RNA-Seq Analysis Identifies Differentially Expressed Genes In

animals

Article RNA-Seq Analysis Identiﬁes Diﬀerentially Expressed Genes in Subcutaneous Adipose Tissue in Qaidaford Cattle, Cattle-Yak, and Angus Cattle

1, 1, 1 2 2 Chengchuang Song †, Yongzhen Huang †, Zhaoxin Yang , Yulin Ma , Buren Chaogetu , Zhaxi Zhuoma 2 and Hong Chen 1,*

1 College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China; [email protected] (C.S.); [email protected] (Y.H.); [email protected] (Z.Y.) 2 Animal Disease Control Center of Haixi Mongolian and Tibetan Autonomous Prefecture, Delingha 817000, China; [email protected] (Y.M.); [email protected] (B.C.); [email protected] (Z.Z.) * Correspondence: [email protected] These authors are contributed equally to this work. † Received: 13 September 2019; Accepted: 22 November 2019; Published: 3 December 2019

Simple Summary: Fat content is an important factor affecting beef quality. Therefore, the screening and identification of differentially expressed genes in adipose tissue between different breeds (Qaidamford cattle, hybrid cattle-yak, and Angus cattle) by RNA high-throughput sequencing can provide useful information to the beef cattle industry. The aim of this work was to identify candidate genes of adipose tissue for future beef breeding efforts. Comparative analysis revealed a significant difference between hybrid cattle and Angus, but the difference between hybrid cattle varieties (cattle-yak vs. Qaidamford cattle) was not significant. Gene ontology (GO) and KEGG pathway enrichment analysis indicated that some differentially expressed genes are involved in lipid metabolism-related biological processes and signaling pathways associated with cell metabolism, such as extracellular matrix (ECM)-receptor interaction and the PI3K-Akt signal pathway. The expression levels of some of the identified genes were further verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR). These data will be helpful for further investigations of meat quality and breeding efforts for different cattle breeds.

Abstract: In the beef industry, fat tissue is closely related to meat quality. In this study, high-throughput RNA sequencing was utilized for adipose tissue transcriptome analysis between cattle-yak, Qaidamford cattle, and Angus cattle. The screening and identification of differentially expressed genes (DEGs) between different breeds of cattle would facilitate cattle breeding. Compared to Angus cattle adipose tissue, a total of 4167 DEGs were identified in cattle-yak adipose tissue and 3269 DEGs were identified in Qaidamford cattle adipose tissue. Considering cattle-yak as a control group, 154 DEGs were identified in Qaidamford cattle adipose tissue. GO analysis indicated the significant enrichment of some DEGs related to lipid metabolism. The KEGG pathway database was also used to map DEGs and revealed that most annotated genes were involved in ECM-receptor interaction and the PI3K-Akt signal pathway, which are closely related to cell metabolism. Eight selected DEGs related to adipose tissue development or metabolism were verified by RT-qPCR, indicating the reliability of the RNA-seq data. The results of this comparative transcriptome analysis of adipose tissue and screening DEGs suggest several candidates for further investigations of meat quality in different cattle breeds.

Keywords: cattle-yak; subcutaneous fat; transcriptome analysis; meat quality

Animals 2019, 9, 1077; doi:10.3390/ani9121077 www.mdpi.com/journal/animals Animals 2019, 9, 1077 2 of 9

