Detection of Genomic Selection Signatures and Identification of Beneficial Mutations for Fat Deposition in Sheep with Different Tail Types

Detection of Genomic Selection Signatures and Identification of Beneficial Mutations for Fat Deposition in Sheep with Different Tail Types | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detection of Genomic Selection Signatures and Identification of Beneficial Mutations for Fat Deposition in Sheep with Different Tail Types caiye Zhu, yue Zhang, yingshi Wei, yang Yang, xiaoyu Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8637217/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chinese indigenous sheep can be classified into two types based on tail morphology: fat-tailed sheep and thin-tailed sheep, with Altay sheep and Tibetan sheep as their representative breeds. Detection of selection signatures can elucidate the molecular mechanism underlying the formation of distinct tail types driven by artificial selection. Based on the Illumina Ovine 600K SNP BeadChip, we identified 163 and 267 candidate selection regions in Altay sheep and Tibetan sheep, with average lengths of 26.61 Mb and 45.68 Mb, respectively. Subsequent bioinformatics analysis suggested that LPIN1 might be involved in fat deposition. Genotyping of the candidate gene LPIN1 in 546 sheep revealed three single nucleotide polymorphisms (SNPs). At the cellular level, the C236T mutation was found to promote the proliferation and differentiation of preadipocytes. Transcriptome analysis demonstrated differential expression of PGC-1α between the CC and TT genotypes of the LPIN1 gene. Secondary structure prediction via SOPMA software indicated that the C236T mutation could alter the number of Alpha helix (Hh) and Random coil (Cc) in the secondary structure of its encoded protein. Yeast two-hybrid assays indicated that this mutation may enhance the binding between LPIN1 and PGC-1α proteins. Quantitative real-time PCR analysis of genes associated with the PPAR signaling pathway revealed concurrent alterations in the expression of these genes. Collectively, these results suggest that the LPIN1 C236T mutation may participate in tail fat deposition in sheep, and thus it can be used as a molecular marker for selective breeding of thin-tailed sheep breeds. sheep fat deposition missense mutation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Chinese sheep breeds can be classified into five types according to their capacity for tail fat deposition (TFD) [ 1 ]. It has been reported that fat-tailed sheep are products of natural selection, where climate, human demands and selection methods exert critical effects on sheep domestication [ 2 , 3 ]. Mutton has attracted consumers’ attention due to its high protein content. However, traits associated with TFD in sheep exert a significant impact on mutton quality and production efficiency. Studies have indicated that excessive TFD in sheep markedly reduces carcass lean meat percentage and feed conversion rate. In addition, excessive TFD impairs sheep reproduction [ 4 ]. Therefore, as an important economic trait, TFD is in urgent need of attention from animal breeders. Furthermore, genes related to TFD in sheep are homologous to those associated with obesity or metabolism in humans. Thus, investigating the regulatory mechanisms underlying TFD also provides a reference for the prevention and treatment of human diseases. TFD is a complex quantitative trait controlled by multiple genes and governed by intricate genetic mechanisms. In recent years, genome-wide association studies (GWAS) have yielded substantial progress in identifying genetic variations associated with TFD, particularly those located in coding regions. With the development and maturation of resequencing and SNP array technologies, a growing number of SNPs have been detected in livestock and poultry [ 5 – 9 ]. However, further validation is required to elucidate the effects of these genetic variations on the TFD trait. In this study, we employed two sheep breeds, namely the Xinjiang Altay sheep (fat-rumped sheep) and Tibetan sheep (thin-tailed sheep), as experimental populations. These two breeds exhibit significant differences in TFD, making them ideal models for investigating the genetic mechanisms underlying adiposity. On this basis, we identified candidate loci/genomic regions via selection signature detection, genotyping and molecular experiments to systematically analyze how genomic variations modulate the regulatory mechanisms governing the TFD trait. Our strategy was as follows: first, candidate variants associated with the TFD trait in sheep were identified via selection signature analysis. Subsequently, we carried out genotyping of functional genes to screen for genotypes linked to the TFD trait. Finally, we established a sheep preadipocyte model to validate these candidate variants at the cellular level, thereby elucidating the roles and regulatory mechanisms of key functional variants in the TFD trait. In conclusion, this study aims to uncover the regulatory mechanisms through which genomic variations affect the TFD trait in sheep, and provide molecular breeding targets for the genetic improvement of the TFD trait in sheep. 2. METHODS 2.1 Ethics Statement All animal experiments were approved by Gansu Agricultural University (Lanzhou, China), with the approval number GSAU-AEW-2017-0003. All animal procedures were strictly performed in accordance with the guidelines formulated by the China Animal Health Commission and the Ministry of Agriculture of the People's Republic of China. Altay sheep were sourced from pastoral areas in Xinjiang, and Tibetan sheep from pastoral areas in Qinghai. 2.2 Experimental Populations and Phenotypic Measurement A total of 546 healthy one-year-old sheep were randomly selected, including 285 Altay sheep (149 rams and 136 ewes) and 261 Tibetan sheep (123 rams and 138 ewes). Sheep of both breeds were sourced from naturally grazed pastures, with no pedigree records available. Blood samples were collected via jugular venipuncture. The phenotypic traits measured included tail length, tail width, and tail circumference. Specifically, tail length was defined as the distance from the anterior edge of the first caudal vertebra to the tail tip; tail width referred to the straight-line distance at the widest part of the tail; and tail circumference represented the length of the tail measured with a flexible tape wrapped around it once. 2.3 Blood DNA Extraction Genomic DNA was extracted from sheep blood samples using a blood DNA extraction kit (Tiangen Biotech, Beijing, China) in strict accordance with the manufacturer’s instructions. The quality of the extracted genomic DNA was evaluated via agarose gel electrophoresis, and the DNA concentration was measured using a NanoDrop 2000 spectrophotometer. 2.4 SNP Array Genotyping and Quality Control A total of 40 Altay sheep (25 rams and 15 ewes) and 40 Tibetan sheep (21 rams and 19 ewes) were selected for whole-genome sequencing analysis. Genotyping of genomic DNA from each sample was performed using the Illumina Ovine SNP 600 Genotyping BeadChip (Illumina, Inc., USA). This chip contains 604,715 SNP loci, with an average distance of 4.2 kb between two adjacent SNPs. To improve the accuracy of selection signature detection, quality control was conducted using the online software PLINK (v1.07; http://pngu.mgh.harvard.edu/purcell/plink ) with the following criteria: individual SNP call rate > 90%; SNP locus call rate > 90%; minor allele frequency (MAF) > 3%; and P -value for Hardy-Weinberg Equilibrium (HWE) < 10⁻⁶. After quality control, the Beagle software was used for imputation of missing genotype data among the SNPs that failed to be genotyped. 2.5 Selection Signature Detection Two methods were adopted for selection signature detection in this study. The first method was population differentiation-based detection, namely the F ST method, which serves as a critical indicator for examining genetic differentiation between populations. The second method was cross-population extended haplotype homozygosity (XP-EHH), which exhibits strong power in detecting selected loci that are either fixed or nearly fixed via an inter-population comparison strategy. For both of the aforementioned methods, Altay sheep were designated as the experimental population, while Tibetan sheep were used as the reference population. 2.6 Gene Annotation and Functional Enrichment Analysis Genes located within the selected regions were retrieved based on the sheep Ovis aries 3.1 genome assembly ( http://www.ncbi.nlm.nih.gov/assembly/447978/ ) using the UCSC and NCBI databases. Functional enrichment analyses for GO (Gene Ontology) and KEGG pathways of the genes in the selected regions were performed via the online DAVID database ( http://david.abcc.ncifcrf.gov ). 2.7 DNA Pool Construction, PCR Amplification and Genotyping for candidate gene To identify potential SNPs of candidate gene, blood samples from 30 randomly selected sheep (15 Tibetan sheep and 15 Altay sheep) out of the initial 546 individuals were used to construct DNA pools (50 ng/µL). All exons and 1000 bp of the 5′-UTR and 3′-UTR regions were amplified based on the reference sequence. The PCR reaction system was as follows: 2 × Taq PCR MasterMix, 12.5 µL; Primer-F (10 pmol/µL), 1 µL; Primer-R (10 pmol/µL), 1 µL; DNA template, 2 µL; ddH₂O was added to make up the total volume to 25 µL. The PCR reaction procedure was set as follows: pre-denaturation at 94°C for 5 min; denaturation at 95°C for 30 s; annealing at 56°C for 30 s; final extension at 72°C for 10 min; with 35 cycles in total. PCR products were detected by 1% agarose gel electrophoresis, and the target bands were sequenced by Bomiao Biotechnology Co., Ltd. (Beijing, China). Based on the sequencing results, sequence alignment was performed using DNAMAN and Chromas2 software to screen for SNP loci. According to the SNP loci identified by pooled sequencing, genotyping of all 546 sheep was conducted via the mass spectrometry-based genotyping method. 2.8 Preadipocyte Culture The sheep tail preadipocytes used in this study were derived from our laboratory. All cells were authenticated and confirmed to be adipocytes. The preadipocytes were cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum (FBS) and 1% antibiotics (penicillin-streptomycin), under the conditions of 37°C and 5% CO₂. Subculture was performed when the cell confluence reached approximately 90%. 2.9 Synthesis of Overexpression Vectors and siRNA The CDS fragment of the LPIN1 (GenBank: NM_001280700) was inserted into the pcDNA3.1 + overexpression vector. The control group was designated as NC, while the wild-type and mutant overexpression vectors were designated as CC and TT, respectively. The construction of vectors carrying specific mutation sites, as well as the preparation of expression vectors, were performed by Gansu Kemeiyi Biotechnology Co., Ltd. (Gansu, China). The primer sequences used for vector construction are provided in the Table S1 . The small interfering RNA (siRNA) oligonucleotide sequences targeting the LPIN1 and the negative control were synthesized by Hongxun Biotechnology Co., Ltd. (Jiangsu, China). Detailed information is provided in the Table S2. 2.10 Cell Transfection Cell transfection was performed strictly in accordance with the manufacturer’s instructions for the Lipofectamine 3000 reagent (Invigentech, USA). 2.11 RNA-seq Forty-eight hours after adipocyte transfection, the medium was replaced with an adipogenic induction medium, followed by 7 days of culture. Subsequently, cells were harvested for RNA-seq analysis, with three biological replicates set for each group. Raw sequencing data were subjected to quality control using fastp (v0.23.2) software. Genome alignment against the sheep reference genome was performed via HISAT2 (v2.2.1) software, followed by differential expression analysis between groups for biological replicates using DESeq2 (v1.22.1) software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the pathways of genes associated with fat deposition. 