Next Article in Journal
Genome-Wide Analysis of the Type-B Authentic Response Regulator Gene Family in Brassica napus
Next Article in Special Issue
Model Comparison of Heritability Enrichment Analysis in Livestock Population
Previous Article in Journal
An Analysis of Phenotype and Genotype in a Large Cohort of Chinese Children with Angelman Syndrome
Previous Article in Special Issue
A Comprehensive Analysis of the Myocardial Transcriptome in ZBED6-Knockout Bama Xiang Pigs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 8
10.3390/genes13081448
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Expression and Genetic Effects of GLI Pathogenesis-Related 1 Gene on Backfat Thickness in Pigs

by
Xin Liu
1,
Hanmei Li
1,2,
Longchao Zhang
1,
Ligang Wang
1 and
Lixian Wang
1,*
1
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
2
Hebei Normal University of Science & Technology, Qinhuangdao 066600, China
*
Author to whom correspondence should be addressed.
Genes 2022, 13(8), 1448; https://doi.org/10.3390/genes13081448
Submission received: 5 July 2022 / Revised: 6 August 2022 / Accepted: 12 August 2022 / Published: 14 August 2022
(This article belongs to the Special Issue Systematic Analysis and Application of Omics Data in Animal Breeding)
Browse Figures
Versions Notes

Abstract

:
Backfat thickness (BFT) is an important carcass composition trait and regarded as a breeding focus. Our initial transcriptome analysis of pig BFT identified GLI pathogenesis-related 1 (GLIPR1) as one of the promising candidate genes. This study was conducted to identify the expression profiles, polymorphisms, and genetic effects of the GLIPR1 gene on BFT in pigs. The expression of the GLIPR1 gene existed in every detected tissue, and there was a significantly higher expression in spleen and adipose tissue than others (p < 0.05). At the different ages of pig, the expression of the GLIPR1 gene was low at an early age, increased with growth, and reached the highest level at 180 days. Genetic polymorphism analysis was detected in 553 individuals of the Large White × Minzhu F2 population. Four SNPs in the promoter significantly associated with 6–7 rib BFT (p < 0.05) were predicted to alter the transcription factor binding sites (TFBS), and the mutations of g.38758089 T>G and g.38758114 G>C were predicted to change the TFs associated with the regulation of adipogenesis. Haplotypes were formed by the detected SNPs, and one block showed a strong association with BFT (p < 0.05). In summary, our results indicate that the expression profiles and genetic variants of GLIPR1 affected the BFT of pigs. To our knowledge, this study is the first to demonstrate the biological function and genetic effects of the GLIPR1 gene on the BFT of pig and provide genetic markers to optimize breeding for BFT in pigs.

1. Introduction

Pork occupies a large part of the meat market. In order to meet consumer demand, reduced fat and increased lean meat content are sought in pig production. Backfat thickness (BFT) is an important carcass composition trait and influences growth, which is why it is regarded as a breeding focus [1,2,3]. Due to the importance of BFT, it has been extensively studied, and some important regulatory genes have been found [4,5,6]. GLI pathogenesis-related 1 (GLIPR1) was identified as a candidate gene by transcriptome analysis of pig BFT in our previous study [7].
GLIPR1 is also called RTVP1, belonging to the GAP protein superfamily [8], found early in human gliomas as a proto-oncogene [9]. The open reading frame of human GLIPR1 contains 926 bp and encodes a polypeptide consisting of 266 amino-acid residues. GLIPR1 has been found to affect various tumors by its expression change [10,11]. Overexpression of GLIPR1 appears to enhance proliferation in glioblastoma, Wilm’s tumor, acute myeloid leukemia, etc. [12,13]. However, a reduction in GLIPR1 expression inhibits and suppresses prostate cancer, lung and bladder cancer, and some other tumors [14,15]. It is indicated that GLIPR1 is necessary for cell growth, proliferation, and pyroptosis [16]. The function of the GLIPR1 gene was queried on the GeneCards (https://www.genecards.org/) (accessed on 5 February 2022), and we found this gene was involved in the peroxisome proliferator-activated receptor α (PPARα) pathway, which regulates lipid metabolism. PPARα is considered a transcriptional regulator of lipid metabolism, involved in fatty-acid transport and fatty-acid β-oxidation [17], and it is also recognized to be a target gene therapy for obesity and dyslipidemia for its effects on energy metabolism in adipocytes and activation in WAT [18]. We concluded that GLIPR1 could regulate the fat formation through interaction with PPARα.
However, compared to the large number of studies in human disease, little information is available regarding GLIPR1 in pigs. Our previous study revealed that different expressions of GLIPR1 could regulate the backfat thickness of pigs, and this gene was located in the lipid related pathway by functional annotation. The objective of this study was to preliminarily clarify the function and genetic effects of GLIPR1 in pig. In this study, we detected the expression profiles in various tissues and throughout the growth stages, and we further explored the polymorphisms and genetic effects of GLIPR1 on pig BFT.