1. Introduction Qaidamford cattle are ternary hybrid cattle with good meat quality [1]. Qaidamford cattle were bred using Yak, Qaidam cattle, and imported Angus cattle, and then reproduced by hybridization technology. Qaidamford cattle have several advantages, including fast growth, high yield, good meat quality, and strong adaptability [1]. Among these excellent traits, meat quality is essential for economic value in the beef cattle industry. Many factors affect meat quality traits, such as softness, hardness, oxidation stability, color, flavor, muscle fiber, fat composition, and fat content [2,3]. Meat quality assessment found a significantly higher ability of Qaidamford cattle to deposit fat compared to that of yak, resulting in improved meat quality [1]. Thus, it is of great significance to analyze meat quality traits by comparing fat tissue transcriptome data for different varieties of cattle. Large amounts of fat deposits in adipose tissue are closely related to obesity and energy metabolism abnormalities, and can contribute to disorders like Type 2 diabetes, insulin resistance, and cardiovascular disease in humans [4]. Numerous studies have shown that fat deposition in animals is a complex biological process that includes regulation by multiple transcriptional factors, such as PPARγ, some members of the C/EBP family, KLFs, STAT5 [5], SREBP-1c [6,7], the E2F family of transcription factors, and Wnts. Thus, the screening of differentially expressed genes between different bovine fat tissues can facilitate further study of fat deposition. The goal of this work was to investigate differences in the subcutaneous fat transcriptome between Qaidamford cattle, cattle-yak, and Angus cattle to guide further improvement of Qaidamford cattle meat quality. RNA-seq sequencing has greatly facilitated the efficiency of identifying differentially expressed genes and this technology has been widely applied in cattle. In bovine embryo transcriptome analysis, RNA-seq technology was first applied in cattle to obtain digital gene expression [8]. In the Wagyu and Holstein subcutaneous fat transcriptome analysis, RNA-seq results revealed 662 differentially expressed genes, with some involved in adipogenesis and lipid metabolism [9]. Recently, comparative transcriptome analysis revealed that Angus Qinchuan × cattle (AQF) have improved performance characteristics compared to Qinchuan cattle (QCF) [10]. These studies provided the theoretical basis for this project. In this study, our purpose was to screen differentially expressed genes in subcutaneous fat tissues of Qaidamford cattle, cattle-yak, and Angus cattle by RNA-seq. Gene ontology (GO) enrichment analysis revealed the involvement of some differentially expressed genes in fatty acid biosynthesis, lipid metabolism, and fatty acid catabolic biological processes. KEGG pathway analysis indicated that some differentially expressed genes participate in signal pathways, such as extracellular matrix (ECM)-receptor interaction and the PI3K-Akt signal pathway, which are important for adipose tissue development. In addition, real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the accuracy of RNA sequencing data. Together, these data can enable genetic improvement of hybrid cattle.

2. Materials and Methods

2.1. Animals and Adipose Tissue Sample Qaidamford cattle and cattle-yak were used in this study. Three biological replicates were tested for each breed. The animals were slaughtered at about 24 months of age. Subcutaneous adipose tissue samples of each animal were collected from backfat and quickly transferred to liquid nitrogen. Sequencing data for Angus cattle (from ﬁve animals) were downloaded from the NCBI database (http://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP070110). Animal care and study protocols were approved by the Animal Care Commission of the College of Veterinary Medicine, Northwest A&F University.

2.2. RNA Extraction and RNA Sequencing Samples of cattle subcutaneous fat tissue were subjected to RNA extraction. Total RNA was extracted using TRIzol (Takara) and treated with DNase I to remove the remaining DNA according to the Animals 2019, 9, 1077 3 of 9 manufacturer’s instructions. The purified RNA was then used for RNA sequencing. First, the mRNA was enriched using magnetic beads with Oligo (dT). Second, the mRNA was fragmented into short segments with fragmentation reagents, which was used for cDNA synthesis with random hexamers. Then, buffer, deoxyribonucleotide triphosphate(dNTPs), and DNA polymerase I were added to synthesize the second strand cDNA. Finally, the products were purified, subjected to sticky end repair, and ligated through the 30-end plus the base “A” to a linker. The obtained fragments were subjected to size selection and PCR amplification enrichment. After qualifying with an Agilent 2100 Bioanalyzer (Davis, CA, USA) and the Real-Time PCR System (StepOnePlus; Applied Biosystems, Waltham, MA, USA), the constructed library was sequenced using the Illumina (San Diego, CA, USA) Sequencing Platform.

2.3. Sequencing Data Analysis The original sequencing data, including low-quality reads, was filtered using Trimmomatic before proceeding to the next step [11]. After filtering, we aligned these high-quality reads to the genome using software HISAT (Johns Hopkins University, Baltimore, MD, USA) [12]. The gene expression level was the transcript abundance. Differential expression analysis was performed to identify differentially expressed genes among different samples and to perform deeper functional mining of differentially expressed genes. EdgeR was used to normalize the data and extract differentially expressed genes (DEGs) with FDR < 0.01. The data was uploaded to NCBI database and BioProject ID: PRJNA343359 (http://www.ncbi.nlm.nih.gov/bioproject/343359).

