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Section = Animal Genetics and Genomics

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17 pages, 2466 KiB  
Article
Genetic Diversity and Population Structure of Largefin Longbarbel Catfish (Hemibagrus macropterus) Inferred by mtDNA and Microsatellite DNA Markers
by Yanling Hou, Huan Ye, Huamei Yue, Junyi Li, Ling Huang, Ziling Qu, Rui Ruan, Danqing Lin, Zhiqiang Liang, Yong Xie and Chuangju Li
Animals 2025, 15(6), 770; https://doi.org/10.3390/ani15060770 (registering DOI) - 8 Mar 2025
Viewed by 13
Abstract
The largefin longbarbel catfish (Hemibagrus macropterus), a freshwater species endemic to China with fundamental economic importance, requires investigation into its genetic structure for effective management. In this study, we employed mitochondrial cytochrome b (Cytb) gene sequences and 14 microsatellite [...] Read more.
The largefin longbarbel catfish (Hemibagrus macropterus), a freshwater species endemic to China with fundamental economic importance, requires investigation into its genetic structure for effective management. In this study, we employed mitochondrial cytochrome b (Cytb) gene sequences and 14 microsatellite loci to elucidate the genetic structure of 195 individuals across eight distinct populations. The Cytb analysis revealed a haplotype number (H) of 31, haplotype diversity (Hd) of 0.853, and nucleotide diversity (π) of 0.0127. Population neutrality tests indicated that Tajima’s D (−0.59467) and Fu and Li’s D* (0.56621) were not statistically significant, and the mismatch distribution exhibited a multimodal pattern. Microsatellite analysis revealed that the mean number of alleles (Na), observed heterozygosity (Ho), and polymorphic information content (PIC) across all loci were 18.500, 0.761, and 0.808, respectively. The UPGMA phylogram constructed based on genetic distance identified two distinct clusters, with paired Fst values ranging from 0.108 to 0.138. These results suggest that the largefin longbarbel catfish is in a state of dynamic equilibrium with high genetic diversity. Furthermore, there was significant genetic differentiation between the YB population and the other seven populations, indicating that the population in the upper reaches of the Yangtze River should be managed as a distinct unit. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Sampling localities for <span class="html-italic">Hemibagrus macropterus</span> populations. YB, Yibin; YC, Yichang; SS, Shishou; WH, Wuhan; NJ, Nanjing; HH, Huaihua; YS, Yangshuo; CQSS, Stock seed; TGD, Three Gorges Dam; GD, Gezhouba Dam.</p>
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<p>Haplotype network diagram of eight populations of <span class="html-italic">Hemibagrus macropterus</span> based on <span class="html-italic">Cytb</span>. YB, Yibin; YC, Yichang; SS, Shishou; WH, Wuhan; NJ, Nanjing; HH, Huaihua; YS, Yangshuo; CQSS, Stock seed.</p>
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<p>UPGMA phylogram for eight populations of <span class="html-italic">Hemibagrus macropterus</span> based on SSRs. YB, Yibin; YC, Yichang; SS, Shishou; WH, Wuhan; NJ, Nanjing; HH, Huaihua; YS, Yangshuo; CQSS, Stock seed.</p>
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<p>PCoA of eight populations of <span class="html-italic">Hemibagrus macropterus</span> based on SSRs. YB, Yibin; YC, Yichang; SS, Shishou; WH, Wuhan; NJ, Nanjing; HH, Huaihua; YS, Yangshuo; CQSS, Stock seed.</p>
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<p>(<b>a</b>) Structural diagram of <span class="html-italic">Hemibagrus macropterus</span> for K = 2; (<b>b</b>) Structural diagram of <span class="html-italic">Hemibagrus macropterus</span> for K = 3; (<b>c</b>) Structural diagram of <span class="html-italic">Hemibagrus macropterus</span> for K = 4; (<b>d</b>) Structural diagram of <span class="html-italic">Hemibagrus macropterus</span> for K = 5. Different colors represent different clustering subgroups. YB, Yibin; YC, Yichang; SS, Shishou; WH, Wuhan; NJ, Nanjing; HH, Huaihua; YS, Yangshuo; CQSS, Stock seed.</p>
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<p>Mismatch distribution of <span class="html-italic">Cytb</span> of <span class="html-italic">Hemibagrus macropterus</span>.</p>
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15 pages, 3160 KiB  
Article
Genomic Insights into the Population Genetics and Adaptive Evolution of Yellow Seabream (Acanthopagrus latus) with Whole-Genome Resequencing
by Yuan Li, Jingyu Yang, Yan Fang, Ran Zhang, Zizi Cai, Binbin Shan, Xing Miao, Longshan Lin, Puqing Song and Jing Zhang
Animals 2025, 15(5), 745; https://doi.org/10.3390/ani15050745 - 5 Mar 2025
Viewed by 189
Abstract
Yellow seabream (Acanthopagrus latus), a species of significant economic importance, predominantly inhabits the warm waters of the Indo-Western Pacific. While previous studies have explored the genetic diversity of A. latus using microsatellites and other nuclear markers, a comprehensive understanding of its [...] Read more.
Yellow seabream (Acanthopagrus latus), a species of significant economic importance, predominantly inhabits the warm waters of the Indo-Western Pacific. While previous studies have explored the genetic diversity of A. latus using microsatellites and other nuclear markers, a comprehensive understanding of its genetic characteristics and adaptive evolution using whole-genome resequencing (WGR) remains limited. In this study, we collected 60 individuals from six distinct geographic locations and performed WGR, achieving an average sequencing depth of 12.59×, which resulted in the identification of 19,488,059 high-quality single-nucleotide polymorphisms (SNPs). The nucleotide polymorphism (πθ) across all populations was consistent, ranging from 0.003042 to 0.003155, indicating low genetic differentiation among populations. Comparative analyses revealed that populations other than that in Xiamen (XM) have undergone adaptive evolution, potentially linked to traits such as growth and development, feeding, immunity, and movement. This study explores the population genetics and adaptive evolutionary patterns of Acanthopagrus latus at the genomic level, providing an essential foundation for the conservation and management of this economically important species in the future. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Sampling sites of <span class="html-italic">A. latus</span> (the red dots indicate the sampling locations, and the arrows represent the coastal currents along the South China coast during autumn and winter. A morphological diagram of <span class="html-italic">A. latus</span> is shown in the lower right corner).</p>
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<p>The distribution of SNPs (<b>A</b>) and InDels (<b>B</b>).</p>
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<p>The phylogenetic relationship of six <span class="html-italic">A. latus</span> populations. (<b>A</b>) The NJ tree of six <span class="html-italic">A. latus</span> populations based on all SNPs. (<b>B</b>) Cross-validation (CV) error for varying values of K. (<b>C</b>) Population genetic structure of <span class="html-italic">A. latus</span>. The length of each color fragment indicates the proportion of individual genes inferred from the ancestral population (K = 2~6), and sample names are at the bottom. Each color represents a different hypothetical ancestor. (<b>D</b>) Gene flow of <span class="html-italic">A. latus</span> among the six populations. The five yellow arrows correspond to the five gene flow events identified in the analysis.</p>
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<p>Demographic history of <span class="html-italic">A. latus</span>.</p>
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<p>GO (<b>A</b>) and KEGG (<b>B</b>) enrichment analyses for selected genes in Xiamen and five other locations with <span class="html-italic">A. latus</span> populations.</p>
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<p>The identification of selection sweeps between Xiamen (XM) and five other locations for <span class="html-italic">A. latus</span>. (<b>A</b>) ROD values along chromosomes (the yellow/blue dots represents the ROD value of all SNPs, and the red dashed line represents the threshold line of the top 5% of ROD). (<b>B</b>) The NJ tree of selected genes. (<b>C</b>) <span class="html-italic">F<sub>st</sub></span>, π<sub>θ</sub>, and Tajima’s D near the <span class="html-italic">NFIC</span> gene. (<b>D</b>) <span class="html-italic">F<sub>st</sub></span>, π<sub>θ</sub>, and Tajima’s D near the <span class="html-italic">RAC2</span> gene. The yellow highlight indicates gene regions with strong selective signals. The green wavy line represents the genetic differentiation analysis between the Xiamen population and other populations.</p>
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27 pages, 381 KiB  
Review
Potential Genetic Markers Associated with Environmental Adaptability in Herbivorous Livestock
by Xiaotong Liu, Yongdong Peng, Xinhao Zhang, Wenting Chen, Yinghui Chen, Lin Wei, Qifei Zhu, Muhammad Zahoor Khan and Changfa Wang
Animals 2025, 15(5), 748; https://doi.org/10.3390/ani15050748 - 5 Mar 2025
Viewed by 170
Abstract
Herbivorous livestock, such as cattle, sheep, goats, horses, and donkeys, play a crucial role in agricultural production and possess remarkable resilience to extreme environmental conditions, driven by complex genetic mechanisms. Recent advancements in high-throughput sequencing, genome assembly, and environmental data integration have enabled [...] Read more.