2. Materials and Methods

2.1. Ethics Statements

All methods and procedures in the study were carried out according to the standard guidelines on experimental animals, which were established by the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (IAS, CAAS).

2.2. Animals and Sample Collection

Large White pigs, selected from the pig farm of IAS, were used for the expression detection of GLIPR1. The three pigs of each age were slaughtered, and backfat samples were collected at ages 3 days, 60 days, 120 days, 150 days, and 180 days. Three 180 day old pigs were slaughtered, and tissue samples were rapidly collected from the backfat, longissimus muscle, heart, liver, lung, and kidney. All of the samples were prepared for mRNA expression detection and stored at −80 °C until use. The 553 pigs for single-nucleotide polymorphism (SNP) detection and association analysis were selected from the Large White × Minzhu F2 generation resource population, which was constructed and described in our previous studies [19]. All pigs were slaughtered at the age of 240 ± 7 days. The slaughter weight was measured, and BFT (6–7 ribs) was determined on each carcass using a Vernier caliper. The ear marginal tissues of individuals were collected for DNA isolation.

2.3. Genomic DNA and RNA Isolation

Genomic DNA exaction was performed using the traditional phenol–chloroform method. The quantity and quality of extracted DNA were evaluated by 1% agarose gel electrophoresis and then stored at −20 °C in a refrigerator. Total RNA was exacted according to the instructions of the RNAprep pure Tissue Kit (TIANGEN, DP431, Beijing, China). The concentration and the purity were measured by Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA, USA), and the RNA integrity was determined by Agilent 2100, bioanalyzed (Agilent Technologies, Santa Clara, CA, USA), and then stored at −80 °C. First-strand cDNA was synthesized by reverse transcription using 1 μg of total RNA and the PrimeScriptTM RT reagent kit (TakaRa, Shiga, Japan) according to the manufacturer’s protocol; cDNAs were stored at −20 °C until use.

2.4. Expression Detection and Analysis of GLIPR1 Gene

To investigate the comparative levels of the expression of GLIPR1 in different tissues, we extracted total RNA from various tissues, including heart, liver, spleen, hypothalamus, muscle tissue, and backfat. To compare the expression of GLIPR1 in backfat among different ages of pig, total RNA was extracted from backfat samples of pigs at different ages. The TB GreenTM Premix Ex TaqTM (TaKaRa, Japan) was used to detect gene relative expression according to the instructions. SYBR Green Real-Time quantitative PCR was performed on an ABI 7500 instrument (Applied Biosystems, Inc., Foster City, CA, USA) to determine the expression level of GLIPR1, and GAPDH was used as the endogenous control gene. The mRNA reference sequences of GLIPR1 and GAPDH were XM_021092042.1 and NM_001206359.1, respectively, which were obtained from NCBI (National Center for Biotechnology Information). The primers of GLIPR1 and GAPDH (shown in Table 1) were designed by Primer Express 3.0 (Applied Biosystems). All primers were synthesized by Invitrogen trade limited company (Shanghai). The reaction system of 20 μL included 10 μL of 2 × TB Green Premix Ex Taq, 0.4 μL each of upstream and downstream primers (10 μmol/L), 0.4 μL of 50 × ROX Reference Dye II, 2 μL of cDNA, and 6.8 μL of ddH2O. The reaction procedure was as follows: 30 s pre-denaturation at 95 °C, then 40 cycles of 5 s denaturation at 95 °C and 34 s annealing at 60 °C, before finally obtaining the melting curve through 15 s at 95 °C, 1 min at 60 °C, and 15 s at 95 °C. The gene expression levels were quantified using the comparative 2−ΔΔCT method [20], and the expression differences were analyzed using the ANOVA method in SAS 9.2 software (SAS INSTITUTE Inc., Cary, NC, USA).