2.4. GO Enrichment and KEGG Pathway Analysis GO enrichment analysis was used to classify the DEGs based on the specific biological functions [13]. Genes with different biological functions interact with each other to achieve the final function or phenotype. Pathway analysis can help characterize the biological function of genes. KEGG is the main public database of gene pathway, and can identify metabolic pathways or signal transduction pathways that are significantly enriched in differentially expressed genes as compared to the background of the entire genome.

2.5. Real-Time Quantitative PCR Analysis To verify the RNA sequencing data, RT-qPCR was used to detect diﬀerential gene expression. Total RNA samples isolated from adipose tissue were reverse transcribed into cDNA using a PrimeScript™ RT reagent Kit (TaKaRa, Kyoto, Japan) with gDNA Eraser to remove genomic DNA. RT-qPCR analysis was performed in triplicate using a SYBR green kit (Genestar, Beijing, China) on an ABI StepOnePlus Real-Time PCR System. The expression levels of the selected diﬀerential genes were normalized against the expression level of the reference genes, β-actin and GAPDH. The reaction conditions were as follows: 95 ◦C for 10 min, 40 cycles of 95 ◦C for 15 s, 60 ◦C for 60 s. The relative ∆∆Ct expression levels of genes were calculated by the2− algorithm. The primers of all tested genes are shown in Table S1.

2.6. Statistical Analysis Results are presented as the mean values standard error of the measurement (SEM). ± The significance of the difference of mRNA expression level (RT-qPCR data) between two groups was evaluated using two-tailed Student’s t-test with SPSS 19.0 statistical software. p value < 0.05 was considered statistically significant. Animals 2019, 9, 1077 4 of 9

3. Results

3.1. Characterization of Bovine Adipose Tissue Transcriptome Sequencing Data RNA-seq data were obtained for adipose tissue samples of cattle-yak (N = 3), Qaidamford cattle

(AnimalsN = 3), 2019 and, 9, 1077 Angus cattle (N = 5). The reads were mapped against the cattle reference genome4 of 10 (UMD_3.1.1) [14](https://bovinegenome.elsiklab.missouri.edu/). The number of total raw reads and the percentagethe percentage of clean of readsclean ofreads each of sample each weresample determined were determined and are listed andin are (Table listed S2). in After (Table ﬁltering, S2). After the totalfiltering, number the oftotal clean number reads of ranged clean from reads 42,023,896 ranged from to 46,456,047, 42,023,896 with to 46,456,047, mapped reads with as mapped a percentage reads ofas thea percentage total ranging of the from total 76.56% ranging to 88.86%. from 76.56% For all to samples, 88.86%. at For least all 72.95% samples, of theat least reads 72.95% uniquely of the mapped reads touniquely the reference mapped genome. to the Countingreference thegenome. reads Counting mapped inthe each reads gene, mapped the total in numbereach gene, of mappedthe total genesnumber for of each mapped sample genes was calculatedfor each sample and is presentedwas calculated in Figure and1 A.is presented The distribution in Figure of mapped 1A. The readsdistribution was similar of mapped for all reads samples. was Thesimilar number for all of samples. genes in The each number expression of genes interval in each (fragments expression per kilobaseinterval (fragments of exon model per perkilobase million of exon mapped model fragments, per million FPKM) mapped in each fragments, cattle adipose FPKM) tissue in each sample cattle wasadipose shown tissue in (Figure sample1B). was These shown ﬁndings in (Figure indicated 1B). These good findings data quality indicated that were good suitable data quality for subsequent that were researchsuitable analysis.for subsequent research analysis.