Herbivorous livestock, such as cattle, sheep, goats, horses, and donkeys, play a crucial role in agricultural production and possess remarkable resilience to extreme environmental conditions, driven by complex genetic mechanisms. Recent advancements in high-throughput sequencing, genome assembly, and environmental data integration have enabled a deeper understanding of the genetic basis of their environmental adaptation. This review identifies key genes associated with high-altitude, heat, cold, and drought adaptation, providing insights into the molecular mechanisms underlying these traits. By elucidating these genetic adaptations, our study aims to support conservation efforts, inform selective breeding programs, and enhance agricultural productivity, ultimately contributing to sustainable livestock farming and economic benefits for farmers. Full article
(This article belongs to the Special Issue Genetic Research for Improving Livestock Heat Stress Resistance)
17 pages, 10662 KiB  
Article
The Regulatory Role of CircAGGF1 in Myogenic Differentiation and Skeletal Muscle Development
by Wei Hei, Yuxuan Gong, Wenrun Cai, Ruotong Li, Jiayi Chen, Wanfeng Zhang, Mengting Ji, Meng Li, Yang Yang, Chunbo Cai, Xiaohong Guo and Bugao Li
Animals 2025, 15(5), 708; https://doi.org/10.3390/ani15050708 - 28 Feb 2025
Viewed by 180
Abstract
Circular RNA (circRNA) has a significant impact on the maturation of skeletal muscle, although their precise functions within this framework remain largely uncharted. This study presents an investigation of the regulatory effect of circAGGF1 on myogenesis in myoblasts, including the potential molecular mechanisms [...] Read more.
Circular RNA (circRNA) has a significant impact on the maturation of skeletal muscle, although their precise functions within this framework remain largely uncharted. This study presents an investigation of the regulatory effect of circAGGF1 on myogenesis in myoblasts, including the potential molecular mechanisms involved. It is revealed that circAGGF1 facilitates the differentiation of myoblasts into other states while simultaneously enhancing the manifestation of type I muscle fibers. In vivo investigations with mice revealed the promotion of skeletal muscle expansion and maturation by circAGGF1, bolstering its regenerative capacity. Mechanistically, circAGGF1 interacts with miR-199a-3p by acting as a sponge, promoting the subsequent expression of Fgf7. Furthermore, rescue experiments indicated a counteraction of the myogenesis induced by circAGGF1 overexpression by miR-199a-3p. To summarize, this research highlights the role played by circAGGF1 in the development of skeletal muscle, providing a valuable resource for enhancing our understanding of skeletal muscle biology. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Effects of circAGGF1 on myogenesis of C2C12 cells. (<b>A</b>) CircAGGF1 expression patterns throughout cell differentiation. (<b>B</b>) CircAGGF1 transfection efficiency. (<b>C</b>,<b>D</b>) Myogenesis genes transcription in C2C12 cells following circAGGF1 overexpression. (<b>E</b>) Protein expression of myogenesis genes in C2C12 cells upon circAGGF1 overexpression. (<b>F</b>) Cell differentiation was assessed via MyHC and visualized using a fluorescence microscope. Scale bar represents 400 μm. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Different lowercase letters mean significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>circAGGF1 Promotes mice myogenesis in vivo. (<b>A</b>) The animal experimental procedure. (<b>B</b>) Transfection efficiency of circAGGF1 in mouse muscle tissue. (<b>C</b>) Body weights progression in mice from 8 to 16 weeks old. (<b>D</b>) Gross morphology of mice in each group. (<b>E</b>,<b>F</b>) The gross morphology and weight of Gas muscle tissues. (<b>G</b>) HE staining. Scale bar indicates 50 μm. (<b>H</b>) Muscle fiber immunofluorescence. Green represents type I fibers, and red for type II fibers. Scale bar indicates 50 μm. (<b>I</b>–<b>K</b>) Expression of myogenesis genes at mRNA and protein in muscle tissue. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>circAGGF1 enhances skeletal muscle regeneration. (<b>A</b>) HE staining of Gas muscles after injection of CTX. (<b>B</b>) The animal experimental procedure. (<b>C</b>) The expression of circAGGF1 and the changes of myogenesis genes in Gas muscle at 5 d, after CTX injury and AAV injection. (<b>D</b>) Following the transfection with circAGGF1, HE staining was performed on muscle 5 and 14 days after CTX injection. Scale bar indicates 50 μm. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>miR-199a-3p inhibits myogenesis in C2C12 Cells. (<b>A</b>) Predicted binding sites of circAGGF1 to miR-199a-3p using RNAhybrid software (<a href="http://bibiserv.techfak.uni-bielefeld.de/rnahybrid" target="_blank">http://bibiserv.techfak.uni-bielefeld.de/rnahybrid</a>). (<b>B</b>) Schematic diagram of circAGGF1 wild-type and mutant luciferase vector construction. (<b>C</b>) Dual luciferase activity assay. (<b>D</b>) Effects of circAGGF1 on miR-199a-3p expression. (<b>E</b>) The miR-199a-3p overexpression and interference efficiency assay. (<b>F</b>,<b>G</b>) The mRNA expression of myogenesis genes in C2C12 cells that overexpress with miR-199a-3p. (<b>H</b>,<b>I</b>) The mRNA expression of myogenesis genes in C2C12 cells that Interference with miR-199a-3p. (<b>J</b>) Protein expression of MyoD, MyHCI, and MyHC IIb. (<b>K</b>) Results of circAGGF1 on myotube production. Scale bar indicates 400 μm. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Fgf7 promotes myogenesis in C2C12 Cells. (<b>A</b>) Predicted binding sites of miR-199a-3p to Fgf7 using RNAhybrid software. (<b>B</b>) Schematic diagram of Fgf7 wild-type and mutant luciferase vector construction. (<b>C</b>) Dual luciferase activity assay. (<b>D</b>) Effects of miR-199a-3p on Fgf7 expression. (<b>E</b>) The Fgf7 overexpression and interference efficiency assay. (<b>F</b>,<b>G</b>) The mRNA expression of myogenesis genes in C2C12 cells that overexpress with Fgf7. (<b>H</b>,<b>I</b>) The mRNA expression of myogenesis genes in C2C12 cells that Interference with Fgf7. (<b>J</b>) Protein expression of MyoD, MyHCI, and MyHC IIb. (<b>K</b>) Results of Fgf7 on myotube production. Scale bar indicates 400 μm. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>circAGGF1 influences myoblast differentiation and the transformation of myofiber types in C2C12 cells via miR-199a-3p. (<b>A</b>) Expression changes of key myogenic factors after transfection with OE-NC + mimics NC, OE-circAGGF1 + mimics NC, and OE-circAGGF1+ miR-199a-3p. (<b>B</b>) Expression changes of key myofiber-type factors after transfection with OE-NC + mimics NC, OE-circAGGF1 + mimics NC, and OE-circAGGF1 + miR-199a-3p. (<b>C</b>) Immunofluorescence staining results after transfection with OE-NC + mimics NC, OE-circAGGF1 + mimics NC, and OE-circAGGF1 + miR-199a-3p. Blue indicates nuclei stained with DAPI; green indicates MyHC protein. Scale bar indicates 400 μm. (<b>D</b>) Protein expression of MyoD, MyHCI, and MyHC IIb. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Mechanism of circAGGF1 in regulating myogenesis.</p>
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27 pages, 5888 KiB  
Article
Multi-Omics Profiling of Lipid Variation and Regulatory Mechanisms in Poultry Breast Muscles
by Hongyuan Zhang, Yaqi Dai, Jinxing Gu, Hongtai Li, Ran Wu, Jiyu Jia, Jingqi Shen, Wanli Li, Ruili Han, Guirong Sun, Wenting Li, Xiaojun Liu, Yinli Zhao and Guoxi Li
Animals 2025, 15(5), 694; https://doi.org/10.3390/ani15050694 - 27 Feb 2025
Viewed by 125
Abstract
This study aimed to elucidate the genetic basis of lipid composition in the breast muscles of poultry, including AA broilers, dwarf guinea fowl, quails, and pigeons, and the impact of artificial selection on lipid traits. By employing lipidomics and transcriptomic sequencing, the research [...] Read more.
