Transcriptional Features of Cattle Visceral and Subcutaneous Adipose Tissues: A Study of RNA-seq ()
1. Introduction
Adipose tissue is an important organ in the storage and release of energy, and regulates various physiologic and pathologic processes through secretion of adipokines [1] [2]. In the beef cattle industry, the composition of the fat in different adipose tissues is of major interest because of the effect of fatty acids on human health and the quality of the beef [3].
Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are the two major types of adipose tissue, according to their distribution in mammals [4]. There are many differences between VAT and SAT including anatomical, cellular, molecular, physiological, clinical, and prognostic [5]. Fat storage and allocation within body depots of farm animals are associated with animal health and meat quality, these differences are caused by a variety of mechanisms such as a different content of immune cells, different sensitivity to lipolytic and antilipolytic stimulation, and different metabolic activities [6] [7]. Evidence from recent studies has confirmed the distinct features of VAT and SAT in livestock. Wang et al. (2013) [8] identified that DEGs between SAT and VAT are potentially associated with the inflammatory features of VAT in pigs. Ji (2014) [9] found that VAT has a greater capacity for expression of pro-inflammatory cytokines such as interleukin 1B (IL1B), IL6, and IL6R compared with SAT, which may lead to an excessive accumulation of visceral lipid in Holstein cows. In addition, through a 2D LC-MS/MS (two-dimensional liquid chromatography tandem mass spectrometry) analysis, Restelli [10] demonstrated different proteomic profiles between VAT and SAT in goats. Romao et al. [11] found that the miRNA may differ among VAT and SAT, suggesting that miRNAs may play a role in the regulation of bovine adipogenesis.
Previous research also reported these differences in cattle, but little research has been undertaken on the molecular differences between VAT and SAT in beef cattle. Thus, we conducted an RNA-seq analysis to detect DEGs between these two tissues in Simmental cattle. We aimed to provide information to support further research into the adipose tissue of cattle and contribute to a better understanding of the metabolic and functional differences between visceral and subcutaneous adipose tissues.
2. Materials and Methods
2.1. Animal Samples
Three female Simmental beef cattle at 20 months of age (from Zhang Jiakou, Hebei Province, China) were used to harvest adipose tissue. All cows were obtained from a same farm, no pregnant, reared under the same conditions. The cattle were weaned at six months of age and then raised on a diet of corn and corn silage. The thickness of backfat was measured right before slaughter (1.07 ± 0.049 cm). Subcutaneous adipose tissue (the hypodermal layer of backfat, namely SAT in this case) and visceral adipose tissue (great omentum, namely VAT in this case) was collected immediately after slaughter, treated with RNAlater (Ambion, NY, USA), and then frozen in liquid nitrogen until RNA extraction.
All animal procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals, and were approved by the Ethical Review Committee on the Welfare of Laboratory Animals of Hebei North University (permit number: 20-0013).
2.2. Library Construction and Sequencing
Three samples from each tissue were used for extracting RNA by using a Fatty Tissue Kit (LC Science, Houston, TX). The integrity of the RNA was confirmed using a 2100 Bioanalyzer (Agilent, USA) and only samples with an RNA integrity number greater than 7 were used in subsequent analyses. After quality control and the removal of ribosomal RNA by using Epicentre ribosomal RNA kit (LC Science, Houston, TX), approximately 20 μg of total RNA was fragmented to 200-nt fragments and was then used as a template for cDNA synthesis. The first strand of cDNA was synthesized with random hexamers, and the second strand was synthesized using a dNTP mix in which dTTP was replaced by dUTP. The double-stranded cDNA was then subjected to end repair, addition of dA to the 3' end, and adaptor ligation; the libraries were further size-selected using a 1:1 volume of AMPure XP beads (Beckman Coulter, Brea, CA). USER enzyme (NEB, USA) was used to degrade the second strand of cDNA, and finally, strand-specific RNA-seq libraries were constructed and sequenced on an Illumina Hiseq 2500 platform at LC Science (Houston, TX).
2.3. RNA-seq Data Analysis
After trimming sequenced reads containing adaptors and reads of low quality (reads with more than 20% bases have a quality value Q ≤ 10), the valid reads for each sample was aligned to the cattle genome (Bos_taurus; UMD3.1.75) using Tophat (v2.0.4) software [12] with the following parameters: --bowtie -r 0 -mate-std-dev 50-N 3--solexa 1.3-quals. Cufflinks (v2.0.2) [13] was used to assemble the RNA-seq alignments, and then Cuffcompare package is used to compare the transcripts to transcripts in ENSEMBL. The Cuffdiff package is used to compare the expression level of transcripts between tissues. Gene expression level was normalized by considering the “fragments per kilobase of transcript per million mapped reads” (FPKM). The R package edgeR (version 1.6.12) was used to identify DEGs. Genes were considered differentially expressed if they had a Benjamin-Hochberg false discovery rate-corrected P-value < 0.05.
The identification of novel transcripts is based on the result of Cuffcompare, transcripts including unknown intergenic transcript, a transfrag falling entirely within a reference intron, and exonic overlap with reference on the opposite strand were considered as novel transcripts.
2.4. Gene Ontology (GO) and Pathway Enrichment Analysis
To identify biological functions, the DEGs were classified into the categories GO cellular component (GO-CC), GO biological processes (GO-BP), and GO molecular function (GO-MF) in the Gene Ontology database (http://www.geneontology.org). In addition, KEGG Pathway analysis was carried out to identify the main metabolic pathways or signal transduction pathways of the DEGs. In these analyses, the whole genome was considered to be the background, and the calculated P-values were corrected using the Bonferroni correction, taking a corrected P-value < 0.05 as the threshold for significance.
2.5. Validation of RNA-seq Data
To validate the results from RNA-seq, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was conducted to measure the expression levels of eight genes that were significantly differentially expressed in different adipose tissues. Total RNA (1 μg) was reverse transcribed to cDNA using a PrimeScript RT reagent kit with gDNA Eraser (Takara, Japan). Primers used for qRT-PCR are shown in TableS1. qRT-PCR was performed on a CFX96 Real Time Detection system (Bio-Rad) using 2× SYBR® Premix ExTaqTM II (Takara). The data were transformed using the formula 2−ΔΔCt. Bovine glyceraldehyde 6-phosphate dehydrogenase (GAPDH) was used as an endogenous control gene, and its expression patterns in these two tissues are measured before qRT-PCR analysis.
3. Results
3.1. RNA Sequence in Adipose Tissues
A total of 65,702,568 reads for SAT and 76,832,410 reads for VAT were obtained by RNA-seq after quality control. Approximately 60% of the valid reads mapped to the reference genome (Bos_taurus_UMD 3.1.75). After assembly of the cattle adipose transcriptome, 13,905 and 15,943 transcripts were expressed in SAT and VAT, respectively (Table 1).
A total of 12,685 transcripts were co-expressed in SAT and VAT. GO analysis indicated that these genes were primarily enriched in the categories of “mitochondrion” (1149 transcripts, P = 3.97E-21), “nucleotide binding” (1277 transcripts, P = 7.11E−17), “metal ion binding” (1605 transcripts, P = 1.39E−11), and “ATP binding” (1037 transcripts, P = 1.55E−11) as shown in TableS2. Notably, some transcripts showed extremely high abundance in both adipose tissues (TableS3), such as FABP4 (7406.8 FPKM in VAT and 11559.1 FPKM in SAT) and SCD (6039.6 FPKM in VAT and 7334.3 FPKM in SAT) these genes play an important role in lipid metabolism processes and can be regarded as “housekeepers” in adipogenesis. Meanwhile, some transcripts presented specific expression patterns in one tissue, we found 3247 transcripts specific expressed in
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Table 1. Summary of RNA-seq alignment.
VAT while 1214 transcripts specific expressed in SAT (TableS4). However, these transcripts expressed at low level, the mean FPKM is 4.39 in VAT and 6.41 in SAT.
3.2. DEGs in Subcutaneous and Visceral Adipose Tissues
A total of 5864 genes were differentially expressed between SAT and VAT, of which 2979 were up-regulated and 2885 were down-regulated in VAT compared with SAT (Figure1). The DEGs showed distinct transcriptome features. A total of 329 GO terms and 27 KEGG pathways were significantly enriched among the DEGs (TableS5). The DEGs were involved in various biological processes and molecular functions (FigureS1) including fatty acid metabolism and immune response processes such as fatty acid biosynthesis, the PPAR (peroxisome proliferator activated receptor) signaling pathway, cytokine receptor binding, and the response to stimuli (Figure2).
3.3. DEGs Involved in Lipid Metabolism
To investigate the function of the DEGs in lipid metabolism, we focused on a
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Figure 1. Volcano map of DEGs in two adipose tissues. Note: The red dot represent the over expressed genes and the green dot represented the lower level expression of the genes, the grey dot represented the gene expression were not significant between two tissues.
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Figure 2. GO (Gene Ontology) and pathway categories enriched for DE transcripts, Gene ontology of DE transcripts. DEGs involved in lipid metabolism and immune response processes.
significantly enriched KEGG category, the PPAR signaling pathway, which has been shown to be important in the regulation of the catabolic and anabolic aspects of lipid metabolism [14] [15]. Twenty-seven DEGs were clustered into this pathway, of which 14 were significantly up-regulated in VAT (Table 2, FigureS2). To validate the RNA-seq, we examined the expression level of eight up/down-regulated genes using qRT-PCR (Figure3(a)). Five DEGs (FABP4, PCK1, CD36, ADIPOQ, and SCD) were down-regulated in VAT compared with SAT, and the other three (LPL, EHADH, and UBB) were up-regulated in VAT compared with SAT. There was a significant correlation between the qRT-PCR and RNA-seq results (Figure3(c)), which indicated that the RNA-seq data accurately reflected the transcriptome features of cattle adipose tissue. The well-known regulator PPARγwas detected in both adipose tissues with no significant difference in expression level.
3.4. DEGs Involved in the Immune Response
Previous research has reported that DEGs in subcutaneous and visceral adipose tissues may be involved in inflammation and immune responses. Our functional analysis revealed that many DEGs were significantly enriched in immune-related categories such as cytokine activity (45 DEGs, P = 2.07E−5), cytokine receptor binding (13 DEGs, P = 0.000573), and immune response (72 DEGs, P = 0.000803). Among the DEGs in the immune response category were five members of the major histocompatibility complex gene family: BoLA-DQA1 and
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Table 2. DEGs involved in PPAR signaling pathway.
BoLA-DQB were up-regulated in VAT while BoLA-DRB3, BoLA-NC, and BoLA-DYA were down-regulated. In addition, several cytokines including TNFSF18, TLR2, IL6, and IL10 were differentially expressed between VAT and SAT (Figure 3(b)).
3.5. Novel Transcripts Involved in Lipid Metabolism
According to the location of sequenced reads and the reference genome, we could identify novel transcripts. A total of 1206 novel transcripts were found to
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Figure 3. Gene expression patterns in visceral and subcutaneous adipose tissues. (a) Relative expression level of eight DEGs in PPAR signaling pathway, data are expressed as mean ± SD, expression level is normalized by GAPDH (consistent expressed in both adipose tissue, P > 0.05, T test); within genes, *indicate differential expression among adipose sites (P < 0.05). (b) Expression of DEGs involved in immune response. (c) Correlation of qRT-PCR and RNA-seq analysis.
have protein coding ability but could not be mapped to a known mRNA of the reference genome (TableS6). Among these, 541 were differentially expressed between the two adipose tissues (244 down-regulated and 297 up-regulated). GO analysis showed these transcripts participated in various biological processes.
4. Discussion
The structure and function of adipose tissue varies according to its distribution [16]. Visceral and subcutaneous are two major types of adipose tissue and play different roles in metabolic processes, such as in insulin action, the regulation of lipolysis, and cytokine production [17]. In our RNA-seq analysis, plenty of genes were found expressed in visceral and subcutaneous adipose tissues which may reveal the transcriptional complexity of different fat depots, more the 1/3 genes were differential expressed between these two tissues, suggested distinct transcriptional features of cattle adipose tissue.
Among the identified DEGs were many well-known regulators of lipid metabolism. For instance, 27 DEGs were enriched in the PPAR signaling pathway, which is important in glucose homeostasis and lipid metabolism in adipose tissue. Lipoprotein lipase (LPL) is a key regulator of fat accumulation in various adipose areas, which, together with acylation stimulating protein, participates in the regulation of in vivo triglyceride uptake in adipose tissue [18] [19]. We found that LPL mRNA was significantly more highly expressed in VAT than in SAT; this may result in higher LPL activity in VAT, and thus influence fat deposition.
In contrast, CD36 was preferentially expressed in SAT. Studies have reported that changes in CD36 expression and subcellular distribution are linked to fatty acid uptake, especially of long-chain fatty acids [20] [21] [22]. Interestingly, another three genes (FABP4, PCK1, and SCD) have also been reported to be involved in fatty acid transport (FABP4), gluconeogenesis (PCK1), and desaturation (SCD) [23] [24] which may affect de novo synthesis of fatty acid. In our study, these four genes were all down-regulated in VAT compared with SAT, which suggests that bovine VAT has lower fatty acid metabolic activity than SAT.
Interestingly, we also observed distinct expression patterns of some lipid metabolism-related genes between our result and previous researches. ADIPOQ andSCD are found more abundant in visceral adipose tissue than in subcutaneous adipose tissue of Holstein cows. These differential expression genes suggested distinct transcriptional features between beef cattle and dairy cattle. Combine analysis of RNA-seq analysis in different cattle breeds will be useful in understanding of gene regulated mechanisms of cattle.
Furthermore, we also detected novel transcripts that are likely to participate in lipid metabolism. For example, ENSBTAT00000043749 on chr15 was expressed in VAT and was classified into the GO categories “lipid metabolic process” and “fatty acid biosynthetic process”.
Adipose tissue is not only an energy storage organ, but is also highly active in expressing and releasing cytokines and initiating an inflammatory or immune response [25] [26]. We found many DEGs that are involved in immune response-related processes. BoLA-DQA1 and BoLA-DQB are two BoLA (bovine leukocyte antigen) class II molecules, which can present antigen to bovine CD4+ T cell lymphocytes and play an important role in immune responses to infectious pathogens [27] [28]. Both BoLA-DQA1 and BoLA-DQB were up-regulated in VAT in our study, together with other pro-inflammatory cytokines and genes such as TNFSF18 and TLR2 [29] [30]. This suggests that VAT can produce more pro-inflammatory cytokines than SAT, and indicates that the former is more sensitive to immune responses. In contrast, we also found several down-regulated cytokines in VAT, such as IL6 and IL10, which may reveal distinct immune characteristics between VAT and SAT and further confirm the transcriptional complexity of bovine adipose tissue.
5. Conclusion
In conclusion, through an RNA-seq analysis between the visceral and subcutaneous adipose tissues of cattle, we detected a large number of DEGs and novel transcripts. Our findings support the complexity of gene regulation governing the physiology of different fat depots, especially relating to lipid metabolism and immune responses.
Acknowledgments
This research was supported by the Natural Science Foundation of Hebei Province (No. C2019405105); Project of Hebei Province Innovation Capacity Promotion Plan (No.20567632H).
Supplementary Figures and Tables
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Supplementary FigureS1. GO analysis of differential expressed genes.
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Supplementary FigureS2. igure S1 DEGs involved in PPAR signaling pathway.
Supplementary TableS1. Primers used for qRT-PCR analysis of mRNA levels.
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Supplementary TableS2. Co-expression of transcripts between VAT and SAT_GO_enrichment.
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Supplementary TableS3. Top ten transcripts with high abundance in both adipose tissues.
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Supplementary TableS4. Specific expressed genes in different adipose tissues.
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Supplementary TableS5. Significantly enriched GO and KEGG.
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Supplementary TableS6. Novel Transcripts.
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