2.12 Oil Red O Staining The culture medium was discarded, and the cells were rinsed twice with PBS. The cells were then fixed with fixative solution for approximately 30 min. After fixation, the cells were rinsed twice with distilled water, followed by immersion in 60% isopropanol for about 20 s. The isopropanol was discarded, and the prepared Oil Red O working solution was added; the cells were stained for 30 min at room temperature in the dark. The staining solution was discarded, and the cells were rinsed with 60% isopropanol for 20 s until the background became clear. The cells were subsequently rinsed with distilled water to remove excess dye. Finally, distilled water was added to cover the cells, which were then observed and photographed under a microscope. 2.13 Cell Proliferation Assay Cells were seeded in 96-well cell culture plates with three replicates per group. Cell viability was measured at 24, 48, and 72 h post-transfection, respectively. Subsequently, 10 µL of CCK-8 reagent (Puxitang, China) was added to each well, and the cells were further incubated at 37°C for 4 h. The absorbance at 450 nm was detected using a multimode microplate reader (Thermo, USA). For the EdU assay, cells were seeded in 24-well plates with three replicates per group, and the EdU detection of adipocytes was performed using the Beyo-Click™ EdU-555 Cell Proliferation Kit (Beyotime, China). Three fields of view were randomly selected from each well, and the ratio of EdU-positive cells was calculated using ImageJ 1.46 software. 2.14 Quantitative Real-time PCR Adipocytes were seeded in 12-well cell culture plates. At 48 h post-transfection, the complete medium was replaced with induction differentiation medium, and induction was maintained for a specified duration. Adipocytes were then harvested for total RNA extraction. Total RNA was isolated using the Trizol method, and cDNA was synthesized using a reverse transcription kit (Genestar, Beijing) and stored at − 20°C. Primers were designed using the online Primer3 tool, with reference to the corresponding gene sequences retrieved from NCBI (Table S5). The expression levels of LPIN1 (GenBank: NM_001280700), LPL (GenBank: NM_001128149.1), PPARγ (GenBank: NM_001100921.1) and CCAAT/enhancer-binding protein α ( C/EBPα ) (GenBank: KF830871.1) genes were determined via qRT-PCR, with β-actin (GenBank: NM_001009784.3) used as the reference gene. Each group was assayed in triplicate. 2.15 Prediction of Protein Secondary Structure The secondary structure of sheep LPIN1 protein was predicted using the SOPMA software. 2.16 Protein-Protein Interaction Validation Protein-protein interactions were verified using the yeast two-hybrid assay. The construction of yeast hybrid vectors and validation of protein interactions were performed by Gansu Kemeiyi Biotechnology Co., Ltd. 2.17 Statistical Analysis Data statistics, calculation, and graphing were performed using Excel 2019 and GraphPad Prism 8.0 software. One-way analysis of variance (ANOVA) was conducted with SPSS 25.0 software, and Duncan’s multiple range test was applied for post-hoc comparisons when significant differences were detected. Protein band quantification was carried out using ImageJ software. All data were presented as mean ± standard error (SEM). P < 0.05 (marked with “*”) indicates a significant difference, P 0.05(marked with “ns”) indicates no significant difference. 3. RESULTS 3.1 Information of SNP chip A total of 80 sheep were genotyped using the Illumina Ovine SNP 600K BeadChip. After quality control, 37 Altay sheep and 37 Tibetan sheep were used in the final analysis (Fig. 1A), a total of 584896 SNPs were remained per breed. The average distance between two SNPs was 4.28 kb. 3.2 Identification of selection signatures In order to accurately detected the selection signal among different type of tail sheep, two methods were used to identify candidate regions involved in selection at genome-wide level for each breed-pairs. Table S3 summaries the selection signatures which were detected in two breed. For F ST , we identify the population genetic differentiation for every SNP, the boxplot method was employed to identify outliers at the genome-wide level because the F ST empirical distribution of a single site was similar to a chi-squared (χ2) distribution with two or three degrees of freedom. Figure 1B show the genome-wide distribution of the outliers on each autosome. For the XPEHH, negative score suggest selection happened in reference, otherwise in observed population. For breed pair of Alaty sheep-Tibetan sheep (A-T), 297 negative values out of 476 outliers indicate that selection happened in the reference population of Tibetan, and the other 179 outliers suggest that selection happened in Altay sheep (Fig. 1C). 3.3 Identifying candidate genes in selection regions As shown in the Table S3, a total of 152,163 and 267 candidate selection regions with average lengths of 30.09 Mb, 26.61 Mb and 45.68 Mb were identified in Altay and Tibetan sheep, respectively. The Ovis_3.1 database were used to identify candidate genes in the selection regions. A total of 1420 and 1520 genes harbored in selection regions in Altay and Tibetan sheep, respectively (Table S4). DAVID V2.1 was used to conduct the GO and KEGG pathway enrichment analyses to investigate the functions of the candidate genes. After this enrichment analysis, followed by the Benjamini correction procedure, there were almost no significant functional terms. The LPIN1 gene was enriched in adipogenesis-related signaling pathways. Figure 1. Selection signal identification and gene mutation identification. (A) test population, (B) genome-wide distribution of selection signatures by XPEHH, (C) genome-wide distribution of selection signatures by F ST , (D) LPIN1 mixing pool sequencing. 3.4 Population genetic analysis of LPIN1 polymorphism Through DNA mixed pool sequencing, three SNPs were identified in the LPIN1 gene (Fig. 1D), including g.134 T/C (rs01), g.269 A/G (rs02), and g.236 C/T (rs03) mutations. These three SNPs were genotyped using MALDI-TOF-MS. It was found that all three loci have three genotypes. According to the criteria for determining polymorphism information content, all three loci are moderately polymorphic (0.25 < PIC 0.05); The number of effective alleles at these loci is close to 2 (Table 1 ). Table 2 shows the gene frequency and polymorphism of three SNP loci in two sheep populations. Table 1 Genetic parameters of the SNPs in Altay and Tibetan population Locus Ho He PIC Hard-Weinberg P χ 2 rs01 T/C 0.42 0.37 0.3 0.051 3.804 rs02 A/G 0.56 0.5 0.37 0.099 2.707 rs03 C/T 0.51 0.5 0.38 0.814 0.055 Table 2 The genotype frequency, allele frequency and Hardy-Weinberg equilibrium test of 3 SNPs in LPIN1 gene Locus Genotype Genotype frequency (%) χ 2 p -value Allele Allele frequency (%) Altay sheep Tibetan sheep Altay sheep Tibetan sheep rs01 CC 166 58.24 35 13.40 10.539 0.0051 C 75.43 52.86 CT 98 34.38 206 78.92 T 24.57 47.14 TT 21 7.36 20 7.66 rs02 AA 58 20.35 27 10.34 12.236 0.0022 A 48.06 18.96 GA 158 55.43 45 17.24 G 51.64 81.04 GG 69 24.21 189 72.42 rs03 CC 79 27.72 200 76.53 5.489 0.044 C 52.28 81.7 CT 140 49.12 27 10.34 T 47.72 18.30 TT 66 23.15 34 13.02 3.5 Functional Verification of the LPIN1 in Preadipocytes We aimed to determine whether altered expression levels of the LPIN1 would affect cellular phenotypes associated with tail fat deposition in sheep. First, we analyzed the effects of LPIN1 knockdown and overexpression on the proliferation of sheep preadipocytes. Cell proliferation assays demonstrated that LPIN1 enhanced cell viability, while EdU staining results indicated that LPIN1 overexpression promoted intracellular DNA synthesis. These findings suggested that LPIN1 facilitates the proliferation of sheep preadipocytes. Subsequently, we evaluated the impacts of LPIN1 knockdown and overexpression on the differentiation of sheep preadipocytes. Results from Oil Red O staining and triglyceride (TG) content assays showed that LPIN1 promoted lipid droplet accumulation in sheep preadipocytes. RT-qPCR analysis revealed that LPIN1 upregulated the expression of fat deposition marker genes including PPARγ , C/EBPα and LPL . Collectively, these data indicated that LPIN1 promotes the differentiation of sheep preadipocytes. 3.6 The effect of mutations on the proliferation, vitality, and differentiation of Precursor adipocytes EdU results show that compared with the control group, overexpression of LPIN1-CC and LPIN1-TT significantly increased the number and ratio of EdU labeled positive cells ( P < 0.05), and the number and ratio of positive cells in the TT group were significantly higher than those in the CC group ( P < 0.01, Fig. 3 A), the CCK-8 results show that at 24 hours after transfection, the cell viability of the TT overexpressing group was significantly higher than that of the CC group ( P 0.05) (Fig. 3 B), indicating that TT genotype has a positive effect on precursor adipocyte proliferation. The results of Oil Red O staining showed that the overexpression group had significantly increased lipid droplets in the CC and TT groups ( P < 0.05), and the lipid droplets in the TT group were significantly more than those in the CC group ( P < 0.05) (Fig. 3 C); The results of triglyceride content measurement showed that compared with the NC group, the overexpression group had significantly increased TG content ( P < 0.01), and the TT group had significantly higher TG content than the CC group ( P < 0.01, Fig. 3 D). The results were consistent with the Oil Red O experiment, indicating that C236T mutation can promote the differentiation of precursor adipocytes. 3.7 Transcriptomic analysis of the impact of C236T missense mutation in LPIN1 on biological pathways Using |log 2 Fold Change| > 1 and FDR < 0.05 as the screening criteria for differentially expressed genes. Overall, the C236T mutation of the LPIN1 affects the target genes associated with fat deposition. Further verification via GO and KEGG enrichment analyses confirmed that these target genes are closely related to lipid metabolism-related pathways, involving cell growth and development, signal transduction, and metabolic processes. 3.8 Expression of related genes on LPIN1 and PPAR signaling pathways during differentiation process PGC-1α acts as a coactivator of PPARs, and it regulates the expression of genes encoding functional proteins related to lipid metabolism and transport via PPARα or PPARγ , thereby affecting the balance of lipid metabolism in vivo. LPIN1 is a target of PGC-1α and its expression is induced by PGC-1α . Transcriptome sequencing results showed that PGC-1α was differentially expressed between the CC and TT groups; therefore, we aimed to investigate whether the C236T mutation of LPIN1 affects the expression of genes related to the PPAR signaling pathway. The qRT-PCR detection results showed that the expression level of LPIN1 in the NC group tended to stabilize during the differentiation stage, while in the CC and TT groups, the LPIN1 showed a trend of first increasing, then decreasing, and then continuing to increase during the differentiation of precursor adipocytes, and the highest expression was observed in adipocytes after 6 days of differentiation; The overall expression level of LPL showed a trend of first increasing and then decreasing, and the expression level of TT group was significantly higher than that of NC and CC groups ( P < 0.05); The overall expression level of PPARγ showed a gradually increasing trend, and the TT group had the highest expression level on the 8th day of differentiation; Compared with the control group, the TT group significantly upregulated the expression level of C/EBPα during adipocyte differentiation, and showed a gradually increasing trend with induction time. 3.9 Prediction of Protein Secondary Structure The LPIN1 protein was subjected to secondary structure prediction, and results showed that the contents of Alpha helix (Hh) and Random coil (Cc) exhibited alterations before and after the mutation, as presented in Table 3 . Table 3 Prediction of secondary structure of Sheep LPIN1 protein Type TT genotype Number CC genotype Number TT genotype Percent CC genotype Percent Alpha helix (Hh) 156 154 17.43% 17.21% Extended strand (Ee) 86 86 9.61% 9.61% Beta turn (Tt) 0 0 0.00% 0.00% Random coil (Cc) 653 655 72.96% 73.18% 3.10 The effect of C236T missense mutation on the binding ability of LPIN1 and PGC-1α protein Positive and negative co transfer groups were set up for co transfer validation, and it was found that all co transfer groups could grow normally. It was preliminarily identified that there is an interaction between LPIN1_CC/TT protein and PGC-1α (Fig. 6 A). The spot results are shown in the figure. All six groups can grow normally on SD-Trp-Leu plates. On SD-Trp-Leu-His and SD-Trp-Leu-His-Ade plates, all co-transformation groups grew normally and turned blue, indicating that there was an interaction between LPIN1-TT, LPIN1-CC proteins and PGC-1α protein. (Fig. 6 B). 