This study aimed to elucidate the genetic basis of lipid composition in the breast muscles of poultry, including AA broilers, dwarf guinea fowl, quails, and pigeons, and the impact of artificial selection on lipid traits. By employing lipidomics and transcriptomic sequencing, the research analyzed the chest muscle tissues of these four poultry. A total of 1542 lipid molecules were identified, with 711 showing significant differences among species. These lipids primarily belonged to subclasses such as TG, PC, Phosphatidylethanolamine (PE), Ceramides (Cer), and Diglyceride (DG), with each species demonstrating distinct profiles in these subclasses. Additionally, 5790 orthologous genes were identified, with 763, 767, 24, and 8 genes in AA broilers, dwarf guinea fowl, quails, and pigeons, respectively, exhibiting positive selection (Ka/Ks > 1). Notably, 114 genes related to lipid metabolism displayed significant differential expression, particularly between AA broilers and dwarf guinea fowl. The findings revealed that the metabolic pathways of PC and LPC lipid molecules in the glycerophospholipid pathway, as well as TG lipid molecules in the glycerolipid pathway, exhibited marked interspecies differences, potentially contributing to variations in breast muscle lipid composition. These results provide a solid foundation for understanding the lipid composition and molecular regulatory mechanisms in diverse poultry, offering valuable insights for further research in poultry lipid metabolism and artificial breeding programs. Full article
(This article belongs to the Special Issue Genetic Analysis of Important Traits in Domestic Animals)
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<p>Characteristics of lipid group in poultry muscle of four kinds of poultry. Different percentages are shown in (<b>A</b>). (<b>B</b>) Radar map of the distribution of four poultry lipid subclasses: the outer circle represents the identified lipid subclass and the length of the green arrow in the inner circle represents the number of identified lipid subclasses. (<b>C</b>) Carbon chain length distribution diagram: the length of the histogram represents the number of lipid molecules under the carbon atom, and different colors represent different types of lipid molecules. The red in the upper right corner represents the proportion of C18-C44 carbon chain length of lipid molecules, and the blue represents the remaining carbon chain length of lipid molecules identified. (<b>D</b>) Carbon chain length distribution radar diagram: the outer circle represents the carbon chain length of the identified lipid molecule, the length of the green arrow in the inner circle represents the number of lipid molecules with the chain length identified. (<b>E</b>) Saturation pie chart: Different colors represent the percentage of saturated lipid molecules.</p>
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<p>Comparative analysis of lipid groups in the breast muscles of four kinds of poultry. (<b>A</b>,<b>B</b>) Chest PCA heat map between three bioreplicates in each group of four kinds of poultry. The stronger the blue color, the stronger the correlation between the samples. (<b>C</b>) Differential lipid venn diagram. The four birds were divided into six comparison groups, and the petal center represented the common differential genes of the four birds. (<b>D</b>) Differential lipid PCA score map. The greater the distance of lipid representatives from the population, the higher the contribution to the difference in lipid distribution in the breast muscle of the four kinds of birds. (<b>E</b>) Lipid heat maps of the top 20 PCA scores in the breast muscles of four kinds of poultry. PCA calculation of the top ten lipid molecules showed that there was a high degree of separation between lipid molecules. (<b>F</b>) Four kinds of poultry breast muscle dominant lipid map. The four kinds of birds were divided into three groups, and the proportion of lipid content was indicated by the length of the column. (<b>G</b>) Dominant lipid carbon chain length. The height of the column represented the number of lipid molecules identified in the breast muscle of the bird; different colors represented the proportion of lipid molecules with different carbon chain lengths. (<b>H</b>) Dominant lipid unsaturated composition stack diagram. The height of the column represents the total lipid number of SFA and PUFA in birds; blue represents the proportion of SFA, and red represents the proportion of PUFA.</p>
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<p>Transcriptome analysis of poultry muscle of four poultry species. (<b>A</b>) PCA heat map. Different colors represent different breeds of poultry breast muscle samples; the number of dots represents the number of samples and the farther the distance, the higher the degree of separation between samples. (<b>B</b>) Circular evolutionary tree. Different colors represent different birds; the greater the distance, the greater the evolutionary distance. (<b>C</b>) Difference orthologous genes Venn diagram. Different colors represent the number of differentially homologous genes between different groups and petal centers represent the common differentially homologous genes between the four birds. (<b>D</b>) Enrichment analysis of dominant lineal homologous gene GO. (<b>E</b>) Orthologous genes expressed in four kinds of common difference between birds GO and KEGG enrichment analysis results. The left side represents the GO enrichment results and the right side represents the KEGG enrichment results.</p>
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<p>Analysis of adaptive evolution of homologous genes in poultry muscle of four poultry species. (<b>A</b>) Map of gene density distribution in the range 0 &lt; <span class="html-italic">Ka/ks</span> &lt; 5. The bar height represents the number of gene contributions under that stress. (<b>B</b>) <span class="html-italic">Ka/ks</span> distribution annular histogram. The column length represents the selective pressure distribution of the poultry. (<b>C</b>) Forward selection gene GO enrichment analysis histogram. (<b>D</b>) Venn map of selected genes. The intersection of circles represents the common gene, yellow and blue represent AC and AD, respectively, and the selected gene is contained within the common gene. (<b>E</b>) The GO enrichment results of the positive selection genes were shared by domestic chickens with quail or pigeons. (<b>F</b>) AA broilers and dwarf chicken wing common KEGG enrichment results by positive selection genes. (<b>G</b>) Only affected by KEGG enrichment of positive selection genes in AA broilers. (<b>H</b>) Only affected by KEGG enrichment of positive selection genes in Pygmy chicks.</p>
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<p>Functional enrichment analysis of positive selection genes related to lipid metabolism. (<b>A</b>) Positive selection for GO enrichment of genes related to lipid metabolism. (<b>B</b>) KEGG enrichment results of positive selection genes related to lipid metabolism with significant differential expression.</p>
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<p>Correlation analysis of lipidome and transcriptome in four kinds of poultry breast muscle. (<b>A</b>) Comparison of characteristics of glycerophospholipid metabolism and glycerlipid metabolism in four kinds of poultry. (<b>B</b>) Positive selection of differentially expressed genes related to lipid metabolism and differential lipid molecular correlation network diagram. The red dot represents the core gene, the yellow dot represents the relevant lipid, and the thickness of the line represents the strength of the correlation (<b>C</b>) Comparison of the expression levels of differential lipid TG (16:1/18:2/20:4), TG (18:2/20:4/20:4), and PC (15:0/18:2) in the breast muscles of four kinds of poultry. The height of the column represents the expression level of the lipid molecule in the breast muscle of different birds.</p>
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13 pages, 818 KiB  
Article
Genome Assembly of Arctica islandica, the Longest-Lived Non-Colonial Animal Species
by Glenn S. Gerhard, John B. Allard, Scott Kaniper, Dorret Lynch, Hayan Lee and Sudhir Kumar
Animals 2025, 15(5), 690; https://doi.org/10.3390/ani15050690 - 27 Feb 2025
Viewed by 342
Abstract
We report the first high-quality genome-wide assembly for Arctica islandica, the longest-lived non-colonial species, with a reported maximum life span of 507 years. The genome was assembled using short- and long-read DNA sequencing and RNA sequencing of four tissues. All assessment approaches [...] Read more.
We report the first high-quality genome-wide assembly for Arctica islandica, the longest-lived non-colonial species, with a reported maximum life span of 507 years. The genome was assembled using short- and long-read DNA sequencing and RNA sequencing of four tissues. All assessment approaches indicated that the assembled genome is complete, contiguous, and accurate. The genome size is estimated at 1781.15 million base pairs (Mbps) with a coverage of 247.8×. The heterozygous rate was 1.15% and the repeat content 67.66%. Genome completeness evaluated by complete BUSCOs was 92.7%. The non-redundant gene set consisted of 39,509 genes with an average transcript length of 15,429 bp. More than 98% of the genes could be annotated across databases. Predicted non-coding RNAs included 801 miRNAs, 11,114 tRNAs, 909 rRNAs, and 349 snRNAs. The Arctica islandica genome, along with the assembly of genomes from other clam species, sets the stage for elucidating the molecular basis for the convergence of extreme longevity across these bivalve species. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Genome annotation strategy.</p>
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<p>Venn diagram of predicted proteins from Swiss-Prot, non-redundant (NR), InterPro, and KEGG.</p>
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16 pages, 6549 KiB  
Article
Mitochondrial Genome of Grapsus albolineatus and Insights into the Phylogeny of Brachyura
by Xue Zhang, Sheng Tang, Yaohui Chen, Qiuning Liu and Boping Tang
Animals 2025, 15(5), 679; https://doi.org/10.3390/ani15050679 - 26 Feb 2025
Viewed by 88
Abstract
Brachyura is among the most diverse groups of crustaceans, with over 7000 described species. Crab mitogenomes are important for understanding molecular evolution and phylogenetic relationships. Grapsus albolineatus exhibits specific rearrangements compared with the Pancrustacean ground pattern and other Brachyura species. The gene arrangement [...] Read more.