FigureFigure 1.1.Characterization Characterization of bovineof bovine adipose adipose tissue tissue transcriptome transcriptome sequencing sequencing data. ( Adata.) The ( totalA) The number total ofnumber genes forof genes each samplefor each was sample calculated was calculated after counting after counting the reads the mapped reads mapped in each gene.in each AB: gene. Angus AB: backfat;Angus backfat; CYB: Cattle-yak CYB: Cattle-yak backfat; FB:backfa Qaidamfordt; FB: Qaidamford cattle backfat. cattle (backfat.B) Statistics (B) Statistics of the number of the of number genes inof eachgenes expression in each intervalexpression (fragments interval per (fragments kilobase ofper exon kilobase model perof millionexon model mapped per fragments, million mapped FPKM) infragments, each cattle FPKM) adipose in tissue.each cattle adipose tissue. 3.2. Identification of Differentially Expressed Genes (DEGs) 3.2. Identification of Differentially Expressed Genes (DEGs) We next investigated the differences in gene expression data for genes of adipose tissues between We next investigated the differences in gene expression data for genes of adipose tissues different cattle breeds. The analysis was performed as in previous studies. Reads were first normalized between different cattle breeds. The analysis was performed as in previous studies. Reads were first as FPKM [15]. Compared to Angus cattle, 4167 differentially expressed genes were identified in normalized as FPKM [15]. Compared to Angus cattle, 4167 differentially expressed genes were cattle-yak, of which 2144 genes were upregulated and 2023 genes were downregulated (Figure2A, identified in cattle-yak, of which 2144 genes were upregulated and 2023 genes were downregulated Supplementary Excel S1). A total of 3269 differentially expressed genes were identified between (Figure 2A, Supplementary Excel S1). A total of 3269 differentially expressed genes were identified Angus cattle and Qaidamford cattle, including 1769 upregulated genes and 1500 downregulated between Angus cattle and Qaidamford cattle, including 1769 upregulated genes and 1500 genes (Figure2B, Supplementary Excel S2). However, there were fewer di fferentially expressed genes downregulated genes (Figure 2B, Supplementary Excel S2). However, there were fewer differentially identified between cattle-yak and Qaidamford cattle. Compared to cattle-yak, only 154 differentially expressed genes identified between cattle-yak and Qaidamford cattle. Compared to cattle-yak, only expressed genes were identified, including 89 upregulated genes and 65 downregulated genes 154 differentially expressed genes were identified, including 89 upregulated genes and 65 (Figure2C, Supplementary Excel S3). These data suggest that there are some di fferences between downregulated genes (Figure 2C, Supplementary Excel S3). These data suggest that there are some hybrid cattle and their ancestors, which may guide the screening conditions for the subsequent breeding. differences between hybrid cattle and their ancestors, which may guide the screening conditions for the subsequent breeding. Animals 2019, 9, 1077 5 of 10

Animals 2019, 9, 1077 5 of 9 Animals 2019, 9, 1077 5 of 10

Figure 2. Volcanic maps represent the number of differentially expressed genes in adipose tissue of Figure 2. Volcanic maps represent the number of diﬀerentially expressed genes in adipose tissue of