4. DISCUSSION In our study, we selected two sheep breeds distributed in different parts of China with obviously different types of tails: Altay sheep and Tibetan sheep. In recent years, more and more research had used SNP chip data to identify selection signature for exploring the potential genetic mechanism of phenotype polymorphisms [ 10 – 16 ], but most of these studies have only implemented a single method, different methods have different categories for selection signatures [ 17 , 18 ]. In order to avoid some unknown bias only used one method to detect selection signature, two method, F ST and XPEHH were employ to explore the selection signature in Altay sheep and Tibetan sheep. The F ST method detect selection signature based on allele frequency differences between population [ 19 , 20 ]. XPEHH is sensitive to detect selection signature based on differences haplotype frequencies, the selected haplotype/allele has approached or achieved fixation in one population but remains polymorphic in the other one [ 6 , 9 ]. In our results, a total of 152,163 and 267 candidate selection regions were identified in Altay and Tibetan sheep, respectively, these results showed that in Tibetan sheep detect more selection regions than Altay sheep, this mean that genome polymorphism of Altay sheep is lower than that of the Tibetan sheep, this results are consistent with Moradi et al [ 1 ] report that the first wild sheep were thin-tailed sheep, from which fat-tailed sheep were produced as the result of selection by humans and by nature. We had used above two breeds to detect CNV [ 21 ], A total of 301 and 66 CNVRs were identified in Altay sheep and Tibetan sheep, respectively. The CNVR regions harbor genes associated with fat deposition and fat synthesis in fat-rumped sheep, such as LPIN1 , PPARA , RXRA , KLF11 , ADD1 , FASN , PPP1CA , PDGFA , and PEX6 . The lipoprotein gene ( LPIN1 ), also known as the lipid phosphate hydrolase gene, is a newly discovered gene family that bidirectionally regulates fat metabolism. Firstly, LPIN1 , as a phospholipid acid phosphatase (PAP), plays an important role in the synthesis of triglycerides and phospholipids [ 22 ]; Secondly, as a transcriptional co activator, LPIN1 participates in the expression of fatty acid metabolism and fat synthesis gene PPARγ , further activating the expression of PGC-1α and PPARα [ 23 ]. Related studies have shown that that the LPIN1 is involved in fat formation and is an important determinant of adipose tissue differentiation and adipocyte function. Moreover, variations in the locus of this gene can also affect fat content [ 24 – 27 ]. We then performed genotyping of LPIN1 and identified three SNPs) in the exon regions, among which the C236T mutation exhibited significant differences in genotype frequencies between Tibetan sheep and Altay sheep. Previous studies on the tail fat of fat tailed and lean tailed sheep using transcriptomics have found that multiple genes in the PPAR signaling pathway, such as FASN / FABP4 , LPL , SCD and PPARγ are differentially expressed in the tail fat tissues of fat tailed and lean tailed sheep [ 28 – 34 ], indicating that the PPAR signaling pathway may play an important role in the deposition of tail fat in sheep. In this study, we systematically elucidated the key genetic variations and regulatory mechanisms underlying the TFD trait in sheep through a combination of selection signature analysis, RNA sequencing (RNA-Seq), genotyping, and molecular experiments. We identified candidate genes and their variants associated with fat deposition in fat-tailed and thin-tailed sheep breeds. Taking the LPIN1 C236T mutation as an example, we validated this mutation site at the cellular level and found that the mutation may enhance the binding ability between LPIN1 and PGC-1α proteins. Concomitantly, the expression levels of genes related to the PPAR signaling pathway were altered accordingly, which affects the differentiation of ovine preadipocytes. Collectively, this study provides a novel perspective for investigating the TFD trait in sheep and offers potential molecular markers for sheep genetic breeding programs. 5. CONCLUSIONS The C236T mutation in the LPIN1 affects the proliferation and differentiation of ovine preadipocytes, and thus this mutation can serve as a molecular marker for the selective breeding of thin-tailed sheep. In this study, we only verified the interaction between LPIN1 and PGC-1α proteins using yeast two-hybrid assays. In addition, regarding the effect of LPIN1 on the PPAR signaling pathway, we only detected the expression of genes related to this pathway and lack corresponding research on the deeper regulatory effects of LPIN1 on the PPAR signaling pathway. In subsequent studies, we will conduct further investigations to provide a theoretical basis for the C236T mutation as a molecular genetic marker in sheep breeding. Declarations Ethics approval and consent to participate Not applicable. All animal experiments were approved by Gansu Agricultural University (Lanzhou, China), with the approval number GSAU-AEW-2017-0003. All animal procedures were strictly performed in accordance with the guidelines formulated by the China Animal Health Commission and the Ministry of Agriculture of the People's Republic of China. Informed consent for the use of the sheep in this study was obtained from the respective owners of the animals. Consent for publication Not applicable. Conflicts of Interest: The authors declare that there is no conflict regarding the publication of this article. Funding: This study is supported by the National Natural Science Foundation of China (3246200236), Fuxi young talents training program of Gansu Agricultural University (GAUfx-04Y012) and Provincial Key Research and Development Program - Agricultural Sector (25YFNA036) . Author Contribution C.Y.Z. : writing – original draft, Investigation, Data curation, Funding acquisition. Y.Z. : Formal analysis, Methodology. Y.S.W. : Formal analysis, Methodology. Y.Y. : Methodology. X.Y.H. : Supervision. Acknowledgement Thank you for the platform provided by the College of animal science and technology of Gansu Agricultural University. Funding: This study is supported by the National Natural Science Foundation of China (3246200236), Fuxi young talents training program of Gansu Agricultural University (GAUfx-04Y012) and Provincial Key Research and Development Program - Agricultural Sector (25YFNA036) . Data availability Data will be made available on request. References Moradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 2012;13. https://doi.org/10.1186/1471-2156-13-10 . Ermias E, Yami A, Rege JEO. Fat deposition in tropical sheep as adaptive attribute to periodic feed fluctuation. J Anim Breed Genet. 2002;119:235–46. https://doi.org/10.1046/j.1439-0388.2002.00344.x . Kashan NEJ, Manafi Azar GH, Afzalzadeh A, Salehi A. Growth performance and carcass quality of fattening lambs from fat-tailed and tailed sheep breeds. Small Ruminant Res. 2005;60:267–71. https://doi.org/10.1016/j.smallrumres.2005.01.001 . Wood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM. Fat deposition, fatty acid composition and meat quality: A review. Meat Sci. 2008;78:343–58. https://doi.org/10.1016/j.meatsci.2007.07.019 . Suzuki Y. Statistical methods for detecting natural selection from genomic data. Genes Genetic Syst. 2010;85:359–76. https://doi.org/10.1266/ggs.85.359 . Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R, Schaffner SF, Lander ES. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449:913–8. https://doi.org/10.1038/nature06250 . McRae KM, McEwan JC, Dodds KG, Gemmell NJ. Signatures of selection in sheep bred for resistance or susceptibility to gastrointestinal nematodes. BMC Genomics. 2014;15:637. https://doi.org/10.1186/1471-2164-15-637 . Zhu CY, Fan HY, Yuan ZH, Hu SJ, Zhang L, Wei CH, Zhang Q, Zhao FP, Du LX. 2015.Detection of Selection Signatures on the X Chromosome in Three Sheep Breeds. Int J Mol Sci. 16, 20360–74. https://doi.org/10.3390/ijms160920360 Ma YL, Wei JL, Zhang Q, Chen L, Wang JY, Liu JF, Ding XD. A Genome Scan for Selection Signatures in Pigs. PLoS ONE. 2015;10:e0116850. https://doi.org/10.1371/journal.pone.0116850 . Zhang C, Bailey DK, Awad T, Liu G, Xing G, Cao M, Valmeekam V, Retief J, Matsuzaki H, Taub M, Seielstad M, Kennedy GC. A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations. Bioinformatics. 2006;22:2122–8. https://doi.org/10.1093/bioinformatics/btl365 . Wang ET, Kodama G, Baldi P, Moyzis RK. Global landscape of recent inferred Darwinian selection for Homo sapiens. Proc Natl Acad Sci USA. 2006;103:135–40. https://doi.org/10.1073/pnas.0509691102 . Voight BF, Kudaravalli S, Wen XQ, Pritchard JK. A Map of Recent Positive Selection in the Human Genome. PLoS Biol. 2006;4:e72. https://doi.org/10.1371/journal.pbio.0040072 . Stephan W. Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation. Mol Ecol. 2016;25:79–88. https://doi.org/10.1111/mec.13288 . Rubin C-J, Megens H-J, Barrio AM, Maqbool K, Sayyab S, Schwochow D, Wang C, Carlborg Ö, Jern P, Jørgensen CB, Archibald AL, Fredholm M, Groenen MAM. L. Andersson.2012. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci USA. 109, 19529–36. https://doi.org/10.1073/pnas.1217149109 Kelley JL, Madeoy J, Calhoun JC, Swanson W, Akey JM. Genomic signatures of positive selection in humans and the limits of outlier approaches. Genome Res. 2006;16:980–9. https://doi.org/10.1101/gr.5157306 . Doerks T, Copley RR, Schultz J, Ponting CP, Bork P. Systematic Identification of Novel Protein Domain Families Associated with Nuclear Functions. Genome Res. 2002;12:47–56. https://doi.org/10.1101/gr.203201 . Oleksyk TK, Smith MW, O’Brien SJ. Genome-wide scans for footprints of natural selection. Philosophical Trans Royal Soc Lond Ser B: Biol Sci. 2010;365:185–205. https://doi.org/10.1098/rstb.2009.0219 . Suzuki Y. Statistical methods for detecting natural selection from genomic data. Genes Genetic Syst. 2010;85:359–76. https://doi.org/10.1266/ggs.85.359 . Randhawa IA, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genet. 2014;15:34. https://doi.org/10.1186/1471-2156-15-34 . Yuan Z, Liu E, Liu Z, Kijas J`W, Zhu C, Hu S, Ma X, Zhang L, Du L, Wang H, Wei C. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim Genet. 2016;48:55–66. https://doi.org/10.1111/age.12477 . Zhu CY, Fan HY, Yuan ZH, Hu SJ, Ma XM, Xuan JL, Wang HW, Zhang L, Wei CH, Zhang Q, Zhao FP, Du LX. Genome-wide detection of CNVs in Chinese indigenous sheep with different types of tails using ovine high-density 600K SNP arrays. Sci Rep. 2016;6:28822. https://doi.org/10.1038/srep27822 . Donkor J, Sariahmetoglu M, Dewald J, Brindley DN, Reue K. Three Mammalian Lipins Act as Phosphatidate Phosphatases with Distinct Tissue Expression Patterns. J Biol Chem. 2007;282:3450–7. https://doi.org/10.1074/jbc.m610745200 . Finck BN, Gropler MC, Chen Z, Leone TC, Croce MA, Harris TE, Lawrence JC, Kelly DP. Lipin 1 is an inducible amplifier of the hepatic PGC-1α/PPARα regulatory pathway. Cell Metabol. 