Brachyura is among the most diverse groups of crustaceans, with over 7000 described species. Crab mitogenomes are important for understanding molecular evolution and phylogenetic relationships. Grapsus albolineatus exhibits specific rearrangements compared with the Pancrustacean ground pattern and other Brachyura species. The gene arrangement of G. albolineatus is similar to that of ancestral crustaceans, barring that of the translocated trnH gene. In phylogenetic analyses, the Bayesian inference estimation was observed to be superior to the maximum likelihood estimation when the nodal support values were compared. Considering the results of the gene rearrangement pattern and phylogenetic analysis, we speculate that G. albolineatus belongs to Grapsidae. Our comparative study indicated that mitogenomes are a useful phylogenetic tool at the subfamily level within Brachyura. The findings indicate that mitogenomes could be a useful tool for systematics in other Brachyuran species. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Circular map of the mitogenome of <span class="html-italic">Grapsus albolineatus</span>. Protein-coding and ribosomal genes are presented with standard abbreviations. Transfer RNA (tRNA) genes are shown by single-letter abbreviations, except for S1 = AGN, S2 = UCN, L1 = CUN, and L2 = UUR. The thick lines outside the circle indicate the heavy strand, whereas those inside the circle indicate the light strand.</p>
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<p>The relative synonymous codon usage (RSCU) values of the mitogenome of <span class="html-italic">Grapsus albolineatus</span>.</p>
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<p>Putative secondary structure of the transfer RNA (tRNA) genes of the mitogenome of <span class="html-italic">Grapsus albolineatus</span>.</p>
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<p>The gene order patterns of the Brachyuran species used in this study.</p>
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<p>Phylogenetic tree inferred from the nucleotide sequences of 13 protein-coding genes (PCGs) of the mitogenome using Bayesian inference (BI) and maximum likelihood (ML) estimation. The Bayesian posterior probability (BPP) and bootstrap value (BP) of each node are shown as BPP/BP, with maxima of 1.00/100.</p>
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<p>Phylogenetic tree inferred from the amino acid sequences of 13 protein-coding genes (PCGs) of the mitogenome using Bayesian inference (BI) and maximum likelihood (ML) estimation. The Bayesian posterior probability (BPP) and bootstrap value (BP) of each node are shown as BPP/BP, with maxima of 1.00/100.</p>
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13 pages, 2247 KiB  
Article
Genetic Evaluation of Resilience Indicators in Holstein Cows
by Eva Kašná, Ludmila Zavadilová and Jan Vařeka
Animals 2025, 15(5), 667; https://doi.org/10.3390/ani15050667 - 25 Feb 2025
Viewed by 233
Abstract
The analysis of resilience indicators was based on daily milk yields recorded from 3347 lactations of 3080 Holstein cows located on 10 farms between 2022 and 2024. Six farms used an automatic milking system. A random regression function with a fourth-degree Legendre polynomial [...] Read more.
The analysis of resilience indicators was based on daily milk yields recorded from 3347 lactations of 3080 Holstein cows located on 10 farms between 2022 and 2024. Six farms used an automatic milking system. A random regression function with a fourth-degree Legendre polynomial was used to predict the lactation curve. The indicators were the natural log-transformed variance (LnVar), lag-1 autocorrelation (r-auto), and skewness (skew) of daily milk yield (DMY) deviations from the predicted lactation curve, as well as the log-transformed variance of DMY (Var). The single-step genomic prediction method (ssGBLUP) was used for genomic evaluation. A total of 9845 genotyped animals and 36,839 SNPs were included. Heritability estimates were low (0.02–0.13). The strongest genetic correlation (0.87) was found between LnVar and Var. The genetic correlation between r-auto and skew was also strong but negative (−0.73). Resilience indicators showed a negative correlation with milk yield per lactation and a positive correlation with fat and protein contents. The negative correlation between fertility and two resilience indicators may be due to the evaluation period (50th–150th day of lactation) being when cows are most often bred after calving, and a decrease in production may accompany a significant oestrus. The associations between resilience indicators and health traits (clinical mastitis, claw health) were weak but mostly favourable. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Average daily milk yield and number of animals in the first and later parities.</p>
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<p>Average accuracies of GEBVs for resilience indicators in particular groups of animals. Resilience indicators are log-transformed variance of daily milk yields (Var), log-transformed variance of deviations (LnVar), lag-1 autocorrelation of deviations (r-auto), and skewness of deviations (skew).</p>
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<p>Pearson correlation coefficients between RBVs of sires considering resilience indicators and RBVs considering milk production traits and the Czech Holstein Selection Index (SIH). Resilience indicators are log-transformed variance of daily milk yields (Var), log-transformed variance of deviations (LnVar), lag-1 autocorrelation of deviations (r-auto), and skewness of deviations (skew). Milk production traits include milk yield (kg), protein yield (kg), fat yield (kg), and protein and fat contents in milk (%). Higher RBVs are desirable; therefore, positive correlations are favourable.</p>
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<p>Pearson correlation coefficients between RBVs of sires considering resilience indicators and RBVs considering selected exterior traits. Resilience indicators were log-transformed variance of daily milk yields (Var), log-transformed variance of deviations (LnVar), lag-1 autocorrelation of deviations (r-auto), and skewness of deviations (skew). Exterior traits include four feet- and leg-type traits and six udder-type traits.</p>
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<p>Pearson correlation coefficients between RBVs of sires considering resilience indicators and RBVs considering fertility traits. Resilience indicators are log-transformed variance of daily milk yields (Var), log-transformed variance of deviations (LnVar), lag-1 autocorrelation of deviations (r-auto), and skewness of deviations (skew). Fertility traits include daughter fertility recorded in heifers, cows, and in both groups (daughters) together, direct calving ease, maternal calving ease, and direct 1st calving ease. Higher RBVs are desirable; therefore, positive correlations are favourable.</p>
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<p>Pearson correlation coefficients between RBVs of sires considering resilience indicators, RBVs considering health traits, RBVs considering body condition score, and longevity and health indexes. Resilience indicators are log-transformed variance of daily milk yields (Var), log-transformed variance of deviations (LnVar), lag-1 autocorrelation of deviations (r-auto), and skewness of deviations (skew). Longevity index combines functional longevity with fertility of cows, body depth, udder depth, foot and leg score, and somatic cell count. Health index consists of resistance to clinical mastitis, infectious claw diseases, claw horn lesions, and overall claw disorders. Higher RBVs are desirable; therefore, positive correlations are favourable.</p>
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19 pages, 4986 KiB  
Article
Analysis of the Transcriptional Control of Bcl11b in Chicken: IRF1 and GATA1 as Negative Regulators
by Lingling Qiu, Haojie Wang, Wenhao Li, Ting Yang, Hao Bai and Guobin Chang
Animals 2025, 15(5), 665; https://doi.org/10.3390/ani15050665 - 25 Feb 2025
Viewed by 167
Abstract
B-cell lymphoma/leukemia 11B (Bcl11b) plays roles in cell proliferation and apoptosis and holds a pivotal position within the immune system. Our previous studies have demonstrated that Bcl11b can promote cell apoptosis to curb ALV-J infection. To gain insights into the molecular mechanisms underlying [...] Read more.
