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Breeding and Functional Genomics in Animals
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Breeding and Functional Genomics in Animals

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

Deadline for manuscript submissions: closed (10 June 2024) | Viewed by 10128

Special Issue Editor

Dr. Tingxian Deng

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Guest Editor
Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning 530001, China
Interests: livestock genomes and transcriptomes; population genetics; whole-genome sequencing; genome-wide association studies; animal biodiversity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Animal breeding is entering the big data era. With the rapid development of high-throughput sequencing technology, the generation of multi-omics data in livestock has been accelerated, leading to the arrival of a new era of "animal breeding big data". This not only provides new opportunities and challenges for animal breeding, but also provides new possibilities for the genetic analysis of economically important traits in animals. Understanding the economically important traits of animals can be used for management interventions in breeding programs and agricultural innovations.

Large-scale molecular markers and precise phenotypic data provide an important basis for the GWAS application and QTL mapping. Population genetics is a subfield of genetics that deals with genetic differences within and between populations, helping to explain the genetic background of the studied population and provide some key information on the application of GWAS. In addition, omic studies will facilitate and accelerate the breeding process and provide applications for genetic improvement, such as GS and MAS.

This Special issue, "Breeding and Functional Genomics in Animals", will cover a range of research topics and review articles on the latest developments in genomics, transcriptomics, population genetics, and other multi-omics that can strengthen breeding strategies and advance the breeding process of livestock species.

Dr. Tingxian Deng
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • animal breeding
  • genetic marker
  • RNA-seq
  • genome
  • population genetics
  • economical important traits
  • omics

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Published Papers (6 papers)

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Research

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13 pages, 30771 KiB  
Article
Transcriptome Analysis Reveals Novel Inflammatory Signalings to Glaesserella parasuis Infection
by Jingwen Lei, Xuexue Chen, Huanhuan Zhou, Zekai Zhang, Zhong Xu, Ke Xu and Hongbo Chen
Genes 2024, 15(8), 1094; https://doi.org/10.3390/genes15081094 - 20 Aug 2024
Viewed by 921
Abstract
Glaesserella parasuis (GPS) can cause severe systemic inflammation in pigs, resulting in huge economic losses to the pig industry. At present, no effective method is available for the prevention and control of GPS infection. Molecular breeding for disease resistance is imminent, but disease-resistance [...] Read more.
Glaesserella parasuis (GPS) can cause severe systemic inflammation in pigs, resulting in huge economic losses to the pig industry. At present, no effective method is available for the prevention and control of GPS infection. Molecular breeding for disease resistance is imminent, but disease-resistance genes have not been identified. To study the mechanism of systemic acute inflammation caused by GPS, we established three in vitro infection models (3D4/21 cells, PK15 cells, and PAVEC cells) according to its infection path. There was no significant difference in apoptosis among the three kinds of cells after 12 h of continuous GPS stimulation, while inflammatory factors were significantly upregulated. Subsequent transcriptome analysis revealed 1969, 1207, and 3564 differentially expressed genes (DEGs) in 3D4/21 cells, PK15 cells, and PAVEC cells, respectively, after GPS infection. Many of the DEGs were predicted to be associated with inflammatory responses (C3, CD44, etc.); cell proliferation, growth and apoptosis; gene expression; and protein phosphorylation. Key signaling pathways, including S100 family signaling, bacteria and virus recognition, and pathogen-induced cytokine storm signaling, were enriched based on Ingenuity Pathway Analysis (IPA). Furthermore, a total of three putative transmembrane receptors and two putative G-protein-coupled receptors, namely F3, ICAM1, PLAUR, ACKR3, and GPRC5A, were identified by IPA among the three types of cells. ACKR3 and GPRC5A play pivotal roles in bacterial adhesion, invasion, host immune response and inflammatory response through the S100 family signaling pathway. Our findings provide new insights into the pathological mechanisms underlying systemic inflammation caused by GPS infection in pigs, and they lay a foundation for further research on disease-resistance breeding to GPS. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)
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Figure 1

