 24 ducks showed white plumage while 56 controls were spotty.</p>
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15 pages, 1844 KiB  
Article
RNA-Seq Profiling between Commercial and Indigenous Iranian Chickens Highlights Differences in Innate Immune Gene Expression
by Ayeh Sadat Sadr, Mohammadreza Nassiri, Mostafa Ghaderi-Zefrehei, Maryam Heidari, Jacqueline Smith and Mustafa Muhaghegh Dolatabady
Genes 2023, 14(4), 793; https://doi.org/10.3390/genes14040793 - 25 Mar 2023
Cited by 1 | Viewed by 3079
Abstract
The purpose of the current study was to examine transcriptomic-based profiling of differentially expressed innate immune genes between indigenous and commercial chickens. In order to compare the transcriptome profiles of the different chicken breeds, we extracted RNA from blood samples of the Isfahan [...] Read more.
The purpose of the current study was to examine transcriptomic-based profiling of differentially expressed innate immune genes between indigenous and commercial chickens. In order to compare the transcriptome profiles of the different chicken breeds, we extracted RNA from blood samples of the Isfahan indigenous chicken (as indigenous) and Ross broiler chicken (as commercial) breeds. RNA-Seq yielded totals of 36,763,939 and 31,545,002 reads for the indigenous and commercial breeds, respectively, with clean reads then aligned to the chicken reference genome (Galgal5). Overall, 1327 genes were significantly differentially expressed, of which 1013 genes were upregulated in the commercial versus the indigenous breed, while 314 were more highly expressed in the indigenous birds. Furthermore, our results demonstrated that the SPARC, ATP6V0D2, IL4I1, SMPDL3A, ADAM7, TMCC3, ULK2, MYO6, THG1L and IRG1 genes were the most significantly expressed genes in the commercial birds and the PAPPA, DUSP1, PSMD12, LHX8, IL8, TRPM2, GDAP1L1, FAM161A, ABCC2 and ASAH2 genes were the most significant in the indigenous chickens. Of notable finding in this study was that the high-level gene expressions of heat-shock proteins (HSPs) in the indigenous breeds could serve as a guideline for future genetic improvement. This study identified genes with breed-specific expression, and comparative transcriptome analysis helped understanding of the differences in underlying genetic mechanisms between commercial and local breeds. Therefore, the current results can be used to identify candidate genes for further breed improvement. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>The methodology pipeline used in this study.</p>
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<p>Analysis of differentially expressed genes (DEGs) between commercial and indigenous birds. (<b>A</b>) Log2-fold change between the commercial versus indigenous (native) breed. Light blue dots represent significant DEGs between breeds (<span class="html-italic">p</span>-values &lt; 0.05). (<b>B</b>) Heat map of differentially expressed genes between the commercial versus indigenous breed. Lighter color represents lower expression.</p>
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<p>Fold-expression changes measured with RNA-Seq and real-time RT-PCR.</p>
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<p>Histogram of gene ontology (GO) classification. The terms are summarized in three categories, biological process (red), cellular component (blue) and molecular function (green), for upregulated DEGs (corrected <span class="html-italic">p-</span>values ≤ 0.05).</p>
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<p>Ingenuity pathway analysis. (<b>A</b>) The biological pathways highlighted in gene expression in commercial birds. (<b>B</b>) The biological pathways highlighted in gene expression in indigenous birds. Orange—pathway activation; blue—pathway repression.</p>
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<p>PPI network of immune-system-related upregulated DEGs. The size of each node is based on degree value; a bigger size is related to a larger degree value. The color of each node is related to one of two subnetwork modules that were obtained (green and pink).</p>
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12 pages, 2610 KiB  
Article
The Expression Patterns of Exogenous Plant miRNAs in Chickens
by Hao Li, Pu Zhang, Diyan Li, Binlong Chen, Jing Li and Tao Wang
Genes 2023, 14(3), 760; https://doi.org/10.3390/genes14030760 - 21 Mar 2023
Cited by 1 | Viewed by 2100
Abstract
(1) Background: MicroRNAs (miRNAs) are involved in a variety of biological processes, such as cell proliferation, cell differentiation, and organ development. Recent studies have shown that plant miRNAs may enter the diet and play physiological and/or pathophysiological roles in human health and disease; [...] Read more.