2006;4:199–210. https://doi.org/10.1016/j.cmet.2006.08.005 . Reue K, Brindley DN. Thematic Review Series: Glycerolipids. Multiple roles for lipins/phosphatidate phosphatase enzymes in lipid metabolism. J Lipid Res. 2008;49:2493–503. https://doi.org/10.1194/jlr.r800019-jlr200 . Grimsey N, Han GS, O'Hara L, Rochford JJ, Carman GM, Siniossoglou S. Temporal and Spatial Regulation of the Phosphatidate Phosphatases Lipin 1 and 2. J Biol Chem. 2008;283:29166–74. https://doi.org/10.1074/jbc.m804278200 . Wang XK, Chen W, Huang YQ, Kang XT, Wang JP, Li GX, Jiang RR. Identification of the transcript isoforms and expression characteristics for chicken Lpin1. Animal. 2012;6:1897–903. https://doi.org/10.1017/s1751731112001358 . Wang X, Zhou HT, Hickford JGH, Li SB, Wang JQ, Liu X, Hu J, Luo YZ. Variation in the yak lipin-1 gene and its association with milk traits. J Dairy Res. 2020;87:166–9. https://doi.org/10.1017/s002202991900089x . Qiu YH, Gan ML, Wang XY, Liao TC, Chen QY, Lei YH, Chen L, Wang JY, Zhao Y, Niu LL, Wang Y, Zhang SH, Zhu L, Shen LY. The global perspective on peroxisome proliferator-activated receptor γ (PPARγ) in ectopic fat deposition: A review. Int J Biol Macromol. 2023;253:127042. https://doi.org/10.1016/j.ijbiomac.2023.127042 . Zheng F, Cai Y. Concurrent exercise improves insulin resistance and nonalcoholic fatty liver disease by upregulating PPAR-γ and genes involved in the beta-oxidation of fatty acids in ApoE-KO mice fed a high-fat diet. Lipids Health Dis. 2019;18:6. https://doi.org/10.1186/s12944-018-0933-z . Yang Q, Zhang Y, Li LQ, Li J, Li YL, Han L, Wang M. D-chiro-Inositol facilitates adiponectin biosynthesis and activates the AMPKα/PPARs pathway to inhibit high-fat diet-induced obesity and liver lipid deposition. Food Funct. 2022;13:7192–203. https://doi.org/10.1039/d2fo00869f . Christofides A, Konstantinidou E, Jani C, Boussiotis VA. The role of peroxisome proliferator-activated receptors (PPAR) in immune responses. Metabolism. 2021;114:154338. https://doi.org/10.1016/j.metabol.2020.154338 . Mao Z, Feng MJ, Li ZR, Zhou MS, Xu LN, Pan K, Wang SX, Su W, Zhang WZ. 2021. ETV5 Regulates Hepatic Fatty Acid Metabolism Through PPAR Signaling Pathway. Diabetes .70, 214–226. https://doi.org/10.2337/db20-0619 Hu TT, Wu QQ, Yao Q, Yu JB, Jiang KB, Wan Y, Tang QZ. PRDM16 exerts critical role in myocardial metabolism and energetics in type 2 diabetes induced cardiomyopathy. Metabolism. 2023;146:155658. https://doi.org/10.1016/j.metabol.2023.155658 . Wang XL, Zhou GX, Xu XC, Geng RQ, Zhou JP, Yang YX, Yang ZX, Chen YL. Transcriptome profile analysis of adipose tissues from fat and short-tailed sheep. Gene. 2014;549:252–7. https://doi.org/10.1016/j.gene.2014.07.072 . Additional Declarations No competing interests reported. Supplementary Files file.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8637217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593182523,"identity":"06f808c3-5437-4299-a2c0-3c637cc9d02e","order_by":0,"name":"caiye Zhu","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"caiye","middleName":"","lastName":"Zhu","suffix":""},{"id":593182525,"identity":"37e5ef9a-4891-4a42-a345-52dd60267eb3","order_by":1,"name":"yue Zhang","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"yue","middleName":"","lastName":"Zhang","suffix":""},{"id":593182526,"identity":"8fa680ba-2456-42ff-bef2-4fda882f1b3a","order_by":2,"name":"yingshi Wei","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"yingshi","middleName":"","lastName":"Wei","suffix":""},{"id":593182527,"identity":"2226e604-c772-4e2f-9c00-85b14eac078f","order_by":3,"name":"yang Yang","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"yang","middleName":"","lastName":"Yang","suffix":""},{"id":593182528,"identity":"6a7eebe4-e7ca-4c6e-8635-133e64bafdd4","order_by":4,"name":"xiaoyu Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RMWvCQBTA8RcO3vTore9Q4le4Koh+mxzCTS10zCBWiJhBxVXBD+HoqAQyRbo6pgidOrRLcVTnShI3h/vN7w/v3QE4zgPCxnGX/5zYlwJMHoT98uQJbO95Oe20VAxHnWdpeeLDS7tGGJr1h/elPkeiwmKQ2hoRe8tI2NAMEWQ8CYoTL0q6qw4LKdAezKYOnO3XxYlAc/gmRhXRJckQNL+WJEiaCZl0Iv/ezFhUSIia6pKwTsBCtYTx+sisVQQ9DrKUSm9pLMT1Kwfvc7k1v6ew78t4Vpz8Q/eNO47jODedAfhARQ37OPzNAAAAAElFTkSuQmCC","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"xiaoyu","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-01-19 08:54:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8637217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8637217/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103061987,"identity":"afca8e10-3880-4bc6-84ea-b7f80fa75567","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":976902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelection signal identification and gene mutation identification.\u003c/strong\u003e (A) test population, (B) genome-wide distribution of selection signatures by XPEHH, (C) genome-wide distribution of selection signatures by F\u003csub\u003eST\u003c/sub\u003e, (D) \u003cem\u003eLPIN1\u003c/em\u003e mixing pool sequencing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/540290e7705fb2de26d4e058.png"},{"id":103061990,"identity":"5a44b9e6-11e6-46c1-8cbf-430e513f6534","added_by":"auto","created_at":"2026-02-20 10:13:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":859844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional verification of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLPIN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (A) EdU-labeled positive cells and Percentage of positive cells , (B) CCK-8 assay for cell viability, (C) Oil Red O staining and triglyceride detection, (D) detection of adipogenic differentiation-related gene expression by qRT-PCR.\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/ca93ef0f63fd97a038f4a03e.jpeg"},{"id":103061989,"identity":"3562e0e8-b4b0-47c3-b056-8290462a3990","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":971687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLPIN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e C236 Mutation on the Proliferation and Differentiation of Preadipocytes.\u003c/strong\u003e (A) EdU assay, (B) CCK-8 assay for cell viability , (C) Oil Red O staining, and (D) Triglyceride (TG) content measurement (n=3).\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/f593f7136d4b901e67d3ca27.jpeg"},{"id":103061986,"identity":"e965beda-df1d-4312-89a6-62fd5f8e8b0d","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":498129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGo and KEGG enrichment analysis dotplot.\u003c/strong\u003e (A) represents CC vs NC GO enrichment terms, (B) represents CC vs NC KEGG enrichment pathway, (C) represents TT vs NC GO enrichment terms, (D) represents TT vs NC KEGG enrichment pathway, (E) represents TT vs CC GO enrichment terms, (F) represents TT vs CC KEGG enrichment pathway (n=3).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/bd70c150cceb0ce51c79439d.png"},{"id":103061985,"identity":"45a6abee-7533-49d9-b04d-c2d6bda6762e","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of C236T mutation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLPIN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e on preadipocytes (n=3).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/81f344766350614b719b9de2.png"},{"id":103061984,"identity":"f1c519b5-28cc-4058-9ac0-aa53456e724a","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":807626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYeast two hybrid verification of protein interaction. \u003c/strong\u003e(A) verification of co-transfection of the bait vector and the prey vector, from left to right, the co-transfection group of pGBKT7-53\u0026amp;pGADT7-T serves as the positive control, the co-transfection group of pGBKT7-lam\u0026amp;pGADT7-T acts as the negative control, the other co-transfection groups include pGBKT7-LPIN1-TT\u0026amp;pGADT7, pGBKT7-LPIN1-TT\u0026amp;pGADT7-PGC-1α, pGBKT7-LPIN1-CC\u0026amp;pGADT7 and pGBKT7-LPIN1-CC\u0026amp;pGADT7-PGC-1α. (B) verification of re-screening by spotting of yeast clones in the yeast two-hybrid experiment, from top to bottom, the bacterial plaques are the positive control, the negative control, pGBKT7-LPIN1-TT \u0026amp; pGADT7, pGBKT7-LPIN1-TT \u0026amp; pGADT7-PGC-1α, pGBKT7-LPIN1-CC \u0026amp; pGADT7, and pGBKT7-LPIN1-CC \u0026amp; pGADT7-PGC-1α.\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/af8a865f21ebf8896c315f00.jpeg"},{"id":104912418,"identity":"d2111d03-df66-4cda-9e7b-b07734761077","added_by":"auto","created_at":"2026-03-18 15:26:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5871611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/1f93794d-cb24-4c92-824b-1db6e28615d3.pdf"},{"id":103061988,"identity":"654d5731-a735-44f5-9e98-9344d584c46f","added_by":"auto","created_at":"2026-02-20 10:13:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36207,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8637217/v1/fb69800362dde7ad47723448.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detection of Genomic Selection Signatures and Identification of Beneficial Mutations for Fat Deposition in Sheep with Different Tail Types","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eChinese sheep breeds can be classified into five types according to their capacity for tail fat deposition (TFD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It has been reported that fat-tailed sheep are products of natural selection, where climate, human demands and selection methods exert critical effects on sheep domestication [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Mutton has attracted consumers\u0026rsquo; attention due to its high protein content. However, traits associated with TFD in sheep exert a significant impact on mutton quality and production efficiency. Studies have indicated that excessive TFD in sheep markedly reduces carcass lean meat percentage and feed conversion rate. In addition, excessive TFD impairs sheep reproduction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, as an important economic trait, TFD is in urgent need of attention from animal breeders. Furthermore, genes related to TFD in sheep are homologous to those associated with obesity or metabolism in humans. Thus, investigating the regulatory mechanisms underlying TFD also provides a reference for the prevention and treatment of human diseases.\u003c/p\u003e \u003cp\u003eTFD is a complex quantitative trait controlled by multiple genes and governed by intricate genetic mechanisms. In recent years, genome-wide association studies (GWAS) have yielded substantial progress in identifying genetic variations associated with TFD, particularly those located in coding regions. With the development and maturation of resequencing and SNP array technologies, a growing number of SNPs have been detected in livestock and poultry [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, further validation is required to elucidate the effects of these genetic variations on the TFD trait.\u003c/p\u003e \u003cp\u003eIn this study, we employed two sheep breeds, namely the Xinjiang Altay sheep (fat-rumped sheep) and Tibetan sheep (thin-tailed sheep), as experimental populations. These two breeds exhibit significant differences in TFD, making them ideal models for investigating the genetic mechanisms underlying adiposity. On this basis, we identified candidate loci/genomic regions via selection signature detection, genotyping and molecular experiments to systematically analyze how genomic variations modulate the regulatory mechanisms governing the TFD trait. Our strategy was as follows: first, candidate variants associated with the TFD trait in sheep were identified via selection signature analysis. Subsequently, we carried out genotyping of functional genes to screen for genotypes linked to the TFD trait. Finally, we established a sheep preadipocyte model to validate these candidate variants at the cellular level, thereby elucidating the roles and regulatory mechanisms of key functional variants in the TFD trait. In conclusion, this study aims to uncover the regulatory mechanisms through which genomic variations affect the TFD trait in sheep, and provide molecular breeding targets for the genetic improvement of the TFD trait in sheep.