Figure 1
<p>Constructions and verification of the in vitro GPS infection model. (<b>a</b>) The fluorescence (lower) and phase contrast microscopy images of 3D4/21 cells in the experimental and control groups. The green, fluorescent marks were Calcein-stained live cells, and the red fluorescent marks were PI-stained dead cells. (<b>b</b>) The difference in the relative fluorescence (RFU) values of 3D4/21 cell apoptosis between the experimental and control groups. (<b>c</b>) The difference in the RFU values of PK15 cell apoptosis between the experimental and control groups. (<b>d</b>) The difference in the RFU values of PAVEC cell apoptosis between the experimental and control groups. (<b>e</b>) Analysis of the difference in the expression levels of inflammatory cytokines in 3D4/21 cells between the experimental and control groups. (<b>f</b>) Analysis of the difference in the expression levels of inflammatory cytokines in PK15 cells between the experimental and control groups. (<b>g</b>) Analysis of the difference in the expression levels of inflammatory cytokines in PAVEC cells between the experimental and control groups. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and ns = not significant.</p> Full article ">Figure 2
<p>Differentially expressed genes (DEGs) analysis. (<b>a</b>) Volcano plot reveals significant DEGs in the comparison of 3D4/21_GPS vs. 3D4/21_WT. (<b>b</b>) Volcano plot reveals significant DEGs in the comparison of PK15_GPS vs. PK15_WT. (<b>c</b>) Volcano plot reveals significant DEGs in the comparison of PAVEC_GPS vs. PAVEC _WT. (<b>d</b>) A Venn diagram showing the DEGs identified from the three comparisons.</p> Full article ">Figure 3
<p>Validation of transcriptome analysis. (<b>a</b>) The qRT-PCR and RNA-seq assay results for common genes in three comparisons. (<b>b</b>) Correlation log2FC from the qRT-PCR and RNA-seq assays.</p> Full article ">Figure 4
<p>Common enriched gene ontology functional classifications of differential expression genes (DEGs) between GPS treatment group and control group.</p> Full article ">Figure 5
<p>Top ten upregulated and downregulated pathways in three types of cells. If there are less than 10 items, all of them are displayed. Categories are shown in terms of the z-score, as represented by the left y-axis and the −log (<span class="html-italic">p</span>-value), represented by the right y-axis. (<b>a</b>) Top ten upregulated and top ten downregulated pathways in the comparison of 3D4/21_GPS vs. 3D4/21_WT. (<b>b</b>) Top ten upregulated and top ten downregulated pathways in the comparison of PK15_GPS vs. PK15_WT. (<b>c</b>) Top ten upregulated and top ten downregulated pathways in the comparison of PAVEC_GPS vs. PAVEC_WT.</p> Full article ">Figure 6
<p>The Sankey diagram of putative common receptors by IPA in three comparisons.</p> Full article ">
18 pages, 7311 KiB  
Article
Transcriptomic Analysis of Newborn Hanwoo Calves: Effects of Maternal Overnutrition during Mid- to Late Pregnancy on Subcutaneous Adipose Tissue and Liver
by Borhan Shokrollahi, Hyun-Jeong Lee, Youl Chang Baek, Shil Jin, Gi-Suk Jang, Sung Jin Moon, Kyung-Hwan Um, Sun Sik Jang and Myung Sun Park
Genes 2024, 15(6), 704; https://doi.org/10.3390/genes15060704 - 28 May 2024
Cited by 1 | Viewed by 950
Abstract
This study investigated the transcriptomic responses of subcutaneous adipose tissue (SAT) and liver in newborn Hanwoo calves subjected to maternal overnutrition during mid- to late gestation. Eight Hanwoo cows were randomly assigned to control and treatment groups. The treatment group received a diet [...] Read more.
This study investigated the transcriptomic responses of subcutaneous adipose tissue (SAT) and liver in newborn Hanwoo calves subjected to maternal overnutrition during mid- to late gestation. Eight Hanwoo cows were randomly assigned to control and treatment groups. The treatment group received a diet of 4.5 kg of concentrate and 6.5 kg of rice straw daily, resulting in intake levels of 8.42 kg DMI, 5.69 kg TDN, and 0.93 kg CP—higher than the control group (6.07 kg DMI, 4.07 kg TDN, and 0.65 kg CP), with respective NEm values of 9.56 Mcal and 6.68 Mcal. Following birth, newly born calves were euthanized humanely as per ethical guidelines, and SAT and liver samples from newborn calves were collected for RNA extraction and analysis. RNA sequencing identified 192 genes that were differentially expressed in the SAT (17 downregulated and 175 upregulated); notably, HSPA6 emerged as the most significantly upregulated gene in the SAT and as the singular upregulated gene in the liver (adj-p value < 0.05). Additionally, differential gene expression analysis highlighted extensive changes across genes associated with adipogenesis, fibrogenesis, and stress response. The functional enrichment pathway and protein–protein interaction (PPI) unraveled the intricate networks and biological processes impacted by overnutrition, including extracellular matrix organization, cell surface receptor signaling, and the PI3K-Akt signaling pathway. These findings underscore maternal overnutrition’s substantial influence on developmental pathways, suggesting profound cellular modifications with potential lasting effects on health and productivity. Despite the robust insights that are provided, the study’s limitations (sample size) underscore the necessity for further research. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)
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Figure 1