B-cell lymphoma/leukemia 11B (Bcl11b) plays roles in cell proliferation and apoptosis and holds a pivotal position within the immune system. Our previous studies have demonstrated that Bcl11b can promote cell apoptosis to curb ALV-J infection. To gain insights into the molecular mechanisms underlying Bcl11b expression regulation in chickens, we constructed various truncated dual luciferase reporter vectors and analyzed the promoter region of Bcl11b. We employed promoter-binding TF profiling assay and the dual luciferase assay of site-directed mutagenesis and the expression level of interfering or overexpressing transcription factors were used to study their transcriptional regulation mechanism of chicken Bcl11b and functions in ALV-J infection. Our findings revealed core regulatory regions of the chicken Bcl11b promoter. By examining the −606~−363 bp region, we identified several transcription factors and their binding sites. Mutational and functional analysis further revealed interferon regulatory factor-1 (IRF1) and GATA-binding protein 1 (GATA1) as critical factors for the repression of chicken Bcl11b, thereby affecting cell apoptosis and ALV-J replication. Furthermore, DNA methylation analysis indicated that methylation may also contribute to changes in Bcl11b promoter activity. These findings offer valuable insights into the regulatory mechanisms of chicken Bcl11b and provide promising targets for molecular breeding and genetic improvement of disease resistance in chickens. Full article
(This article belongs to the Special Issue Livestock and Poultry Genetics and Breeding Management)
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<p>Promoter analysis of Chicken <span class="html-italic">Bcl11b</span> gene. (<b>A</b>) Online prediction of <span class="html-italic">Bcl11b</span> promoter region. (<b>B</b>) Dual luciferase activity assay with various truncated fragments from the upstream region of TSS of the chicken <span class="html-italic">Bcl11b</span> gene. Mean ± SEM, different letters (a–f) represent significant differences between groups at a significance level of <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Bioinformatics analysis and visualization of the <span class="html-italic">Bcl11b</span> promoter region. (<b>A</b>) Phylogenetic tree of <span class="html-italic">Bcl11b</span> promoter sequence (−2999~+19 bp) of 27 species. The same color represents groups that are clustered together. (<b>B</b>) Venn diagram analysis to visualize the set relationships of predicted transcription factors across different regulatory regions of chicken <span class="html-italic">Bcl11b</span> promoter. (<b>C</b>–<b>E</b>) Binding predictions for transcription factors across three regulatory regions of chicken <span class="html-italic">Bcl11b</span> promoter. Each individual bar corresponds to one transcription factor, with the bar length indicative of the number of binding sites.</p>
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<p>Identification of core regulatory regions and crucial transcription factors of the chicken <span class="html-italic">Bcl11b</span> promoter. (<b>A</b>,<b>B</b>) A dual luciferase reporter assay was conducted using different truncated fragments spanning three regulatory regions of the chicken <span class="html-italic">Bcl11b</span> as described earlier. (<b>C</b>) A schematic representation of the profiling assay for TFs binding to the promoter. (<b>D</b>) Identification of TFs that bind to the region spanning from −2259 to −2140 bp of the chicken <span class="html-italic">Bcl11b</span> promoter. (<b>E</b>) Identification of TFs that bind to the region spanning from −606 to −363 bp of the chicken <span class="html-italic">Bcl11b</span> promoter. Data are present as mean ± SEM, with different letters (a–g) indicating statistically significant differences between groups (<span class="html-italic">p</span> &lt; 0.05). * for <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Detection and confirmation of key transcription factors binding sites within the second core region of the chicken <span class="html-italic">Bcl11b</span> promoter. (<b>A</b>) Dual luciferase activity test with site-directed mutations at the C/EBPβ, GATA1, SMAD2, and IRF1 sites in the pGL3-708+19 vector, using pGL3-708+19 as a comparison. (<b>B</b>) Alignment of multiple sequences focusing on the IRF1 and GATA1 transcription factor binding sites in the <span class="html-italic">Bcl11b</span> promoter across species. The red box indicates GATA1 transcription factor binding site. (<b>C</b>,<b>D</b>) Overexpression of chicken IRF1 and GATA1, with the pcDNA3.1(+) empty vector serving as negative control. (<b>E</b>,<b>F</b>) Interference of chicken IRF1 and GATA1 expression, with the siRNA NC serving as negative control. (<b>G</b>,<b>H</b>) Luciferase reporter assessments following IRF1 or GATA1 overexpression, achieved through co-transfection with either the pGL3-708+19 vector in DF-1 cells. (<b>I</b>,<b>J</b>) Luciferase reporter assessments following IRF1 or GATA1 knockdown, achieved through co-transfection with either the pGL3-708+19 vector in DF-1 cells. The results are presented as Mean ± SEM. Compared to the control group, significance levels are indicated by asterisks: * for <span class="html-italic">p</span> &lt; 0.05, ** for <span class="html-italic">p</span> &lt; 0.01, *** for <span class="html-italic">p</span> &lt; 0.001, and **** for <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>IRF1 and GATA1 suppress <span class="html-italic">Bcl11b</span> transcription and promote ALV-J replication. (<b>A</b>,<b>B</b>) The expression levels of <span class="html-italic">Bcl11b</span> after we altered GATA1 and IRF1 expression, respectively. (<b>C</b>) The expression levels of <span class="html-italic">Bcl11b</span>, the pcDNA3.1(+) empty vector was used as negative control. (<b>D</b>) When Bcl11b, GATA1, and IRF1 expressions were modified, changes in cell viability were noted. (<b>E</b>,<b>F</b>) Changes in cell apoptosis when <span class="html-italic">Bcl11b</span>, <span class="html-italic">GATA1</span>, and <span class="html-italic">IRF1</span> expressions were altered. (<b>G</b>) A detailed procedure for intervening in ALV-J-infected cells. (<b>H</b>) Immunofluorescence staining was conducted using an antibody specific to the viral protein gp85 to detect ALV-J virus infection (green). Nuclear staining was achieved using DAPI (Blue). The scale bar indicates 50 μm. (<b>I</b>) The impact on ALV-J replication was assessed when Bcl11b, GATA1, and IRF1 expressions were altered. Data are presented as mean ± SEM. Compared to the control group, * indicates significance at <span class="html-italic">p</span> &lt; 0.05, ** indicates significance at <span class="html-italic">p</span> &lt; 0.01, and *** indicates significance at <span class="html-italic">p</span> &lt; 0.001. Different letters (a–e) denote significant differences between groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Methylation analysis of the second core region of the chicken <span class="html-italic">Bcl11b</span> promoter. (<b>A</b>) CpG island in the entire <span class="html-italic">Bcl11b</span> promoter predicted by METHPRIMER. (<b>B</b>) Methylation of the second core promoter region of chicken <span class="html-italic">Bcl11b</span>. (<b>C</b>) DNA demethylation represses the <span class="html-italic">Bcl11b</span> promoter activity. (<b>D</b>) DNA demethylation in the second core promoter region of <span class="html-italic">Bcl11b</span> enhances the inhibition of promoter activity mediated by negative regulatory elements such as GATA1. Data are presented as mean ± SEM. Compared to the control group, * indicates significance at <span class="html-italic">p</span> &lt; 0.05, *** indicates significance at <span class="html-italic">p</span> &lt; 0.001, and **** indicates significance at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Schematic diagram of how transcription factor IRF1 and GATA1 regulate the transcription of chicken <span class="html-italic">Bcl11b</span> and thereby affect cell apoptosis and ALV-J replication. The orange boxes represent transcription factor binding sites.</p>
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11 pages, 279 KiB  
Article
Genetic Analysis of Litter Size Across Parities in Prolific and Conventional Populations of Tunisian Barbarine Sheep Using a Random Regression Model
by Chiraz Ziadi, Juan Manuel Serradilla, Sonia Bedhiaf-Romdhani and Antonio Molina
Animals 2025, 15(5), 638; https://doi.org/10.3390/ani15050638 - 22 Feb 2025
Viewed by 197
Abstract
Litter size records from two lines of Tunisian Barbarine sheep were analysed across parities using an RRM. A total of 2751 and 2562 litter records from the first to the sixth parity from the prolific and the conventional lines, respectively, were included in [...] Read more.
Litter size records from two lines of Tunisian Barbarine sheep were analysed across parities using an RRM. A total of 2751 and 2562 litter records from the first to the sixth parity from the prolific and the conventional lines, respectively, were included in the analysis. The total number of animals in the pedigree was 1277 for the prolific line and 1102 for the conventional line. The estimation of genetic parameters was based on Bayesian inference under categorical distribution. Fixed effects included the year and month of lambing and a fixed quadratic regression coefficient for the lambing number with Legendre polynomials. The random additive and permanent environmental effects were modelled by second-order Legendre polynomials. Heritability ranged from 0.04 to 0.18 for the prolific line and from 0.17 to 0.39 for the conventional line. Genetic correlations within trait through parities showed a wide range of values, from 0.25 to 0.96 for the prolific line and from zero to 0.93 for the conventional line. Due to the changes in the variances and the genetic correlations different from unity across parities, the use of an RRM is recommended to analyse litter size in the Barbarine sheep. Full article
(This article belongs to the Special Issue Genetics and Genomics of Small Ruminants Prolificacy)
19 pages, 3737 KiB  
Article
Heterozygosity-Rich Regions in Canine Genome: Can They Serve as Indicators of Balancing Selection?
by Adrián Halvoník, Nina Moravčíková, Luboš Vostrý, Hana Vostra-Vydrova, Gábor Mészáros, Eymen Demir, Monika Chalupková and Radovan Kasarda
Animals 2025, 15(4), 612; https://doi.org/10.3390/ani15040612 - 19 Feb 2025
Viewed by 209
Abstract
Compared to the negative effect of directional selection on genetic diversity, balancing selection acts oppositely and maintains variability across the genome. This study aims to articulate whether balancing selection leads to heterozygosity-rich region islands (HRRIs) forming in the canine genome by investigating 1000 [...] Read more.