2.5. SNP Identification and Association Analysis

On the basis of the genomic sequence of pig GLIPR1 (GenBank accession number: NC_010447.5), we identified the SNPs located in the promoter regions of GLIPR1 using PCR amplification and sequencing for 553 F2 resource population individuals. The PCR primers were designed using the Primer 5.0 software (Premier Biosoft International, Palo Alto, CA, USA) (shown in Table 1). The reaction system of 25 μL included 2.5 μL of 10 × PCR buffer (including 15 mM Mg2+), 1 μL of dNTPs (2.5 mM each), 0.5 μL of each primer (10 μM), 1 μL of genomic DNA, 0.5 μL of Taq DNA polymerase (5 units/μL) (TaKaRa, Japan), and ddH2O made up the final volume. The reaction procedure was as follows: 94 °C for 5 min, followed by 35 cycles at 95 °C for 30 s, 1 min at annealing temperature, 72 °C for 30 s, and final extension at 72 °C for 10 min. PCR products were detected by 1.5% agarose gel electrophoresis.
As for the SNPs in the promoter region, we predicted whether these mutations altered the transcription factor binding sites (TFBS) using JASPAR software (https://jaspar.genereg.net/) (accessed on 5 February 2022). Genotypes and population genetic characteristics, including genotype frequency, allele frequency, polymorphic information content (PIC), gene heterozygosity (He), and the Hardy–Weinberg equilibrium test of polymorphic loci, were calculated. The association between genotypes and BFT was performed using the GLM procedure of SAS, as follows:
Yij = μ + Gi + bW + eij,
where Yij is the phenotypic observation, μ is the overall mean, Gi is the fixed effect of genotype, W is the covariate of slaughter weight, b is the covariate regression coefficient, and eij is the random error. The data are presented as the mean ± standard error, and significant differences were considered at p < 0.05.

2.6. Linkage Disequilibrium (LD) and Haplotype Analysis

Haploview 4.1 (Broad Insititute, Cambridge, MA, USA) was used to perform the LD analysis among the identified SNPs and constructed blocks. Then, Package Haplo.stats was used to perform association analysis between haplotypes and BFT in R.

3. Results

3.1. Tissue Expression Profiles of GLIPR1

Comparative analysis of GLIPR1 expression was performed in six different tissues via real-time quantitative PCR (qPCR). The analysis showed that the levels of GLIPR1 mRNA expression differed considerably among tissues. The highest relative expression was found in spleen and backfat (p < 0.05), and the lowest expression was found in muscle (p < 0.05) (Figure 1A). The relative expression of GLIPR1 in backfat was examined in different ages of pigs by qPCR, and the results showed that the expression level of GLIPR1 was low in the early growth stage of pig, then increased, and remained at a high expression in the middle and late stages. The expression level of GLIPR1 in BFT of 3 day old pigs was significantly lower than other ages (p < 0.05), and the expression was highest in 180 day old pigs, which was significantly higher than that in the other ages of pigs (p < 0.05) (Figure 1B).

3.2. SNP Identification and Association with BFT

Since the mutations of promoter location may play an important role in the regulation of gene expression, the promoter region was first selected as the research object in this study. SNPs in the promoter regions of GLIPR1 were identified for 553 individuals. A total of 33 SNPs in promoter regions were detected. We performed association analyses between these SNPs and 6–7 rib BFT in the F2 resource population, along with bioinformatics analysis. The results showed that four SNPs in promoter regions were not only significantly associated with BFT but also predicted at TFBS, which caused changes in transcription factors (TFs) (shown in Figure 2 and Table 2).
Genotyping of the identified SNPs significantly associated with BFT in the F2 resource population was performed, and the analysis of the genotype frequency and population genetic characteristics is shown in Table 3. The diversity parameters showed that the He values ranged from 0.2818 to 0.4050, and the PIC values ranged from 0.2421 to 0.3230. According to these data, all SNPs, except g.38757892 G>C, reached a medium level of genetic diversity (0.25 < PIC < 0.5).

3.3. Haplotype Analysis of GLIPR1 Gene

LD analysis was performed among the 33 SNPs in promoter regions. We found five haplotype blocks in promoter regions (D’ = 0.86–0.95, Figure 3).
In the promoter regions of GLIPR1, the five blocks were correlated with BFT. Only block 2 had significant associations with BFT (p = 0.00143), while the other four blocks did not achieve significant correlations (p > 0.05). Block 2 had three haplotypes, among which the TA haplotype was significantly associated with BFT (p < 0.05). However, some haplotypes, including in blocks 1, 4, and 5, showed a strong association with BFT. The TAG haplotype in block 1 and GA haplotype in block 5 had significant negative effects (p < 0.05). The CCT haplotype in block 1, the TATCGACCG haplotype in block 4, and the AG haplotype in block 5 showed significant positive effects (p < 0.05) (Table 4).