Figure 1
<p>Gene filtering based on zero counts in the SAT and liver: Panel (<b>A</b>) depicts the process of gene exclusion due to zero counts in the SAT, while Panel (<b>B</b>) illustrates the analogous procedure for liver.</p> Full article ">Figure 2
<p>Volcano plots depicting the influence of maternal overnutrition during mid- to late gestation on the gene expression in subcutaneous adipose tissue (SAT) (<b>A</b>) and liver (<b>B</b>) of newborn Hanwoo calves. The Y-axis illustrates the negative logarithm of <span class="html-italic">p</span>-values (−log10 <span class="html-italic">p</span>-values), and the X-axis represents the fold change (FC; log<sub>2</sub> fold change). Blue dots to the left signify downregulated, differentially expressed genes (DEGs), while orange dots to the right mark upregulated DEGs. DEGs significantly affected by maternal overnutrition (<span class="html-italic">p</span>-value &lt; 0.05) are emphasized.</p> Full article ">Figure 3
<p>Heatmaps illustrating DEGs in the SAT of newborn Hanwoo calves. Within the heatmap, columns correspond to individual samples, where sample T was from the treatment group and samples C1, C2, and C3 were from the control group. A dendrogram is provided to display the overall expression trends visually. The spectrum of colors from red to blue signifies the extent of gene upregulation and downregulation, respectively.</p> Full article ">Figure 4
<p>GO enrichment and KEGG pathway analysis for the SAT: This figure showcases the outcomes of both Gene Ontology (GO) enrichment and KEGG pathway analyses, focusing on the top 10 identified terms across biological processes, molecular functions, and cellular components, as well as the key KEGG pathways affected in the SAT as a result of maternal overnutrition. The biological processes depict general categories where gene expression is modified, the molecular functions show changes in protein activity or binding capabilities, and the cellular components indicate the locations within the cell where these changes occur. The KEGG pathways offer insights into the complex metabolic and signaling pathways that are affected. The Y-axis indicates the number of differentially expressed genes (DEGs), with blue and red bars representing downregulated and upregulated DEGs for each identified term, respectively, providing an indication of the directionality of the expression changes. The X-axis shows the number of DEGs associated with each of the top 10 GO terms and KEGG pathways. On the opposing Y-axis, the −log10 adjusted <span class="html-italic">p</span>-values for each term are illustrated by dots, providing a measure of significance for the enrichment of each term.</p> Full article ">Figure 5
<p>GO enrichment and KEGG pathway analysis for liver: This figure showcases the outcomes of both Gene Ontology (GO) enrichment and KEGG pathway analyses, focusing on the top 10 identified terms across biological processes, molecular functions, and cellular components, as well as key KEGG pathways. The Y-axis indicates the number of differentially expressed genes (DEGs), with blue and red bars representing downregulated and upregulated DEGs for each identified term, respectively. The X-axis details the top 10 GO terms and KEGG pathways. On the opposing Y-axis, the −log10 adjusted <span class="html-italic">p</span>-values for each term are illustrated by dots, providing a measure of significance for the enrichment of each term.</p> Full article ">Figure 6
<p>Protein–Protein Interaction (PPI) Network for the SAT: This network illustrates the interactions between proteins encoded by DEGs as a response to maternal overnutrition. Nodes symbolize the individual proteins, while the connecting lines (edges) indicate the protein interactions.</p> Full article ">Figure 7
<p>Protein–Protein Interaction (PPI) Network for the liver: This network illustrates the interactions between proteins encoded by DEGs as a response to maternal overnutrition. Nodes symbolize the individual proteins, while the connecting lines (edges) indicate the protein interactions.</p> Full article ">
15 pages, 8478 KiB  
Article
Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding
by Dong Jae Lee, Yoonsik Kim, Phuong Thanh N. Dinh, Yoonji Chung, Dooho Lee, Yeongkuk Kim, Soo Hyun Lee, Inchul Choi and Seung Hwan Lee
Genes 2023, 14(10), 1839; https://doi.org/10.3390/genes14101839 - 22 Sep 2023
Viewed by 1346
Abstract
This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits [...] Read more.
This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein’s function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)
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Figure 1