Compared to the negative effect of directional selection on genetic diversity, balancing selection acts oppositely and maintains variability across the genome. This study aims to articulate whether balancing selection leads to heterozygosity-rich region islands (HRRIs) forming in the canine genome by investigating 1000 animals belonging to 50 dog breeds via 153,733 autosomal SNPs. A consecutive SNP-based approach was used to identify heterozygosity-rich regions (HRRs). Signals of balancing selection in the genome of studied breeds were then assessed with Tajima’s D statistics. A total of 72,062 HRRs with an average length of 324 kb were detected to be unevenly distributed across the genome. A total of 509 and 450 genomic regions were classified as HRRIs and balancing selection signals, respectively. Although the genome-wide distributions of HRRIs varied across breeds, several HRRIs were found in the same locations across multiple breeds. A total of 109 genomic regions were classified as both HRRIs and signals of balancing selection. Even though the genomic coordinates of HRRIs and balancing selection signals did not fully overlap across all genomic regions, balancing selection may play a significant role in maintaining diversity in regions associated with various cancer diseases, immune response, and bone, skin, and cartilage tissue development. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Pairwise Pearson correlations (*** indicates <span class="html-italic">p</span> &lt; 0.001) between <span class="html-italic">H<sub>o</sub></span>, percentage of genome covered by HRRs, and number of HRRs (<b>A</b>) and violin plot showing variability in the length of detected HRRs (<b>B</b>).</p>
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<p>Distribution of HRRIs per chromosome in the whole population (<b>A</b>) and distribution of HRRIs (blue) and ROHIs (red) per breed (<b>B</b>). The gaps between visualised regions within and between chromosomes do not correspond to the real distance.</p>
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<p>Distribution of balancing selection signals derived from Tajima’s D statistics per chromosome in the whole population (<b>A</b>) and per breed (<b>B</b>). The gaps between visualised regions within and between chromosomes do not correspond to the real distance.</p>
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<p>Venn diagrams showing overlaps between balancing selection signatures and HRRIs in regions identified as hot spots of balancing selection signatures and HRRIs.</p>
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14 pages, 4098 KiB  
Article
Genetic Comparison and Selection of Reproductive and Growth-Related Traits in Qinchuan Cattle and Two Belgian Cattle Breeds
by Xiaopeng Li, Peng Niu, Xueyan Wang, Fei Huang, Jieru Wang, Huimin Qu, Chunmei Han and Qinghua Gao
Animals 2025, 15(4), 608; https://doi.org/10.3390/ani15040608 - 19 Feb 2025
Viewed by 196
Abstract
This study investigates the genetic structure of Belgian Red (BR), Belgian Red and White (BWR), and Qinchuan (QinC) cattle, with a focus on identifying genes associated with reproductive functions, growth, and development. A total of 270 Belgian cattle (91 BR and 179 BWR) [...] Read more.
This study investigates the genetic structure of Belgian Red (BR), Belgian Red and White (BWR), and Qinchuan (QinC) cattle, with a focus on identifying genes associated with reproductive functions, growth, and development. A total of 270 Belgian cattle (91 BR and 179 BWR) and 286 Qinchuan cattle were genotyped using the Illumina Bovine SNP 50K microarray. Data analysis was conducted using PLINK and Beagle 5.1 to estimate linkage disequilibrium (LD) and effective population size (Ne). Candidate SNP loci were identified by selecting the top 5% based on the weighted fixation index (Fst) and nucleotide diversity (θπ ratio), followed by gene annotation. The analysis revealed 160 candidate genes under selection between Qinchuan and Belgian Red cattle, and 98 candidate genes between Qinchuan and Belgian Red and White cattle. Key genes associated with reproductive functions, including NFKBIA, PTHLH, UGT2B10, TRPC4, and ALOX5AP, were identified. Additionally, genes involved in growth and muscle development were highlighted, particularly those influencing protein synthesis, fatty acid metabolism, and collagen synthesis. These findings provide valuable molecular markers for enhancing reproductive efficiency, growth, and meat production through genetic selection and selective breeding strategies. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>(<b>A</b>) The PCA results of three cattle populations with PC1 on the x axis and PC2 on the y axis; (<b>B</b>) Evolutionary tree of the three bovine populations, blue for QinC, green for BWR, and yellow for BR; (<b>C</b>) genome-wide LD (r<sup>2</sup>) to estimate Ne in different populations; (<b>D</b>) LD decay plots of three cattle populations where the X axis represents the distance, the Y axis represents the chain imbalance coefficient, red represents BR, black represents BWR, blue represents QinC; (<b>E</b>) LD(r2) distribution pattern of three different populations.</p>
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<p>A multi-panel visualization of selection signals in the QinC and BR populations based on different genetic selection metrics. The figure consists of four subplots (<b>A</b>–<b>D</b>), each illustrating different aspects of selection patterns: (<b>A</b>) Chromosomal distribution of selection degree (θπ ratio). X axis: Represents different chromosomes, showing genome-wide selection patterns across the chromosomes. Y axis: Represents the selection degree values θπ ratio (πQinC /πBR), which compares nucleotide diversity (π) between QinC and BR populations. Red-dashed lines: The upper-dashed line (0.415) and lower-dashed line (−0.771) indicate the top and bottom 2.5% threshold values for θπ ratio, which define regions under selection. Data points above the upper threshold represent genomic regions that experienced strong positive selection in QinC (higher diversity in QinC relative to BR). Data points below the lower threshold indicate genomic regions under stronger selection in BR (lower diversity in QinC compared to BR). (<b>B</b>) Venn diagram of selected genes. Three main categories are depicted: Genes identified in the top 5% of Fst-selected regions (representing highly differentiated genes between QinC and BR); Genes within the top 2.5% of θπ ratio upregulated genes (genes that show higher nucleotide diversity in QinC compared to BR, suggesting positive selection in QinC). Genes within the top 2.5% of θπ ratio downregulated genes (genes that show lower nucleotide diversity in QinC compared to BR, suggesting positive selection in BR)<b>.</b> Overlap among these categories: The intersection of these sets highlights genes that are subject to both differentiation and strong selection, making them potential candidates for adaptive evolution. (<b>C</b>) Scatterplot of θπ ratio vs. Fst values. X axis: Represents θπ ratio (πQinC /πBR), showing the relative selection strength between the two populations. Y axis: Represents Fst values, measuring genetic differentiation between QinC and BR populations at each genomic locus. Dot colors indicate population-specific selection: Blue dots: Represent regions predominantly under selection in QinC. Green dots: Represent regions predominantly under selection in BR. Higher Fst values suggest stronger genetic divergence between populations, while extreme θπ ratio values indicate selection pressure acting more intensely on one population. (<b>D</b>) Chromosomal distribution of WEIGHTED_Fst Values. X axis: Represents different chromosomes, similar to panel (<b>A</b>). Y axis: Represents WEIGHTED_Fst values, a metric indicating genetic differentiation across different genomic regions. Red-dashed line (threshold = 0.231): Represents the top 5% threshold of WEIGHTED_Fst values. Genomic regions above this threshold are significantly differentiated between QinC and BR populations, indicating strong selective pressure leading to population-specific divergence.</p>
