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Article

Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle

by
Gustavo Pimenta Schettini
1,*,
Elisa Peripolli
2,
Pâmela Almeida Alexandre
3,
Wellington Bizarria dos Santos
1,
Angélica Simone Cravo Pereira
2,
Lúcia Galvão de Albuquerque
1,
Fernando Baldi
1 and
Rogério Abdallah Curi
4
1
School of Agricultural and Veterinary Sciences, São Paulo State University, Jaboticabal 14884-900, SP, Brazil
2
School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga 13635-900, SP, Brazil
3
Commonwealth Scientific and Industrial Research Organization, Agriculture & Food, St Lucia, QLD 4067, Australia
4
School of Veterinary Medicine and Animal Science, São Paulo State University, Botucatu 18618-681, SP, Brazil
*
Author to whom correspondence should be addressed.
Metabolites 2022, 12(5), 471; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12050471
Submission received: 26 March 2022 / Revised: 5 May 2022 / Accepted: 16 May 2022 / Published: 23 May 2022
(This article belongs to the Special Issue Insights into Increasing Meat Production Using Omics Technologies)
Browse Figure
Review Reports Versions Notes

Abstract

:
Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

1. Introduction

Beef is a source of fats, proteins, vitamins, and minerals necessary in several metabolic pathways in humans [1]. Among the fatty acids (FA), the basic unit of lipids, the beef provides essential fatty acids (EFAs) such as linoleic (LA—C18:2 n6) and alpha-linolenic (ALA—C18:3 n3). These FA are not naturally synthesized by mammals and have been associated with lower incidences of inflammatory and cardiovascular diseases in humans [2]. The lipid absorption and deposition in cattle are influenced by the effects of genetic and ruminal metabolism [3,4,5,6].
Moreover, based on genetic parameters and variability, the FA profile, especially LA and ALA, can improve their concentration in beef through genomic selection [7,8]. This is an important feature since the FA profile is a difficult and expensive trait to measure.
Although genomic-wide association studies (GWAS) have provided information to support the understanding of complex traits, i.e., FA profile and intramuscular fat (IMF) deposition in cattle [9,10,11], this approach has displayed some limitations in pointing out genomic regions that explain a high proportion of genetic variance, as well as the cause-and-effect relationship among the single-nucleotide polymorphism (SNP) genotypes and EFA profile. This can be explained due to the complex and polygenic features of the FA profile. To address these limitations, the results generated by transcriptomic analyses such as differentially expressed genes (DEG), co-expression (COE), and differential co-expression (DCO) could be used.
Notwithstanding that the DEG analysis has been widely used to achieve a better understanding of genetic mechanisms, it is not sufficient to fully identify the gene groups related to complex traits since it does not allow the identification of potential genes that are not differentially expressed [12]. Hence, the DCO emerges due to the capacity to evaluate the global gene expression and its interrelationships to indicate sub-networks and better-connected genes to the phenotype [12,13,14].
The combined use of two co-expression algorithms, weighted gene co-expression network analysis (WGCNA) and partial correlation information theory (PCIT), can point out potential gene groups related to complex traits through different ways [15,16]. The WGCNA clusters genes in co-expression modules based on gene expression patterns between the samples, indicating gene groups correlated to the trait evaluated [15]. Otherwise, the PCIT incorporates theoretical information about transcription factors, which allows, through different metrics, the indication not only of hub genes but also the identification of transcription factors responsible for modulating the expression of differentially expressed genes between contrasting groups [12]. Moreover, the DCO can also assist the GWAS to indicate genomic regions with higher accuracy in integrated -omics studies [17,18].
Nevertheless, studies evaluating the beef FA profile of EFAs in indicine cattle (Bos taurus indicus) are incipient, especially incorporating different methodologies of differential co-expression analysis. Therefore, we aimed to identify potential regulatory transcription factors and hub gene groups related to the EFA profile in the Longissimus thoracis muscle of Nellore cattle finished in feedlot based on two methodologies, WGCNA and PCIT.

2. Results

2.1. Differentially Expressed Genes Analysis

The DEG were identified among the contrasting groups for LA and ALA. The LA groups encompassed 151 genes differentially expressed and enriched in 36 GO:Terms (2 BP, 2 MF, 34 CC) and six KEGG pathways, while the ALA groups displayed 352 DEG in 207 GO:Terms (102 BP, 18 MF, 87 CC) and 10 KEGG pathways (Supplementary Tables S1 and S2). Among the genes identified, we can highlight the acyl-CoA oxidase 2 (ACOX2) and 3-hydroxybutyrate dehydrogenase type 1 (BDH1), listed as upregulated in the LA-H group and enriched in the terms “oxidation-reduction process” (GO:0055114) and “metabolic pathways” (bta01100). The genes succinate dehydrogenase complex iron sulfur subunit B (SDHB), enriched in the terms “oxidation-reduction process” (GO:0055114) and “oxidative phosphorylation” (bta00190), and the fat storage inducing transmembrane protein 1 (FITM1), inserted in the “metabolic process” (GO:0008152), were found upregulated in the ALA-H group. Moreover, the gene acyl-CoA synthetase long-chain family member 3 (ACSL3), also enriched in the GO:0008152, was found downregulated in the ALA-H group. Furthermore, the enoyl-CoA hydratase, short-chain 1 (ECHS1), and isovaleryl-CoA dehydrogenase (IVD) genes, involved in “oxidation-reduction processes” (GO:0055114) and “metabolic pathways” (bta01100), were upregulated in both groups (LA-H and ALA-H).

2.2. Differential Co-Expression Analysis (PCIT—DH)

The correlation matrices for each FA group were performed using the PCIT algorithm and only the correlations ≥|0.9| were used for the identification of DH genes between the contrasting groups. A total of 5649 genes inserted in 732 GO:Terms (419 BP, 115 MF, 198 CC) and 68 KEGG pathways were identified between LA-H and LA-L groups (Supplementary Files S3 and S4). For the ALA-H and ALA-L, a total of 5559 differential hubbing genes and 774 GO terms (454 BP, 106 MF, 214 CC) and 73 KEGG pathways were described (Supplementary Tables S5 and S6). Of these, we can underscore the genes WD repeat domain (WDR43)—“regulation of catalytic activity” (GO:0050790) and “RNA binding” (GO:0003723); ankyrin repeat and SOCS box containing 5 (ASB5)—“protein ubiquitination” (GO:0016567); ER lipid raft associated 1 (ERLIN1)—“regulation of gene expression” (GO:0010468); and TRAF-type zinc finger domain containing 1 (TRAFD1), which presented a high number of connections in the LA-H and ALA-H groups (Supplementary Tables S3–S6 and Table 1). However, when considering the low concentration groups, no common genes were found between LA and ALA.

2.3. Differential Co-Expression Analysis (PCIT—RIF)

In the RIF analysis, 20 potential TF were listed for each FA (Supplementary Tables S7 and S8). Such TF were enriched in 86 GO:Terms (52 BP, 28 MF, 6 CC) to LA and 81 GO:Terms (38 BP, 31 MF, 12 CC) to ALA; in none of the fatty acids were there enriched KEGG pathways (Supplementary Table S9). Six TF were common to both fatty acids, in which we can highlight the nuclear factor IA (NFIA) and histone H4 transcription factor (HINFP), both enriched in the term “regulation of gene expression” (GO:0010468), as well as the zinc finger protein 473 (ZNF473), which was ranked with the more expressive RIF scores (Table 2 and Table 3).
More specifically to the LA, the enriched TF nuclear transcription factor Y subunit alpha (NFYA) and beta (NFYB)—“gene expression” (GO:0010467), as well as the BTB domain and CNC homolog 1 (BACH1) and nuclear factor of activated T cells 5 (NFAT5)—“regulation of biological process” (GO:0050789) positively impacted the target genes’ (DEG) expression in the LA-H group (Supplementary Table S9; Table 2). The presence of the TF zinc finger protein 134 (ZNF134), zinc finger protein 584 (ZNF584), zinc finger and BTB domain containing 43 (ZBTB43), and SLC2A4 regulator (SLC2A4RG) was also pointed out in the RIF analysis (Table 2). On the other hand, impacting more the DEG in LA-L, we highlight the TF WIZ zinc finger (WIZ)—“regulation of biological process” (GO:0050789) (Supplementary Table S9; Table 2).
With the emphasis on the ALA, the TF RAR-related orphan receptor A (RORA), nuclear transcription factor X-box binding 1 (NFX1), melanocyte inducing transcription factor (MITF), and activating transcription factor 6 (ATF6)—“gene expression” (GO:0010467)—and HIVEP zinc finger 2 (HIVEP2) impacted the expression of DEG for the ALA-H group (Supplementary Table S9; Table 3). The DnaJ heat shock protein family (Hsp40) member C1 (DNAJC1), TSC22 domain family member 2 (TSC22D2), and zinc finger protein 800 (ZNF800) were also ranked, although they were not listed within any significant GO:Terms (Supplementary Table S11; Table 3). In contrast, the TF forkhead box S1 (FOXS1), upstream binding protein 1 (UBP1), and nuclear receptor subfamily 2 group C member 1 (NR2C1)—“gene expression” (GO:0010467); GDNF inducible zinc finger protein 1 (GZF1)—“DNA binding” (GO:0003677); and the zinc finger protein FOG family member 1 (ZFPM1) were indicated with the lowest values for RIF1 and RIF2 (Supplementary Table S9; Table 3).

2.4. Differential Co-Expression Analysis (WGCNA)

The DCO-WGCNA identified 521 genes inserted in six modules significantly correlated (p ≤ 0.1) with values higher than |0.4| in the LA-H group (Supplementary Table S10 and Supplementary Figure S3). However, only the lightgreen (−0.59, p = 0.02, n = 123 genes) and skyblue (−0.47, p = 0.08, n = 126 genes) modules had genes enriched in some GO:Terms. It pointed out seven unique component cellular terms, but no KEGG pathway, biological process, or molecular function (Supplementary Table S14). Among these terms, we highlight the “intracellular” (GO:0005622), in which the ATP binding cassette subfamily A member 6 (ABCA6) gene—light green module—was inserted.
Under the same parameters, five modules were identified, harboring 1682 co-expressed genes in the LA-L group (Supplementary Table S11 and Supplementary Figure S4) enriched in unique 245 GO:Terms, 174 BP, 15 MF, 56 CC (Supplementary Table S14), and 9 unique KEGG pathways. We can highlight the SREBF chaperone (SCAP) gene in the darkseagreen4 module (−0.46, p = 0.08, n = 248 genes) enriched in “intracellular” (GO:0005622), as well as the fatty acid desaturase 1 (FADS1) and 2 (FADS2), fatty acid synthase (FASN), and peroxisome proliferator activated gamma receptor (PPARG) genes in the purple module (0.45, p = 0.10, n = 800 genes), enriched in the term “cell differentiation” (GO:0030154) (Supplementary Table S14).
For the ALA, a total of 852 genes were listed in six modules (ALA-H, Supplementary Table S12 and Supplementary Figure S5) enriched in 111 GO:Terms (69 BP, 5 MF, 37 CC) and 17 KEGG pathways (Supplementary Table S14), and 247 genes were inserted in two modules (ALA-L, Supplementary Table S13 and Supplementary Figure S6) enriched in 19 GO:Terms (1 BP, 18 CC). Among them, we can emphasize the ELOVL fatty acid elongase 5 (ELOVL5)—“single-organism metabolic process” (GO:0044710) in the dark olive-green module (−0.49, p = 0.07, n = 474 genes, ALA-H) and the acetyl-CoA carboxylase beta (ACACB) and 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) inserted in the floral-white module (−0.44, p = 0.10, n = 177 genes, ALA-L) enriched in the term “intracellular” (GO:0005622) (Supplementary Table S14).

3. Discussion

3.1. Differentially Expressed Genes

Among the DEG described for the LA, we can highlight the ACOX2, BDH1, ECHS1, and IVD genes, enriched in the term “oxidation-reduction process” (GO:0055114), with higher expression in the LA-H group. The ACOX2 gene corroborates with results from Lim et al. [19], in which it was also identified in Hanwoo cattle with a high marbling score. This gene was also associated with the BDH1 gene’s overexpression in Nellore cattle [10], which suggests a relationship with higher concentrations of FA. This relationship can still be justified by the regulatory action of such genes due to the high concentration of unsaturated fatty acids, as can be seen with the oxidative action genes ECHS1 and IVD [20,21], which were also pointed out as differentially expressed between ALA groups.
Particularly in the ALA-H group, the SDHB gene, enriched in the BP “oxidation-reduction process” (GO:0055114), was identified as upregulated. This gene encodes a subunit that forms the SDH enzyme, responsible for ATP synthesis steps through the Krebs cycle [22]. Moreover, this same enzyme was related to FA concentration in cattle. According to Jeong et al. [23], the SDH enzyme was overexpressed in samples of Longissimus dorsi muscle in Korean cattle after castration, which had a higher rate of IMF compared to non-castrated animals. This fact reiterates the relationship of some genes with the oxidative action and FA profile (i.e., SDHB gene), and it suggests a possible regulatory mechanism associated with a high FA concentration.
The ACSL3 and FITM1 genes, enriched in the “metabolic process” (GO:0008152), were identified as differentially expressed between the ALA groups. The ACSL3 gene has regulatory potential in the lipogenesis process due to its relationship with the intracellular uptake of FA, as well as the enzymatic action triggered by its protein product by providing substrate for the triglyceride synthesis and β-oxidation steps [24,25]. This gene was downregulated in the ALA-H group, a finding justified by the high FA rate. On the other hand, the FITM1 gene was correlated with small deposits of lipids in muscle cells [26] and higher fat accumulation in cell cultures [27]. This fact corroborates our results that showed it to be more expressed in the ALA-H group and suggests a relationship with the high FA concentration.

3.2. Differential Co-Expression Analysis (PCIT—DH)

For the DCO analysis aiming to identify DH genes (PCIT-DH), we highlighted the WDR43, ASB5, TRAFD1, and ERLIN1 genes due to their implications in the LA-H and ALA-H groups. The WDR43 gene acts in the transcriptional control by binding to RNA polymerase II modulating proteins and promoter regions for mRNA formation [28]. Members of the same gene family were related to the FA profile in Nellore cattle [9,11,29], as well as in the marbling score in Hanwoo cattle [30]. The ASB5 gene was associated with cell differentiation and proliferation in muscle tissue [31], besides being related to the marbling score in the Nellore [10] and crossbred Wagyu × Hereford cattle [32]. Although the function of the TRAFD1 gene is not clear, it regulated the DEG expression for IMF content in Iberian swine [33]. The appointment of these genes as DH in the ALA-H and LA-H groups is justified not only by their potential function by regulating gene expression but also because of their impact on lipid metabolism. A similar explanation can be attributed to the ERLIN1 gene, enriched in the term “regulation of gene expression” (GO:0010468), due to its relationship with the ER lipid raft associated 2 (ERLIN2), SCAP, and insulin induced gene 1 (INSIG1) and 2 (INSIG2) for the modulation of the SREBF1 gene, and consequently, to cholesterol homeostasis establishment [34]. This fact is also related to fat deposition, as observed by Huber et al. [35], when indicating the ERLIN1 gene as responsible for triglyceride formation in a cell culture study.
The ORC4 gene—“DNA metabolic process” (GO:0006259) and NAA15—“positive regulation of gene expression” (GO:0010628) were highlighted as some of the most expressive DH in the LA-H and ALA-H groups, respectively. The protein encoded by the ORC4 gene belongs to a protein complex associated with DNA replication [36]. Complementarily, the NAA15 gene was associated with cell proliferation in tumor cells [37], indicating a possible relationship between these genes and the mechanisms underlying cell proliferation and differentiation in the high groups due to the high cell activity in fat synthesis and storage.
Although no common DH genes were identified between the LA-L and ALA-L groups, the FN1, MMP14, FSTL1, LRP1, and DAB2 genes can modulate the gene expression in the LA-L group. Among them, FN1 and MMP14 are closely related to extracellular matrix formation, a fundamental step for cell proliferation and fat deposit formation in muscle tissue [32,38]. The FN1 gene was overexpressed in pre-adipocytes in cell culture [39], besides being differentially expressed in Nellore cattle with contrasting ribeye muscle areas [40]. The FSTL1 gene, enriched in the term “response to starvation” (GO:0042594), acts by promoting adipogenesis and fat accumulation in pre-adipocytes and adipocytes differentiated in cell culture, as well as being upregulated in human non-obese patients [41]. The relationship of these genes with the LA-L group can be justified by the low FA concentration, likewise hypothesizing that the animals in this group are later than those in the high group. The DAB2 gene—“cellular response to lipid” (GO:0071396) and LRP1—“positive regulation of gene expression” (GO:0010628) are related to fat deposition in tissues since they act intimately in cholesterol homeostasis [42,43]. According to Tao et al. [44], the DAB2 gene acts in pre-adipocyte differentiation stages, which is shared by the LRP1 gene, as presented by Masson et al. [45] in a cell culture study. This association with the early stages of adipocyte formation suggests that the animals in the LA-L group are in prior stages to those in the high group.
Finally, the DEK and KMT2E genes, enriched in the term “gene expression” (GO:0010467), were identified as the DH genes in the ALA-L group. Although the DEK gene is related to tumor processes in humans, it is involved in stages of cell differentiation [46], and complementarity to the KMT2E gene, which acts on cell proliferation [47], allows us to reinforce the possibility that ALA-L animals are less precocious. The identification of the PRPF38B, SEC62, and USP8 genes as major DH in the ALA-L group does not indicate a direct association with the FA phenotype due to functions related to the protein complex involved in general transcriptional processes [48,49,50].

3.3. Differential Co-Expression Analysis (PCIT—RIF)

Common to both FA, the TF NFIA and ZNF473 displayed the highest positive RIF1 and RIF2 values, respectively. The NFIA gene was related to the adipogenic processes and cell differentiation since it could regulate the expression of genes such as PPARG in non-differentiated adipocytes of cell cultures [51] and in mice brown fat [52]. The transcription factor ZNF473 was identified as one of the potential regulators of feed efficiency in pigs [53]. Further, this TF was related to energy and lipid metabolism in genomic-wide association and gene co-expression studies in the Nellore cattle [54,55]. These facts corroborate with our results, indicating a relationship of these TF with the EFA metabolism. Moreover, the LA and ALA also shared the TF HINFP with lower RIF2 scores. The TF HINFP can regulate lipid metabolism due to its interaction with the SREBP2 protein, which regulates cholesterol levels and lipid deposition [56]. In this way, the TF HINFP suggests a negative regulatory action under FA biosynthesis.
Similarly, among the TF ranked with the highest RIF1 and RIF2 scores for the LA, we highlight NFYA and NFYB, which modulate the FASN expression in mouse adipocytes, and interact with the SREBP1 protein for FA synthesis and cholesterol homeostasis [57]. Moreover, the identified TF NFAT5 and SLC2A4RG were related to adipogenic processes linked to glucose uptake and cell differentiation [58,59]. This last trait was also linked to the TF BACH1 in a study carried out by Nishizuka et al. [60] in cell cultures, which indicated it as a potential regulator in adipocyte differentiation. These studies corroborate the potential relationship of these TF with DEG expression and, consequently, fat deposition and the EFA profile, especially for the LA. Moreover, the TF ZNF134, ZNF584, and ZBTB43 in RIF1, and WIZ (RIF2), also were highlighted. Although these genes have an unclear relationship with lipid metabolism, the zinc finger family members act in the gene expression of several metabolic processes [61], and therefore, these TF can be crucial in regulating DEG expression.
When considering the RIF analysis for the ALA, the TF with the highest RIF1 (MITF) and RIF2 (ATF6 and HIVEP2) scores were enriched in the term “gene expression” (GO:0010467). The TF MITF indication may have been due to its relationship with the Stearoyl-CoA desaturase (SCD) gene, which acts in unsaturated fatty acid synthesis. According to Vivas-García et al. [62], the FT MITF promoted higher expression of the SCD gene and, consequently, a higher concentration of unsaturated fatty acids in tumor cells. The TF ATF6 and HIVEP2 share a similar potential to regulate adipogenic processes since both interact with the PPARG gene. According to Lowe et al. [63], the ATF6 gene knockout significantly reduced the PPARG expression in cell culture. The TF HIVEP2, also known as Schnurri-2, acts as a co-activator in the expression of the PPARG gene, and, in knockout situations, restricted the PPARG expression during adipocyte differentiation [64]. These relationships reinforce the regulatory potential of these TF under DEG expression in the ALA-H group, and consequently, the EFA profile.
Furthermore, with the ability to impact lipid deposition, TF with higher RIF1 values (ALA, RORA, and NFX1) were described enriched in the term “gene expression” (GO:0010467). Such impact may be due to the interrelation between these TF and lipogenesis-promoting genes. According to Lau et al. [65], the TF RORA has regulatory potential in lipid homeostasis, given that, in knockout situations, this TF induced the lower expression of genes such as FASN and SCD, crucial for FA synthesis. The FT NFX1 encodes a protein with the same name that binds to the NF-Y protein, which regulates the FASN expression, and, consequently, impacts FA synthesis [57,66]. A relationship was also observed by Fernandes Júnior et al. [67] when pointing to the NFX1 gene as a candidate gene to explain a part of the genetic variance for backfat thickness in Nellore cattle.
Otherwise, the TF DNAJC1 (RIF1), TSC22D2 (RIF2), and ZNF800 (RIF2) for the ALA act mainly in cell growth control. The DNAJC1 and TSC22D2 genes were related to a large number of genes to suppress cell proliferation in tumor processes and adverse conditions [68,69]. The ZNF800 gene has a similar function, in which it can inhibit cell proliferation by blocking the AKT/mTOR pathway [70]. These TF with higher positive scores suggest a potential regulatory action to control cell proliferation in animals in the ALA-H group.
With lower RIF1 scores for the ALA, TF such as the FOXS1 and UBP1—“gene expression” (GO:0010467) and GZF1—“DNA binding” (GO:0003677) were identified. The FT FOXS1 is associated with several cell proliferation steps [71], and its overexpression inhibited tumor cell proliferation. The FT GZF1 is also involved in the cell proliferation process; however, it was related to cell proliferation promotion in cell culture [72]. The FT UBP1 acts in angiogenesis by stimulating neovascularization, an important step in pre-adipocytes to promote growth and differentiation in the cellular as well as extracellular matrix [73,74]. These TF allow us to hypothesize that animals in the ALA-L group are more delayed than those in the ALA-H, indicating early stages of adipocyte formation, and consequently, less lipid deposition.
With low RIF1 scores for the ALA, the TF ZFPM1 and NR2C1 are worth highlighting. The FT ZFPM1 (alias name: FOG1) acts as a co-regulator in early adipocyte differentiation stages since it was overexpressed in pre-adipocytes and showed lower expression in mature adipocytes in cell culture [75]. Moreover, ZFPM1 was pointed out as a DH gene in a low genomic value (GEBV) group for IMF in Nellore cattle [76]. Further related to fat metabolism, the NR2C1 gene encodes the TR2 protein, which, when interacting with the TR4 protein product, is related to pathways opposite to those presented by the PPARG gene, and suppresses the expression of Retinoid X receptors [77]. These findings corroborate with our results, by showing that these TF are related to a lower FA concentration.

3.4. Differential Co-Expression Analysis (WGCNA)

The WGCNA methodology employs a different algorithm from that used by the PCIT regarding the gene networks’ formation as well as the gene clustering, complementing each other. This complementarity allowed us to identify other potential DCO genes to improve the understanding of the mechanisms underlying the phenotypes.
The DCO analysis through the WGCNA package identified gene groups important to FA profile development. In the LA-H group, only two modules had GO:Terms enriched, but we highlight the lightgreen module (−0.59, p = 0.02), in which the ABCA6 gene was inserted and pointed out in the component cellular “intracellular” (GO:0005622). According to Wenzel et al. [78], the ABCA6 gene is related to cholesterol uptake, which is closely linked to the SREBF1 gene, and its target gene functions in cholesterol homeostasis, impacting the EFA profile [34]. This fact justifies the presence of ABCA6 in a significant module correlated to LA concentration. For the LA-L group, the SCAP gene, enriched in “intracellular” (GO:0005622) and clustered in the darkseagreen4 module (−0.46, p = 0.08), also interacts with the SREBF1 gene, activating it [34]. The protein complex SCAP:SREBP1 promotes, in low-cholesterol conditions, higher synthesis of FA and cholesterol [34], reinforcing the relationship with the EFA profile, justifying the SCAP co-expression in a module negatively correlated to the LA-L group. Notwithstanding, the purple module (0.45, p = 0.10) pointed out clustered hub genes for the FA phenotype, i.e., PPARG, FASN, FADS1, and FADS2, enriched in “cell differentiation” (GO: 0030154). The PPARG and the FASN genes act in crucial cell proliferation and differentiation stages in adipocytes [79,80,81]. The PPARG impacted the lipid deposition in the Hanwoo cattle since it was overexpressed in high marbling score groups [19]. Similarly, the FASN gene was correlated (0.44) with IMF (Longissimus dorsi) in Korean cattle [82], and it was overexpressed in Iberian pigs with high IMF content [33]. Further, it explained part of the genetic variance for the FA profile in Angus and crossbred cattle musculature [83]. The FADS1 and FADS2 genes act in the FA desaturation process. According to Ralston et al. [84], in adipocyte cell cultures, the highest LA concentration triggered the higher expression of FADS1 and FADS2. Moreover, SNP-type mutations in the FADS2 gene were associated with higher marbling in the Holstein breed [85]. These studies corroborate our results that indicate a positive correlation of the purple module with the LA concentration.
Regarding the ALA, gene groups related to the FA synthesis were identified. The dark olive-green module (−0.49, p = 0.07, ALA-H) encompassing the ELOVL5 gene—“single-organism metabolic process” (GO:0044710) and the floral white module (−0.44, p = 0.10, ALA-L) harboring the ACACB and HMGCS1 genes—“intracellular” (GO:0005622) should be underscored. The ELOVL5 gene acts in stages of unsaturated fatty acid elongation (i.e., C18–20 chains), especially ALA and eicosatrienoic acid (C20:3 n3) [86]. This result corroborates the negative correlation of the dark olive-green module with the ALA concentration. On the other hand, the ACACB and HMGCS1 genes act in the FA oxidation stages [87]. The ACACB gene was related to lower lipid deposition in cattle since it was identified by Zhang et al. [47] as downregulated in castrated animal groups, which showed high lipid deposition, compared to non-castrated animals. The HMGCS1 gene, besides participating in the formation of ketone bodies, is also involved in cholesterol homeostasis and FA uptake together with the same family member, 3-hydroxy-3-methylglutaryl-coenzyme reductase (HMGCR) [34,88]. These results corroborate the module’s correlation with the ALA concentration and suggest that the ACACB and HMGCS1 genes’ co-expression is linked to a lower FA concentration.
In summary, the combination of two complementary differential co-expression methodologies, WGCNA and PCIT, with the differentially expressed genes analysis, allowed us to identify hub genes already described in the literature. Furthermore, unexplored genes and transcription factors able to model the EFA profile in zebu meat cattle were identified, revealing regulatory mechanisms linked to lipid and energy metabolism, as well as cell proliferation and differentiation. Although more studies are needed, these findings show the possible genetic potential in zebu breeds raised in tropical climates to improve meat quality and achieve a healthier EFA profile in meat.

4. Materials and Methods

4.1. Animals and Sampling

We were approved to carry out procedures involving animals by the Animal Use Ethics Committee of the Faculty of Agricultural and Veterinary Sciences (FCAV), Unesp, Jaboticabal/SP (certificate number 18340/16). We used a total of 44 young Nellore bulls, the progeny of six sires, belonging to the Capivara farm (São Paulo state, Brazil), which participated in the Nelore Qualitas breeding program. On this farm, the mating season occurred two times, between the months of February and April, and from mid-November to January. The female animals at 10 to 14 months of age were submitted for three months of the breeding season. Then, the heifers were evaluated by rectal palpation ~60 days after finishing the breeding season to confirm pregnancy. The females who did not get pregnant were exposed again to a breeding season at two years old.
During the raise period, the nutritional management of the animals used in this study was based on grazing conditions using Brachiaria sp. and Panicum sp. forages; moreover, during the dry season, they were offered creep feeding and supplementation. Then, after yearling, the breeding animals were selected, and the remaining were kept in a feedlot for at least 90 days with a diet based on whole-plant silage, sorghum grain, and soybean meal, with a concentrate/roughage ratio of 70/30. All animals were managed in the same lot from birth to the finishing phase.
Next, on the same day, all animals were slaughtered in commercial slaughterhouses with approximately 550 kg of live weight and an average age of 24 months. At the time of the slaughter, the Longissimus thoracis (LT) muscle samples—between the 12th and 13th ribs from the left half carcasses—were collected, properly identified, and subjected to liquid nitrogen. Subsequently, they were stored at −80 °C until further RNA-seq analysis. At the deboning stage, after 48 h of carcass cooling (0–2 °C), in the same place, LT samples (2.5 cm thick) were collected to measure the intramuscular fatty acid profile and content.

4.2. Lipid Extraction and Quantification

The lipid fraction quantification was performed at the Animal Product Technology Laboratory in the Technology Department of FCAV/UNESP (Jaboticabal, São Paulo, Brazil) according to Bligh and Dyer [89]. Approximately 3 g of raw and ground samples of Longissimus thoracis muscle was subjected to lipid extraction, in which, after being transferred to a 250 mL flask, chloroform (10 mL), methanol (20 mL), and distilled water (8 mL) were added. After this, the solutions were homogenized and centrifuged for 30 min on a horizontal shaker table (HITACHI High-Speed Micro Centrifuge model CF16RN himac). Then, an additional 10 mL of chloroform was added together with 10 mL of an aqueous sodium sulfate solution (1.5%) and agitated for 2 min. This solution, in a 50 mL falcon tube, was centrifuged (1000× g-force) at room temperature. The supernatant was discarded and the rest was filtered into measuring cylinders (25 mL) through a paper filter in order to separate the extracted lipid solution. From this last solution, 5 mL was transferred to a 50 mL beaker, dried in an oven, and cooled in a desiccator for at least 24 h. Afterwards, the beaker with the solution was placed in an oven at 110 °C for the evaporation of the solvent, cooled in a desiccator (O/N), and, at the end, weighed. The differences between the initial and final weight of the beaker were used to determine the lipid concentration of the samples.

4.3. Fatty Acid Profile Identification

The FA profile was determined for each sample according to Folch et al. [90]. Approximately 100 g of the ground muscle samples was used. The lipids were extracted and isolated by homogenizing a chloroform and methanol solution in 2:1 ratio and subsequent addition of a 1.5% NaCl solution. After this, as proposed by Kramer et al. [91], the methylation process of the isolated lipids was carried out, and this resulted in the formation of methyl esters; from these FA profile compositions, it was possible to perform quantification using a gas chromatographer (GC-2010 Plus—Shimadzu AOC 20i auto-injector) with an SP-2560 capillary column (100 m × 0.25 mm in diameter with 0.02 mm thickness, Supelco, Bellefonte, PA, USA). With an initial temperature of 70 °C, under continuous heating of 13 °C per minute, an intermediate temperature of 175 °C was established and, when reached, was maintained for 27 min. Thereafter, at a heating speed of 4 °C per minute, the temperature was increased to 215 °C and maintained for 31 min.
The FA identification was performed by comparing the retention time of the methyl esters of the samples with standards C4-C24 (F.A.M.E mix Sigma®), vaccenic acid C18:1 trans-11 (V038-1G, Sigma®), C18:2 trans-10 cis-12 (UC-61 M 100 mg), CLA e C18:2 cis-9, trans-11 (UC-60 M 100 mg), and tricosanoic acid (Sigma®). Then, using the GS solution 2.42 software, the FA were quantified, by normalizing the area under the methyl esters curve, and expressed as a percentage of the total FA methyl ester. This step was performed at the Meat Science Laboratory (LCC) in the Animal Nutrition and Production Department at FMVZ/USP. Among the quantified FA, we selected the fatty acids linoleic (C18:2 ω-6) and alpha-linolenic (C18:3 ω-3) due their importance for human health.