Figure 1
<p>Comprehensive information on missense variants in the Hanwoo genome. (<b>A</b>) Functional effects of mutations on the sequence-level data. (<b>B</b>) The heritability of variants by genome region. (<b>C</b>) Missense mutations in Illumina Bovine SNP50 BeadChips (BovineSNP50 v2, BovineSNP50 v3) and the customized Hanwoo 50K chip.</p> Full article ">Figure 2
<p>Manhattan plots of the exon-specific association study (ESAS) using missense variants for carcass traits. (<b>A</b>) CW; (<b>B</b>) BFT; (<b>C</b>) EMA; and (<b>D</b>) MS.</p> Full article ">Figure 3
<p>Distribution of individuals’ GEBV depending on the type of haplotype combination (favorable vs. unfavorable) in BTA4, BTA6, and BTA14.</p> Full article ">Figure 4
<p>Protein structure prediction of wild-type and mutant-type for protein kinase, DNA-activated, catalytic polypeptide (PRKDC), mitochondrial ribosomal protein L9 (MRPL9), and ankyrin repeat and fibronectin type III domain containing 1 (ANKFN1) protein. (<b>A</b>) 3D protein structures of wild-type (left) and mutant-type (right) PRKDC protein using 1400 amino acids including the target sequence (p.Ala895Thr); (<b>B</b>) MRPL9 protein structure with wild-type (left) and mutant-type (right) (p.Thr226Ala); (<b>C</b>) ANKFN1 protein structure with wild-type (left) and mutant-type (right) (p.Leu174Val).</p> Full article ">Figure 4 Cont.
<p>Protein structure prediction of wild-type and mutant-type for protein kinase, DNA-activated, catalytic polypeptide (PRKDC), mitochondrial ribosomal protein L9 (MRPL9), and ankyrin repeat and fibronectin type III domain containing 1 (ANKFN1) protein. (<b>A</b>) 3D protein structures of wild-type (left) and mutant-type (right) PRKDC protein using 1400 amino acids including the target sequence (p.Ala895Thr); (<b>B</b>) MRPL9 protein structure with wild-type (left) and mutant-type (right) (p.Thr226Ala); (<b>C</b>) ANKFN1 protein structure with wild-type (left) and mutant-type (right) (p.Leu174Val).</p> Full article ">
11 pages, 3141 KiB  
Article
ranchSATdb: A Genome-Wide Simple Sequence Repeat (SSR) Markers Database of Livestock Species for Mutant Germplasm Characterization and Improving Farm Animal Health
by Naveen Duhan, Simardeep Kaur and Rakesh Kaundal
Genes 2023, 14(7), 1481; https://doi.org/10.3390/genes14071481 - 20 Jul 2023
Cited by 1 | Viewed by 1774
Abstract
Microsatellites, also known as simple sequence repeats (SSRs), are polymorphic loci that play an important role in genome research, animal breeding, and disease control. Ranch animals are important components of agricultural landscape. The ranch animal SSR database, ranchSATdb, is a web resource [...] Read more.
Microsatellites, also known as simple sequence repeats (SSRs), are polymorphic loci that play an important role in genome research, animal breeding, and disease control. Ranch animals are important components of agricultural landscape. The ranch animal SSR database, ranchSATdb, is a web resource which contains 15,520,263 putative SSR markers. This database provides a comprehensive tool for performing end-to-end marker selection, from SSRs prediction to generating marker primers and their cross-species feasibility, visualization of the resulting markers, and finding similarities between the genomic repeat sequences all in one place without the need to switch between other resources. The user-friendly online interface allows users to browse SSRs by genomic coordinates, repeat motif sequence, chromosome, motif type, motif frequency, and functional annotation. Users may enter their preferred flanking area around the repeat to retrieve the nucleotide sequence, they can investigate SSRs present in the genic or the genes between SSRs, they can generate custom primers, and they can also execute in silico validation of primers using electronic PCR. For customized sequences, an SSR prediction pipeline called miSATminer is also built. New species will be added to this website’s database on a regular basis throughout time. To improve animal health via genomic selection, we hope that ranchSATdb will be a useful tool for mapping quantitative trait loci (QTLs) and marker-assisted selection. The web-resource is freely accessible at https://bioinfo.usu.edu/ranchSATdb/. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)
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Figure 1