4. Discussion

The BFT of pig has been a research hotspot for a long time because of its significance in pig breeding and production. With the development of omics technology and the reduction in chip and sequencing costs, there have been many studies on the important economic traits of pigs using omics methods, including BFT, and many new and interesting genes or mutations have been discovered [21,22,23]. In our previous study on the transcriptome analysis of pig BFT, the GLIPR1 gene was identified as a candidate gene by analyzing the differentially expressed genes, comparing the high-BFT group with the low-BFT group, and finding its biological function related to adipogenesis [7]. In this study, further functional verification of the GLIPR1 gene on the fat of pig was performed. The expression profiles, polymorphisms, and genetic effects were determined.
The expression of GLIPR1 was detected among six tissues in 180 day old pigs. Although GLIPR1 mRNA was present in all tissues, the levels varied considerably, with a higher expression observed in spleen and backfat and the lowest expression observed in muscle. Our results were basically consistent with those presented by NCBI (https://www.ncbi.nlm.nih.gov/gene/100523551) (accessed on 5 February 2022), which summarizes the detection results of this gene expression in the related literature [24]. The expression of GLIPR1 in different growth ages showed that this gene had low expression at an early age and high expression at a late growth age, which revealed the regulation and action mode of this gene on adipogenesis in pigs. Research has reported that GLIPR1 can affect cell-cycle regulation, and its changes in expression can regulate cell proliferation and apoptosis [12,25]. We hypothesize that GLIPR1 may affect adipocyte differentiation and regulate adipogenesis in pig. At present, there is no report on the expression regulation of the GLIPR1 gene on fat deposition in pigs; hence, this study provides some basis for further research.
According to the quantitative trait locus mapping on the PigQTL database (https://www.animalgenome.org/cgi-bin/QTLdb/SS/index) (accessed on 5 February 2022), the location of the GLIPR1 gene on chromosome 5 is in the significant effect regions of backfat at the tenth rib [26] and backfat at the last lumbar [27]. It can be inferred that GLIPR1 may play an important role in BFT. SNPs are genetic variations of single base pairs occurring in the genomic sequence impacting gene expression activities, amino-acid substitution, and dysfunction [28]. The occurrence and frequency of SNPs have contributed greatly to the assessment of genetic diversity and the genetic variation of complex quantitative traits [29,30]. Therefore, we detected the polymorphism of the GLIPR1 gene and analyzed the association between the mutations and 6–7 rib BFT. The SNPs, which were significantly associated with BFT, can be genetic markers for pig breeding. The results which we obtained in the previous study showed that GLIPR1 gene regulated fat deposition of pig through the differential expression. The mutations of promoter location may play an important role in the regulation of gene expression; hence, we first selected the promoter region for SNP polymorphism study. Among these significant associated mutations, four SNPs in promoter regions were predicted at TFBS, and their mutations lead to changes in binding TFs. TFs commonly control the gene expression through binding to specific sequence elements, and the regular regions [31] and the disruption of TFBs will show phenotypic diversity [32]. In our study, we found that four SNPs in the promoter altered TFBs, and some of them were associated with adipogenesis. The allele C of g.38758114 G>C created a TF NR2C2. NR2C2, known as a testicular receptor (tr4), is reported to regulate lipogenic gene expression, which is inactivated by the 5′-AMP-activated protein kinase (AMPK) and has been shown to play a potentially important role in insulin sensitivity [33]. The allele of G of g.38758089 T>G caused the disappearance of NFATC2. NFATC2 has been reported to regulate adipocyte differentiation by interacting with the transcription factor CCAAT/enhancer binding protein (C/EBP) and induce adipogenesis, as well as in obese mice [34,35].
Haplotypes formed by the SNPs have important implications for identifying associations with complex traits [36]. After haplotype and association analysis on GLIPR1, we obtained one block and some haplotypes significantly correlated with BFT. The significantly associated block in the promoter region contained the SNP g.38756953 T>G, which was also significantly correlated with BFT and predicted to cause TF change. The identified strongly significantly associated SNPs and haplotypes could be used as markers to optimize the selection and breeding of the BFT trait in pigs.
By studying genetic polymorphisms of the promoter region of GLIPR1, we obtained four potentially candidate SNPs related to BFT in pigs. However, this study had several limitations. The variants in other regions of this gene, including exons, introns, and UTR-regulated regions, as well as its surrounding regions, need more comprehensive studies in subsequent work.

5. Conclusions

Overall, our results indicate that the expression profiles and genetic variants of GLIPR1 affected the BFT trait of pigs. This study is the first to demonstrate that the GLIPR1 gene has a significant association with the BFT of pigs, preliminarily showing the biological effect of the GLIPR1 gene on the subcutaneous fat of pig and providing genetic markers to optimize breeding for BFT in pigs.

Author Contributions

X.L. and H.L. performed the molecular experiments; X.L., H.L., L.Z. and L.W. (Ligang Wang) collected the samples and the phenotypic data; X.L. and L.W. (Ligang Wang) performed the analysis; X.L. wrote the manuscript; L.W. (Ligang Wang) and L.W. (Lixian Wang) revised the manuscript; L.W. (Lixian Wang) provided funding support. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Key R&D Program of China (2021YFD1301101) and the Agricultural Science and Technology Innovation Project (ASTIP-IAS02).

