Identification of Key Pathways and Genes in the Subcutaneous Fat Tissue from Different Sexes of Pigs using WGCNA Method

doi:10.21203/rs.3.rs-965422/v1

Download PDF

Research Article

Identification of Key Pathways and Genes in the Subcutaneous Fat Tissue from Different Sexes of Pigs using WGCNA Method

https://doi.org/10.21203/rs.3.rs-965422/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Obesity is a complex disease that will endanger human life and health, and obesity is closely related to subcutaneous fat deposition. Some studies have shown the significant association between subcutaneous fat deposition and sex. However, the molecular mechanisms for this association are still unclear. The objective of this study is to identify key pathways and genes related to the subcutaneous adipose tissue between different sexes of pigs.

Results: A total of 16 coexpression modules were detected, of which two sex-related modules were found. Functional enrichment analyses showed that the genes in the purple module were mainly enriched in some pathways related to lipid metabolism such as Regulation of lipolysis in adipocytes. The genes in the magenta module mainly involved in some pathways related to immunity such as Defense response to virus and Immune response. Furthermore, 7 extracellular matrix (ECM)-related genes and 7 lipid metabolism-related genes were identified in the purple module. In the magenta module, 17 genes and 2 transcription factors participating in Type I interferon response were identified which were associated with immunity.

Conclusions: The present study identified key pathways and genes in subcutaneous adipose tissue from different sexes of pigs, which provided some insights into the molecular mechanism involving in fat formation and immunoregulation between pigs with different sexes. These findings may be helpful for breeding in pig industry and obesity treatment in medicine.

Epigenetics & Genomics

Sex

Pigs

Subcutaneous fat tissue

WGCNA

Key pathways and genes

Obesity is a complex disease that will endanger human life and health. Pigs are important domestic animals and important biomedical models for human diseases, such as obesity, type II diabetes, and cardiovascular diseases, because their body size and physiological/anatomical features are similar to those of humans [1]. As far as we know, adipose tissue is a central metabolic tissue of complex and highly metabolically active which participates in regulating systemic energy homeostasis [2]. The subcutaneous adipose tissue of pigs is very important for the study of fat deposition in pigs and metabolic diseases for human such as obesity. At present, most of the studies mainly focused on the comparative study of differentially expressed genes (DEGs) in subcutaneous adipose tissue between different pig breeds, and identified some important pathways and genes related to fat deposition. For example, Huang et al. analyzed the gene expression profiles of subcutaneous adipose tissues in Laiwu and Large White pigs and found that DEGs were significantly enriched in PPAR signaling pathway and two biological processes, including fatty acid metabolism and fat cell differentiation [3]. Albuquerque et al. identified some important genes involved in de novo fatty acid biosynthesis, elongation and desaturation by analyzing the gene expression profiles of dorsa subcutaneous fat tissues in Iberian and Alentejano pig breeds, such as ACLY, FASN, ME1, ELOVL6, and SCD [4]. Lim et al. identified 136 DEGs in subcutaneous adipose tissue between the Korean native pig and Yorkshire using a cDNA microarray, and found that some DEGs were involved in fat metabolism processes such as adipogenesis, adipocyte differentiation and lipolysis [5]. Madeira et al. found that the mechanisms regulating fat deposition in pigs were genotype and tissue specific, and were associated with the expression regulation of the key lipogenic genes including ACACA, CEBPA, FASN and SCD [6].

In recent years, some studies showed that fat deposition and characteristics were influenced by the sex of pigs [7, 8]. Zhou et al. found that the fat cells of female pigs were larger in volume and the mRNA abundance of related genes was higher by comparing with Rongchang and Landrace male pigs in six different adipose tissues [9]. Mentzel et al. found that many differentially expressed miRNAs were significantly associated with gender by analyzing the expression profile of miRNAs in subcutaneous adipose tissues from male and female crossbred F2 of Göttingen minipigs, Duroc and Yorkshire pigs, which demonstrated that the fat deposition were potentially regulated by miRNAs between genders [10]. Despite some progress, the molecular mechanisms of fat formation involved in gender were still unclear. Especially, the gene coexpression relationship with the sex remained unknown.

Weighted Gene Coexpression Network Analysis (WGCNA) is a systematic biology method to describe the correlation patterns among genes across samples [11]. Compared with other methods, WGCNA focuses on the relationship between coexpression modules and phenotypes [12], and using WGCNA can find the gene coexpression modules with higher reliability and biological significance and identify “driver” genes in the modules [13]. Presently, WGCNA has become the most important way to study the coexpression relationships between genes and been successfully applied in various research fields, such as complex diseases including bladder cancer [14], lung squamous cell carcinoma [15], Hepatocellular carcinoma [16], uveal melanoma [17], hyperlipidaemia [18], obesity [19], and economic traits including meat quality [20], hypoxic adaptation [21] and skin color [22], and so on. Lim et al. identified functional modules and hub genes related to marbling trait in Hanwoo (Korean) cattle using WGCNA method, and these hub genes mainly involved in biological processes correlated with fat or muscle formation [23]. Xing et al. found that four coexpression modules were significantly correlated with backfat thickness in Songliao black and Landrace with high and low backfat using WGCNA method [24]. Besides, Protein and protein interaction (PPI) networks are also viable tools to construct a gene coexpression network and to understand cell functions and disease machinery [25]. However, interpreting a PPI network and identifying core proteins and genes are a particularly challenging task because of the network complexity. For this reason, some algorithms have been developed, including Molecular complex detection, cytoHubba and centiScape, and so on. Zhao et al. identified ADIPOQ, PPARG, LIPE, CIDEC, PLIN1, CIDEA and FABP4 as potentially candidate genes affecting intramuscular fat (IMF) content in 28 purebred Duroc pigs by integrating the results from WGCNA and PPI methods [26].

In the present study, we applied the WGCNA and PPI network analysis method, using RNA-Seq data of subcutaneous fat tissue of male and female pigs (crossbred F2 of Duroc and Göttingen miniature pig) from Gene Expression Omnibus (GEO) database, with the aim to identify the significant modules closely related to sex of pigs, and further identify key pathways and genes. These findings may contribute to further understand the function of adipose tissue of pigs and the mechanisms regulating fat deposition in subcutaneous fat tissue from pigs with different sexes, and provide some insights into the obesity treatment in medicine. Moreover, the identified key genes may serve as the potential biomarkers in pig breeding and potential targets in obesity treatment.

WGCNA and the significant module identification

The expression matrix containing 5000 genes were used to reconstruct the gene coexpression network using WGCNA method. Pearson correlation matrix among genes were converted into a strengthened adjacency matrix by power β=5 based on scale-free topology criterion with R²=0.9 (Fig. 1a). Topological overlap measure (TOM) of each gene pair was calculated, and 16 gene coexpression modules were identified by average linkage hierarchical clustering according to TOM-based dissimilarity (1-TOM) (Fig. 1b). There were large differences in the number of genes between the modules, and the smallest midnightblue module contained 59 transcripts, while the largest turquoise module contained 914 transcripts. The purple module contained 227 transcripts, and the magenta module contained 254 transcripts (Table S1).

The eigengene adjacency heatmap depicting the interaction of the identified modules was showed in Fig. 1c, while the network heatmap plot of all the genes was showed in Fig. 1d. As shown in Fig. 1e, the correlation analysis between Module eigengene (ME) and sex showed that the purple module was the most significantly representative module (cor=-0.73 and P=5e-07), and the magenta module was the third significantly representative module (cor=0.5 and P=0.002). Gene significance (GS) analysis across modules (P=2.2e-259) showed that the purple module and magenta module had the highest and second highest GS, respectively (Fig. 1f). The module membership (MM) in the purple module possessed the most significant correlation across all modules (cor=0.71 and P<4e-36, Fig. 2a), and the MM in the magenta module possessed the second significant correlation across all modules (cor=0.63 and P<1.7e-29, Fig. 2b). Thus, the purple module and magenta module were selected for further analyses. The expression heatmap of all genes in the purple and magenta module were respectively showed in Fig. 2c and 2d.