Figure 1
<p>An overall workflow of <span class="html-italic">ranchSATdb</span>.</p> Full article ">Figure 2
<p>Distribution of SSRs based on motif type.</p> Full article ">Figure 3
<p>Distribution of SSRs based on genomic annotations.</p> Full article ">Figure 4
<p>Distribution of SSRs based on promoter and non-promoter regions.</p> Full article ">

Review

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22 pages, 339 KiB  
Review
Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality
by Rugang Tian, Maryam Mahmoodi, Jing Tian, Sina Esmailizadeh Koshkoiyeh, Meng Zhao, Mahla Saminzadeh, Hui Li, Xiao Wang, Yuan Li and Ali Esmailizadeh
Genes 2024, 15(8), 1104; https://doi.org/10.3390/genes15081104 - 22 Aug 2024
Viewed by 1733
Abstract
Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) [...] Read more.
Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)

Other

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7 pages, 253 KiB  
Case Report
Demonstration of Parthenogenetic Reproduction in a Pet Ball Python (Python regius) through Analysis of Early-Stage Embryos
by Francesco Di Ianni, Sara Albarella, Alessandro Vetere, Marco Torcello, Michela Ablondi, Mariagiulia Pugliano, Susanna Di Mauro, Pietro Parma and Francesca Ciotola
Genes 2023, 14(9), 1744; https://doi.org/10.3390/genes14091744 - 31 Aug 2023
Viewed by 2668
Abstract
Parthenogenesis is an asexual form of reproduction, normally present in various animal and plant species, in which an embryo is generated from a single gamete. Currently, there are some species for which parthenogenesis is supposed but not confirmed, and the mechanisms that activate [...] Read more.
Parthenogenesis is an asexual form of reproduction, normally present in various animal and plant species, in which an embryo is generated from a single gamete. Currently, there are some species for which parthenogenesis is supposed but not confirmed, and the mechanisms that activate it are not well understood. A 10-year-old, wild-caught female ball python (Python regius) laid four eggs without any prior contact with a male. The eggs were not incubated and, after 3 days, were submitted to the University of Parma for analysis due to the suspicion of potential embryo presence. Examination of the egg content revealed residual blood vessels and a small red spot, indicative of an early-stage embryo. DNA was extracted from the three deceased embryos and from the mother’s blood, five microsatellites were analyzed to ascertain the origin of the embryos. The captive history data, together with the genetic microsatellite analysis approach, demonstrated the parthenogenetic origin of all three embryos. The embryos were homozygous for each of the maternal microsatellites, suggesting a terminal fusion automixis mode of development. Full article
(This article belongs to the Special Issue Breeding and Functional Genomics in Animals)
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