different cattle breeds. (A) Compared with the adipose tissue of Angus, differentially expressed genes different cattle breeds. (A) Compared with the adipose tissue of Angus, differentially expressed genes were identified in the adipose tissue of cattle-yak. Up: Upregulated genes; Down: Downregulated wereFigure identified 2. Volcanic in the maps adipose represent tissue the of number cattle-yak. of differen Up: Upregulatedtially expressed genes; genes Down: in adipose Downregulated tissue of genes; no DEG: No differentially expressed genes. (B) Compared with the adipose tissue of Angus, genes;different no DEG:cattle Nobreeds. differentially (A) Compared expressed withgenes. the adipose (B) Compared tissue of Angus, with the differentia adipose tissuelly expressed of Angus, genes the the number of differentially expressed genes were identified in the adipose tissue of Qaidamford numberwere identified of differentially in the adipose expressed tissue genes of cattle-yak. were identified Up: Upregulated in the adipose genes; tissue Down: of Qaidamford Downregulated cattle. cattle. (C) Compared with the adipose tissue of cattle-yak, the number of differentially expressed (Cgenes;) Compared no DEG: with No the differentially adipose tissue expressed of cattle-yak, genes. the (B) number Compared of di withfferentially the adipose expressed tissue genes of Angus, were genes were identified in the adipose tissue of Qaidamford cattle. identifiedthe number in theof differentially adipose tissue expressed of Qaidamford genes cattle. were identified in the adipose tissue of Qaidamford cattle. (C) Compared with the adipose tissue of cattle-yak, the number of differentially expressed 3.3.3.3. GO GO Enrichment Enrichment and and KEGG KEGG Pathway Pathway Analysis Analysis of of Related Related DEGs DEGs genes were identified in the adipose tissue of Qaidamford cattle. ToTo investigate thethe relationship relationship between between diff erentiallydifferentially expressed expressed genes andgenes adipose and tissueadipose formation, tissue formation,we3.3. used GO Enrichment GO we analysisused GOand using KEGGanalysis David Pathway using software AnalysisDavid (https: software of Related//david.ncifcrf.gov (https://david.ncifcrf.gov/) DEGs /) to analyze theto analyze differentially the expressed genes. We found that some differentially expressed genes were enriched in fatty acid differentiallyTo investigate expressed the genes. relationship We found between that some diff differentiallyerentially expressed expressed genes genes and were adipose enriched tissue in fattybeta-oxidationformation, acid beta-oxidation we biological used GO biological process analysis in processusing cattle-yak David in adiposecattle-yak software tissue adipose (https://david.ncifcrf.gov/) compared tissue to compared Angus cattle to Angusto adipose analyze cattle tissue the adipose(Figuredifferentially 3tissueA). Further (Figure expressed analysis 3A). genes. Further revealed We analysis found that allthat revealed 14 some di ff erentially thatdifferentially all 14 expressed differentially expressed genes expressedgenes were were downregulated, genes enriched were in downregulated,includingfatty acid keybeta-oxidation genesincluding influencing keybiological genes fat depositioninfluencingprocess in such cattle-yakfat deposition as ADIPOQ. adipose such Compared tissue as ADIPOQ. compared with Compared Angus to Angus cattle, with cattle the Angusdiadiposefferentially cattle, tissue the expressed (Figure differentially 3A). genes Further inexpressed Qaidamford analysis genes cattlerevealed in Qaidamford were that significantly all 14cattle differentially were enriched significantly forexpressed the process enriched genes of fattywerefor theaciddownregulated, process metabolism of fatty including (Figureacid metabolism3B). key However, genes (Figure influencing the 3B). di Hofferentiallywever, fat deposition the expressed differentially such genes as expressedADIPOQ. between genesCompared cattle-yak between with and cattle-yakQaidamfordAngus cattle, and cattle Qaidamford the diff showederentially nocattle significant expressed showed enrichmentno genes significant in Qaidamford in biologicalenrichment cattle processes in were biological (Figuresignificantly processes3C), which enriched (Figure might for 3C),suggestthe which process very might of little fatty suggest di acidfference metabolism very in little fat content.difference (Figure 3B). in fatHo content.wever, the differentially expressed genes between cattle-yak and Qaidamford cattle showed no significant enrichment in biological processes (Figure 3C), which might suggest very little difference in fat content.