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<p>A multi-panel visualization of selection signals in the QinC and BWR populations based on different genetic selection metrics. The figure consists of four subplots (<b>A</b>–<b>D</b>), each illustrating different aspects of selection patterns. (<b>A</b>) Chromosomal distribution of selection degree (θπratio) <b>X axis:</b> Represents different chromosomes, showing genome-wide selection patterns. Y axis: Represents the selection degree θπratio (πQinC/πBWR), which compares nucleotide diversity (π) between QinC and BWR populations. Red-dashed lines: The upper threshold (0.283) and lower threshold (−0.789) mark the top and bottom 2.5% quantiles of θπ ratio, defining regions under selection. Data points above the upper threshold indicate genomic regions under strong positive selection in QinC (higher diversity in QinC relative to BWR). Data points below the lower threshold indicate genomic regions under stronger selection in BWR (lower diversity in QinC relative to BWR). (<b>B</b>) Venn diagram of selected genes. This panel depicts the overlap among three categories of selected genes: Genes in the top 5% of Fst-selected regions, representing loci with high genetic differentiation between QinC and BWR (blue). Genes in the top 2.5% of θπ ratio upregulated genes, showing higher nucleotide diversity in QinC compared to BWR, suggesting positive selection in QinC (green).Genes in the top 2.5% of θπ ratio downregulated genes, showing lower nucleotide diversity in QinC compared to BWR, suggesting positive selection in BWR (orange). Overlap among these categories: The intersection of these sets highlights genes subject to both strong differentiation (Fst) and selection (θπratio). These genes are potential candidates for adaptive evolution in either population. (<b>C</b>) Scatterplot of θπratio vs. Fst values. This panel visualizes the relationship between genetic differentiation (Fst) and nucleotide diversity ratio (θπratio) in the QinC and BWR populations, helping to identify regions under selection. X axis: Represents the θπ ratio (πQinC/πBWR), reflecting the relative selection strength between the two populations. Y axis: Represents Fst values, quantifying genetic differentiation between the populations. Higher Fst values indicate strong genetic divergence, suggesting that selection may have driven population-specific allele frequency shifts. Dot colors indicate selection patterns in different populations: Blue dots: Represent regions predominantly under selection in QinC. Green dots: Represent regions predominantly under selection in BWR. Higher Fst values indicate stronger genetic divergence between populations, while extreme θπ ratio values suggest intense selection pressure acting on one population. (<b>D</b>) Chromosomal distribution of WEIGHTED_Fst Values. X axis: Represents different chromosomes, as in panel (<b>A</b>). Y axis: Represents WEIGHTED_Fst values, measuring genetic differentiation across genomic regions. Red-dashed line (threshold = 0.178): Marks the top 5% quantile of WEIGHTED_Fst values. Genomic regions above this threshold show significant differentiation between QinC and BWR, indicating strong selective pressure driving population divergence.</p>
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<p>A circular chord diagram illustrating the functional enrichment of the top 5% candidate genes in the BWR population. This visualization highlights the relationships between key genes (left side) and their corresponding enriched pathways (right side).</p>
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<p>The red line in the figure represents the IKBA phosphorylation process, and the inhibition of the phosphorylation process improves sperm motility and thus enhances the reproductive performance of bulls.</p>
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19 pages, 15205 KiB  
Article
N6-Methyladenosine (m6A)-Circular RNA Pappalysin 1 (circPAPPA) from Cashmere Goats: Identification, Regulatory Network and Expression Potentially Regulated by Methylation in Secondary Hair Follicles Within the First Intron of Its Host Gene
by Man Bai, Jincheng Shen, Yixing Fan, Ruqing Xu, Taiyu Hui, Yubo Zhu, Qi Zhang, Jialiang Zhang, Zeying Wang and Wenlin Bai
Animals 2025, 15(4), 581; https://doi.org/10.3390/ani15040581 - 18 Feb 2025
Viewed by 266
Abstract
N6-methyladenosine (m6A) is one of the most abundant modifications in eukaryotic RNA molecules and mediates the functional exertion of RNA molecules. We characterized the circPAPPA and validated its potential m6A modification sites in secondary hair follicles (SHFs) [...] Read more.
N6-methyladenosine (m6A) is one of the most abundant modifications in eukaryotic RNA molecules and mediates the functional exertion of RNA molecules. We characterized the circPAPPA and validated its potential m6A modification sites in secondary hair follicles (SHFs) of cashmere goats. Furthermore, we generated integrated regulatory networks of the circPAPPA along with enrichment analysis of signaling pathways. We also explored the potential relationship of circPAPPA expression with the first intron methylation of the PAPPA gene in SHFs of cashmere goats. Host source analysis revealed that circPAPPA is derived from the complete exon 2 of the PAPPA gene, spliced in reverse orientation, and predominantly localized in the cytoplasm of SHF stem cells in cashmere goats. The circPAPPA was verified to contain at least four m6A modification sites in SHFs of cashmere goats, including m6A-450/456, m6A-852, m6A-900, and m6A-963. The generated regulatory network indicated complex and diverse regulatory relationships of m6A-circPAPPA with its putative regulatory molecules, including miRNAs, mRNAs, and proteins. Enrichment analysis of signaling pathways showed that m6A-circPAPPA might play multiple functional roles in the growth and development of SHF in cashmere goats through the putative regulatory network mediated by its target miRNAs and regulatory proteins. The first intron methylation of the PAPPA gene most likely is significantly involved in the dynamic expression of m6A-circPAPPA in cashmere goat SHFs. Results from this study provided novel information to elucidate the biological roles and functional regulatory pathways of m6A-circPAPPA in SHF development and the growth of cashmere goat fiber. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>A diagram of circPAPPA host source in cashmere goats along with its sequence characteristics. (<b>A</b>) Structural feature diagram of the host gene of circPAPPA along with the reverse splicing size of 1062-nt. (<b>B</b>) Sequence display of circPAPPA that harbours seven possible binding sites of miRNAs, including chi-let7b-5p, chi-let7d-5p, chi-miR-21-5p, chi-miR-199a-5p, chi-miR-17-5p, chi-miR-103-3p, and chi-miR-24-3p. Also, five potential m<sup>6</sup>A modification sites were revealed within the circPAPPA molecule, including m<sup>6</sup>A-450, m<sup>6</sup>A-456, m<sup>6</sup>A-852, m<sup>6</sup>A-900, and m<sup>6</sup>A-963 with the motif of GAACU, GGACA, GAACU, GGACU, and GGACU, respectively.</p>
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<p>Validation of circPAPPA m<sup>6</sup>A modification sites and its subcellular localization, along with potential target miRNA prediction based on in silico analysis. (<b>A</b>) A diagram of potential m<sup>6</sup>A modification sites within circPAPPA along with their validation through Me-RIP technique followed by qPCR analysis. (<b>B</b>) Evaluation of circPAPPA coding potentiality along with a screening of potential ORFs. (<b>C</b>) Subcellular localization of circPAPPA in SHF stem cells of cashmere goats where the relative expression of <span class="html-italic">snRNA-U6</span> and <span class="html-italic">GAPDH</span> were also measured as internal controls of the nuclear and cytoplasm RNA, respectively. (<b>D</b>) Binding structure features of circPAPPA with its potential target miRNAs: chi-let7b-5p, chi-let7d-5p, chi-miR-21-5p, chi-miR-199a-5p, chi-miR-17-5p, chi-miR-103-3p and chi-miR-24-3p. The ∆G value was calculated using an online service program, RNAhybrid, under default settings (<a href="https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/" target="_blank">https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/</a>, last access: 25 September 2024).</p>
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<p>CeRNA regulatory network of cashmere goat circPAPPA that was constructed using the Cytoscape software (version 2.8.3). CircPAPPA was indicated by the brown hexagon. The miRNAs were indicated by purple swallowtail shapes. The potential target genes of miRNAs were indicated by green circles.</p>
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<p>Signaling pathway enrichment of circPAPPA regulatory genes mediated by its potential target miRNAs. Enrichment of signaling pathways was performed by the CluePedia plugin embedded in Cytoscape software. Enriched results were presented as a network where each term of signaling pathways and its associated genes shared the same color.</p>
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<p>Regulatory network of m<sup>6</sup>A-circPAPPA in cashmere goats with its direct and indirect regulatory proteins. M<sup>6</sup>A-circPAPPA was indicated by a dark red circle. The proteins (DIPs) that directly interact with m<sup>6</sup>A-circPAPPA were indicated by dark red round balls where the binding motifs with each protein were provided in corresponding outer ring. The indirect regulatory proteins (IRPs) of m<sup>6</sup>A-circPAPPA mediated by DIPs were indicated by dark green round balls.</p>
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<p>Signaling pathway enrichment of the potential regulatory proteins by the circPAPPA molecule. The enrichment analysis of signaling pathways was conducted using the CluePedia plugin embedded in Cytoscape software under default settings. The significantly enriched pathways were provided as a chordmap that was generated by the SRplot procedure [<a href="#B23-animals-15-00581" class="html-bibr">23</a>].</p>