GO and KEGG analysis and key genes identification

GO and KEGG enrichment analysis were performed on all genes in the purple module and magenta module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. In the purple module, GO enrichment results showed that 9 biological processes (BPs), 7 cellular components (CCs) and 4 molecular functions (MFs) were significantly enriched (P<0.05). KEGG enrichment analysis showed that 6 KEGG pathways were significantly enriched (P<0.05). Top 1 BP, CC and MF terms and Top 4 KEGG pathways were showed in Table 1. KEGG pathway analysis showed that the genes in the purple module were significant enriched in Regulation of lipolysis in adipocytes, Aldosterone synthesis and secretion, Ras signaling pathway and MAPK signaling pathway, and so on. GO analysis showed that the most significant enriched CC term was Extracellular space. The most significant enriched BP term was Muscle organ morphogenesis. The most representative MF term was Heparin binding. In addition, the functional analysis for the magenta module was performed, GO and KEGG enrichment results showed that 23 BPs, 3 CCs, 11 MFs and 12 KEGG pathways were significantly enriched (P<0.05). Top 2 BPs, Top 1 CC and MF terms and top 4 KEGG pathways were showed in Table 2. In KEGG analysis, genes in the magenta module mainly enriched in pathways related to immunity disease, including Influenza A, Hepatitis C, Measles and Herpes simplex infection, and so on. The most significant enriched CC term was External side of plasma membrane, and the significant enriched BP terms were related to immunity process, including Defense response to virus and Immune response. The most representative MF term was Double-stranded RNA binding.

In the purple module, the selection criteria of key genes were that genes participated in two terms, and at least involved in one KEGG pathway. Besides, all the genes involved in the pathway of regulation of lipolysis in adipocytes were selected as key genes. So, 10 key genes, LIPE, NPR1, AQP7, NPY1R, PLA2G16, LOC100522721, FGF10, FGFR2, CACNA1G and IGF1 were identified and showed in Fig. 3a. In the magenta module, the selection criterion of key genes was that genes involved in at least three terms. 12 genes, FAS, TNFSF10, DDX58, MX1, IFIT1, ADAR, OAS2, OAS1, CXCL9, CXCL10, STAT1 and IRF9 were selected as key genes (Fig. 3b).

PPI network construction and hub genes identification

All genes in the purple and magenta modules were used to perform PPI network analysis. At combined score > 0.4, a PPI network including 116 nodes and 169 edges was constructed for the purple module (called the first PPI network), and was showed in Fig. 4a. For the magenta module, a PPI network containing 153 nodes and 814 edges was established and showed in Fig. 4b (called the second PPI network). Top genes with higher scores were identified by centrality analyses in the two modules using the centiScape. The result of centrality analysis for genes with top 10 centrality scores in the first PPI network and top 20 centrality scores in the second PPI network were respectively showed in Table 3 and Table 4. In the purple module, top 10 genes were COL1A2, LOC100738989, MMP2, COL1A1, SPARC, POSTN, SERPINH1, IGF1, FMOD and FKBP10, separately. In the magenta module, top 20 genes were USP18, MX1, STAT1, ISG15, IRG6, MX2, IFIT3, IFIT2, IFIT1, DDX58, OAS1, PARP9, DDX60, IFI44L, LFI35, IFI44, PARP14, IRF9, XAF1 and OAS2, separately.

To further excavate the hub gene in the two modules, the cytoHubba was used to screen out hub genes in the whole PPI network. According the Maximal Clique Centrality (MCC) score, top 10 genes were selected as hub genes in the first PPI network. A sub-network including 10 nodes and 36 edges in the first PPI network was identified and showed in Fig. 4c. These hub genes were COL1A2, MMP2, COL1A1, SPARC, POSTN, SERPINH1, FMOD, ENSSSCG00000017581, ENSSSCG00000029074 and LOC100738989. According to the MCC score, top 20 genes were selected as hub genes in the second PPI network. A sub-network containing 20 nodes and 187 edges in the second PPI network was identified and showed in Fig. 4d. These hub genes were USP18, MX1, STAT1, ISG15, IRG6, ENSSSCG00000027918, MX2, IFIT3, IFIT2, IFIT1, DDX58, OAS1, PARP9, DDX60, IFI35, IFI44, IFI44L, IRF9, XAF1 and OAS2.

Ultimately, 8 hub genes, COL1A2, LOC100738989, MMP2, COL1A1, SPARC, POSTN, SERPINH1 and FMOD in the purple module were identified simultaneously using centiScape and cytoHubba. Function enrichment analysis showed that the 8 genes mainly involved in some pathways including AGE-RAGE signaling pathway in diabetic complications, Relaxin signaling pathway and Proteoglycans in cancer (Fig. 5d). The significant enriched BP terms were Extracellular matrix (ECM) organization and Extracellular structure organization, and so on (Fig. 5a). The significant enriched MF terms were Collagen binding and Extracellular matrix binding, and so on (Fig. 5b). The significant enriched CC terms were Extracellular matrix and Collagen-containing extracellular matrix, and so on (Fig. 5c).

In the magenta module, 19 hub genes, USP18, MX1, STAT1, ISG15, IRG6, MX2, IFIT3, IFIT2, IFIT1, DDX58, OAS1, PARP9, DDX60, IFI35, IFI44, IFI44L, IRF9, XAF1 and OAS2 were identified simultaneously using centiScape and cytoHubba. The functional enrichment analysis was performed for these hub genes. KEGG pathways had Hepatitis C, Coronavirus disease-COVID-19, Measles, Influenza A, Epstein-Barr virus infection, Herpes simplex virus 1 infection, NOD-like receptors signal pathway, RIG-I-like receptors signal pathway and Human papillomavirus infection and so on (Figure 6d). The significant enriched BP terms were Defense response to virus, Response to virus, Immune effector process, Defense response to other organism and Response to type I interferon (Fig. 6a).The enriched MF terms were Double-stranded RNA binding and GTP binding, and so on (Figure 6b). The enriched CC terms were Dendritic spine and Neuron spine, and so on (Fig. 6c).

Differential expression analysis

By the functional analysis and PPI network analysis, 18 genes and 24 genes were selected as key genes in the purple and magenta module, respectively. In the purple module, differential expression analysis showed that 17 genes except for NPY1R (P=0.0502) had significant differences in the purple module between two sex groups (P<0.05 or 0.01, Fig. 7a and 7b). Among 17 genes with significant differences in expression, FMOD, SPARC, POSTN, SERPINH1 and LOC100522721 were significantly different (0.01< P<0.05), and other 12 genes were remarkable significant different (P<0.01). Expression level of NPR1, LIPE, AQP7, PLA2G16 and LOC100522721 in female pigs were significantly higher than that in male pigs, while that of COL1A2, MMP2, COL1A1, SPARC, POSTN, SERPINH1, FMOD, LOC100738989, IGF1, FGF10, FGFR2 and CACNA1G in females were significantly lower than that in males. In the magenta module, 19 genes except for USP18, MX1, IRG6, DDX60 and ADAR including DDX58 (P<0.05), OAS2 (P<0.05), IRF9 (P<0.05), STAT1 (P<0.05), IFIT3 (P<0.05), IFI44L (P<0.05), IFI44 (P<0.05), IFI35 (P<0.05), ISG15 (P<0.05), MX2 (P<0.05), PARP9 (P<0.05), CXCL9 (P<0.05), CXCL10 (P<0.05), XAF1(P<0.05), IFIT1 (P<0.01), IFIT2 (P<0.01), OAS1 (P<0.01), FAS (P<0.01) and TNFSF10 (P<0.01) were significantly differentially expressed between two sex groups, and all the genes were more highly expressed in female pigs (Fig. 7c and 7d).