FigureFigure 3. 3. GeneGene ontology ontology (GO) (GO) enrichment enrichment analysis analysis of of re relatedlated differentially differentially expressed expressed genes genes (DEGs). (DEGs). (A(A) GO) GO enrichment enrichment analysis analysis of of differentially differentially expre expressedssed genes genes of of cattle-yak cattle-yak compared compared with with Angus. Angus. BP: BP: BiologicalBiological process; process; CC: CC: Cell Cell comp components;onents; MF: MF: Molecular Molecular function. function. (B ()B )GO GO enrichment enrichment analysis analysis of of Figure 3. Gene ontology (GO) enrichment analysis of related differentially expressed genes (DEGs). differentiallydifferentially expressed expressed genesgenes ofof Qaidamford Qaidamford cattle cattle compared compared with with Angus. Angus. (C) GO (C enrichment) GO enrichment analysis (A) GO enrichment analysis of differentially expressed genes of cattle-yak compared with Angus. BP: analysisof differentially of differentially expressed expressed genes of genes Qaidamford of Qaidamford cattle compared cattle compared with cattle-yak. with cattle-yak. Biological process; CC: Cell components; MF: Molecular function. (B) GO enrichment analysis of differentially expressed genes of Qaidamford cattle compared with Angus. (C) GO enrichment ToTo further further studystudy thethe differentially differentially expressed expressed genes genes involved involved in the in molecular the molecular pathway pathway of adipose of analysis of differentially expressed genes of Qaidamford cattle compared with cattle-yak. adiposetissue development, tissue development, the KEGG the database KEGG was database used to identifywas used key candidateto identify genes. key Comparedcandidate togenes. Angus cattle, 3079 out of 4167 differentially expressed genes of cattle-yak were enriched in 300 pathways ComparedTo further to Angus study cattle, the 3079 differentially out of 4167 differentiexpressedally genes expressed involved genes in of the cattle-yak molecular were pathway enriched of (Supplementary Excel S4). Some differentially expressed genes were enriched in important pathways inadipose 300 pathways tissue (Supplementarydevelopment, the Excel KEGG S4). Somedatabase differentially was used expressed to identify genes key were candidate enriched genes. in like PI3K-Akt signaling (Figure4A). A total of 2439 genes out of 3269 differentially expressed genes in importantCompared pathways to Angus likecattle, PI3K-Akt 3079 out ofsignaling 4167 differenti (Figureally 4A). expressed A total genes of 2439of cattle-yak genes outwere of enriched 3269 Qaidamford cattle, relative to Angus cattle, were enriched in 299 pathways (Supplementary Excel S5). in 300 pathways (Supplementary Excel S4). Some differentially expressed genes were enriched in important pathways like PI3K-Akt signaling (Figure 4A). A total of 2439 genes out of 3269 AnimalsAnimals 20192019,, 99,, 10771077 6 6of of 10 9 differentially expressed genes in Qaidamford cattle, relative to Angus cattle, were enriched in 299 Somepathways differentially (Supplementary expressed Excel genes S5). were Some also differentially associated with expressed the PI3K-Akt genes signal were pathways also associated (Figure with4B). Comparedthe PI3K-Akt to the signal levels pathways in cattle-yak, (Figure there 4B). were Compared 154 differentially to the expressedlevels in cattle-yak, genes of Qaidamford there were cattle. 154 Animals 2019, 9, 1077 6 of 10 Ofdifferentially these 154, expressed 114 were genes involved of Qaidamford in 143 pathways cattle. (SupplementaryOf these 154, 114 Excelwere involved S4). Some in 143 differentially pathways expressed(Supplementarydifferentially geneswere Excel expressed enriched S4). Somegenes in the indifferentially PI3K-AktQaidamford signaling cattle, expressed relative pathway genesto Angus but were werecattle, enriched notwere particularly enriched in the in significant299PI3K-Akt (Figuresignaling4pathwaysC). pathway (Supplementary but were not Excel particularly S5). Some significantdifferentially (Figure expressed 4C). genes were also associated with the PI3K-Akt signal pathways (Figure 4B). Compared to the levels in cattle-yak, there were 154 differentially expressed genes of Qaidamford cattle. Of these 154, 114 were involved in 143 pathways (Supplementary Excel S4). Some differentially expressed genes were enriched in the PI3K-Akt signaling pathway but were not particularly significant (Figure 4C).