Institutional Review Board Statement

The study was conducted according to the standard guidelines on experimental animals, which were established by the Animal Ethical Committee of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (IAS-CAAS) (Beijing, China).

Data Availability Statement

Not applicable.

Acknowledgments

We thank the researchers at our laboratories for their hard work and dedication.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gozalo-Marcilla, M.; Buntjer, J.; Johnsson, M.; Batista, L.; Diez, F.; Werner, C.R.; Chen, C.Y.; Gorjanc, G.; Mellanby, R.J.; Hickey, J.M.; et al. Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds. Genet. Sel. Evol. 2021, 53, 76. [Google Scholar] [CrossRef] [PubMed]
  2. Lonergan, S.M.; Huff-Lonergan, E.; Rowe, L.J.; Kuhlers, D.L.; Jungst, S.B. Selection for lean growth efficiency in Duroc pigs influences pork quality. J. Anim. Sci. 2001, 79, 2075–2085. [Google Scholar] [CrossRef] [PubMed]
  3. Zambonelli, P.; Gaffo, E.; Zappaterra, M.; Bortoluzzi, S.; Davoli, R. Transcriptional profiling of subcutaneous adipose tissue in Italian Large White pigs divergent for backfat thickness. Anim. Genet. 2016, 47, 306–323. [Google Scholar] [CrossRef] [PubMed]
  4. Kojima, M.; Nakajima, I.; Arakawa, A.; Mikawa, S.; Matsumoto, T.; Uenishi, H.; Nakamura, Y.; Taniguchi, M. Differences in gene expression profiles for subcutaneous adipose, liver, and skeletal muscle tissues between Meishan and Landrace pigs with different backfat thicknesses. PLoS ONE 2018, 13, e0204135. [Google Scholar] [CrossRef]
  5. Meidtner, K.; Schwarzenbacher, H.; Scharfe, M.; Severitt, S.; Blöcker, H.; Fries, R. Haplotypes of the porcine peroxisome proliferator-activated receptor delta gene are associated with backfat thickness. BMC Genet. 2009, 10, 76. [Google Scholar] [CrossRef]
  6. Seo, Y.J.; Lim, B.; Kim, D.Y.; Lim, K.S.; Kim, J.M. Regulation of Swine Growth by Backfat Tissue during Growing and Finishing Stages. Animals 2021, 11, 3511. [Google Scholar] [CrossRef]
  7. Liu, X.; Gong, J.; Wang, L.; Hou, X.; Gao, H.; Yan, H.; Zhao, F.; Zhang, L.; Wang, L. Genome-Wide Profiling of the Microrna Transcriptome Regulatory Network to Identify Putative Candidate Genes Associated with Backfat Deposition in Pigs. Animals 2019, 9, 313. [Google Scholar] [CrossRef]
  8. Gibbs, G.M.; Roelants, K.; O’Bryan, M.K. The CAP Superfamily: Cysteine-Rich Secretory Proteins, Antigen 5, and Pathogenesis-Related 1 Proteins—Roles in Reproduction, Cancer, and Immune Defense. Endocr. Rev. 2008, 29, 865–897. [Google Scholar] [CrossRef]
  9. Murphy, E.V.; Zhang, Y.; Zhu, W.; Biggs, J. The human glioma pathogenesis-related protein is structurally related to plant pathogenesis-related proteins and its gene is expressed specifically in brain tumors. Gene 1995, 159, 131–135. [Google Scholar] [CrossRef]
  10. Chilukamarri, L.; Hancock, A.L.; Malik, S.; Zabkiewicz, J.; Baker, J.A.; Greenhough, A.; Dallosso, A.R.; Huang, T.H. Hypomethylation and aberrant expression of the glioma pathogenesis-related 1 gene in Wilms tumors. Neoplasia 2007, 9, 970–978. [Google Scholar] [CrossRef]
  11. Jacoby, E.; Yalon, M.; Leitner, M.; Cohen, Z.R.; Cohen, Y.; Fisher, T.; Eder, S.; Amariglio, N.; Rechavi, G.; Cazacu, S.; et al. Related to testes-specific, vespid and pathogenesis protein-1 is regulated by methylation in glioblastoma. Oncol. Lett. 2014, 7, 1209–1212. [Google Scholar] [CrossRef]