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethics Statement\u003c/h2\u003e \u003cp\u003e All animal experiments were approved by Gansu Agricultural University (Lanzhou, China), with the approval number GSAU-AEW-2017-0003. All animal procedures were strictly performed in accordance with the guidelines formulated by the China Animal Health Commission and the Ministry of Agriculture of the People's Republic of China. Altay sheep were sourced from pastoral areas in Xinjiang, and Tibetan sheep from pastoral areas in Qinghai.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental Populations and Phenotypic Measurement\u003c/h2\u003e \u003cp\u003eA total of 546 healthy one-year-old sheep were randomly selected, including 285 Altay sheep (149 rams and 136 ewes) and 261 Tibetan sheep (123 rams and 138 ewes). Sheep of both breeds were sourced from naturally grazed pastures, with no pedigree records available. Blood samples were collected via jugular venipuncture. The phenotypic traits measured included tail length, tail width, and tail circumference. Specifically, tail length was defined as the distance from the anterior edge of the first caudal vertebra to the tail tip; tail width referred to the straight-line distance at the widest part of the tail; and tail circumference represented the length of the tail measured with a flexible tape wrapped around it once.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Blood DNA Extraction\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from sheep blood samples using a blood DNA extraction kit (Tiangen Biotech, Beijing, China) in strict accordance with the manufacturer\u0026rsquo;s instructions. The quality of the extracted genomic DNA was evaluated via agarose gel electrophoresis, and the DNA concentration was measured using a NanoDrop 2000 spectrophotometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 SNP Array Genotyping and Quality Control\u003c/h2\u003e \u003cp\u003eA total of 40 Altay sheep (25 rams and 15 ewes) and 40 Tibetan sheep (21 rams and 19 ewes) were selected for whole-genome sequencing analysis. Genotyping of genomic DNA from each sample was performed using the Illumina Ovine SNP 600 Genotyping BeadChip (Illumina, Inc., USA). This chip contains 604,715 SNP loci, with an average distance of 4.2 kb between two adjacent SNPs. To improve the accuracy of selection signature detection, quality control was conducted using the online software PLINK (v1.07; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pngu.mgh.harvard.edu/purcell/plink\u003c/span\u003e\u003cspan address=\"http://pngu.mgh.harvard.edu/purcell/plink\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the following criteria: individual SNP call rate\u0026thinsp;\u0026gt;\u0026thinsp;90%; SNP locus call rate\u0026thinsp;\u0026gt;\u0026thinsp;90%; minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;3%; and \u003cem\u003eP\u003c/em\u003e-value for Hardy-Weinberg Equilibrium (HWE)\u0026thinsp;\u0026lt;\u0026thinsp;10⁻⁶. After quality control, the Beagle software was used for imputation of missing genotype data among the SNPs that failed to be genotyped.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Selection Signature Detection\u003c/h2\u003e \u003cp\u003eTwo methods were adopted for selection signature detection in this study. The first method was population differentiation-based detection, namely the F\u003csub\u003eST\u003c/sub\u003e method, which serves as a critical indicator for examining genetic differentiation between populations. The second method was cross-population extended haplotype homozygosity (XP-EHH), which exhibits strong power in detecting selected loci that are either fixed or nearly fixed via an inter-population comparison strategy. For both of the aforementioned methods, Altay sheep were designated as the experimental population, while Tibetan sheep were used as the reference population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene Annotation and Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGenes located within the selected regions were retrieved based on the sheep Ovis aries 3.1 genome assembly (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/assembly/447978/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/assembly/447978/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the UCSC and NCBI databases. Functional enrichment analyses for GO (Gene Ontology) and KEGG pathways of the genes in the selected regions were performed via the online DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.abcc.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"http://david.abcc.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 DNA Pool Construction, PCR Amplification and Genotyping for candidate gene\u003c/h2\u003e \u003cp\u003eTo identify potential SNPs of candidate gene, blood samples from 30 randomly selected sheep (15 Tibetan sheep and 15 Altay sheep) out of the initial 546 individuals were used to construct DNA pools (50 ng/\u0026micro;L). All exons and 1000 bp of the 5\u0026prime;-UTR and 3\u0026prime;-UTR regions were amplified based on the reference sequence. The PCR reaction system was as follows: 2 \u0026times; Taq PCR MasterMix, 12.5 \u0026micro;L; Primer-F (10 pmol/\u0026micro;L), 1 \u0026micro;L; Primer-R (10 pmol/\u0026micro;L), 1 \u0026micro;L; DNA template, 2 \u0026micro;L; ddH₂O was added to make up the total volume to 25 \u0026micro;L. The PCR reaction procedure was set as follows: pre-denaturation at 94\u0026deg;C for 5 min; denaturation at 95\u0026deg;C for 30 s; annealing at 56\u0026deg;C for 30 s; final extension at 72\u0026deg;C for 10 min; with 35 cycles in total. PCR products were detected by 1% agarose gel electrophoresis, and the target bands were sequenced by Bomiao Biotechnology Co., Ltd. (Beijing, China). Based on the sequencing results, sequence alignment was performed using DNAMAN and Chromas2 software to screen for SNP loci. According to the SNP loci identified by pooled sequencing, genotyping of all 546 sheep was conducted via the mass spectrometry-based genotyping method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Preadipocyte Culture\u003c/h2\u003e \u003cp\u003eThe sheep tail preadipocytes used in this study were derived from our laboratory. All cells were authenticated and confirmed to be adipocytes. The preadipocytes were cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum (FBS) and 1% antibiotics (penicillin-streptomycin), under the conditions of 37\u0026deg;C and 5% CO₂. Subculture was performed when the cell confluence reached approximately 90%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Synthesis of Overexpression Vectors and siRNA\u003c/h2\u003e \u003cp\u003eThe CDS fragment of the \u003cem\u003eLPIN1\u003c/em\u003e (GenBank: NM_001280700) was inserted into the pcDNA3.1\u0026thinsp;+\u0026thinsp;overexpression vector. The control group was designated as NC, while the wild-type and mutant overexpression vectors were designated as CC and TT, respectively. The construction of vectors carrying specific mutation sites, as well as the preparation of expression vectors, were performed by Gansu Kemeiyi Biotechnology Co., Ltd. (Gansu, China). The primer sequences used for vector construction are provided in the Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe small interfering RNA (siRNA) oligonucleotide sequences targeting the \u003cem\u003eLPIN1\u003c/em\u003e and the negative control were synthesized by Hongxun Biotechnology Co., Ltd. (Jiangsu, China). Detailed information is provided in the Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Cell Transfection\u003c/h2\u003e \u003cp\u003eCell transfection was performed strictly in accordance with the manufacturer\u0026rsquo;s instructions for the Lipofectamine 3000 reagent (Invigentech, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 RNA-seq\u003c/h2\u003e \u003cp\u003eForty-eight hours after adipocyte transfection, the medium was replaced with an adipogenic induction medium, followed by 7 days of culture. Subsequently, cells were harvested for RNA-seq analysis, with three biological replicates set for each group. Raw sequencing data were subjected to quality control using fastp (v0.23.2) software. Genome alignment against the sheep reference genome was performed via HISAT2 (v2.2.1) software, followed by differential expression analysis between groups for biological replicates using DESeq2 (v1.22.1) software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the pathways of genes associated with fat deposition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Oil Red O Staining\u003c/h2\u003e \u003cp\u003eThe culture medium was discarded, and the cells were rinsed twice with PBS. The cells were then fixed with fixative solution for approximately 30 min. After fixation, the cells were rinsed twice with distilled water, followed by immersion in 60% isopropanol for about 20 s. The isopropanol was discarded, and the prepared Oil Red O working solution was added; the cells were stained for 30 min at room temperature in the dark. The staining solution was discarded, and the cells were rinsed with 60% isopropanol for 20 s until the background became clear. The cells were subsequently rinsed with distilled water to remove excess dye. Finally, distilled water was added to cover the cells, which were then observed and photographed under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Cell Proliferation Assay\u003c/h2\u003e \u003cp\u003eCells were seeded in 96-well cell culture plates with three replicates per group. Cell viability was measured at 24, 48, and 72 h post-transfection, respectively. Subsequently, 10 \u0026micro;L of CCK-8 reagent (Puxitang, China) was added to each well, and the cells were further incubated at 37\u0026deg;C for 4 h. The absorbance at 450 nm was detected using a multimode microplate reader (Thermo, USA). For the EdU assay, cells were seeded in 24-well plates with three replicates per group, and the EdU detection of adipocytes was performed using the Beyo-Click\u0026trade; EdU-555 Cell Proliferation Kit (Beyotime, China). Three fields of view were randomly selected from each well, and the ratio of EdU-positive cells was calculated using ImageJ 1.46 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Quantitative Real-time PCR\u003c/h2\u003e \u003cp\u003eAdipocytes were seeded in 12-well cell culture plates. At 48 h post-transfection, the complete medium was replaced with induction differentiation medium, and induction was maintained for a specified duration. Adipocytes were then harvested for total RNA extraction. Total RNA was isolated using the Trizol method, and cDNA was synthesized using a reverse transcription kit (Genestar, Beijing) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Primers were designed using the online Primer3 tool, with reference to the corresponding gene sequences retrieved from NCBI (Table S5). The expression levels of \u003cem\u003eLPIN1\u003c/em\u003e (GenBank: NM_001280700), \u003cem\u003eLPL\u003c/em\u003e (GenBank: NM_001128149.1), \u003cem\u003ePPARγ\u003c/em\u003e (GenBank: NM_001100921.1) and CCAAT/enhancer-binding protein α (\u003cem\u003eC/EBPα\u003c/em\u003e) (GenBank: KF830871.1) genes were determined via qRT-PCR, with \u003cem\u003eβ-actin\u003c/em\u003e (GenBank: NM_001009784.3) used as the reference gene. Each group was assayed in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Prediction of Protein Secondary Structure\u003c/h2\u003e \u003cp\u003eThe secondary structure of sheep LPIN1 protein was predicted using the SOPMA software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Protein-Protein Interaction Validation\u003c/h2\u003e \u003cp\u003eProtein-protein interactions were verified using the yeast two-hybrid assay. The construction of yeast hybrid vectors and validation of protein interactions were performed by Gansu Kemeiyi Biotechnology Co., Ltd.