FigureFigure 4.4. KEGG pathway analysis of relatedrelated DEGs.DEGs. ( A)) Cattle-yak compared to thethe Angus,Angus, thethe didifferentiallyfferentially expressedexpressed genesgenes werewere subjectedsubjected toto pathwaypathway enrichmentenrichment analysis.analysis. ((BB)) QaidamfordQaidamford cattle cattle Figure 4. KEGG pathway analysis of related DEGs. (A) Cattle-yak compared to the Angus, the comparedcompared toto Angus, Angus, the the diff differentiallyerentially expressed expressed genes genes were subjectedwere subjected to pathway to pathway enrichment enrichment analysis. differentially expressed genes were subjected to pathway enrichment analysis. (B) Qaidamford cattle (C) Qaidamford cattle compared to the cattle-yak, the differentially expressed genes were performed to analysis.(comparedC) Qaidamford to Angus, cattle the differentially compared toexpressed the cattle- genesyak, were the subjected differentially to pathway expressed enrichment genes were pathwayperformedanalysis.( enrichment to pathwayC) Qaidamford analysis. enrichment cattle comparedanalysis. to the cattle-yak, the differentially expressed genes were performed to pathway enrichment analysis. 3.4. DEGs Were Validated Using RT-qPCR 3.4. DEGs Were Validated Using RT-qPCR To confirm3.4. DEGs that Were the Validated mRNA Using expression RT-qPCR levels of the differentially expressed genes were consistent To confirm that the mRNA expression levels of the differentially expressed genes were with the levelsTo confirm shown that in thesethe mRNA sequencing expression analysis levels data,of the wedifferentially selected severalexpressed genes genes involvedwere in consistent with the levels shown in these sequencing analysis data, we selected several genes lipid-relatedconsistent processes with the for verification.levels shown RT-qPCRin these sequencing was performed analysis to data, confirm we selected the transcriptome several genes analysis involvedinvolved in lipid-related in lipid-related processes processes for for verification. verification. RT-qPCRRT-qPCR was was performed performed to confirm to confirm the the differences, as shown in Figure5A. RT-qPCR was used to detect the expression of SLC16A11, NR2F2, transcriptometranscriptome analysis analysis differences, differences, as asshown shown inin Figure 5A.5A. RT-qPCR RT-qPCR was wasused usedto detect to detectthe the BAMBI, MXRA8, FAR2, FLT1, PFKFB4, and LPIN1 between different groups (Figure5B). The gene expressionexpression of SLC16A11 of SLC16A11, NR2F2, NR2F2, BAMBI, BAMBI, MXRA8, MXRA8,, FAR2FAR2,, FLT1 FLT1, PFKFB4,, PFKFB4, and and LPIN1 LPIN1 between between different different expression levels showed that the RT-qPCR results were consistent with the RNA-seq analysis, groups (Figuregroups (Figure 5B). The 5B). gene The gene expression expression levels levels show showed thatthat the the RT-qPCR RT-qPCR results results were were consistent consistent with with suggestingthe RNA-seq the reliability analysis, of suggesting RNA-seq the results. reliability of RNA-seq results. the RNA-seq analysis, suggesting the reliability of RNA-seq results.

Figure 5. Verification of the differentially expressed genes by real-time quantitative polymerase chain reaction (RT-qPCR). (A) The RNA sequencing results revealed differentially expressed genes in adipose tissue of different cattle breeds. (B) Differentially expressed genes were confirmed by RT-qPCR. Data are

represented as mean values SEM. n = 3. * p < 0.05, ** p < 0.01. ± Animals 2019, 9, 1077 7 of 9