  12. Capalbo, G.; Mueller-Kuller, T.; Koschmieder, S.; Klein, H.U.; Ottmann, O.G.; Hoelzer, D.; Scheuring, U.J. Endoplasmic reticulum protein GliPR1 regulates G protein signaling and the cell cycle and is overexpressed in AML. Oncol. Rep. 2013, 30, 2254–2262. [Google Scholar] [CrossRef]
  13. Rosenzweig, T.; Ziv-Av, A.; Xiang, C.; Lu, W.; Cazacu, S.; Taler, D.; Miller, C.G.; Reich, R.; Shoshan, Y.; Anikster, Y.; et al. Related to testesspecifc, vespid, and pathogenesis protein-1 (RTVP-1) is overexpressed in gliomas and regulates the growth, survival, and invasion of glioma cells. Cancer Res. 2006, 66, 4139–4148. [Google Scholar] [CrossRef]
  14. Li, L.; Ren, C.; Yang, G.; Fattah, E.A.; Goltsov, A.A.; Kim, S.M.; Lee, J.S.; Park, S.; Demayo, F.J.; Ittmann, M.M.; et al. GLIPR1 suppresses prostate cancer development through targeted oncoprotein destruction. Cancer Res. 2011, 71, 7694–7704. [Google Scholar] [CrossRef]
  15. Sheng, X.; Bowen, N.; Wang, Z. GLI pathogenesis-related 1 functions as a tumor-suppressor in lung cancer. Mol. Cancer 2016, 15, 25. [Google Scholar] [CrossRef]
  16. Scheuring, U.J.; Ritter, S.; Martin, D.; Schackert, G.; Temme, A.; Tietze, S. GliPR1 knockdown by RNA interference exerts anti-glioma effects in vitro and in vivo. J. Neurooncol. 2021, 153, 23–32. [Google Scholar] [CrossRef]
  17. Jump, D.B.; Botolin, D.; Wang, Y.; Xu, J.; Christian, B.; Demeure, O. Fatty acid regulation of hepatic gene transcription. J. Nutr. 2005, 135, 2503–2506. [Google Scholar] [CrossRef]
  18. Goto., T.; Lee, J.; Teraminami, A.; Kim, Y.I.; Hirai, S.; Uemura, T.; Inoue, H.; Takahashi, N.; Kawada, T. Activation of peroxisome proliferator-activated receptor-α stimulates both differentiation and fatty acid oxidation in adipocytes. J. Lipid Res. 2011, 52, 873–884. [Google Scholar] [CrossRef]
  19. Luo, W.; Cheng, D.; Chen, S.; Wang, L.; Li, Y.; Ma, X.; Song, X.; Liu, X.; Li, W.; Liang, J.; et al. Genome-wide association analysis of meat quality traits in a porcine Large White × Minzhu intercross population. Int. J. Biol. Sci. 2012, 8, 580–595. [Google Scholar] [CrossRef]
  20. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Zhang, J.; Gong, H.; Cui, L.; Zhang, W.; Ma, J.; Chen, C.; Ai, H.; Xiao, S.; Huang, L.; et al. Genetic correlation of fatty acid composition with growth, carcass, fat deposition and meat quality traits based on GWAS data in six pig populations. Meat Sci. 2019, 150, 47–55. [Google Scholar] [CrossRef] [PubMed]
  22. Xing, K.; Wang, K.; Ao, H.; Chen, S.; Tan, Z.; Wang, Y.; Xitong, Z.; Yang, T.; Zhang, F.; Liu, Y.; et al. Comparative adipose transcriptome analysis digs out genes related to fat deposition in two pig breeds. Sci. Rep. 2019, 9, 12925. [Google Scholar] [CrossRef] [PubMed]
  23. Chen, Q.; Zhang, W.; Cai, J.; Ni, Y.; Xiao, L.; Zhang, J. Transcriptome analysis in comparing carcass and meat quality traits of Jiaxing Black Pig and Duroc × Duroc × Berkshire × Jiaxing Black Pig crosses. Gene 2022, 808, 145978. [Google Scholar] [CrossRef] [PubMed]
  24. Li, M.; Chen, L.; Tian, S.; Lin, Y.; Tang, Q.; Zhou, X.; Li, D.; Yeung, C.K.L.; Che, T.; Jin, L.; et al. Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies. Genome Res. 2017, 27, 865–874. [Google Scholar] [CrossRef] [PubMed]
  25. Dong, J.; Bi, B.; Zhang, L.; Gao, K. GLIPR1 inhibits the proliferation and induces the differentiation of cancer-initiating cells by regulating miR-16 in osteosarcoma. Oncol. Rep. 2016, 36, 1585–1591. [Google Scholar] [CrossRef]
  26. Lee, S.S.; Chen, Y.; Moran, C.; Stratil, A.; Reiner, G.; Bartenschlager, H.; Moser, G.; Geldermann, H. Linkage and QTL mapping for Sus scrofa chromosome 5. J. Anim. Breed Genet. 2003, 120, 38–44. [Google Scholar] [CrossRef]