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<p>Expression features of circPAPPA in cashmere goat SHFs during hair follicle cycles and its potential relationships with the first intron methylation of the host gene PAPPA. (<b>A</b>) Relative expression of circPAPPA in cashmere goat SHFs at differential stages of hair follicle cycles. (<b>B</b>) Relative expression of the <span class="html-italic">PAPPA</span> gene in cashmere goat SHFs at differential stages of hair follicle cycles. (<b>C</b>) Expression correlation of circPAPPA and its host gene <span class="html-italic">PAPPA</span> in cashmere goat SHFs at differential stages of hair follicle cycles. (<b>D</b>) Prediction of CpG islands within the <span class="html-italic">PAPPA</span> gene’s first intron where the CpG sites were designated by pink vertical lines. The nucleotide positions were designated based on the <span class="html-italic">PAPPA</span> gene sequence in goat genome datasets: NC_030815.1 (Genome assembly ARS1.2, <a href="https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_001704415.2" target="_blank">https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_001704415.2</a>). BSP stands for bisulfite sequencing PCR. (<b>E</b>) Prediction analysis of potential binding sites of transcription factors (underlined with yellow) in BSP analysis region within the <span class="html-italic">PAPPA</span> first intron of goats. The CpG sites were designated by yellow shadow regions. (<b>F</b>) Methylation analysis results of the first intron of the <span class="html-italic">PAPPA</span> gene in cashmere goat SHFs during differential stages of the hair follicle: telogen, anagen and telogen. The methylated and unmethylated CpG sites were designated by the filled black and unfilled white circles, respectively. The corresponding percentages of methylated CpG sites within the first intron of the <span class="html-italic">PAPPA</span> gene were presented by pie charts for each investigated stage of cashmere goat SHFs. The ‘ns’ represents no significant difference, and the ‘****’ indicates <span class="html-italic">p</span> &lt; 0.0001.</p>
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11 pages, 1595 KiB  
Article
Strategic Sampling of Eurasian Otter Spraints for Genetic Research in South Korea: Enhancing PCR Success and Data Accuracy
by Jee Hyun Kim, Jangmi Lee, Dong Youn Kim, Yoon-Do Yang, Sujoo Cho, Han-Chan Park, Sung Yong Han, Mi-Sook Min, Hang Lee, Je-Yoel Cho and Puneet Pandey
Animals 2025, 15(4), 574; https://doi.org/10.3390/ani15040574 - 17 Feb 2025
Viewed by 250
Abstract
Non-invasive genetic approaches, particularly using fecal samples, are commonly used to study endangered and elusive species, as they are easy to collect with minimal permission and cause little disturbance to the subject population. However, such studies face limitations due to poor DNA yield, [...] Read more.
Non-invasive genetic approaches, particularly using fecal samples, are commonly used to study endangered and elusive species, as they are easy to collect with minimal permission and cause little disturbance to the subject population. However, such studies face limitations due to poor DNA yield, which affects the overall utilization of collected samples and increases data errors. Here, we evaluated the impact of sample age and collection season on the performance of DNA extracted from feces (spraints) of the Eurasian otter (Lutra lutra), a semi-aquatic apex predator in South Korean freshwater ecosystems. We found that PCR amplification success rates decreased more rapidly in summer (79.3–58.2%) compared to winter (99.2–84.8%) with extended environmental exposure. Genotyping error rates were higher in samples collected during summer, with the rate of error increase over time being significantly greater in summer than in winter. The hot and humid South Korean summer fosters microbial growth and fecal degradation, which negatively impacts DNA yield, reducing PCR amplification success and increasing genotyping errors. We recommend collecting otter feces during winter for better DNA quality. If sampling in summer is unavoidable, it is crucial to collect fresh samples, which can be facilitated by conducting frequent surveys of latrine sites. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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Figure 1
<p>(<b>a</b>) Metal enclosure with otter spraints exposed to semi-natural conditions in summer (<b>upper</b>) and winter (<b>below</b>). (<b>b</b>) Hair trap used to collect otter hairs.</p>
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<p>Average PCR success rates from DNA extracted from spraints collected in summer (●) and winter (○), along with the average of allelic dropout rates [summer (■) and winter (□)] and false allele rates [summer (▲) and winter (△)] over time. PCR success and error rates are presented on a scale from 0 to 1, where 0 indicates no success or no errors, and 1 represents 100% success or 100% error.</p>
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11 pages, 1679 KiB  
Article
Missense Mutations in FDNC5 Associated with Morphometric Traits and Meat Quality in Hainan Black Goats
by Jing Huang, Mengning Xu, Yuelang Zhang, Jiancheng Han, Hanlin Zhou and Ke Wang
Animals 2025, 15(4), 565; https://doi.org/10.3390/ani15040565 - 15 Feb 2025
Viewed by 307
Abstract
Goats are widely recognized for their adaptability and resource efficiency, making them an excellent choice for sustainable farming. However, the Hainan Black goat (HNBG), a vital breed in southern China’s tropical regions, faces significant challenges that threaten its productivity and economic viability. Specifically, [...] Read more.
Goats are widely recognized for their adaptability and resource efficiency, making them an excellent choice for sustainable farming. However, the Hainan Black goat (HNBG), a vital breed in southern China’s tropical regions, faces significant challenges that threaten its productivity and economic viability. Specifically, young HNBGs exhibit stunted growth and poor muscle development, indicating the breed may have more genetic defects that cause the poor phenotypes. The FNDC5 gene, which encodes the protein irisin, plays a key role in promoting mitochondrial biogenesis and oxidative metabolism by activating critical signaling molecules such as PGC-1α, thereby enhancing muscle endurance and metabolic efficiency. This study aimed to investigate the impact of missense mutations in the FNDC5 gene on growth and meat quality traits in HNBGs. We sequenced a population of HNBGs and identified three SNPs that could lead to amino acid substitutions. Notably, SNP1 (p.119A/V) and SNP2 (p.135R/H) showed strong linkage. Predictions on the structural effects of these mutations indicated that SNP1 (p.119A/V) and SNP3 (p.170W/G) could alter the secondary structure of the FNDC5 protein. Association analyses revealed that SNP1 (p.119A/V) and SNP2 (p.135R/H) were significantly associated with morphometric traits and meat quality. The phenotypic values of SNP1 and SNP2 co-mutants were significantly lower than those of other combined genotypes. Furthermore, gene expression levels of FNDC5 varied notably across individuals with different SNP1 genotypes. These findings suggest that FNDC5-SNP1 (p.119A/V) could serve as a promising genetic marker for selecting HNBGs with improved growth and muscle development, offering a potential pathway for enhancing key economic traits in this breed. Full article
(This article belongs to the Special Issue Genetics and Breeding in Ruminants)
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<p>Identification of SNPs in the <span class="html-italic">FNDC5</span> gene and expression of the <span class="html-italic">FNDC5</span> gene in goats. (<b>A</b>) Localization and identification of SNPs in the <span class="html-italic">FNDC5</span> gene by Sanger sequencing. The black module indicates the exon region where the two spliceosomes overlap, and the mosaic module represents the region where the two spliceosomes do not overlap. The three missense mutations are marked in red. (<b>B</b>) Tissue expression profile of the <span class="html-italic">FNDC5</span> gene in adult female goats. Letters (a–c) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) in expression levels among tissues. (<b>C</b>) Temporal expression profile of the <span class="html-italic">FNDC5</span> gene in longissimus dorsi muscle. Letters (a,b) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) in expression levels across time points. (<b>D</b>) Expression of the <span class="html-italic">FNDC5</span> gene in individuals with different extreme carcass weights. The asterisk (*) indicates significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups. (<b>E</b>) Expression of the <span class="html-italic">FNDC5</span> gene in individuals with different extreme cross-sectional areas of longissimus dorsi muscle. The asterisk (*) indicates significant differences (<span class="html-italic">p</span> &lt; 0.05) between groups.</p>
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<p>Structural prediction of FNDC5 protein based on reference genome (<b>A</b>) and three missense mutations (<b>B</b>). The blue arrows represent the strand structure, which forms the beta sheet, and the red waves represent the alpha helix. The blue and red dashed squares show the contrast of secondary structure changes. The orange bars represent other regions where the secondary structure is not predicted. The bar between blue and orange indicates the difference in solvent accessibillity, where blue indicates water affinity and orange indicates fat affinity.</p>
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<p>Missense mutations affect the expression of the <span class="html-italic">FNDC5</span> gene in goat longissimus dorsi muscle. Comparison with normal genotype; ** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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