Key pathways

In our study, two modules including the purple and magenta modules closely related to sex in the subcutaneous fat tissue of pigs were identified using WGCNA method. Function enrichment analysis showed that the genes in the purple module were mainly enriched in some pathways including Regulation of lipolysis in adipocytes, Aldosterone synthesis and secretion, Ras signaling pathway and MAPK signaling pathway. These pathways are related to lipid metabolism [27, 28]. Yao et al. found that male gonadal steroids deficiency caused by castration significantly raised the serum lipid levels and increased fat accumulation in the pigs, and DEGs identified between the boars and barrows were mainly involved in metabolism of lipid, carbohydrate, amino acid and immune diseases pathways [29]. A study showed that male gonosteroids in boars could significantly reduce blood lipid levels and fat accumulation in pigs. Moreover, functional enrichment analysis showed that the downregulated genes in entire males as compared with immunocastrated males were significantly enriched in Adipocytokine signaling pathway, AMPK signaling pathway, Insulin signaling pathway and PPAR signaling pathway [30]. However, The genes in the magenta module were mainly involved in some pathways related to disease immunity, such as Influenza A, Hepatitis C, Measles, Herpes simplex infection, Defense response to virus and Response to virus, and so on. A study demonstrated that sex steroids also could regulate the immune system [31]. After hepatitis C virus and influenza A virus infection, inflammatory immune responses and repair of damaged tissue differed between the sex and impacted the outcome of infection [32, 33]. Therefore, it could be inferred that sex differences might affect fat deposition and immunity in subcutaneous fat tissue of pigs by affecting relevant pathways related to lipogenesis, lipid metabolism and disease immune.

Hub genes in the purple module

In the purple module, 8 hub genes including COL1A2, MMP2, COL1A1, SPARC, POSTN, SERPINH1, FMOD and LOC100738989 were identified by the PPI analysis. Functional enrichment analysis showed that these genes were significantly enriched in AGE-RAGE signaling pathway in diabetic complications, Relaxin signaling pathway, Extracellular matrix (ECM) organization, Extracellular structure organization, ECM, Collagen-containing ECM, Collagen binding and ECM binding. The result was very similar to that from the study of Poklukar et al. [30], and their findings showed that the upregulated genes in entire males as compared with immunocastrated males and surgical castrates were significantly enriched in extracellular region/matrix (ECM) cellular components, ECM receptor interaction and focal adhesion pathways. Some genes responsible for the differences in backfat deposition between the three male sex categories were identified including COL1A2, COL6A3, POSTN, P4HA3, DCN, FMOD, MMP2 and MMP27 [30]. In our study, COL1A2 gene was identified to have the highest degree in the PPI network, which indicated that the gene was at the core of the network. In the ECM remodeling, COL1A2 and COL1A1 genes involves in the synthesis of collagen, which are the major components of ECM [34]. POSTN gene is crucial for the collagen cross-linking and the ECM maintenance [35, 36]. Similarly, FMOD gene is required for proper collagen folding and ECM stabilization [37]. SPARC gene can promote the collagen fibrils development and post-translational modifications [38] and stabilizes the tissue and maintains the balance of lipogenesis and lipolysis [39]. SERPINH1 gene is responsible for the correct folding and secretion of collagen [40], and can regulate the ECM proteins expression [41]. MMP2 gene involves in the ECM degradation [42]. In the ECM remodeling, COL1A2, COL1A1, FMOD, POSTN, SPARC, SERPINH1 and MMP2 play a key role, which influence adipose tissue remodeling. In our study, the 7 genes were all significant differences in expression between different sexes, and expression of the 7 genes in boars were higher compared with sows. Jeong et al. measured the expression levels of ECM-related genes in different adipose tissues from bulls, cows and steers of Korean cattle (Hanwoo), and found that the expressions of ECM-related genes in the omental adipose tissue of cows and steers are decreased, and expression levels of most ECM-related genes were generally similar between cows and steers [43]. Poklukar et al. found that castration of male pigs resulted in the downregulation of genes involved in ECM dynamics [30]. The results of these studies are similar to this study. Thus, it might be inferred that sex differences could affect the expression levels of ECM-related genes (COL1A2, COL1A1, SPARC, POSTN, FMOD, SERPINH1 and MMP2), progress to influence adipose tissue remodeling, and the higher expression of these genes in boars might make boars have a greater fat remodeling capacity than sows.

In the purple module, 10 hub genes including LIPE, NPR1, NPY1R, AQP7, PLA2G16, LOC100522721, IGF1, FGF10, FGFR2 and CACNA1G were identified by the function enrichment analysis. Except for NPY1R, other genes were significant differences in expression between different sexes. Among these DEGs, the expression levels of LIPE, NPR1, AQP7, PLA2G16 and LOC100522721 in sows were significantly higher than that in boars, and other four genes including LIPE, NPR1, AQP7 and PLA2G16 excepting for LOC100522721 participated in Regulation of lipolysis in adipocytes. LIPE gene encodes hormone sensitive lipase which cleaves free fatty acids from the intracellular triacylglycerol [44], and LIPE gene is significantly associated with intramuscular fat content and fatty acid composition of pigs [45, 46]. NPR1 gene promotes the proliferation, differentiation and lipolysis of preadipocytes [47]. AQP7 promotes glycerine excretion in adipose tissue and are highly expressed in adipose tissue [48], and AQP7 deficiency is associated with adipose tissue triglyceride accumulation and adult obesity [49]. Similarly, PLA2G16 is a major phospholipase A2 of adipose tissues that is specific to adipocytes [50]. PLA2G16 encodes a major negative regulator of adipocyte lipolysis, and PLA2G16 deficient mice has a markedly higher rate of lipolysis, and a significantly reduced adipose tissue mass [51, 52]. According to the above, LIPE, NPR1, AQP7 and PLA2G16 mainly involve in the regulation of lipolysis. At present, the four genes had not been reported in the study of fat deposition in pigs between different sexes.

Furthermore, among these DEGs, the expression levels of IGF1, FGF10, FGFR2 and CACNA1G were significantly lower in sows than in boars. Functional enrichment analysis showed that IGF1 participated in Ras signaling pathway, and FGF10 and FGFR2 participated in Ras signaling pathway and MAPK signaling pathway, while CACNA1G participated in Aldosterone synthesis and secretion and MAPK signaling pathway. A study showed that IGF1 action is inhibited in castrated animals, which affects adipocyte proliferation and differentiation [30]. FGF10, belongs to the fibroblast growth factor family, which is widely involved in the regulation of cell growth, proliferation, differentiation and regulation of metabolism [53, 54]. FGFR2 appears to be the most commonly distributed growth factor receptors and mediates most biological functions of the FGF family. Some studies suggested that FGF10 stimulated preadipocyte proliferation and differentiation through activating FGFR2 [55, 56]. Additionally, CACNA1G gene is reported to be associated with RFI, DMI and FCR [57, 58], not been reported in adipocyte proliferation and differentiation of pigs at present. As the above, IGF1, FGF10 and FGFR2 promote adipocyte proliferation and differentiation. Currently, the three genes had not been reported in the adipocyte proliferation and differentiation of pigs between different sexes. Because the expressions of the three genes were higher in boars, which indicated that the three genes might more promote adipocyte proliferation and differentiation of boars. In summary, LIPE, NPR1, NPR1, PLA2G16, IGF1, FGF10 and FGFR2 in the purple module were lipid metabolism-related genes, which might play a different regulatory role in the process of fat deposition and adipocyte proliferation and differentiation of pigs with different sexes.

Hub genes in the magenta module

In the magenta module, 19 genes were identified as hub genes by the PPI analysis. Functional enrichment analysis showed that these genes were mainly enriched in some diseases and signal pathways, such as Hepatitis C, Coronavirus disease-COVID-19, RIG-I-like receptors (RLRs) signal pathway and NOD-like receptors (NLRs) signal pathway, and so on. RLRs and NLRs can sense viral components, such as double/single-strand RNA and DNA. The RLRs play essential roles in the production of type I interferons (IFNs) and proinflammatory cytokines in cell type-specific manners [59]. Among these genes, 15 genes, DDX58, STAT1, IRF9, OAS2, OAS1, IFIT1, IFIT2, IFIT3, ISG15, IFI35, IFI44, IFI44L, MX2, XAF1 and PARP9 were significant differences in subcutaneous adipose tissue of pigs between two sex groups. It has been reported that DDX58 gene belongs to one of the crucial members of RLRs family, and DDX58 can produce type I interferon (IFN) [60, 61], which activates STAT1 and STAT2 and recruits IRF9 gene. The STAT1-STAT2-IRF9 complex activates IFN-stimulated gene factor 3 (ISGF3), which drives the expression of IFN-stimulated genes (ISGs) [62, 63]. A study showed that STAT1 and IRF9 were the transcription factors (TFs) for interferon and played important roles in the immune system [64]. Some studies showed that OAS2 [65], OAS1 [65], IFIT1 [66], IFIT2 [67], IFIT3 [67], ISG15 [68], IFI35 [69], IFI44 [70], IFI44L [70], PARP9 [70], XAF1 [71] and MX2 [72] belonged to the Type I ISGs. OAS1 and OAS2 are the 2′-5′ oligoadenylate synthetase family which consists of antiviral enzymes induced by IFN and is responsible for destroying virus-derived dsRNA [65]. IFIT1, IFIT2 and IFIT3 belong to interferon induced proteins with tetratricopeptide repeats family, which have a great importance in antiviral and immune regulation [67] and restrict virus infection through altering cellular protein synthesis [73]. ISG15, IFI35, IFI44 and IFI44L belong to IFN-induced protein family. ISG15 gene is one of the most abundantly induced ISGs and can effectively control certain viral and bacterial infections [74]. A study indicated that IFI35, IFI44 and IFI44L played important roles in interferon signaling, anti-bacterial and immune regulation [75]. Beside, PARP9 gene promotes the expression of pro-inflammatory genes [76]. XAF1 gene involves in immunoregulatory [71]. MX2 gene mediates antiviral innate immunity [77].

In the magenta module, 12 genes were identified as key genes by the function enrichment analysis. Among these genes, 10 genes, DDX58, STAT1, IRF9, OAS2, OAS1, IFIT1, CXCL9, CXCL10, TNFSF10/TRAIL and FAS were significant differences between two sex groups. The six genes, DDX58, STAT1, IRF9, OAS2, OAS1 and IFIT1 have been discussed before. Some studies showed that CXCL9 [78], CXCL10 [79] and TNFSF10/TRAIL [70] were also Type I ISGs. The proinflammatory chemokines CXCL9 and CXCL10 associate with inflammation and proliferation [80, 81]. TRAIL-system plays a role in regulating autoimmunity [82]. FAS gene involves in activation of the Type I IFN/STAT1 axis [83]. From the above, a total of 19 DEGs in the magenta module participated in Type I IFN response, and it could be inferred that DDX58 gene first promoted the production of type I IFN, while FAS gene participated in activating the Type I IFN/STAT1 axis. Second, the combination of STAT1, STAT2 with IRF9, which drove the expression of 15 ISGs (OAS2, OAS1, IFIT1, IFIT2, IFIT3, ISG15, IFI35, IFI44, IFI44L, PARP9, XAF1, MX2, CXCL9, CXCL10 and TNFSF10) which encoded corresponding immune proteins to carry on specific immune action. Currently, the 17 genes and two TFs (STAT1 and IRF9) had not been reported in the disease immunity of pigs between different sexes. In this study, the expressions of these genes were higher in female pigs. Therefore, the 17 genes, DDX58, FAS, OAS2, OAS1, IFIT1, IFIT2, IFIT3, ISG15, IFI35, IFI44, IFI44L, PARP9, XAF1, MX2, CXCL9, CXCL10 and TNFSF10) and two TFs (STAT1 and IRF9) participated in Type I IFN response which might play a different regulatory role in immunity of pigs between different sexes and might enhance the immunity ability of female pigs though Type I IFN stimulated pathway in cells.

However, there were some limitations in this study. On one hand, small sample size limited the statistical power to identify the hub genes. On the other hand, molecular biological experiments were required to validate the function of these hub genes.

In summary, 16 coexpression modules were identified using WGCNA method, and two of these modules were closely related to sex. Functional enrichment analysis showed that the purple module was related to lipid metabolism, while the magenta module was associated with immunity. 7 ECM-related genes (COL1A2, COL1A1, SPARC, POSTN, FMOD, SERPINH1 and MMP2) and 7 genes related to lipid metabolism (LIPE, AQP7, NPR1, PLA2G16, IGF1, FGF10 and FGFR2) were identified in the purple module. In the magenta module, 17 genes and two TFs participating in Type I IFN response were identified which were associated with immunity. In conclusion, the identified key pathways and genes in subcutaneous adipose tissue from different sexes of pigs, which provided some insights into the molecular mechanism involving in fat formation and immunoregulation between male and female pigs. These findings may be helpful for breeding in pig industry and obesity treatment in medicine.

RNA-seq data and samples information collection

The normalized gene expression dataset of GSE61271 was used in this study, and was retrieved from the public NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/). The GSE61271 dataset was from a study of Kogelman et al. [84], consisting of 36 pig (crossbred F2 of Duroc and Göttingen miniature pig) samples with 100 kg weight (17 females and 19 males).

WGCNA

WGCNA was used to construct the gene coexpression network, and identify the coexpression gene modules. The WGCNA package (version 1.13) based on R was used to perform WGCNA [13]. First, the expression matrix was converted into an adjacency matrix, and an unsupervised coexpression relationship was constructed based on the adjacency matrix using Pearson correlation coefficients for gene pairs. The correlation adjacency matrix was strengthened by power β (soft threshold), and the power parameter was selected based on scale-free topology criterion.

Second, the adjacency matrix was transformed into a topology matrix, and TOM was used to measure the correlation of gene pairs. According to 1-TOM, average linkage hierarchical clustering was performed to classify genes with coherent expression profiles into gene modules. Dynamic cutting algorithm was used to identify gene modules from the system cluster tree. Module eigengene (ME) was defined as the first principal components, and was the representative in module genes. Module membership (MM) was defined as the correlation between ME and gene module. Gene significance (GS) was indexed by log10 transformation of the P value of T-test. The only requirement was that GS of 0 indicates that the gene was not significant with regard to the biological question of interest. The GS could take on positive or negative values. Module significance (MS) was defined as the average GS for all the genes in the module. More detailed description of WGCNA was presented by an original article [13].

Finally, the statistical significance of the relationship between modules and sexes was analyzed by calculating the Pearson correlation coefficient. For studying the genes in the module related to sex, the top two modules with the high GS scores among all modules were selected as the modules of significant for further analysis.

PPI network construction and analysis

The interactive relationships among genes encoding proteins in the significant gene coexpression module were analyzed by constructing a PPI network. The interactive information among genes encoding proteins was retrieved from online the Search Tool for the Retrieval of Interacting Genes (STRING) database (version 11.5, https://string-db.org/). The gene pairs with a combined score ≥ the medium confidence 0.4 were used to construct the PPI network. Cytoscape (v3.8.0) software was used to construct and visualize the interactive relationships among genes in whole PPI network [85].

GO and KEGG analysis

GO and KEGG pathway terms of all genes in the significant module were analyzed using the online DAVID database (version 6.80, https://david.ncifcrf.gov/). The cut-off criterion was set as P-value<0.05. Cytoscape (v3.8.0) software was used to construct and visualize the interactive relationships between genes and functional enrichment terms in the whole network. GO and KEGG enrichment analyses for hub genes in PPI network were implemented using the R-package clusterProfiler software [86]. The cut-off criterion was set as q-value<0.5. GO annotation result includes three main bodies: biological process (BP), molecular function (MF) and cellular component (CC).

Hub genes identification

Hub genes were identified by the cytoHubba algorithm and centiScape 2.2 plugin in the cytoscape software, and key genes were identified using GO and KEGG network analysis. CytoHubba algorithm was used to gain top genes in the whole PPI network, and the criterion of selecting hub genes was that top nodes ranked by Maximal Clique Centrality (MCC) [87]. The centiScape algorithm was applied to calculate the centrality of each gene in the whole PPI network, including Degree Centrality, Betweenness Centrality, Closeness Centrality, EigenVector Centrality, Stress Centrality and Radiality Centrality, and so on [88]. The genes with higher Centrality scores were identified as hub genes.

Statistical analysis

All statistical analyses were performed using R software. The comparison of gene expression in subcutaneous fat tissues with females and male pigs was analyzed using Mean±Standard Deviation. T-test was used to estimate the statistical difference, and 0.01<P<0.05 was considered as significant difference, and P<0.01 was considered as remarkable significant different, and P>0.05 was considered as not significant difference in expression level between two groups.

WGCNA: Weighted gene coexpression network analysis; PPI: Protein and Protein Interaction; GEO: Gene Expression Omnibus; DAVID: Database for Annotation, Visualization and Integrated Discovery; STRING: Search Tool for the Retrieval of Interacting Genes; BP: Biological process; CC: Cellular component; MF: Molecular function; DEGs: Differential expression genes; TOM: Topological overlap measure; 1-TOM: TOM-based dissimilarity; ME: Module eigengene; GS: Gene significance; MM: Module membership; MS: Module significance; MCC: Maximal clique centrality; ECM: Extracellular matrix; IFN: Interferon; ISG: IFN-stimulated gene; RLR: RIG-I-like receptor; NLR: NOD-like receptor; TF: Transcription factor

Acknowledgements

Not applicable.

Authors’ contributions

HYW, XYW, SXL and QC processed and analyzed the data. MLL and SYW assisted with the processing of data. HYW and XYW wrote the manuscript that was subsequently revised by SXL and QC. All authors have read and approved the final manuscript.

Funding

This study was supported by the Yunnan Swine Industry Technology System Program (2019KJTX0013) and Yunnan Province Important National Science & Technology Specific Projects (202102AE090039). These funding agencies played no role in the design of the study, data collection, analysis and interpretation, or in writing the manuscript.

Availability of data and materials

The datasets analysed during the current study are available in the public NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61271)[84].

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Author details

^1.Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan 650201, China; ^2.Faculty of Animal Science, Xichang University, Xichang, Sichuang 615000, China.

Robich MP, Osipov RM, Nezafat R, Feng J, Clements RT, Bianchi C, et al. Resveratrol improves myocardial perfusion in a swine model of hypercholesterolemia and chronic myocardial ischemia. 2010;Circulation122: S142-S49. https://doi.org/10.1161/CIRCULATI ONAHA.109.920132.
Galic S, Oakhill JS, Steinberg GR. Adipose tissue as an endocrine organ. Molecular and Cellular Endocrinology. 2010;316(2):129–39. https://doi.org/10.1016/j.mce.2009.08.018.
Huang WL, Zhang XX, Li A, Xie LL, Miao XY. Differential regulation of mRNAs and lncRNAs related to lipid metabolism in two pig breeds. Oncotarget. 2017;8(50):87539–53. https://doi.org/10.18632/oncotarget. 20978.
Albuquerque A, Óvilo C, Núñez Y, Benítez R, López-Garcia A, García F, et al. Comparative transcriptomic analysis of subcutaneous adipose tissue from local pig breeds. Genes. 2020;11(4):422. https://doi.org/10. 3390/genes11040422.
Lim D, Lee KT, Park JE, Kim H, Kim TH, Choi BH, et al. Analysis of gene expression profiles from subcutaneous adipose tissue of two pig breeds. Genes & Genomics. 2011;33(6):693–99. https://doi.org/10. 1007/s13258-011-0083-4.
Madeira MS, Pires VM, Alfaia CM, Costa AS, Luxton R, Doran O, et al. Differential effects of reduced protein diets on fatty acid composition and gene expression in muscle and subcutaneous adipose tissue of Alentejana purebred and Large White×Landrace×Pietrain crossbred pigs. British Journal of Nutrition. 2013;110(2):216–29. https://doi.org/10.1017/S0007114512004916.
Correa J, Gariepy C, Marcoux M, Faucitano L. Effects of growth rate, sex and slaughter weight on fat characteristics of pork bellies. Meat Science. 2008;80(2):550–54. https://doi.org/10.1016/j.meatsci.2007.12. 018.
Dunshea F, D’souza D. A review: fat deposition and metabolism in the pig. Manipulating Pig Production IX. 2003;127–50.
Zhou S, Li M, Li Q, Guan J, Li X. Differential expression analysis of porcine MDH1, MDH2 and ME1 genes in adipose tissues. Genetics and Molecular Research. 2012;11(2):1254-59. http://dx.doi.org/10.4238/ 2012. May.9.4.
Mentzel CMJ, Anthon C, Jacobsen MJ, Karlskov-Mortensen P, Bruun CS, Jørgensen CB, et al. Gender and obesity specific microRNA expression in adipose tissue from lean and obese pigs. Plos One. 2015;10(7). https://doi.org/10.1371/journal.pone.0131650.
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics & Molecular Biology. 2005;4(1):17. https://doi.org/10.2202/1544-6115.1128.
Chou W, Cheng A, Brotto M, Chuang C. Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BioMed Central. 2014;15(1). https://doi.org/10.1186/1471-2164-15-300.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. https://doi.org/10.1186/1471-2105-9-559.
Di Y, Chen D, Yu W, Yan L. Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis. BioMed Central. 2019;156(1). https://doi.org/10.1186/s41065-019-0083-y.
Jing H, tong XL, Tao S, Qiang C. Systematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy. Translational Lung Cancer Research. 2019;8(5):614–35. https://doi.org/10.21037/tlcr.2019.09.13.
Yin L, Cai Z, Zhu B, Xu C. Identification of key pathways and genes in the dynamic progression of HCC based on WGCNA. Genes. 2018;9(2):92. https://doi.org/10.3390/genes9020092.
Wan Q, Tang J, Han Y, Wang D. Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma. Experimental Eye Research. 2018;166:13–20. https://doi.org/10. 1016/j.exer.2017.10.007.
Liao FJ, Zheng PF, Guan YZ, Pan HW, Li W. Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia. Nutrition & Metabolism. 2021;18(1):24. https://doi.org/ 10.1186/s12986-021-00555-2.
Han Y, Wang W, Jia J, Sun X, Kuang D, Tong P, et al. WGCNA analysis of the subcutaneous fat transcriptome in a novel tree shrew model. Experimental Biology and Medicine. 2020;245(11). https://doi. org/10.1177/1535370220915180.
Kogelman LJ, Byrne K, Vuocolo T, Watson-Haigh NS, Kadarmideen HN, Kijas JW, et al. Genetic architecture of gene expression in ovine skeletal muscle. BMC Genomics. 2011;12(1):607. https://doi.org/ 10.1186/1471-2164-12-607.
Bao Q, Zhang X, Bao P, Liang C, Guo X, Chu M, et al. Using weighted gene co-expression network analysis (WGCNA) to identify the hub genes related to hypoxic adaptation in yak (Bos grunniens). Genes & Genomics. 2021;43:1231–46. https://doi.org/10.1007/s13258-021-01137-5.
Wang L, Liu H, Hu B, Hu J, Li L. Transcriptome reveals genes involving in black skin color formation of ducks. Genes & Genomics. 2021;43(2). https://doi.org/10.1007/s13258-020-01026-3.
Lim D, Lee S, Kim N, Cho Y, Chai H, Seong H, et al. Gene co-expression analysis to characterize genes related to marbling trait in Hanwoo (Korean) cattle. Asian Australasian Journal of Animal Sciences. 2013;26(1):19–29. https://doi.org/10.5713/ajas.2012.12375.
Xing K, Liu H, Zhang F, Liu Y, Shi Y, Ding X, et al. Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes. Journal of Animal Science and Biotechnology. 2021;12:100. https://doi.org/10.1186/s40104-021-00616-9.
Vella D, Marini S, Vitali F, Di Silvestre D, Mauri G, Bellazzi R. MTGO: PPI network analysis via topological and functional module identification. Scientific Reports. 2018;8(1):5499. https://doi.org/10.1038/ s41598-018-23672-0.
Zhao X, Hu H, Lin H, Wang C, Wang Y, Wang J. Muscle transcriptome analysis reveals potential candidate genes and pathways affecting intramuscular fat content in pigs. Frontiers in Genetics. 2020;11:877. https:// doi.org/10.3389/fgene.2020.00877.
Ahmadian M, Wang Y, Sul HS. Lipolysis in adipocytes. International Journal of Biochemistry & Cell Biology. 2010;42(5):555–59. https://doi.org/10.1016/j.biocel.2009.12.009.
Xiao Y, Liu H, Yu J, Zhao Z, Xiao F, Xia T, et al. MAPK1/3 regulate hepatic lipid metabolism via ATG7-dependent autophagy. Autophagy. 2016;12(3):592–93. https://doi.org/10.1080/15548627.2015. 1135282
Yao Y, Ma H, Wu K, Shao Y, Han W, Cai Z, et al. Body composition, serum lipid levels, and transcriptomic characterization in the adipose tissue of male pigs in response to sex hormone deficiency. Gene. 2018;646: 74–82. https://doi.org/10.1016/j.gene.2017.12.057.
Poklukar K, Čandek-Potokar M, Vrecl M, Batorek-Lukač N, Fazarinc G, Kress K, et al. Adipose tissue gene expression of entire male, immunocastrated and surgically castrated pigs. International Journal of Molecular Sciences. 2021;22(4):1768. https://doi.org/10.3390/ijms22041768.
Grossman CJ. Regulation of the immune system by sex steroids. Endocrine Reviews. 1984;5(3):435–55. https://doi.org/10.1210/edrv-5-3-435.
Baden R, Rockstroh JK, Buti M. Natural history and management of hepatitis C: does sex play a role? The Journal of Infectious Diseases. 2014;209 (suppl.3):S81-S85. https://doi.org/10.1093/infdis/jiu057.
Morgan R, Klein SL. The intersection of sex and gender in the treatment of influenza. Current Opinion in Virology. 2019;35:35–41. https://doi.org/10.1016/j.coviro.2019.02.009.
Ruiz-Ojeda FJ, Méndez A, Aguilera CM, Plaza-Díaz J. Extracellular matrix remodeling of adipose tissue in obesity and metabolic diseases. International Journal of Molecular Sciences. 2019;20(19):4888. https://doi. org/10.3390/ijms20194888.
Fumiko N, Masataka N, Takahiro H, Hitoo N, Yasuhiro N, Osamu B, et al. Loss of periostin ameliorates adipose tissue inflammation and fibrosis in vivo. Scientific Reports. 2018;8(1): 8553. https://doi.org/10. 1038/ s41598-018-27009-9.
Schwanekamp JA, Lorts A, Vagnozzi RJ, Vanhoutte D, Molkentin JD. Deletion of periostin protects against atherosclerosis in mice by altering inflammation and extracellular matrix remodeling. Arteriosclerosis, Thrombosis, and Vascular Biology. 2016;36(1):60–68. https://doi.org/10.1161/ATVBAHA.115.306397.
Font B, Eichenberger D, Rosenberg LM, Rest MVD. Characterization of the interactions of type XII collagen with two small proteoglycans from fetal bovine tendon, decorin and fibromodulin. Matrix Biology. 1996;15 (5):341–48. https://doi.org/10.1016/S0945-053X(96)90137-7.
Nie J, Sage EH. SPARC functions as an inhibitor of adipogenesis. Journal of Cell Communication and Signaling. 2009;3(3):247–54. https://doi.org/10.1007/s12079-009-0064-4.
Tartare-Deckert S, Chavey C, Monthouel MN, Gautier N, Obberghen EV. The matricellular protein SPARC/osteonectin as a newly identified factor up-regulated in obesity. Journal of Biological Chemistry. 2001;276(25):22231–37. https://doi.org/10.1074/jbc.M010634200.
Beatriz, Dal, Pont, Duarte, Diego, Bonatto. The heat shock protein 47 as a potential biomarker and a therapeutic agent in cancer research. Journal of Cancer Research & Clinical Oncology. 2018;144:2319–28. https://doi.org/10.1007/s00432-018-2739-9.
Zhu J, Xiong G, Fu H, Evers BM, Zhou BP, Xu R. Chaperone Hsp47 drives malignant growth and invasion by modulating an ECM gene network. Cancer Research. 2015;75(8):1580. https://doi.org/10.1158/0008-5472. CAN-14-1027.
Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. Nature Reviews Molecular Cell Biology. 2014;15:786–801. https://doi.org/10.1038/nrm3904.
Jeong JY, Ibrahim M, Kim MJ, So K, Jeong YD, Park S, et al. Comparisons of extracellular matrix-related gene expression levels in different adipose tissues from Korean cattle. Livestock Science. 2017;198:138–46. https://doi.org/10.1016/j.livsci.2017.02.020.
Large V, Reynisdottir S, Langin D, Fredby K, Arner P. Decreased expression and function of adipocyte hormone-sensitive lipase in subcutaneous fat cells of obese subjects. Journal of Lipid Research. 1999;40(11): 2059–66. https://doi.org/10.1016/S0022-2275(20)32429-9.
Jin B, Xue W, Xu X, Wang Z. Association of the ADRB3, FABP3, LIPE, and LPL gene polymorphisms with pig intramuscular fat content and fatty acid composition. Czech Journal of Animal Science. 2015;60(2):60–66. https://doi.org/10.17221/7975-CJAS.
Zappaterra M, Deserti M, Mazza R, Braglia S, Zambonelli P, Davoli R. A gene and protein expression study on four porcine genes related to intramuscular fat deposition. Meat Science. 2016;121:27–32. https://doi.org/ 10.1016/j.meatsci.2016.05.007.
Huang HY, Zhao G, Liu RR, Li QH, Zheng MQ, Li SF, et al. Brain natriuretic peptide stimulates lipid metabolism through its receptor NPR1 and the glycerolipid metabolism pathway in chicken adipocytes. Biochemistry. 2015;54:6622–30. https://doi.org/10.1021/acs.biochem.5b00714.
Li X, Lei T, Xia T, Chen X, Feng S, Chen H, et al. Molecular characterization, chromosomal and expression patterns of three aquaglyceroporins (AQP3, 7, 9) from pig. Comparative Biochemistry and Physiology. Part B, Biochemistry & Molecular Biology. 2008;149(3). https://doi.org/10. 1016/ j.cbpb.2007.11.014.
Janne L. Metabolic impact of the glycerol channels AQP7 and AQP9 in adipose tissue and liver. Journal of Molecular Endocrinology. 2014;52(2):R165-R78. https://doi.org/10.1530/JME-13-0268.
Duncan RE, Sarkadi-Nagy E, Jaworski K, Ahmadian M, Sul HS. Identification and functional characterization of adipose-specific phospholipase A2 (AdPLA). Journal of Biological Chemistry. 2008; 283 (37). https://doi.org/10.1074/jbc.M804146200.
Ahmadian M, Duncan RE, Sul HS. The skinny on fat: lipolysis and fatty acid utilization in adipocytes. Trends in Endocrinology & Metabolism Tem. 2009;20(9):424–28. https://doi.org/10.1016/j.tem.2009.06.002.
Jaworski K, Ahmadian M, Duncan RE, Sarkadi-Nagy E, Varady KA, Hellerstein MK, et al. AdPLA ablation increases lipolysis and prevents obesity induced by high-fat feeding or leptin deficiency. Nature Medicine. 2009;15:159–168. https://doi.org/10.1038/nm.1904.
Fernandes-Freitas I, Owen BM. Metabolic roles of endocrine fibroblast growth factors. Current Opinion in Pharmacology. 2015;25:30–35. https://doi.org/10.1016/j.coph.2015.09.014.
Itoh N, Ornitz DM. Fibroblast growth factors: from molecular evolution to roles in development, metabolism and disease. Journal of Biochemistry. 2010;149(2):121–30. https://doi.org/10.1093/jb/mvq121.
Fischer C, Seki T, Lim S, Nakamura M, Andersson P, Yang Y. A miR-327-FGF10-FGFR2-mediated autocrine signaling mechanism controls white fat browning. Nature Communications. 2017;8(1):2079. https://doi.org/ 10.1038/s41467-017-02158-z.
Ohta H, Konishi M, Itoh N. FGF10 and FGF21 as regulators in adipocyte development and metabolism. Endocrine, Metabolic & Immune Disorders-Drug Targets. 2011;11(4):302–09. https://doi.org/10.2174/1871 53011797881166.
Abo-Ismail MK, Lansink N, Akanno E, Karisa B, Crowley JJ, Moore S, et al. Development and validation of a small SNP panel for feed efficiency in beef cattle. Journal of Animal Science. 2018;96(2):375–97. https:// doi.org/10.1093/jas/sky020.
Abo-Ismail MK, Vander Voort G, Squires JJ, Swanson KC, Mandell IB, Liao X, et al. Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle. BMC Genetics. 2014;15(1): 1–14. https://doi.org/10.7939/R3RV56.
Takeuchi O, Akira S. Innate immunity to virus infection. Immunological Reviews. 2010;227(1):75–86. https://doi.org/10.1111/j.1600-065X.2008.00737.x.
Chiang JJ, Davis ME, Gack MU. Regulation of RIG-I-like receptor signaling by host and viral proteins. Cytokine & Growth Factor Reviews. 2014;25(5):491–505. https://doi.org/10.1016/j.cytogfr.2014.06.005.
Eisenächer K, Krug A. Regulation of RLR-mediated innate immune signaling-it is all about keeping the balance. European Journal of Cell Biology. 2012;91(1):36–47. https://doi.org/10.1016/j.ejcb.2011.01.011.
Borden EC, Sen GC, Uze G, Silverman RH, Ransohoff RM, Foster GR, et al. Interferons at age 50: past, current and future impact on biomedicine. Nature Reviews Drug Discovery. 2007;6(12):975–90. https:// doi.org/10.1038/nrd2422.
González-Navajas JM, Lee J, David M, Raz E. Immunomodulatory functions of type I interferons. Nature Reviews Immunology. 2012;12(2):125–35. https://doi.org/10.1038/nri3133.
Nguyen H, Hiscott J, Pitha PM. The growing family of interferon regulatory factors. Cytokine and Growth Factor Reviews. 1997;8(4):293–312. https://doi.org/10.1016/S1359-6101(97)00019-1.
Helle K, Henrik GH, Signe E-L, Philippe D, Rune H. The oligoadenylate synthetase family: an ancient protein family with multiple antiviral activities. Journal of Interferon & Cytokine Research: the Official Journal of the International Society for Interferon and Cytokine Research. 2011;31(1). https://doi.org/10. 1089/ jir.2010.0107.
Reynaud JM, Kim DY, Atasheva S, Rasalouskaya A, White JP, Diamond MS, et al. IFIT1 differentially interferes with translation and replication of alphavirus genomes and promotes induction of type I interferon. Plos Pathogens. 2015;11(4). https://doi.org/10.1371/journal.ppat.1004863.
Diamond MS, Farzan M. The broad-spectrum antiviral functions of IFIT and IFITM proteins. Nature Reviews Immunology. 2013;13(1):46–57. https://doi.org/10.1038/nri3344.
Lenschow DJ. Antiviral properties of ISG15. Viruses. 2010;2(10):2154–68. https://doi.org/10.3390/v2102 154.
Imaizumi T, Yano C, Numata A, Tsugawa K, Hayakari R, Matsumiya T, et al. Interferon (IFN)- induced protein 35 (IFI35), a type I interferon-dependent transcript, upregulates inflammatory signaling pathways by activating Toll-like receptor 3 in human mesangial cells. Kidney & Blood Pressure Research. 2016;41(5): 635–42. https://doi.org/10.1159/000447932.
Imgenberg-Kreuz J, Sandling JK, Björk A, Nordlund J, Kvarnström M, Eloranta ML, et al. Transcription profiling of peripheral B cells in antibody-positive primary Sjögren's syndrome reveals upregulated expression of CX3CR1 and a type I and type II interferon signature. Scandinavian Journal of Immunology. 2018;87(5):e12662. https://doi.org/10.1111/sji.12662.
Gilli F, Marnetto F, Caldano M, Sala A, Malucchi S, Capobianco M, et al. Biological markers of interferon-beta therapy: comparison among interferon-stimulated genes MxA, TRAIL and XAF-1. Multiple Sclerosis. 2006;12(1):47. https://doi.org/10.1191/135248506ms1245oa.
Liu K, Liao X, Zhou B, Yao H, Fan S, Chen P, et al. Porcine alpha interferon inhibit Japanese encephalitis virus replication by different ISGs in vitro. Research in Veterinary Science. 2013;95(3):950–56. https://doi. org/10.1016/j.rvsc.2013.08.008.
Sarkar SN, Sen GC. Novel functions of proteins encoded by viral stress-inducible genes. Pharmacology & Therapeutics. 2004;103(3):245–59. https://doi.org/10.1016/j.pharmthera.2004.07.007.
Campbell JA, Lenschow DJ. Emerging roles for immunomodulatory functions of free ISG15. Journal of Interferon & Cytokine Research the Official Journal of the International Society for Interferon & Cytokine Research. 2013;33(12):728. https://doi.org/10.1089/jir.2013.0064.
Maleki-Fischbach M, Leti F, O’connor B, Fingerlin T. Transcription profiling of peripheral B-cells in antibody positive primary Sjögren's syndrome reveals interferon signature and upregulated expression of genes which may be predictive of transformation to lymphoma and correlation to chronic pulmonapy non-tuberculous mycobacterial (NTM) infection. Annals of the Rheumatic Diseases. 2019;78:1547–48. https://doi.org/10.1136/annrheumdis-2019-eular.707.
Iwata H, Goettsch C, Sharma A, Ricchiuto P, Goh W, Halu A, et al. PARP9 and PARP14 cross-regulate macrophage activation via STAT1 ADP-ribosylation. Nature Communications. 2016;7:12849. https://doi. org/10.1038/ncomms12849.
Betancor G, Jimenez-Guardeño J, Lynham S, Antrobus R, Khan H, Sobala A, et al. Phosphorylation of MX2 regulates innate immunity against HIV-1. Research Square. 2021; https://doi.org/10.21203/rs.3.rs-274544/v1.
Hyrcza MD, Kovacs C, Loutfy M, Halpenny R, Heisler L, Yang S, et al. Distinct transcriptional profiles in Ex vivo CD4+ and CD8+ T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8+ T cells. Journal of Virology. 2007;81(7):3477–86. https://doi.org/10.1128/JVI.01552-06.
Brownell J, Bruckner J, Wagoner J, Thomas E, Loo YM, Gale JM, et al. Direct, interferon-independent activation of the CXCL10 promoter by NF-κB and interferon regulatory factor 3 during hepatitis C virus infection. Journal of Virology. 2014;88(3):1582–90. https://doi.org/10.1128/JVI.02007-13.
Luster AD, Ravetch JV. Biochemical characterization of a γinterferon-inducible cytokine (CXCL10). Journal of Experimental Medicine. 1987;166(4):1084–97. https://doi.org/10.1084/jem.166.4.1084.
Marshall A, Celentano A, Cirillo N, Mccullough M, Porter S. Tissue-specific regulation of CXCL9/10/11 chemokines in keratinocytes: Implications for oral inflammatory disease. Plos One. 2017;12(3):e0172821. https://doi.org/10.1371/journal.pone.0172821.
Matsumura R, Umemiya K, Kagami M, Tomioka H, Okumura K. Expression of TNF-related apoptosis inducing ligand (TRAIL) on infiltrating cells and of TRAIL receptors on salivary glands in patients with Sjören's syndrome. Clinical & Experimental Rheumatology. 2002;20(6):791–98. https://doi.org/10.1136/jcp. 2004.022988.
Qadir AS, Stults AM, Murmann AE, Peter ME. The mechanism of how CD95/Fas activates the Type I IFN/STAT1 axis, driving cancer stemness in breast cancer. Scientific Reports. 2020;10(1):1310. https:// doi.org/ 10.1038/s41598-020-58211-3.
Kogelman LJA, Cirera S, Zhernakova DV, Fredholm M, Franke L, Kadarmideen HN. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC Medical Genomics. 2014;7:57. https://doi.org/10.1186/1755-8794-7 -<background-color:#D279AA;>57</background-color:#D279AA;>.
Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods in Molecular Biology (Clifton, N.J.). 2011;696: 291–303. https://doi.org/10.1007/978-1-60761-987 -1_18.
Yu G, Wang L, Han Y, He Q. ClusterProfiler: an R package for comparing biological themes among gene clusters. Omics: a journal of integrative biology. 2012;16(5). https://doi.org/10.1089/omi.2011.0118.
Chin CH, Chen SH, Wu HH, Ho CW, Ko M, Lin CY. CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology. 2014;8(Suppl 4):S11-S11. https://doi.org/10.1186/1752-0509-8-S4-S11.
Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with centiScaPe. Bioinformatics. 2009;25(21):2857–59. https://doi.org/10.1093/bioinformatics/btp517.

Table 1

The results of GO and KEGG enrichment analysis for the purple module using DAVID tool

ID	KEGG/GO term	Gene symbols	P value	Benjamini
KEGG
ssc04923	Regulation of lipolysis in adipocytes	NPR1、LIPE、AQP7、 PLA2G16、NPY1R	0.001646123	0.24856458

ssc04925	Aldosterone synthesis and secretion	NPR1、LIPE、CACNA1G、 PLCB4、CREB3L1	0.004708168	0.355466659

ssc04014	Ras signaling pathway	FGFR2、FGF10、IGF1、PLA2G16、FOXO4、PLA1A、LOC100522721	0.013749858	0.692076201

ssc04010	MAPK signaling pathway	FGFR2、FGF10、DDIT3、CACNA1G、GADD45G、LOC100620270、LOC100522721	0.021823082	0.823821335
Biological process
GO:0048644	Muscle organ morphogenesis	CNTF、GSC、ARID5B	0.001720395	1
Cellular component
GO:0005615	Extracellular space	FGF10、IGF1、MMP2、CHRD、SMPDL3B、VASN、CNTF、SLIT2、CPXM1、RAB11FIP4、LOC100739844、JAM3、C1QTNF6、KERA、AEBP1、IL34、C1QTNF2、FMOD、C1QTNF5、POSTN、MYOC、TINAGL1、PCOLCE、LOC100737806	6.10e-06	7.14e-04

Molecular function
GO:0008201	Heparin binding	FGF10、CHRD、FMOD、SLIT2、 POSTN、PCOLCE、CCDC80	2.26e-04	0.039022012

Table 2

The results of GO and KEGG enrichment analysis for the magenta module using DAVID tool

ID	KEGG/GO term	Gene symbols	P value	Benjamini
KEGG
ssc05164	Influenza A	FAS、STAT1、DDX58、OAS2、OAS1、MX1、IRF9、CXCL10、TNFSF10、ADAR、RSAD2、LOC100157336	1.01e-06	1.26e-04

ssc05162	Measles	FAS、STAT1、DDX58、OAS2、OAS1、MX1、IRF9、TNFSF10、ADAR、IL2RB	8.02e-06	4.97e-04

ssc05168	Herpes simplex infection	FAS、STAT1、DDX58、OAS2、OAS1、IRF9、IFIT1、LOC100157336、TAP2、IL15、SLA-1	1.65e-05	6.84e-04

ssc05160	Hepatitis C	STAT1、DDX58、OAS2、OAS1、 IRF9、IFIT1、IRF1、LDLR	3.30e-04	0.0102426
Biological process
GO:0051607	Defense response to virus	CXCL9、CXCL10、MX1、OAS2、OAS1、IRF1、IFIT1、IFIT2、IFIT3、ISG15、LOC733579、CD8A、RSAD2、PRF1、ADAR	7.95e-12	4.05e-09

GO:0006955	Immune response	FAS、CXCL9、CXCL10、OAS2、OAS1、IL15、CD244、LOC106504143、CTSW、TNFSF10、SMAD6、LTB、SLA-1、LOC100513601	8.50e-08	2.17e-05
Cellular component
GO:0009897	External side of plasma membrane	FAS、CXCL9、CXCL10、CD8A、 CD244、LDLR、CD2、KLRK1	1.77e-04	0.017692488
Molecular function
GO:0003725	Double-stranded RNA binding	DDX58、OAS2、OAS1、DHX58	0.007629956	0.67811581

Table 3

Top 10 genes with higher scores identified by centrality analyses using centiScape in the purple module

Gene	Degree	Betweenness	Closeness	EigenVector	Stress	Radiality
COL1A2	19	837.8553114	0.005747126	0.363597997	2948	4.8
LOC100738989	16	563.4065018	0.005649718	0.329498621	2276	4.773913043
MMP2	15	889.0868132	0.005882353	0.318458854	3766	4.834782609
COL1A1	13	161.1777473	0.005	0.289918992	1024	4.573913043
SPARC	12	45.19917582	0.004878049	0.3010476	334	4.530434783
POSTN	11	215.0187729	0.004975124	0.268061831	1462	4.565217391
IGF1	10	2121.740842	0.00617284	0.124305981	4578	4.904347826
SERPINH1	10	396.5845238	0.00456621	0.214246397	1790	4.408695652
FMOD	9	270.4775641	0.004807692	0.215407852	832	4.504347826
FKBP10	8	464.2095238	0.004504505	0.166622554	2116	4.382608696

Table 4

Top 20 genes with higher scores identified by centrality analyses using centiScape in the magenta module

Gene	Degree	Betweenness	Closeness	EigenVector	Stress	Radiality
USP18	49	1035.767393	0.003861004	0.214286633	8528	6.717105263
MX1	47	1467.536254	0.003891051	0.210942217	9596	6.730263158
STAT1	46	1597.399356	0.003937008	0.199111377	14050	6.75
ISG15	44	646.8937011	0.003745318	0.207317681	5516	6.664473684
IRG6	43	394.756316	0.003745318	0.205145769	5600	6.664473684
MX2	40	1425.201521	0.003717472	0.191899951	9226	6.651315789
IFIT3	39	195.9297022	0.003636364	0.196834362	3234	6.611842105
IFIT2	38	172.7290035	0.003496503	0.189962271	2596	6.539473684
IFIT1	38	1115.323993	0.003597122	0.183495334	7916	6.592105263
DDX58	38	382.6723592	0.003649635	0.187862494	4164	6.618421053
OAS1	37	748.0567294	0.003484321	0.181182841	4892	6.532894737
PARP9	32	135.1189157	0.003436426	0.166187174	2012	6.506578947
DDX60	32	752.0138271	0.003355705	0.164950398	6274	6.460526316
IFI44L	31	65.3413002	0.003333333	0.169057372	1230	6.447368421
IFI35	31	109.2484837	0.003367003	0.161532744	1644	6.467105263
IFI44	31	51.3264405	0.003355705	0.171500135	1016	6.460526316
PARP14	29	86.78062865	0.00330033	0.156260384	1400	6.427631579
IRF9	28	288.8620542	0.00330033	0.153975259	2196	6.427631579
XAF1	26	59.40043809	0.003322259	0.145687373	976	6.440789474
OAS2	26	17.67701199	0.003246753	0.149119245	438	6.394736842

No competing interests reported.

TableS1Thenumberofgenesineachofthe16modules.docx

Download PDF

Editorial decision: Major revision
18 Nov, 2021
Reviews received at journal
04 Nov, 2021
Reviews received at journal
29 Oct, 2021
Reviewers agreed at journal
25 Oct, 2021
Reviewers agreed at journal
25 Oct, 2021
Reviewers invited by journal
25 Oct, 2021
Editor assigned by journal
25 Oct, 2021
Editor invited by journal
25 Oct, 2021
Submission checks completed at journal
25 Oct, 2021
First submitted to journal
12 Oct, 2021

You are reading this latest preprint version

Identification of Key Pathways and Genes in the Subcutaneous Fat Tissue from Different Sexes of Pigs using WGCNA Method

Status:

Version 1

Abstract

Figures

Background

Results

WGCNA and the significant module identification

GO and KEGG analysis and key genes identification

PPI network construction and hub genes identification

Differential expression analysis

Discussion

Key pathways

Hub genes in the purple module

Hub genes in the magenta module

Conclusions

Methods

RNA-seq data and samples information collection

WGCNA

PPI network construction and analysis

GO and KEGG analysis

Hub genes identification

Statistical analysis

Abbreviations

Declarations

Acknowledgements

Authors’ contributions

Funding

Availability of data and materials

Ethics approval and consent to participate

Consent for publication

Competing interests

Author details

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1