  27. Rohrer, G.A.; Keele, J.W. Identification of quantitative trait loci affecting carcass composition in swine: I. Fat deposition traits. J. Anim. Sci. 1998, 76, 2247–2254. [Google Scholar] [CrossRef]
  28. Robert, F.; Pelletier, J. Exploring the Impact of Single-Nucleotide Polymorphisms on Translation. Front. Genet. 2018, 9, 507. [Google Scholar] [CrossRef]
  29. Krupa, E.; Moravčíková, N.; Krupová, Z.; Žáková, E. Assessment of the Genetic Diversity of a Local Pig Breed Using Pedigree and SNP Data. Genes 2021, 12, 1972. [Google Scholar] [CrossRef]
  30. Ramos, A.M.; Crooijmans, R.P.; Affara, N.A.; Amaral, A.J.; Archibald, A.L.; Beever, J.E.; Bendixen, C.; Churcher, C.; Clark, R.; Dehais, P.; et al. Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE 2009, 4, e6524. [Google Scholar] [CrossRef]
  31. Orphanides, G.; Reinberg, D. A unified theory of gene expression. Cell 2002, 108, 439–451. [Google Scholar] [CrossRef]
  32. Deplancke, B.; Alpern, D.; Gardeux, V. The genetics of transcription factor DNA binding variation. Cell 2016, 166, 538–554. [Google Scholar] [CrossRef]
  33. Kang, H.S.; Okamoto, K.; Kim, Y.S.; Takeda, Y.; Bortner, C.D.; Dang, H.; Wada, T.; Xie, W.; Yang, X.P.; Liao, G.; et al. Nuclear orphan receptor TAK1/TR4-deficient mice are protected against obesity-linked inflammation, hepatic steatosis, and insulin resistance. Diabetes 2011, 60, 177–188. [Google Scholar] [CrossRef]
  34. Yang, T.T.; Suk, H.Y.; Yang, X.; Olabisi, O.; Yu, R.Y.; Durand, J.; Jelicks, L.A.; Kim, J.Y.; Scherer, P.E.; Wang, Y.; et al. Role of transcription factor NFAT in glucose and insulin homeostasis. Mol. Cell Biol. 2006, 26, 7372–7387. [Google Scholar] [CrossRef]
  35. Yang, T.T.; Chow, C.W. Transcription cooperation by NFAT.C/EBP composite enhancer complex. J. Biol. Chem. 2003, 278, 15874–15885. [Google Scholar] [CrossRef]
  36. Patil, N.; Berno, A.J.; Hinds, D.A.; Barrett, W.A.; Doshi, J.M.; Hacker, C.R.; Kautzer, C.R.; Lee, D.H.; Marjoribanks, C.; McDonough, D.P.; et al. Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science 2001, 294, 1719–1723. [Google Scholar] [CrossRef]
Genes 13 01448 g001 550
Figure 1. Comparison of relative expression of GLIPR1 gene in different tissues of 180 day old pigs (A) and in backfat tissues among different growth ages of Large White pigs (B). Different letters indicate significant differences among the expression levels of GLIPR1 gene at p < 0.05. Three biological replicates of each set were performed for qPCR.
Figure 1. Comparison of relative expression of GLIPR1 gene in different tissues of 180 day old pigs (A) and in backfat tissues among different growth ages of Large White pigs (B). Different letters indicate significant differences among the expression levels of GLIPR1 gene at p < 0.05. Three biological replicates of each set were performed for qPCR.
Genes 13 01448 g001
Genes 13 01448 g002 550
Figure 2. Significant association analysis between SNPs and BFT. * Significant difference in the association analysis between two genotypes and 6–7 rib backfat thickness at p < 0.05.
Figure 2. Significant association analysis between SNPs and BFT. * Significant difference in the association analysis between two genotypes and 6–7 rib backfat thickness at p < 0.05.
Genes 13 01448 g002
Genes 13 01448 g003 550
Figure 3. Linkage disequilibrium (LD) analysis of SNPs on promoter of GLIPR1; r2 is the correlation coefficient between the two loci.