(1) Background: MicroRNAs (miRNAs) are involved in a variety of biological processes, such as cell proliferation, cell differentiation, and organ development. Recent studies have shown that plant miRNAs may enter the diet and play physiological and/or pathophysiological roles in human health and disease; however, little is known about plant miRNAs in chickens. (2) Methods: Here, we analyzed miRNA sequencing data, with the use of five Chinese native chicken breeds and six different tissues (heart, liver, spleen, lung, kidney, and leg muscle), and used Illumina sequencing to detect the expression of plant miRNAs in the pectoralis muscles at fourteen developmental stages of Tibetan chickens. (3) Results: The results showed that plant miRNAs are detectable in multiple tissues and organs in different chicken breeds. Surprisingly, we found that plant miRNAs, such as tae-miR2018, were detectable in free-range Tibetan chicken embryos at different stages. The results of gavage feeding experiments also showed that synthetic tae-miR2018 was detectable in caged Tibetan chickens after ingestion. The analysis of tae-miR2018 showed that its target genes were related to skeletal muscle organ development, regulation of mesodermal cell fate specification, growth factor activity, negative regulation of the cell cycle, and regulation of growth, indicating that exogenous miRNA may regulate the development of chicken embryos. Further cell cultures and exogenous miRNA uptake assay experiments showed that synthetic wheat miR2018 can be absorbed by chicken myoblasts. (4) Conclusions: Our study found that chickens can absorb and deposit plant miRNAs in various tissues and organs. The plant miRNAs detected in embryos may be involved in the development of chicken embryos. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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Figure 1
<p>Expression scheme of plant miRNAs in different chicken breeds and tissues. (<b>a</b>) Spearman’s correlation heatmap of the plant miRNA expression among different chicken breeds and tissues. (<b>b</b>) Mean expression levels (reads counts) of 15 plant miRNAs were detected in 7 tissues or organs from 5 Chinese native free-range chicken breeds. Note: <span class="html-italic">O. sativa</span> (rice, osa), <span class="html-italic">T. aestivum</span> (wheat, tae).</p>
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<p>Expression of plant miRNAs across different chicken developmental stages. (<b>a</b>) Heatmap showing the expression of plant miRNAs with more than 40 reads. Expression levels (read counts) of <span class="html-italic">osa-miRf11479-akr</span> (<b>b</b>) and <span class="html-italic">tae-miR2018</span> (<b>c</b>) at 14 chicken developmental stages. Note: ‘E’, ‘D’, and ‘Y’ indicate the sampling times in ‘embryonic days’, ‘days after hatching’ and ‘years’, respectively. Expression of plant miRNAs in Ross301 chicken feed without exogenous plant miRNA is also shown.</p>
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<p>Enrichment analysis of target genes of <span class="html-italic">tae-miR2018</span>. The top 20 enrichment clusters are shown. The genes in each cluster are marked in the second circle. The third circle shows the correlation degrees of the target genes and the pathways arranged by <span class="html-italic">p</span> value. The connections in the chord diagram indicate that the corresponding gene is associated with multiple pathways and GO terms.</p>
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<p>Expression levels of <span class="html-italic">tae-miR2018</span>. Relative expression levels of <span class="html-italic">tae-miR2018</span> in myoblasts (<b>a</b>) after incubation with synthetic tae-miR2018 and in the liver (<b>b</b>) or pectoralis (<b>c</b>) of chickens at the indicated time points after gavage feeding of synthetic dietary miRNAs. One-way ANOVA with the Tukey method was used to evaluate significance (<span class="html-italic">p</span> &lt; 0.001); ‘h’ means hour. Expression levels of tae-miR2018 (<b>d</b>) and <span class="html-italic">BMPR1A</span> (<b>e</b>) in chicken myoblasts transfected with wild type, <span class="html-italic">tae-miR2018</span> mimic, <span class="html-italic">tae-miR2018</span> mimic, and inhibitor ncRNA constructs.</p>
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10 pages, 2082 KiB  
Article
Three-Dimensional Organization of Chicken Genome Provides Insights into Genetic Adaptation to Extreme Environments
by Dan Shao, Yu Yang, Shourong Shi and Haibing Tong
Genes 2022, 13(12), 2317; https://doi.org/10.3390/genes13122317 - 9 Dec 2022
Cited by 2 | Viewed by 2289
Abstract
The high-throughput chromosome conformation capture (Hi-C) technique is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. However, the knowledge of the 3D genome structure and its dynamics during extreme environmental adaptations remains poor. Here, we characterized 3D [...] Read more.
The high-throughput chromosome conformation capture (Hi-C) technique is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. However, the knowledge of the 3D genome structure and its dynamics during extreme environmental adaptations remains poor. Here, we characterized 3D genome architectures using the Hi-C technique for chicken liver cells. Upon comparing Lindian chicken (LDC) liver cells with Wenchang chicken (WCC) liver cells, we discovered that environmental adaptation contributed to the switching of A/B compartments, the reorganization of topologically associated domains (TADs), and TAD boundaries in both liver cells. In addition, the analysis of the switching of A/B compartments revealed that the switched compartmental genes (SCGs) were strongly associated with extreme environment adaption-related pathways, including tight junction, notch signaling pathway, vascular smooth muscle contraction, and the RIG-I-like receptor signaling pathway. The findings of this study advanced our understanding of the evolutionary role of chicken 3D genome architecture and its significance in genome activity and transcriptional regulation. Full article
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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<p>Hi-C contact heatmaps of liver cells in LDC (<b>A</b>) and WCC (<b>B</b>). The color of each dot on the heatmaps represents the log of the interaction probability for the corresponding pair of genomic loci according to standard JuiceBox color scheme.</p>
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<p>Association between A/B compartment switches and gene expression. (<b>A</b>) Distribution of the A/B compartments in the whole genome of two liver cells. A compartments are shown in orange, B compartments are shown in blue. The diff means the absolute difference between the PC1 value of two Hi-C data, and the value greater than 0 are shown in red, less than 0 are shown in green. (<b>B</b>) Genome-wide proportions of A/B compartment changes in the whole genome of two liver cells. Gene numbers (<b>C</b>,<b>E</b>) and expression (<b>D</b>,<b>F</b>) volume map of A/B compartments in the whole genome of LDC and WCC liver cells.</p>
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<p>Distribution of topologically associated domains (TADs) on chromosome.</p>
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<p>Enrichment analysis of switched compartmental genes (SCGs) in 5% switched A/B compartments between LDC and WCC liver cells. (<b>A</b>) GO enrichment analysis. The 30 most common GO terms are presented. (<b>B</b>) KEGG enrichment analysis. The 20 most common KEGG pathways are presented. The <span class="html-italic">y</span>-axis and <span class="html-italic">x</span>-axis indicate pathway name and rich factor, respectively. The size of the circle dot means gene number.</p>
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