4. Discussion The aim of this study was to identify differentially expression genes related to adipose tissue formation indifferent cattle breeds. We used RNA-Seq technology to analyze transcriptome differences in the subcutaneous tissue of Qaidamford cattle, cattle-Yak and Angus cattle. The comparison of different bovine adipose tissue transcripts levels revealed some differentially expressed genes that are likely involved in the biological processes of fat formation, and some differentially expressed genes that participate in vital signaling pathways involved in adipose tissue development. Overall, understanding difference in gene expression in adipose tissue in Qaidamford cattle, cattle-yak, and Angus cattle can lead to future breed improvement. RNA-seq is a high-throughput sequencing technology that is a powerful way to obtain large quantities of transcriptome data from many organisms, tissue types, and cell contexts, making it a convenient and efficient way to study gene expression on a genome-wide scale [16,17]. This method has been widely used in chicken [18], ducks [19], pigs [20], and cattle [8]. Especially in the study of adipose tissue and adipocytes, RNA-seq was used to identify differentially expressed RNAs including mRNA [21], miRNAs [22], and LncRNAs [23]. In this work, we applied RNA-seq to analyze the adipose tissue transcriptome in Qaidamford cattle, cattle-yak, and Angus cattle, and identified 4167 differentially expressed genes (Angus vs. cattle-yak), 3269 differentially expressed genes (Angus vs. Qaidamford cattle), and 154 differentially expressed genes (cattle-yak vs. Qaidamford cattle). Many differentially expressed genes were identified in the comparison of hybrid cattle and Angus cattle, suggesting significant differences in the adipose tissue. However, fewer differentially expressed genes were identified in the comparison of cattle-yak and Qaidamford cattle adipose tissue, suggesting little difference in adipose tissue. GO analysis of these transcripts showed significant enrichment in GO categories related to fatty acid beta-oxidation and fatty acid metabolic process. Compared to Angus cattle, 14 differential expression transcripts were involved in fatty acid beta-oxidation of cattle-yak adipose tissue. These genes are closely related to fat deposition, and include adiponectin (ADIPOQ). Recent studies have shown that ADIPOQ plays a vital role in adipocyte development [24]. Twelve differentially expressed transcripts were related to fatty acid metabolic process in Qaidamford cattle adipose tissue. However, the number of difference genes between Qaidamford cattle and cattle-yak were not significant, suggesting little difference in adipose tissue. KEGG pathway annotation of these genes revealed that ECM-receptor interaction and PI3K-Akt signaling were enriched pathways in the adipose tissues of the three breeds of cattle. ECM-receptor interaction was previously investigated in depot-specific adipogenesis in cattle [25]. The PI3K-Akt signaling pathway is a classic insulin signaling pathway [26], and is involved in proliferation, differentiation, apoptosis, and glucose transport. Moreover, the PI3K-Akt pathway may also regulate adipogenesis [27,28]. The detailed function and regulatory mechanisms of the differentially expressed genes involved in these pathways should be carefully elucidated in subsequent studies. To ensure the accuracy and reliability of our sequencing results, it was necessary to use RT-qPCR. In this study, we prioritized the validation of genes differentially expressed in processes associated with adipose development. BAMBI has been investigated to negative regulate adipogenesis [29]. SLC16A11 might have a role in hepatic lipid metabolism and its genetic variation might result in diabetes risk [30,31]. NR2F2 (Coup-TF2) is a member of the steroid/thyroid hormone receptor family, which has been reported to negatively regulate adipogenesis [32]. The meat analysis experiment showed that the fat content of cattle was higher than that of hybrid cattle, which might be related to the expression of NR2F2 gene. Hence, the relationship of NR2F2 expression with fat content in cattle needs further study and discussion. Studies have shown that MXRA8 expression level is associated with increased marbling in beef cattle [33]. In addition, LPIN1 is a candidate gene for fat deposition in pigs [34]. These differentially expressed genes may have important roles in adipogenesis, and the regulatory functions of these candidate genes should be investigated in future studies. Animals 2019, 9, 1077 8 of 9

5. Conclusions The RNA-seq analysis identified differentially expressed genes in adipose tissue of Angus cattle, cattle-yak, and Qaidamford cattle. Some differentially expressed genes are involved in important signaling pathways, such as the PI3K-Akt and ECM-receptor interaction pathways. These transcriptome analysis data should allow subsequent bovine cross-breeding for improved meat quality.

Supplementary Materials: The following are available online at http://www.mdpi.com/2076-2615/9/12/1077/s1, Table S1. Specific primers used for RT-qPCR. Table S2. Summary of sequence read alignments to the reference genome. Supplementary Excel S1. AB-vs.-CYB. DESeq. difffilter. Supplementary Excel S2. FB-vs.-AB. DESeq. difffilter. Supplementary Excel S3. CYB-vs.-FB. DESeq. difffilter. Supplementary Excel S4. Pathway enrichment analysis of AB-vs.-CYB. Supplementary Excel S5. Pathway enrichment analysis of AB-vs.-FB. Supplementary Excel S6. Pathway enrichment analysis of CYB-vs.-FB. Author Contributions: C.S. and Y.H. are responsible for designing experiments and writing manuscripts. Z.Y. conducts experimental research. Y.M., B.C., and Z.Z. are responsible for data collection and result analysis. H.C. is responsible for the final design of research ideas, manuscript review and modification and financial support. Funding: This work was supported by the Haixi Project of Qinghai Province: Identification of key genes of growth and high-quality meat in Yak Multi hybrids, Program of National Beef Cattle and Yak Industrial Technology System (CARS-37), the National Natural Science Foundation of China (31772574, 31601926). Acknowledgments: Thanks to Xiukai Cao for his support in experimental analysis and sample collection. Conflicts of Interest: The authors declare that they have no competing interests.

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