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData statistics, calculation, and graphing were performed using Excel 2019 and GraphPad Prism 8.0 software. One-way analysis of variance (ANOVA) was conducted with SPSS 25.0 software, and Duncan\u0026rsquo;s multiple range test was applied for post-hoc comparisons when significant differences were detected. Protein band quantification was carried out using ImageJ software. All data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SEM). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (marked with \u0026ldquo;*\u0026rdquo;) indicates a significant difference, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (marked with: \u0026ldquo;**\u0026rdquo;) indicates an extremely significant difference, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05(marked with \u0026ldquo;ns\u0026rdquo;) indicates no significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Information of SNP chip\u003c/h2\u003e \u003cp\u003eA total of 80 sheep were genotyped using the Illumina Ovine SNP 600K BeadChip. After quality control, 37 Altay sheep and 37 Tibetan sheep were used in the final analysis (Fig.\u0026nbsp;1A), a total of 584896 SNPs were remained per breed. The average distance between two SNPs was 4.28 kb.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of selection signatures\u003c/h2\u003e \u003cp\u003eIn order to accurately detected the selection signal among different type of tail sheep, two methods were used to identify candidate regions involved in selection at genome-wide level for each breed-pairs. Table S3 summaries the selection signatures which were detected in two breed. For F\u003csub\u003eST\u003c/sub\u003e, we identify the population genetic differentiation for every SNP, the boxplot method was employed to identify outliers at the genome-wide level because the F\u003csub\u003eST\u003c/sub\u003e empirical distribution of a single site was similar to a chi-squared (χ2) distribution with two or three degrees of freedom. Figure\u0026nbsp;1B show the genome-wide distribution of the outliers on each autosome.\u003c/p\u003e \u003cp\u003eFor the XPEHH, negative score suggest selection happened in reference, otherwise in observed population. For breed pair of Alaty sheep-Tibetan sheep (A-T), 297 negative values out of 476 outliers indicate that selection happened in the reference population of Tibetan, and the other 179 outliers suggest that selection happened in Altay sheep (Fig.\u0026nbsp;1C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identifying candidate genes in selection regions\u003c/h2\u003e \u003cp\u003eAs shown in the Table S3, a total of 152,163 and 267 candidate selection regions with average lengths of 30.09 Mb, 26.61 Mb and 45.68 Mb were identified in Altay and Tibetan sheep, respectively.\u003c/p\u003e \u003cp\u003eThe Ovis_3.1 database were used to identify candidate genes in the selection regions. A total of 1420 and 1520 genes harbored in selection regions in Altay and Tibetan sheep, respectively (Table S4). DAVID V2.1 was used to conduct the GO and KEGG pathway enrichment analyses to investigate the functions of the candidate genes. After this enrichment analysis, followed by the Benjamini correction procedure, there were almost no significant functional terms. The \u003cem\u003eLPIN1\u003c/em\u003e gene was enriched in adipogenesis-related signaling pathways.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1. Selection signal identification and gene mutation identification.\u003c/b\u003e (A) test population, (B) genome-wide distribution of selection signatures by XPEHH, (C) genome-wide distribution of selection signatures by F\u003csub\u003eST\u003c/sub\u003e, (D) \u003cem\u003eLPIN1\u003c/em\u003e mixing pool sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Population genetic analysis of \u003cem\u003eLPIN1\u003c/em\u003e polymorphism\u003c/h2\u003e \u003cp\u003eThrough DNA mixed pool sequencing, three SNPs were identified in the \u003cem\u003eLPIN1\u003c/em\u003e gene (Fig.\u0026nbsp;1D), including g.134 T/C (rs01), g.269 A/G (rs02), and g.236 C/T (rs03) mutations. These three SNPs were genotyped using MALDI-TOF-MS. It was found that all three loci have three genotypes. According to the criteria for determining polymorphism information content, all three loci are moderately polymorphic (0.25\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ePIC\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5). All three loci reached Hardy-Weinberg equilibrium (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); The number of effective alleles at these loci is close to 2 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the gene frequency and polymorphism of three SNP loci in two sheep populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic parameters of the SNPs in Altay and Tibetan population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHard-Weinberg\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe genotype frequency, allele frequency and Hardy-Weinberg equilibrium test of 3 SNPs in \u003cem\u003eLPIN1\u003c/em\u003e gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eGenotype frequency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eAllele frequency (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAltay sheep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTibetan sheep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAltay sheep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTibetan sheep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e75.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional Verification of the \u003cem\u003eLPIN1\u003c/em\u003e in Preadipocytes\u003c/h2\u003e \u003cp\u003eWe aimed to determine whether altered expression levels of the \u003cem\u003eLPIN1\u003c/em\u003e would affect cellular phenotypes associated with tail fat deposition in sheep. First, we analyzed the effects of \u003cem\u003eLPIN1\u003c/em\u003e knockdown and overexpression on the proliferation of sheep preadipocytes. Cell proliferation assays demonstrated that \u003cem\u003eLPIN1\u003c/em\u003e enhanced cell viability, while EdU staining results indicated that \u003cem\u003eLPIN1\u003c/em\u003e overexpression promoted intracellular DNA synthesis. These findings suggested that \u003cem\u003eLPIN1\u003c/em\u003e facilitates the proliferation of sheep preadipocytes.\u003c/p\u003e \u003cp\u003eSubsequently, we evaluated the impacts of \u003cem\u003eLPIN1\u003c/em\u003e knockdown and overexpression on the differentiation of sheep preadipocytes. Results from Oil Red O staining and triglyceride (TG) content assays showed that \u003cem\u003eLPIN1\u003c/em\u003e promoted lipid droplet accumulation in sheep preadipocytes. RT-qPCR analysis revealed that \u003cem\u003eLPIN1\u003c/em\u003e upregulated the expression of fat deposition marker genes including \u003cem\u003ePPARγ\u003c/em\u003e, \u003cem\u003eC/EBPα\u003c/em\u003e and \u003cem\u003eLPL\u003c/em\u003e. Collectively, these data indicated that \u003cem\u003eLPIN1\u003c/em\u003e promotes the differentiation of sheep preadipocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The effect of mutations on the proliferation, vitality, and differentiation of Precursor adipocytes\u003c/h2\u003e \u003cp\u003eEdU results show that compared with the control group, overexpression of LPIN1-CC and LPIN1-TT significantly increased the number and ratio of EdU labeled positive cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the number and ratio of positive cells in the TT group were significantly higher than those in the CC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the CCK-8 results show that at 24 hours after transfection, the cell viability of the TT overexpressing group was significantly higher than that of the CC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and there was no significant difference between 48 and 72 hours (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), indicating that TT genotype has a positive effect on precursor adipocyte proliferation. The results of Oil Red O staining showed that the overexpression group had significantly increased lipid droplets in the CC and TT groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the lipid droplets in the TT group were significantly more than those in the CC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC); The results of triglyceride content measurement showed that compared with the NC group, the overexpression group had significantly increased TG content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the TT group had significantly higher TG content than the CC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The results were consistent with the Oil Red O experiment, indicating that C236T mutation can promote the differentiation of precursor adipocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Transcriptomic analysis of the impact of C236T missense mutation in \u003cem\u003eLPIN1\u003c/em\u003e on biological pathways\u003c/h2\u003e \u003cp\u003eUsing |log\u003csub\u003e2\u003c/sub\u003eFold Change| \u0026gt; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the screening criteria for differentially expressed genes. Overall, the C236T mutation of the \u003cem\u003eLPIN1\u003c/em\u003e affects the target genes associated with fat deposition. Further verification via GO and KEGG enrichment analyses confirmed that these target genes are closely related to lipid metabolism-related pathways, involving cell growth and development, signal transduction, and metabolic processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Expression of related genes on \u003cem\u003eLPIN1\u003c/em\u003e and PPAR signaling pathways during differentiation process\u003c/h2\u003e \u003cp\u003ePGC-1α acts as a coactivator of PPARs, and it regulates the expression of genes encoding functional proteins related to lipid metabolism and transport via \u003cem\u003ePPARα\u003c/em\u003e or \u003cem\u003ePPARγ\u003c/em\u003e, thereby affecting the balance of lipid metabolism in vivo. \u003cem\u003eLPIN1\u003c/em\u003e is a target of \u003cem\u003ePGC-1α\u003c/em\u003e and its expression is induced by \u003cem\u003ePGC-1α\u003c/em\u003e. Transcriptome sequencing results showed that \u003cem\u003ePGC-1α\u003c/em\u003e was differentially expressed between the CC and TT groups; therefore, we aimed to investigate whether the C236T mutation of \u003cem\u003eLPIN1\u003c/em\u003e affects the expression of genes related to the PPAR signaling pathway. The qRT-PCR detection results showed that the expression level of \u003cem\u003eLPIN1\u003c/em\u003e in the NC group tended to stabilize during the differentiation stage, while in the CC and TT groups, the \u003cem\u003eLPIN1\u003c/em\u003e showed a trend of first increasing, then decreasing, and then continuing to increase during the differentiation of precursor adipocytes, and the highest expression was observed in adipocytes after 6 days of differentiation; The overall expression level of \u003cem\u003eLPL\u003c/em\u003e showed a trend of first increasing and then decreasing, and the expression level of TT group was significantly higher than that of NC and CC groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); The overall expression level of \u003cem\u003ePPARγ\u003c/em\u003e showed a gradually increasing trend, and the TT group had the highest expression level on the 8th day of differentiation; Compared with the control group, the TT group significantly upregulated the expression level of \u003cem\u003eC/EBPα\u003c/em\u003e during adipocyte differentiation, and showed a gradually increasing trend with induction time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Prediction of Protein Secondary Structure\u003c/h2\u003e \u003cp\u003eThe LPIN1 protein was subjected to secondary structure prediction, and results showed that the contents of Alpha helix (Hh) and Random coil (Cc) exhibited alterations before and after the mutation, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of secondary structure of Sheep LPIN1 protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT genotype Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC genotype Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTT genotype Percent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC genotype Percent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha helix (Hh)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtended strand (Ee)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta turn (Tt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom coil (Cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.10 The effect of C236T missense mutation on the binding ability of LPIN1 and PGC-1α protein\u003c/h2\u003e \u003cp\u003ePositive and negative co transfer groups were set up for co transfer validation, and it was found that all co transfer groups could grow normally. It was preliminarily identified that there is an interaction between LPIN1_CC/TT protein and PGC-1α (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The spot results are shown in the figure. All six groups can grow normally on SD-Trp-Leu plates. On SD-Trp-Leu-His and SD-Trp-Leu-His-Ade plates, all co-transformation groups grew normally and turned blue, indicating that there was an interaction between LPIN1-TT, LPIN1-CC proteins and PGC-1α protein. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn our study, we selected two sheep breeds distributed in different parts of China with obviously different types of tails: Altay sheep and Tibetan sheep. In recent years, more and more research had used SNP chip data to identify selection signature for exploring the potential genetic mechanism of phenotype polymorphisms [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], but most of these studies have only implemented a single method, different methods have different categories for selection signatures [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In order to avoid some unknown bias only used one method to detect selection signature, two method, F\u003csub\u003eST\u003c/sub\u003e and XPEHH were employ to explore the selection signature in Altay sheep and Tibetan sheep. The F\u003csub\u003eST\u003c/sub\u003e method detect selection signature based on allele frequency differences between population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. XPEHH is sensitive to detect selection signature based on differences haplotype frequencies, the selected haplotype/allele has approached or achieved fixation in one population but remains polymorphic in the other one [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In our results, a total of 152,163 and 267 candidate selection regions were identified in Altay and Tibetan sheep, respectively, these results showed that in Tibetan sheep detect more selection regions than Altay sheep, this mean that genome polymorphism of Altay sheep is lower than that of the Tibetan sheep, this results are consistent with Moradi et al [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] report that the first wild sheep were thin-tailed sheep, from which fat-tailed sheep were produced as the result of selection by humans and by nature. We had used above two breeds to detect CNV [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], A total of 301 and 66 CNVRs were identified in Altay sheep and Tibetan sheep, respectively. The CNVR regions harbor genes associated with fat deposition and fat synthesis in fat-rumped sheep, such as \u003cem\u003eLPIN1\u003c/em\u003e, \u003cem\u003ePPARA\u003c/em\u003e, \u003cem\u003eRXRA\u003c/em\u003e, \u003cem\u003eKLF11\u003c/em\u003e, \u003cem\u003eADD1\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e, \u003cem\u003ePPP1CA\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e, and \u003cem\u003ePEX6\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe lipoprotein gene (\u003cem\u003eLPIN1\u003c/em\u003e), also known as the lipid phosphate hydrolase gene, is a newly discovered gene family that bidirectionally regulates fat metabolism. Firstly, \u003cem\u003eLPIN1\u003c/em\u003e, as a phospholipid acid phosphatase (PAP), plays an important role in the synthesis of triglycerides and phospholipids [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; Secondly, as a transcriptional co activator, \u003cem\u003eLPIN1\u003c/em\u003e participates in the expression of fatty acid metabolism and fat synthesis gene \u003cem\u003ePPARγ\u003c/em\u003e, further activating the expression of \u003cem\u003ePGC-1α\u003c/em\u003e and \u003cem\u003ePPARα\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Related studies have shown that that the \u003cem\u003eLPIN1\u003c/em\u003e is involved in fat formation and is an important determinant of adipose tissue differentiation and adipocyte function. Moreover, variations in the locus of this gene can also affect fat content [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We then performed genotyping of \u003cem\u003eLPIN1\u003c/em\u003e and identified three SNPs) in the exon regions, among which the C236T mutation exhibited significant differences in genotype frequencies between Tibetan sheep and Altay sheep.\u003c/p\u003e \u003cp\u003ePrevious studies on the tail fat of fat tailed and lean tailed sheep using transcriptomics have found that multiple genes in the PPAR signaling pathway, such as \u003cem\u003eFASN\u003c/em\u003e / \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eLPL\u003c/em\u003e, \u003cem\u003eSCD\u003c/em\u003e and \u003cem\u003ePPARγ\u003c/em\u003e are differentially expressed in the tail fat tissues of fat tailed and lean tailed sheep [\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], indicating that the PPAR signaling pathway may play an important role in the deposition of tail fat in sheep.\u003c/p\u003e \u003cp\u003eIn this study, we systematically elucidated the key genetic variations and regulatory mechanisms underlying the TFD trait in sheep through a combination of selection signature analysis, RNA sequencing (RNA-Seq), genotyping, and molecular experiments. We identified candidate genes and their variants associated with fat deposition in fat-tailed and thin-tailed sheep breeds. Taking the \u003cem\u003eLPIN1\u003c/em\u003e C236T mutation as an example, we validated this mutation site at the cellular level and found that the mutation may enhance the binding ability between LPIN1 and PGC-1α proteins. Concomitantly, the expression levels of genes related to the PPAR signaling pathway were altered accordingly, which affects the differentiation of ovine preadipocytes.\u003c/p\u003e \u003cp\u003eCollectively, this study provides a novel perspective for investigating the TFD trait in sheep and offers potential molecular markers for sheep genetic breeding programs.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eThe C236T mutation in the \u003cem\u003eLPIN1\u003c/em\u003e affects the proliferation and differentiation of ovine preadipocytes, and thus this mutation can serve as a molecular marker for the selective breeding of thin-tailed sheep. In this study, we only verified the interaction between LPIN1 and PGC-1α proteins using yeast two-hybrid assays. In addition, regarding the effect of \u003cem\u003eLPIN1\u003c/em\u003e on the PPAR signaling pathway, we only detected the expression of genes related to this pathway and lack corresponding research on the deeper regulatory effects of \u003cem\u003eLPIN1\u003c/em\u003e on the PPAR signaling pathway. In subsequent studies, we will conduct further investigations to provide a theoretical basis for the C236T mutation as a molecular genetic marker in sheep breeding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable. All animal experiments were approved by Gansu Agricultural University (Lanzhou, China), with the approval number GSAU-AEW-2017-0003. All animal procedures were strictly performed in accordance with the guidelines formulated by the China Animal Health Commission and the Ministry of Agriculture of the People's Republic of China. Informed consent for the use of the sheep in this study was obtained from the respective owners of the animals.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that there is no conflict regarding the publication of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study is supported by the National Natural Science Foundation of China (3246200236), Fuxi young talents training program of Gansu Agricultural University (GAUfx-04Y012) and Provincial Key Research and Development Program - Agricultural Sector (25YFNA036) .\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.Y.Z. : writing \u0026ndash; original draft, Investigation, Data curation, Funding acquisition. Y.Z. : Formal analysis, Methodology. Y.S.W. : Formal analysis, Methodology. Y.Y. : Methodology. X.Y.H. : Supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThank you for the platform provided by the College of animal science and technology of Gansu Agricultural University. Funding: This study is supported by the National Natural Science Foundation of China (3246200236), Fuxi young talents training program of Gansu Agricultural University (GAUfx-04Y012) and Provincial Key Research and Development Program - Agricultural Sector (25YFNA036) .\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMoradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 2012;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2156-13-10\u003c/span\u003e\u003cspan address=\"10.1186/1471-2156-13-10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErmias E, Yami A, Rege JEO. Fat deposition in tropical sheep as adaptive attribute to periodic feed fluctuation. J Anim Breed Genet. 2002;119:235\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1439-0388.2002.00344.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1439-0388.2002.00344.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashan NEJ, Manafi Azar GH, Afzalzadeh A, Salehi A. Growth performance and carcass quality of fattening lambs from fat-tailed and tailed sheep breeds. Small Ruminant Res. 2005;60:267\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.smallrumres.2005.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.smallrumres.2005.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM. Fat deposition, fatty acid composition and meat quality: A review. Meat Sci. 2008;78:343\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.meatsci.2007.07.019\u003c/span\u003e\u003cspan address=\"10.1016/j.meatsci.2007.07.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki Y. Statistical methods for detecting natural selection from genomic data. Genes Genetic Syst. 2010;85:359\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1266/ggs.85.359\u003c/span\u003e\u003cspan address=\"10.1266/ggs.85.359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R, Schaffner SF, Lander ES. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449:913\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature06250\u003c/span\u003e\u003cspan address=\"10.1038/nature06250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcRae KM, McEwan JC, Dodds KG, Gemmell NJ. Signatures of selection in sheep bred for resistance or susceptibility to gastrointestinal nematodes. BMC Genomics. 2014;15:637. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2164-15-637\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-15-637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu CY, Fan HY, Yuan ZH, Hu SJ, Zhang L, Wei CH, Zhang Q, Zhao FP, Du LX. 2015.Detection of Selection Signatures on the X Chromosome in Three Sheep Breeds. Int J Mol Sci. 16, 20360\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms160920360\u003c/span\u003e\u003cspan address=\"10.3390/ijms160920360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa YL, Wei JL, Zhang Q, Chen L, Wang JY, Liu JF, Ding XD. A Genome Scan for Selection Signatures in Pigs. PLoS ONE. 2015;10:e0116850. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0116850\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0116850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Bailey DK, Awad T, Liu G, Xing G, Cao M, Valmeekam V, Retief J, Matsuzaki H, Taub M, Seielstad M, Kennedy GC. A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations. Bioinformatics. 2006;22:2122\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btl365\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btl365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang ET, Kodama G, Baldi P, Moyzis RK. Global landscape of recent inferred Darwinian selection for Homo sapiens. Proc Natl Acad Sci USA. 2006;103:135\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0509691102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0509691102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoight BF, Kudaravalli S, Wen XQ, Pritchard JK. A Map of Recent Positive Selection in the Human Genome. PLoS Biol. 2006;4:e72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pbio.0040072\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.0040072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephan W. Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation. Mol Ecol. 2016;25:79\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.13288\u003c/span\u003e\u003cspan address=\"10.1111/mec.13288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin C-J, Megens H-J, Barrio AM, Maqbool K, Sayyab S, Schwochow D, Wang C, Carlborg \u0026Ouml;, Jern P, J\u0026oslash;rgensen CB, Archibald AL, Fredholm M, Groenen MAM. L. Andersson.2012. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci USA. 109, 19529\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1217149109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1217149109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelley JL, Madeoy J, Calhoun JC, Swanson W, Akey JM. Genomic signatures of positive selection in humans and the limits of outlier approaches. Genome Res. 2006;16:980\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.5157306\u003c/span\u003e\u003cspan address=\"10.1101/gr.5157306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoerks T, Copley RR, Schultz J, Ponting CP, Bork P. Systematic Identification of Novel Protein Domain Families Associated with Nuclear Functions. Genome Res. 2002;12:47\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.203201\u003c/span\u003e\u003cspan address=\"10.1101/gr.203201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOleksyk TK, Smith MW, O\u0026rsquo;Brien SJ. Genome-wide scans for footprints of natural selection. Philosophical Trans Royal Soc Lond Ser B: Biol Sci. 2010;365:185\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2009.0219\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2009.0219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki Y. Statistical methods for detecting natural selection from genomic data. Genes Genetic Syst. 2010;85:359\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1266/ggs.85.359\u003c/span\u003e\u003cspan address=\"10.1266/ggs.85.359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandhawa IA, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genet. 2014;15:34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2156-15-34\u003c/span\u003e\u003cspan address=\"10.1186/1471-2156-15-34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Z, Liu E, Liu Z, Kijas J`W, Zhu C, Hu S, Ma X, Zhang L, Du L, Wang H, Wei C. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim Genet. 2016;48:55\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/age.12477\u003c/span\u003e\u003cspan address=\"10.1111/age.12477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu CY, Fan HY, Yuan ZH, Hu SJ, Ma XM, Xuan JL, Wang HW, Zhang L, Wei CH, Zhang Q, Zhao FP, Du LX. Genome-wide detection of CNVs in Chinese indigenous sheep with different types of tails using ovine high-density 600K SNP arrays. Sci Rep. 2016;6:28822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep27822\u003c/span\u003e\u003cspan address=\"10.1038/srep27822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonkor J, Sariahmetoglu M, Dewald J, Brindley DN, Reue K. Three Mammalian Lipins Act as Phosphatidate Phosphatases with Distinct Tissue Expression Patterns. J Biol Chem. 2007;282:3450\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.m610745200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.m610745200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinck BN, Gropler MC, Chen Z, Leone TC, Croce MA, Harris TE, Lawrence JC, Kelly DP. Lipin 1 is an inducible amplifier of the hepatic PGC-1α/PPARα regulatory pathway. Cell Metabol. 2006;4:199\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2006.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2006.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReue K, Brindley DN. Thematic Review Series: Glycerolipids. Multiple roles for lipins/phosphatidate phosphatase enzymes in lipid metabolism. J Lipid Res. 2008;49:2493\u0026ndash;503. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1194/jlr.r800019-jlr200\u003c/span\u003e\u003cspan address=\"10.1194/jlr.r800019-jlr200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimsey N, Han GS, O'Hara L, Rochford JJ, Carman GM, Siniossoglou S. Temporal and Spatial Regulation of the Phosphatidate Phosphatases Lipin 1 and 2. J Biol Chem. 2008;283:29166\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.m804278200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.m804278200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XK, Chen W, Huang YQ, Kang XT, Wang JP, Li GX, Jiang RR. Identification of the transcript isoforms and expression characteristics for chicken Lpin1. Animal. 2012;6:1897\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s1751731112001358\u003c/span\u003e\u003cspan address=\"10.1017/s1751731112001358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Zhou HT, Hickford JGH, Li SB, Wang JQ, Liu X, Hu J, Luo YZ. Variation in the yak lipin-1 gene and its association with milk traits. J Dairy Res. 2020;87:166\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s002202991900089x\u003c/span\u003e\u003cspan address=\"10.1017/s002202991900089x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu YH, Gan ML, Wang XY, Liao TC, Chen QY, Lei YH, Chen L, Wang JY, Zhao Y, Niu LL, Wang Y, Zhang SH, Zhu L, Shen LY. The global perspective on peroxisome proliferator-activated receptor γ (PPARγ) in ectopic fat deposition: A review. Int J Biol Macromol. 2023;253:127042. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijbiomac.2023.127042\u003c/span\u003e\u003cspan address=\"10.1016/j.ijbiomac.2023.127042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng F, Cai Y. Concurrent exercise improves insulin resistance and nonalcoholic fatty liver disease by upregulating PPAR-γ and genes involved in the beta-oxidation of fatty acids in ApoE-KO mice fed a high-fat diet. Lipids Health Dis. 2019;18:6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12944-018-0933-z\u003c/span\u003e\u003cspan address=\"10.1186/s12944-018-0933-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q, Zhang Y, Li LQ, Li J, Li YL, Han L, Wang M. D-chiro-Inositol facilitates adiponectin biosynthesis and activates the AMPKα/PPARs pathway to inhibit high-fat diet-induced obesity and liver lipid deposition. Food Funct. 2022;13:7192\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d2fo00869f\u003c/span\u003e\u003cspan address=\"10.1039/d2fo00869f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristofides A, Konstantinidou E, Jani C, Boussiotis VA. The role of peroxisome proliferator-activated receptors (PPAR) in immune responses. Metabolism. 2021;114:154338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2020.154338\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2020.154338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao Z, Feng MJ, Li ZR, Zhou MS, Xu LN, Pan K, Wang SX, Su W, Zhang WZ. 2021. ETV5 Regulates Hepatic Fatty Acid Metabolism Through PPAR Signaling Pathway. \u003cem\u003eDiabetes\u003c/em\u003e.70, 214\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/db20-0619\u003c/span\u003e\u003cspan address=\"10.2337/db20-0619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu TT, Wu QQ, Yao Q, Yu JB, Jiang KB, Wan Y, Tang QZ. PRDM16 exerts critical role in myocardial metabolism and energetics in type 2 diabetes induced cardiomyopathy. Metabolism. 2023;146:155658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2023.155658\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2023.155658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XL, Zhou GX, Xu XC, Geng RQ, Zhou JP, Yang YX, Yang ZX, Chen YL. Transcriptome profile analysis of adipose tissues from fat and short-tailed sheep. Gene. 2014;549:252\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gene.2014.07.072\u003c/span\u003e\u003cspan address=\"10.1016/j.gene.2014.07.072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sheep, fat deposition, missense mutation","lastPublishedDoi":"10.21203/rs.3.rs-8637217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8637217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChinese indigenous sheep can be classified into two types based on tail morphology: fat-tailed sheep and thin-tailed sheep, with Altay sheep and Tibetan sheep as their representative breeds. Detection of selection signatures can elucidate the molecular mechanism underlying the formation of distinct tail types driven by artificial selection. Based on the Illumina Ovine 600K SNP BeadChip, we identified 163 and 267 candidate selection regions in Altay sheep and Tibetan sheep, with average lengths of 26.61 Mb and 45.68 Mb, respectively. Subsequent bioinformatics analysis suggested that \u003cem\u003eLPIN1\u003c/em\u003e might be involved in fat deposition. Genotyping of the candidate gene \u003cem\u003eLPIN1\u003c/em\u003e in 546 sheep revealed three single nucleotide polymorphisms (SNPs). At the cellular level, the C236T mutation was found to promote the proliferation and differentiation of preadipocytes. Transcriptome analysis demonstrated differential expression of PGC-1α between the CC and TT genotypes of the \u003cem\u003eLPIN1\u003c/em\u003e gene. Secondary structure prediction via SOPMA software indicated that the C236T mutation could alter the number of Alpha helix (Hh) and Random coil (Cc) in the secondary structure of its encoded protein. Yeast two-hybrid assays indicated that this mutation may enhance the binding between LPIN1 and PGC-1α proteins. Quantitative real-time PCR analysis of genes associated with the PPAR signaling pathway revealed concurrent alterations in the expression of these genes.\u003c/p\u003e \u003cp\u003eCollectively, these results suggest that the \u003cem\u003eLPIN1\u003c/em\u003e C236T mutation may participate in tail fat deposition in sheep, and thus it can be used as a molecular marker for selective breeding of thin-tailed sheep breeds.\u003c/p\u003e","manuscriptTitle":"Detection of Genomic Selection Signatures and Identification of Beneficial Mutations for Fat Deposition in Sheep with Different Tail Types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 10:13:35","doi":"10.21203/rs.3.rs-8637217/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88d0c1ce-4089-4546-932e-71a274486200","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T15:25:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 10:13:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8637217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8637217","identity":"rs-8637217","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}