Figure 3. Linkage disequilibrium (LD) analysis of SNPs on promoter of GLIPR1; r2 is the correlation coefficient between the two loci.
Genes 13 01448 g003
Table
Table 1. Real-time quantitative PCR primes of GLIPR1 and GAPDH and promoter primers of GLIPR1.
Table 1. Real-time quantitative PCR primes of GLIPR1 and GAPDH and promoter primers of GLIPR1.
Primer NamePrimer F/R (5′–3′)
GLIPR1-FCAACAAGCTCCGATCAGAAGTG
GLIPR1-RGGGTGTAGCCTGTAGGGTGACT
GAPDH-FTGGAGGCAAGGACCTAAAAGC
GAPDH-RTCCACCACCCTGTTGCTGTAG
PRIMER1-FAAGAAACTGAGCGGGCAAGG
PRIMER1-RAGTGAGGTCAGGGATCAAAC
PRIMER2-FATACTTTACCAAAATGCTGC
PRIMER2-RAACCCTCTTGAGTGATCTGC
PRIMER3-FGTATGAGCAGATCACTCAAG
PRIMER3-RAAAGCCAGTAGACCTAAAAG
Table
Table 2. Changes of TFs caused by SNPs in promoter regions.
Table 2. Changes of TFs caused by SNPs in promoter regions.
LocationSequenceTF
g.38756593 T>CCGGTCATATCTGAGTCTATGCATGCCTCTC *
CGGTCATATCTGAGCCTATGCATGCCTCTCRHOXF1
g.38757892 G>CCAGATCACTCAAGAGGGTTTTTCTCTTGTTNKX2–4, NKX2–8
CAGATCACTCAAGACGGTTTTTCTCTTGTT
g.38758089 T>GATGCACCTCTGCTATTTTCCCTGGCAACAGNFATC3, NFATC2
ATGCACCTCTGCTAGTTTCCCTGGCAACAGNFATC3
g.38758114 G>CAACAGATGAGCCAAGCTGACTTCAATTCCNFIX
AACAGATGAGCCAACCTGACTTCAATTCCNFIX, NR2C2
* The identified SNPs are highlighted in bold.
Table
Table 3. Population genetic characteristics of SNPs of GLIPR1.
Table 3. Population genetic characteristics of SNPs of GLIPR1.
Polymorphism SiteGenotype FrequencyGene Frequencyχ2PICHe
g.38756953 T>CTTTCCCTC0.45080.27430.3282
0.62430.33760.03810.79310.2069
g.38757892 G>CGGGAAAGA26.83860.24210.2818
0.05990.21960.72050.16970.8303
g.38758089 T>GTTTGGGTG3.56560.32300.4050
0.09580.37250.53610.28210.7179
g.38758114 G>CAAAGGGAG3.56560.32300.4050
0.09580.37250.53160.28210.7179
Table
Table 4. Correlation analysis between haplotype of GLIPR1 promoter and backfat thickness.
Table 4. Correlation analysis between haplotype of GLIPR1 promoter and backfat thickness.
BlockHaplotypeHaplotype FrequencyHaplotype Scorep-ValueTotal p-Value
Block 1TAG0.70877−2.252280.02430.1263
TCT0.01094−0.318620.75002
CAG0.009130.667590.5044
CCT0.271162.237290.02527
Block 2TA0.5125−3.375770.000740.00143
CA0.203441.836310.06631
TG0.28061.849420.0644
Block 3GG0.22531−0.912450.361530.09193
AG0.49587−0.83570.40332
AA0.274481.908870.05628
Block 4CGGTCAGTA0.05715−1.626150.103920.12506
CGGTCTGTA0.00861−1.4620.14374
CGGTCAGCA0.58356−1.331940.18288
CGGTCTGCA0.04678−0.605370.54493
CGGTCACCG0.00482−0.077270.93841
TATCGACCG0.265712.102390.03552
Block 5GA0.70004−2.400650.016370.09626
GG0.01290.019440.98449
AA0.009170.922250.3564
AG0.277892.289960.02202
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Liu, X.; Li, H.; Zhang, L.; Wang, L.; Wang, L. Expression and Genetic Effects of GLI Pathogenesis-Related 1 Gene on Backfat Thickness in Pigs. Genes 2022, 13, 1448. https://doi.org/10.3390/genes13081448

AMA Style

Liu X, Li H, Zhang L, Wang L, Wang L. Expression and Genetic Effects of GLI Pathogenesis-Related 1 Gene on Backfat Thickness in Pigs. Genes. 2022; 13(8):1448. https://doi.org/10.3390/genes13081448

Chicago/Turabian Style

Liu, Xin, Hanmei Li, Longchao Zhang, Ligang Wang, and Lixian Wang. 2022. "Expression and Genetic Effects of GLI Pathogenesis-Related 1 Gene on Backfat Thickness in Pigs" Genes 13, no. 8: 1448. https://doi.org/10.3390/genes13081448

APA Style

Liu, X., Li, H., Zhang, L., Wang, L., & Wang, L. (2022). Expression and Genetic Effects of GLI Pathogenesis-Related 1 Gene on Backfat Thickness in Pigs. Genes, 13(8), 1448. https://doi.org/10.3390/genes13081448

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop