Table of Contents Author Guidelines Submit a Manuscript
International Journal of Genomics
Volume 2019, Article ID 5070975, 12 pages
https://doi.org/10.1155/2019/5070975
Research Article

Identification of lncRNAs and Genes Responsible for Fatness and Fatty Acid Composition Traits between the Tibetan and Yorkshire Pigs

1Animal Science College, Tibet Agriculture and Animal Husbandry University, Linzhi, Xizang, China
2College of Animal Sciences and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China
3National Center for Preservation and Utilization of Animal Genetic Resources, National Animal Husbandry Service, Beijing 100193, China

Correspondence should be addressed to Kejun Wang; moc.361@em.nujekgnaw and Yangzom Chamba; moc.621@826zybq

Received 24 November 2018; Revised 10 February 2019; Accepted 3 March 2019; Published 2 June 2019

Guest Editor: Katarzyna Ropka-Molik

Copyright © 2019 Peng Shang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Tibetan pigs from the Tibetan Plateau are characterized with a significant phenotypic difference relative to lowland pigs. In this study, a significant difference of the fatness and fatty acid composition traits was observed between the Tibetan and Yorkshire pigs. To uncover the involved mechanism, the expression profile of long noncoding RNAs (lncRNAs) and genes was compared between them. After serial filtered steps, 1,964 lncRNAs were obtained through our computational pipeline. In total, 63 and 715 lncRNAs and genes were identified to be differentially expressed. Evidence from cis- and trans-targeting analysis of lncRNAs demonstrated that some lncRNAs, such as MSTRG.14097 and MSTRG.8034, played important roles in the fatness and fatty acid composition traits. Bioinformatics analysis revealed that many candidate genes were responsible for the two traits. Of these, FASN, ACACA, SCD, ME3, PDHB, ACSS1, ACSS2, and ACLY were identified, which functioned in regulating the level of hexadecanoic acid, hexadecenoic acid, octadecenoic acid, and monounsaturated fatty acid. And LPGAT1, PDK4, ACAA1, and ADIPOQ were associated with the content of stearic acid, octadecadienoic acid, and polyunsaturated fatty acid. Candidate genes, which were responsible for fatness trait, consisted of FGF2, PLAG1, ADIPOQ, IRX3, MIF, IL-34, ADAM8, HMOX1, Vav1, and TLR8. In addition, association analysis also revealed that 34 and 57 genes significantly correlated to the fatness and fatty acid composition trait, respectively. Working out the mechanism caused by these lncRNAs and candidate genes is proven to be complicated but is invaluable to our understanding of fatness and fatty acid composition traits.

1. Introduction

Tibetan pigs mainly originated from the Tibetan Plateau, which had undergone the long-term natural selection in the harsh environment. It indicated that many candidate genes related to germplasm of the Tibetan pigs, such as energy metabolism, might contribute to the adaptability to hostile environment [1]. However, the heritable adaptions to the extreme environment is quite sophisticated, which is influenced by many factors [2], for example, cardiopulmonary function, immunity, blood pressure, and fat metabolism [35]. Furthermore, previous publication also demonstrated that excessive caloric intake led to fat deposition, and further, the excessive fat would participate in hypoxia and inflammation (Heinonen et al. 2016).

So we took another look on this scientific question that is due to thicker back fat and higher intramuscular fat in the Tibetan pigs, which could stably deliver the energy to the body and guarantee the inflammatory response in such a harsh environment. Additionally, phenotype difference of the fatness and fatty acid composition traits was also observed between the Tibetan and Yorkshire pigs in this study. Multiple elements, including coding genes [6], miRNA [7], and DNA methylation [8], had been confirmed to be functioned in the pig fatness-related traits. But long noncoding RNAs (lncRNAs) involved in the process of fatness and fatty acid composition has not been reported in the Tibetan pigs yet. LncRNAs, groups of RNAs more than 200 nucleotides, had been demonstrated to play a crucial role in various biological processes, including metabolic activity, fat deposition, and biological development process [9]. In this study, comparative analysis of lncRNAs and mRNAs was performed between the Tibetan and Yorkshire pigs to identify the candidate genes responsible for fatness and fatty acid composition traits. These findings will help us to further decipher the molecular mechanism of adaptability to hypoxic conditions of the Tibetan pig from the sight of fat metabolism.

2. Materials and Methods

2.1. Collection of Phenotype and Sequencing Data

All phenotypes of the Tibetan and Yorkshire pig group, which included the fatness and fatty acid composition traits, were collected at days 180 [10]. Each group have three individuals. Phenotypic statistics of the fatness trait, which consisted of left leaf fat percentage, back-fat thinness at shoulder, back-fat thinness at rib, back-fat thinness at first lumbar, back-fat thinness at last lumbar, and average back-fat thinness, were adjusted to the live-weight. Meanwhile, 14 fatty acid compositions were obtained, including tetradecanoic acid, hexadecanoic acid, hexadecenoic acid, stearic acid, octadecenoic acid, octadecadienoic acid, octadecatrienoic acid, eicosenoic acid, eicosadienoic acid, eicosatrienoic acid, eicosatetraenoic acid, saturated fatty acid, monounsaturated fatty acid, and polyunsaturated fatty acid. Total RNA was extracted from fat tissue. The sequence data was collected based on the Illumina Hiseq 2000 platform [10]. All the RNA-Seq data was collected in the NCBI database (SRP090525) [10].

2.2. Identification Pipeline of lncRNAs

Sequencing data were assessed for quality using FastQC. High quality clean data was used in RNA-Seq analysis. Clean pair-end reads with FASTQ format were aligned to the Sscrofa 11.1 genome assembly, and novel splice junctions were determined using Hisat2 (v2.0.0-beta). The default parameters were used in the analysis, except for the “-G” option tighter with the GTF file of Ensembl gene annotation (Sscrofa 11.1.91). The alignment data with the BAM format were subjected to the StringTie (v1.3.0) to assemble transcripts. All transcripts from all samples were merged together using StringTie. Then, gffcompare (v0.10.1) was applied to match the transcript pool to Ensembl gene sets (Sscrofa 11.1.91). Transcripts, of which class code with “i,” “u,” “o,” and “x,” were subjected to lncRNA identification. To avoid incomplete assemble and false positive rate, the putative lncRNA transcripts were filtered with transcript bp and exon . Remaining transcripts were scored with the CNCI tool [11] and CPC (Coding Potential Calculator) [12]. The transcripts, which both the CNCI and CPC scores were below 0, would be considered as the lncRNAs.

2.3. Differential Expression and Functional Enrichment Analysis

The number of reads that mapped to each gene was calculated with HTSeq [13]. Then, DESeq2 [14] was used to identify the differentially expressed genes with the criterion (fold and adjusted value < 0.05). Enrichment analysis (GO and KEGG pathway) was performed with the Metascape database based on human homology gene list [15]. Association analysis was performed between the expression level of each differentially expressed gene and phenotypes from the two traits. “Corrplot” and “psych” packages were used to compute and visualize the Pearson correlation coefficients under environment. The Bonferroni correction was used to determine the significance level of each association event.

2.4. Targeting Gene Prediction and Functional Analysis

The cis-regulating role of lncRNAs was defined as those exerting effects on neighboring genes [16]. Differentially expressed genes (DEGs) located in 100 kb upstream or downstream of lncRNAs were identified by Bedtools [17]. The positional relationship between differentially expressed lncRNAs and DEGs was firstly investigated. Further, the expression correlation between lncRNA-DEGs pairs was computed with the Pearson method. Additionally, for transregulation relationship, the expression correlation was computed between differentially expressed lncRNA and DEGs with the Pearson correlation. The FDR correction was performed to determine the significance level.

2.5. Quantitative Real-Time PCR (qPCR)

Seven differentially expressed mRNAs and six differential lncRNAs were validated via qPCR. Total RNA was extracted from back-fat tissue using TRIzol reagent according to the manufacturer’s instructions (Life Technologies). cDNA synthesis was performed with 1 μg of total RNA, following the protocol accompanied with the FastQuant RT Kit (Tiangen). Quantitative PCR (qPCR) amplification was conducted as described previously [7].

3. Results and Discussion

3.1. Descriptive Statistics of the Fatness and Fatty Acid Composition Traits

Phenotypes of the fatness and fat composition traits from the Tibetan and Yorkshire pigs were collected and analyzed. Higher fat content was observed in the Tibetan group than the Yorkshire group, such as back-fat thickness at different positions and leaf fat percentage (Figure 1(a)). Evidence from the fat composition trait demonstrated that the levels of fatty acids, including hexadecenoic acid, hexadecanoic acid, octadecenoic acid, and monounsaturated fatty acid, were significantly higher in the Tibetan group compared to the Yorkshire group, while stearic acid, octadecadienoic acid, eicosadienoic acid, and polyunsaturated fatty acid were significantly abundant in the Yorkshire group relative to the Tibetan group (Figure 1(b)). In this case, RNA-Seq data was collected from back-fat tissues between three Tibetan pigs and three Yorkshire pigs.

Figure 1: Descriptive statistics of phenotypes and RNA-Seq data. (a) Phenotypic statistics of the fatness trait. Back-fat thinness was adjusted to the body weight. (b) Phenotypic statistics of the fatty acid composition trait. (c) The number of quantified transcripts in the Tibetan and Yorkshire pigs. (d) Volcano plot of differentially expressed transcripts between the Tibetan and Yorkshire pigs. (e) Classification of identified lncRNAs. (f) The number of quantified LncRNAs in the Tibetan and Yorkshire pigs. (g) Volcano plot of differentially expressed lncRNAs between the Tibetan and Yorkshire pigs. LFP: left leaf fat percentage; SFT: back-fat thinness at shoulder; RFT: back-fat thinness at rib; BFT: back-fat thinness at first lumbar; LFT: back-fat thinness at last lumbar; AFT: average back-fat thinness; TA (14:0): tetradecanoic acid; HA (16:0): hexadecanoic acid; HA (16:1): hexadecenoic acid; SA (18:0): stearic acid; OA (18:1): octadecenoic acid; OA (18:2): octadecadienoic acid; OA (18:3): octadecatrienoic acid; EA (20:1): eicosenoic acid; EZ (20:2): eicosadienoic acid; EA (20:3): eicosatrienoic acid; EA (20:4): eicosatetraenoic acid; SFA: saturated fatty acid; MUFA: monounsaturated fatty acid; PUFA: polyunsaturated fatty acid.
3.2. Identification of Differentially Expressed lncRNAs and Genes

Clean reads were mapped to the Sscrofa 11.1 genome assembly, which mapping rates more than 83.1%. Of these, more than 84% reads were aligned on the CDS, 5UTR, and 3UTR region (Figure S1). A total of 17,334 genes were commonly quantified, then 776 and 493 genes were uniquely expressed in the Tibetan group and the Yorkshire group, respectively (Figure 1(c)). Specific expression is the extreme situation of differential expression, so all genes were subjected to the identification of differentially expressed genes. After statistical testing, 715 differentially expressed genes (DEGs) were identified, including 472 upregulated and 243 downregulated genes (Figure 1(d) and Table S1). After a series of filtered steps (Materials and Methods), 1,964 lncRNAs were identified based on our computational pipeline, which consist of 874 intergenic lncRNAs (lincRNAs), 687 natural antisense lncRNAs (lncNATs), and 403 intronic lncRNAs (ilncRNAs) (Figure 1(e) and Table S2). Of these, 1,323 lncRNA genes were commonly quantified, while 35 and 30 lncRNAs were uniquely expressed in the Tibetan and Yorkshire pigs (Figure 1(f)). Relative to the Yorkshire group, we found 63 lncRNAs differentially expressed in the Tibetan group, which included 44 upregulated and 19 downregulated lncRNAs (Figure 1(g) and Table S3). To validate the RNA-Seq results, six DEGs and four differentially expressed lncRNAs were randomly selected for qPCR assay (Figure 2). Figure 2 showed the result of the relative expression level between two groups, which was consistent with the RNA-Seq data. It indicated that identification and abundance estimation of genes were highly reliable in this study.

Figure 2: Validation of DEGs and lncRNAs with qPCR.
3.3. Candidate Genes Related to Fatty Acid Metabolism

To further understand the potential genes responsible for the phenotype difference, differentially expressed genes were subjected to functional enrichment analysis. Biological process and KEGG pathway terms significantly enriched by upregulated DEGs included small molecule biosynthetic process (consists of “fatty acid biosynthetic process,” “lipid biosynthetic process,” etc.), many immune-related classes (“activation of immune response,” “macrophage differentiation,” “interleukin-3-mediated signaling pathway,” etc.), and 4 KEGG pathway classes (“complement and coagulation cascades,” “lysosome,” “phagosome,” and “osteoclast differentiation”) (Figure 3 and Table S4).

Figure 3: Functional enrichment analysis with upregulated DEGs. Enriched ontology clusters which is colored by cluster ID. value of clusters was embedded at the bottom.

Many upregulated DEGs were enriched in fatty acid biosynthetic-related terms, which included FASN, SCD, ME3, PDHB, ACACA, ACLY, ACSS1, and ACSS2. It had been demonstrated that the polymorphism of the FASN gene is associated with blood monounsaturated fatty acid and saturated fatty acid [18]. FASN is a metabolic enzyme that catalyzed the synthesis of long-chain fatty acids through condensing acetyl-CoA and malonyl-CoA, and its main product is hexadecanoic acid [19]. Malonyl-CoA is the main substrate which was synthesized with acetyl-CoA by ACACA (acetyl-CoA carboxylase) [20]. Furthermore, the synthesis of monounsaturated fatty acid, including hexadecenoic acid and octadecenoic acid, was processed by SCD (stearoyl-CoA desaturase) [18]. Variation of SCD was demonstrated to correlate with the oleic acid, SFA, and MUFA content in pork [21]. ME3 is an important paralog of ME1, together with ACACA and PDHB (Pyruvate Dehydrogenase E1 Beta Subunit) involved in the fatty acid synthesis process and pyruvate metabolism pathway (Table S4). ACSS1 and ACSS2 are both members of the acyl-CoA synthetase family, which was reported to function in the oleic acid (octadecenoic acid) synthesis process [22]. ACLY was demonstrated to be associated with the fatty acid content of milk in dairy cattle [23]. It suggested that many genes, such as FASN, ACACA, SCD, ME3, PDHB, ACSS1, ACSS2, and ACLY, were responsible for the higher monounsaturated fatty acid, octadecenoic acid, hexadecenoic acid, and hexadecanoic acid in the Tibetan group relative to the Yorkshire group.

The biological process class of the “monocarboxylic acid metabolic process” was also enriched by downregulated DEGs (Figure 4 and Table S5), which consisted of “fatty acid biosynthetic process,” “fatty acid metabolic process,” “carboxylic acid biosynthetic process,” “monocarboxylic acid biosynthetic process,” etc. Some DEGs involved in fatty acid-related terms were highlighted, since higher level of stearic acid, octadecadienoic acid, eicosadienoic acid, and polyunsaturated fatty acid was observed in the Yorkshire group relative to the Tibetan group (Figure 1(b)). LPGAT1 (lysophosphatidylglycerol specific acyltransferase) recognized various acyl-CoAs and LPGs as substrates but preferred to long-chain saturated fatty acyl-CoAs and oleoyl-CoA as acyl donors [24]. Genome-wide association analysis found that the LPGAT1 gene was associated with stearic acid content of plasma [25]. PDK4-specific DNA methylation was positively associated with polyunsaturated fatty acid, i.e., eicosapentaenoic and arachidonic acid [26]. ACAA1 was reported to be involved in the fatty acid β-oxidation signal pathway [27]. The ADIPOQ level was associated with the polyunsaturated fatty acid content in animal models [28]. ELVO6 is the enzyme that catalyzed the transformative reaction from hexadecanoic acid into stearic acid, but no significant difference was observed between groups in this study. These findings revealed the LPGAT1, PDK4, ACAA1, and ADIPOQ genes were associated with the higher level of stearic acid, octadecadienoic acid, and polyunsaturated fatty acid in the Yorkshire pigs.

Figure 4: Functional enrichment analysis with downregulated DEGs. Enriched ontology clusters which is colored by cluster ID. value of clusters was embedded at the bottom.
3.4. Candidate Genes Involved in the Immune Function Affecting Fat Metabolism

It has been surveyed that fat mass will be augmented in response to excess caloric intake leading to obesity and further to inflammatory signaling [29]. As part of the immune system, adipose tissue had been identified to be involved in inflammatory response which also inversely participated in obesity and insulin resistance [30]. GO terms, included “leukocyte migration,” “activation of immune response,” and “macrophage differentiation,” were enriched by upregulated DEGs (Figure 3 and Table S4). MIF (macrophage migration inhibitory factor) deficiency ameliorated high-fat diet-induced insulin resistance in mice and reduced adipose inflammation [31]. IL-34 is associated with obesity, chronic inflammation, and insulin resistance [32]. ADAM8 was involved in chronic inflammation in fat tissue that is contributing to the development of obesity-related insulin resistance [33]. HMOX1 (heme oxygenase 1) could rescue adipocyte dysfunction in mice, and this gene is important to the adaption of chronic hypoxemia [34, 35]. Loss of Vav1 in MSCs led to spontaneous adipogenic and displayed an increase in fat content [36]. The TLR8 gene was significantly upregulated in obesity as compared with lean individuals, the increase of which has consensus with inflammatory signatures [37]. These findings revealed that many genes, involving in immune-related processes, played an important role in adipocyte metabolism.

3.5. Opposite Effect of FGF2, PLAG1, ADIPOQ, and IRX3 on Fat and Other Tissues

Many interesting terms of biological process were also enriched by downregulated DEGs (Figure 4 and Table S5), including “skeletal system morphogenesis,” “organ growth,” “kidney epithelium development,” and “striated muscle tissue development.” These genes, such as FGF2, PLAG1, ADIPOQ, and IRX3, might participate in the development of skeletal muscle, kidney, or other organs [3840] but would have a negative effect on fat deposition. It had been demonstrated that FGF2 played a negative effect on adipogenesis [41]. Variants located in PLAG1 had confirmed to be associated with the fatness and growth traits in wild boar and Yorkshire F2 intercross pig population [42]. Adiponectin (ADIPOQ) is secreted from adipose tissue into the bloodstream to regulate other organ and to be inversely correlated with body mass index [43, 44]. IRX3 could promote browning of white adipocyte and correlate with human obesity [45]. It is well-known that the growth rate of the Yorkshire pigs is significantly higher than the Tibetan pigs. Furthermore, adipose tissue is a true endocrine organ secreting some mediators, which regulated adipose, skeletal muscle, and other organ using exosomes or via auto-paracrine manner [46, 47]. So, these genes might exert a negative effect on fat deposition but a positive role in skeletal muscle and other gran by cross-talking. KEGG pathway analysis also demonstrated that some downregulated genes were involved in “cytokine-cytokine receptor interaction” (Figure 4 and Table S5). It might reflect the communications between adipose and other tissues.

3.6. Association Analysis between Expression Level and Phenotypes

To better identify the potential function of differentially expressed gene, association analysis between gene expression level and phenotypes was investigated. After statistical testing, 34 DEGs were significantly associated with the fatness trait, which consisted of 14 positive and 20 negative correlation events (Figure 5(a)). A total of 10 genes, consist of LPXN, IL7R, GRPEL2, ACTG2, WISP2, ST14, ZNF277, SCEL, CAMK2A, and HACD1, were related to the RFT trait. Eight genes, including ND1, PLEK, RHOBTB3, HSDL2, GPR83, SCN3B, KIAA1211, and CBR1, were associated with the LFP trait. There were also 8 genes, including ENSSSCT00000014378, MXRA5, PENK, SGCG, ESPN, FERMT3, AARD, and ARMCX6, associated with the LFT trait. Five genes were correlated to the SFT trait, including SLC36A2, SHMT1, GSTM3, PFKFB1, and TIMP3. Four genes were significantly correlated to the AFT trait, such as DPP6, SLC36A2, PFKFB1, and TIMP3. Only 2 genes, S100B and XKR6, were involved in BFT. Furthermore, three genes, SLC36A2, TIMP3, and PFKFB1, were associated with both the SFT and AFT traits. Of these genes, some had been demonstrated to be involved in fat or adipocyte-related physiological process. TIMP3 overexpression in macrophages resulted in reduced activation of oxidative stress signals related to lipid peroxidation in fat tissue [48]. IL-7R deletion ameliorated diet-induced obesity and insulin resistance in mice [49]. SLC36A2 was identified as membrane surface marker of brown and beige adipocytes [50]. A significant SNP, near the XKR6 gene, was associated with body fat distribution [51].

Figure 5: Association analysis between DEGs and phenotypes. (a) Significant correlation events on six phenotypes of the fatness trait. (b) Significant correlation events on fourteen phenotypes of the fatty acid composition trait. Note: circle color and circle size represent the correlation coefficients. Significance level was tested by Bonferroni correction. value < 0.05; value < 0.01; value < 0.001. The abbreviation of phenotype is the same as in Figure 1.

There were also 57 DEGs significantly associating with the fat composition trait (Figure 5(b)). A total of 14 genes were significantly associated with the eicosenoic acid content, which included CYBB, CLU, CD84, ADGRE1, S100B, RGS17, C1QA, CFP, ENSSSCG00000028376, CST3, INPP5D, FAAH, NYAP2, and ALDH3A1. There were also 10 genes correlated to polyunsaturated fatty acid, which included CES1, CYBB, CD84, S100B, RGS17, C1QA, CFP, MYO1F, ENSSSCG00000028376, and CST3. Nine genes, such as ACACA, KDELC2, CAMKK1, PRUNE2, MUM1L1, SLIT2, SLC37A2, PARM1, and ATP1B1, were related to hexadecanoic acid level. Totally, 7 genes, LCP1, CRLF2, CD53, C1QC, SYCP2, ENSSSCG00000013144, and TIMELESS, were associated with the octadecenoic acid content. PENK, RGS2, SGCG, ESPN, AARD, and KIT were associated with the stearic acid content. SMPD3, ANO2, INPP5D, FAAH, NYAP2, and ALDH3A1 were related to the octadecadienoic acid content. For octadecatrienoic acid, ND1, LSAMP, LGALS12, VSTM4, EGR2, and ENSSSCG00000001252 were related. There were also 5 genes, SPHK1, NCS1, CTSB, SHROOM1, and SPOCK2, associated with the eicosadienoic acid level. Four genes, SMPD3, HCLS1, FABP7, and RUNX1, were correlated to monounsaturated fatty acid. There were only 3 genes, LEPR, TNFAIP6, and HSPA5, related to hexadecenoic acid. Some genes were associated with more than one trait. For example, CYBB, CD84, S100B, RGS17, C1QA, CFP, ENSSSCG00000028376, and CST3 were associated with both the eicosenoic acid and polyunsaturated fatty acid levels. However, no gene was observed to significantly associate with the other phenotypes of fatty acid. Most genes were first reported to be associated with the fatty acid content in this study. The reason behind is that most genes might indirectly involve in fatty acid composition, and less studies about fatty acid composition were reported. SNPs of LEPR were reported to be associated with SFA in subcutaneous fat. In this study, the LEPR expression level significantly correlated with the hexadecenoic acid content. To some extent, these findings are partly similar to published result and would be regarded as important foundational evidence for further study.

3.7. Prediction of Regulatory Actions for Differentially Expressed LncRNAs

To find the potentially biological function of differentially expressed lncRNAs, the cis- and trans-regulating models were analyzed. For the cis-acting model, DEGs physically located within 100 kb near the differentially expressed lncRNAs were obtained. The 9 pairs of lncRNA-DEGs with cis-regulating effect were identified (Table 1). Of these, 7 pairs passed the significant threshold of FDR correction. The cis-regulatory effect of MSTRG.8034 on ACACA is positive, but it is not significant. It was reported that ACACA catalyzed the first committed step of fatty acid synthesis, from carboxylation of acetyl-CoA to malonyl-CoA, leading to the biosynthesis of long-chain fatty acids [52]. A significant cis-regulatory effect of MSTRG.29167 on the RDH16 gene was also observed (Table 1). No obvious evidence was reported that RDH16 was correlated to fatness or fat composition trait, but it also showed differentially expressed in fat tissue between lean and obese pig breeds [53].

Table 1: The DEGs within 100 kb upstream or downstream of differentially expressed lncRNAs.

Trans-acting correlations of expression between differential lncRNAs and DEGs were examined. A total of 49 significant targeting events between 21 lncRNAs and 42 DEGs were found (Table S6). Two pairs of lncRNA-DEGs, MSTRG.14097-SCD and MSTRG.42256-NPDC1, were identified by both the Trans- and Cis-acting models. SCD was discussed to be involved in the fatty acid composition trait in pigs as before. It was demonstrated that NPDC1 might participate in antiobesity effects of the sodium alginate in high-fat diet-induced obese mice [54]. Functional enrichment analysis revealed that 42 target DEGs were involved in the unsaturated fatty acid biosynthetic process, iron ion transport, electron transport chain, antigen processing and presentation of peptide antigen, etc. (Table S7). Of these, CBR1, CD74, and SCD were enriched in unsaturated fatty acid biosynthetic process term. The regulatory lncRNAs of CBR1 and CD74 were MSTRG.30986 and MSTRG.4275, respectively. The CD74 and CBR1 expression of adipocyte is involved in fatty acid synthesis process and metabolic complications of obesity [55, 56]. It indicated that these lncRNAs would be involved in the fatness and fatty acid composition traits.

4. Conclusions

In this study, the lncRNA and gene profiles from back-fat tissue were investigated in the Tibetan and Yorkshire pigs. In total, 1,964 lncRNAs were identified through our computational pipeline. Evidence from lncRNAs cis- and trans-targeting analysis demonstrated that some lncRNAs functioned as regulators of fatness and fatty acid composition. Functional analysis revealed that 12 candidate genes contributed to fat acid composition and 10 genes are involved in the pig fatness trait. Association analysis also identified 34 and 57 genes correlated to fatness and fatty acid composition trait, respectively. These findings will help us decipher the fatness and fatty acid composition trait of pig and deserve to be further explored.

Data Availability

All the RNA-seq data was collected in the NCBI database (SRP090525).

Conflicts of Interest

We declare that we have no conflict of interest.

Authors’ Contributions

The conception and design of the study were done by KJW and YC. The performance and analysis of data were by KJW, PS, WTL, GL, JZ, ML, and LW. KJW, PS, and WTL wrote the manuscript and did the revision. Peng Shang and Wenting Li contributed equally to this work and were as co-first authors.

Acknowledgments

This work was supported by the Key Research and Development & Transformation Program of Tibet Autonomous Region of China (XZ201801NB06), the Natural Science Foundation of Tibet Autonomous Region of China (XZ2017ZRG-33), and the Foundation of Henan Educational Committee (19A230006).

Supplementary Materials

Figure S1: summary of RNA-Seq read alignment. Table S1: the list of differentially expressed genes between the Tibetan and Yorkshire group. Table S2: the annotation of identified lncRNAs with the gtf format. Table S3: the list of differentially expressed lncRNAs between the Tibetan and Yorkshire group. Table S4: the functional enrichment results of upregulated DEGs using Metascape. Table S5: the functional enrichment results of downregulated DEGs. Table S6: the trans-targeting gene prediction of lncRNAs. Table S7: functional enrichment analysis of the trans-targeting genes of lncRNAs. (Supplementary Materials)

References

  1. M. Li, S. Tian, L. Jin et al., “Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars,” Nature Genetics, vol. 45, no. 12, pp. 1431–1438, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Gou, J. Peng, Q. Wu, Q. Zhang, H. Zhang, and C. Wu, “Expression pattern of heme oxygenase 1 gene and hypoxic adaptation in chicken embryos,” Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, vol. 174, pp. 23–28, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Dong, N. Yao, Y. Pu et al., “Genomic scan reveals loci under altitude adaptation in Tibetan and Dahe pigs,” PLoS One, vol. 9, no. 10, article e110520, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Ai, B. Yang, J. Li, X. Xie, H. Chen, and J. Ren, “Population history and genomic signatures for high-altitude adaptation in Tibetan pigs,” BMC Genomics, vol. 15, no. 1, p. 834, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Zhang, Y. Chamba, P. Shang et al., “Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in high-altitude adaptation in the Tibetan pig,” Scientific Reports, vol. 7, no. 1, p. 3654, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. K. Wang, Y. Liu, Q. Xu et al., “A post-GWAS confirming GPAT3 gene associated with pig growth and a significant SNP influencing its promoter activity,” Animal Genetics, vol. 48, no. 4, pp. 478–482, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Wang, W. Li, Y. Bai, W. Yang, Y. Ling, and M. Fang, “ssc-miR-7134-3p regulates fat accumulation in castrated male pigs by targeting MARK4 gene,” International Journal of Biological Sciences, vol. 13, no. 2, pp. 189–197, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Li, H. Wu, Z. Luo et al., “An atlas of DNA methylomes in porcine adipose and muscle tissues,” Nature Communications, vol. 3, no. 1, p. 850, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Miao, S. Wang, J. Zhang et al., “Identification and comparison of long non-conding RNA in Jinhua and Landrace pigs,” Biochemical and Biophysical Research Communications, vol. 506, no. 3, pp. 765–771, 2018. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Tao, Y. Liang, X. Yang et al., “Transcriptomic profiling in muscle and adipose tissue identifies genes related to growth and lipid deposition,” PLoS One, vol. 12, no. 9, article e0184120, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Sun, H. Luo, D. Bu et al., “Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts,” Nucleic Acids Research, vol. 41, no. 17, article e166, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Kong, Y. Zhang, Z. Q. Ye et al., “CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine,” Nucleic Acids Research, vol. 35, Supplement 2, pp. W345–W349, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Anders, P. T. Pyl, and W. Huber, “HTSeq—a Python framework to work with high-throughput sequencing data,” Bioinformatics, vol. 31, no. 2, pp. 166–169, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. M. I. Love, W. Huber, and S. Anders, “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2,” Genome Biology, vol. 15, no. 12, p. 550, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Tripathi, M. O. Pohl, Y. Zhou et al., “Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding,” Cell Host & Microbe, vol. 18, no. 6, pp. 723–735, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Guil and M. Esteller, “Cis-acting noncoding RNAs: friends and foes,” Nature Structural & Molecular Biology, vol. 19, no. 11, pp. 1068–1075, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. A. R. Quinlan, “BEDTools: the Swiss-army tool for genome feature analysis,” Current Protocols in Bioinformatics, vol. 47, no. 1, pp. 11.12.1–11.12.34, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. J. E. Chavarro, S. A. Kenfield, M. J. Stampfer et al., “Blood levels of saturated and monounsaturated fatty acids as markers of de novo lipogenesis and risk of prostate cancer,” American Journal of Epidemiology, vol. 178, no. 8, pp. 1246–1255, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Q. Han, T. Y. Gao, G. Y. Yang, and J. J. Loor, “Overexpression of SREBF chaperone (SCAP) enhances nuclear SREBP1 translocation to upregulate fatty acid synthase (FASN) gene expression in bovine mammary epithelial cells,” Journal of Dairy Science, vol. 101, no. 7, pp. 6523–6531, 2018. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Ronnett, E. Kim, L. Landree, and Y. Tu, “Fatty acid metabolism as a target for obesity treatment,” Physiology & Behavior, vol. 85, no. 1, pp. 25–35, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Ros-Freixedes, S. Gol, R. N. Pena et al., “Genome-wide association study singles out SCD and LEPR as the two main loci influencing intramuscular fat content and fatty acid composition in Duroc pigs,” PLoS One, vol. 11, no. 3, article e0152496, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. J. R. Marszalek, C. Kitidis, A. Dararutana, and H. F. Lodish, “Acyl-CoA synthetase 2 overexpression enhances fatty acid internalization and neurite outgrowth,” Journal of Biological Chemistry, vol. 279, no. 23, pp. 23882–23891, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. A. C. Bouwman, H. Bovenhuis, M. H. Visker, and J. A. van Arendonk, “Genome-wide association of milk fatty acids in Dutch dairy cattle,” BMC Genetics, vol. 12, no. 1, p. 43, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Yang, J. Cao, and Y. Shi, “Identification and characterization of a gene encoding human LPGAT1, an endoplasmic reticulum-associated lysophosphatidylglycerol acyltransferase,” Journal of Biological Chemistry, vol. 279, no. 53, pp. 55866–55874, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. J. H. Y. Wu, R. N. Lemaitre, A. Manichaikul et al., “Genome-wide association study identifies novel loci associated with concentrations of four plasma phospholipid fatty acids in the de novo lipogenesis pathway: results from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium,” Circulation: Cardiovascular Genetics, vol. 6, no. 2, pp. 171–183, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. C. de la Rocha, J. E. Pérez-Mojica, S. Z. D. León et al., “Associations between whole peripheral blood fatty acids and DNA methylation in humans,” Scientific Reports, vol. 6, no. 1, article 25867, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Bouchard-Mercier, I. Rudkowska, S. Lemieux, P. Couture, and M. C. Vohl, “Polymorphisms in genes involved in fatty acid β-oxidation interact with dietary fat intakes to modulate the plasma TG response to a fish oil supplementation,” Nutrients, vol. 6, no. 3, pp. 1145–1163, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Warodomwichit, J. Shen, D. K. Arnett et al., “ADIPOQ polymorphisms, monounsaturated fatty acids, and obesity risk: the GOLDN study,” Obesity, vol. 17, no. 3, pp. 510–517, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. I. H. A. Heinonen, R. Boushel, and K. K. Kalliokoski, “The circulatory and metabolic responses to hypoxia in humans – with special reference to adipose tissue physiology and obesity,” Frontiers in Endocrinology, vol. 7, 2016. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Zhang, J. Ma, K. Long et al., “Dynamic gene expression profiles during postnatal development of porcine subcutaneous adipose,” PeerJ, vol. 4, article e1768, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. O. M. Finucane, C. M. Reynolds, F. C. McGillicuddy et al., “Macrophage migration inhibitory factor deficiency ameliorates high-fat diet induced insulin resistance in mice with reduced adipose inflammation and hepatic steatosis,” PLoS One, vol. 9, no. 11, article e113369, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. E. J. Chang, S. K. Lee, Y. S. Song et al., “IL-34 is associated with obesity, chronic inflammation, and insulin resistance,” The Journal of Clinical Endocrinology & Metabolism, vol. 99, no. 7, pp. E1263–E1271, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Xu, G. T. Barnes, Q. Yang et al., “Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance,” The Journal of Clinical Investigation, vol. 112, no. 12, pp. 1821–1830, 2003. View at Publisher · View at Google Scholar · View at Scopus
  34. J. A. Neubauer and J. Sunderram, “Heme oxygenase-1 and chronic hypoxia,” Respiratory Physiology & Neurobiology, vol. 184, no. 2, pp. 178–185, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Burgess, M. Li, L. Vanella et al., “Adipocyte heme oxygenase-1 induction attenuates metabolic syndrome in both male and female obese mice,” Hypertension, vol. 56, no. 6, pp. 1124–1130, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Qu, L. Wang, Y. Min, L. McKennett, J. R. Keller, and P. C. Lin, “Vav1 regulates mesenchymal stem cell differentiation decision between adipocyte and chondrocyte via Sirt1,” Stem Cells, vol. 34, no. 7, pp. 1934–1946, 2016. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Ahmad, S. Kochumon, R. Thomas, V. Atizado, and S. Sindhu, “Increased adipose tissue expression of TLR8 in obese individuals with or without type-2 diabetes: significance in metabolic inflammation,” Journal of Inflammation, vol. 13, no. 1, p. 38, 2016. View at Publisher · View at Google Scholar · View at Scopus
  38. J. S. Kim, D. H. Yoon, H. J. Kim, M. J. Choi, and W. Song, “Resistance exercise reduced the expression of fibroblast growth factor-2 in skeletal muscle of aged mice,” Integrative Medicine Research, vol. 5, no. 3, pp. 230–235, 2016. View at Publisher · View at Google Scholar
  39. D. E. Maridas, V. E. DeMambro, P. T. Le et al., “IGFBP-4 regulates adult skeletal growth in a sex-specific manner,” Journal of Endocrinology, vol. 233, no. 1, pp. 131–144, 2017. View at Publisher · View at Google Scholar · View at Scopus
  40. K. Kurpinski, H. Lam, J. Chu et al., “Transforming growth factor-β and notch signaling mediate stem cell differentiation into smooth muscle cells,” Stem Cells, vol. 28, no. 4, pp. 734–742, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. L. Xiao, T. Sobue, A. Esliger et al., “Disruption of the Fgf2 gene activates the adipogenic and suppresses the osteogenic program in mesenchymal marrow stromal stem cells,” Bone, vol. 47, no. 2, pp. 360–370, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. C. J. Rubin, H. J. Megens, A. M. Barrio et al., “Strong signatures of selection in the domestic pig genome,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 48, pp. 19529–19536, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Chen, B. Tan, E. Karteris et al., “Secretion of adiponectin by human placenta: differential modulation of adiponectin and its receptors by cytokines,” Diabetologia, vol. 49, no. 6, pp. 1292–1302, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. O. Ukkola and M. Santaniemi, “Adiponectin: a link between excess adiposity and associated comorbidities?” Journal of Molecular Medicine, vol. 80, no. 11, pp. 696–702, 2002. View at Publisher · View at Google Scholar · View at Scopus
  45. Y. Zou, P. Lu, J. Shi et al., “IRX3 promotes the browning of white adipocytes and its rare variants are associated with human obesity risk,” EBioMedicine, vol. 24, pp. 64–75, 2017. View at Publisher · View at Google Scholar · View at Scopus
  46. T. Romacho, M. Elsen, D. Rohrborn, and J. Eckel, “Adipose tissue and its role in organ crosstalk,” Acta Physiologica, vol. 210, no. 4, pp. 733–753, 2014. View at Publisher · View at Google Scholar · View at Scopus
  47. Y. Yu, H. du, S. Wei et al., “Adipocyte-derived exosomal miR-27a induces insulin resistance in skeletal muscle through repression of PPARγ,” Theranostics, vol. 8, no. 8, pp. 2171–2188, 2018. View at Publisher · View at Google Scholar · View at Scopus
  48. R. Menghini, V. Casagrande, S. Menini et al., “TIMP3 overexpression in macrophages protects from insulin resistance, adipose inflammation, and nonalcoholic fatty liver disease in mice,” Diabetes, vol. 61, no. 2, pp. 454–462, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. M. Lee, S. J. Song, M. S. Choi, R. Yu, and T. Park, “IL-7 receptor deletion ameliorates diet-induced obesity and insulin resistance in mice,” Diabetologia, vol. 58, no. 10, pp. 2361–2370, 2015. View at Publisher · View at Google Scholar · View at Scopus
  50. S. Ussar, K. Y. Lee, S. N. Dankel et al., “ASC-1, PAT2, and P2RX5 are cell surface markers for white, beige, and brown adipocytes,” Science Translational Medicine, vol. 6, no. 247, article 247ra103, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Rask-Andersen, T. Karlsson, W. E. Ek, and A. Johansson, “Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects,” Nature Communications, vol. 10, no. 1, p. 339, 2019. View at Publisher · View at Google Scholar
  52. J. E. Cronan Jr. and G. L. Waldrop, “Multi-subunit acetyl-CoA carboxylases,” Progress in Lipid Research, vol. 41, no. 5, pp. 407–435, 2002. View at Publisher · View at Google Scholar · View at Scopus
  53. S. S. Sodhi, W. C. Park, M. Ghosh et al., “Comparative transcriptomic analysis to identify differentially expressed genes in fat tissue of adult Berkshire and Jeju native pig using RNA-seq,” Molecular Biology Reports, vol. 41, no. 9, pp. 6305–6315, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. X. Wang, F. Liu, Y. Gao, C. H. Xue, R. W. Li, and Q. J. Tang, “Transcriptome analysis revealed anti-obesity effects of the sodium alginate in high-fat diet -induced obese mice,” International Journal of Biological Macromolecules, vol. 115, pp. 861–870, 2018. View at Publisher · View at Google Scholar · View at Scopus
  55. R. A. Morgan, K. R. Beck, M. Nixon et al., “Carbonyl reductase 1 catalyzes 20β-reduction of glucocorticoids, modulating receptor activation and metabolic complications of obesity,” Scientific Reports, vol. 7, no. 1, article 10633, 2017. View at Publisher · View at Google Scholar · View at Scopus
  56. P. C. Chan, T. N. Wu, Y. C. Chen et al., “Targetted inhibition of CD74 attenuates adipose COX-2-MIF-mediated M1 macrophage polarization and retards obesity-related adipose tissue inflammation and insulin resistance,” Clinical Science, vol. 132, no. 14, pp. 1581–1596, 2018. View at Publisher · View at Google Scholar · View at Scopus