4.4. RNA Extraction

The extraction of total RNA from 44 bovine LT muscle samples (~100 mg) was performed using the extraction protocol via TRIzol® (Life Technologies, Carlsbad, CA, USA). All the samples used in this study obtained RNA integrity number (RIN) ≥ 8, measured by the Agilent equipment 2100 Bioanalyzer® (Agilent, Santa Clara, CA, USA), and were used. The preparation of the cDNA libraries followed the TruSeq® RNA Sample Preparation v2 protocol (Illumina, San Diego, CA, USA) and their quantifications were performed using the KAPA Library Quantification Kit® (KAPA Biosystems, Foster City, CA, USA). Subsequently, barcode sequences were added for individual identification of the samples, which were subjected to sequencing. The method used for sequencing was the paired-end, which produces fragments of 2 × 100 bp. The HiSeq 2500 equipment (Illumina, San Diego, CA, USA) and the TruSeq PE Cluster kit v3-cBot-HS and TruSeq SBS v3-HS kit (Illumina, San Diego, CA, USA) were used for its realization. The sequencing analysis was performed at the Genome Center at ESALQ/USP (Piracicaba, São Paulo, Brazil).

4.5. Read Alignment and Gene Count

HiSeq platform raw sequencing data were converted into FASTQ files and separated into individual libraries using the Casava v.1.8.2 software (Illumina, San Diego, CA, USA). These files were submitted to the software FastQC v. 0.11.9 [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ accessed on 26 November 2019] and Trimmomatic v. 0.32 [92] to perform quality control, and to identify and remove PCR adapters and primers, as well as low-quality and/or small sequences (<36 bp). The reads were then aligned to the Bos taurus taurus genome assembly ARS-UCD1.2 using the STAR v.2.7.0 program [93]. Subsequently, from the SAMtools v1.09 software [94], a second quality control procedure was performed to remove low-quality alignments, secondary alignments, and PCR duplicates. Afterwards, the gene counts were performed by the HTSeq Python package [95], and they were used for subsequent gene expression analyses (DEG and DCO).
Moreover, we evaluated gene expression records on a principal component analysis and hierarchical clustering. However, it was not able to identify a clear group (Figure 1; Supplementary Figures S1 and S2).

4.6. Differentially Expressed Genes Analysis

For the differentially expressed analysis (DEG), animals were grouped based on their concentrations for each selected FA (linoleic, LA—C18:2 n6 and alpha-linolenic, ALA—C18:3 n3). Two groups were formed by considering those samples with the 15 highest (high—H) values/phenotypes and the 15 lowest (low—L), totaling four main groups: LA-H, LA-L, ALA-H, and ALA-L. A significant (p < 0.0001) difference was observed, by t test [96], between the means of the highest and lowest FA concentration groups (Table 4). The DEG analysis was performed through the R package DESeq2 [96] and was used in two EFA-specific groups, where each group had 30 samples (15 high and 15 low). The input gene set was filtered, and genes that had less than one count per million (cpm) in 90% of the samples were filtered out.

4.7. Gene Co-Expresion Analysis: Partial Correlation and Information Theory (PCIT)

For the differential co-expression analyses (DCO), the partial correlation and information theory (PCIT) algorithm implemented in R software [16,97] was used (DCO-PCIT). The quality control based on filter genes with less than 1 cpm resulted in 10,739 genes from 27,270 genes. Next, the raw count data were normalized (cpm), and their variance was stabilized by the variance stabilizing transformation function on DESeq2 [96]. The correlation matrices for the gene expression values were performed separately for each of the four FA groups evaluated (LA-H, LA-L, ALA-H, and ALA-L) using the PCIT package [97]. From these matrices, only significant correlations ≥|0.9| were used to perform the differential hubbing (DH) analysis. The measurement of DH values was based on the difference in the quantity of significant correlations ≥|0.9| of each gene between the contrasting groups. Genes were then ranked based on the top five DH values (highest and lowest) for each FA.
The regulatory impact factor (RIF) scores were calculated as described by Reverter et al. [98] and they are displayed as z-scores. These scores reflect the co-expression of each transcription factor (TF), previously informed, and its potential target genes, i.e., differentially expressed genes identified among contrasting EFA groups. There are two different ways to calculate the RIF measurements. In the first one (RIF1), the RIF score is strongly influenced by large differences in the correlation values between TF and DEG between the divergent groups. In the second one (RIF2), the RIF score is affected mainly by the magnitude of the potential target genes’ (DEG) expression induced by TF expression. The positive values for both scores indicate a better connection with the groups with high concentrations of the assessed fatty acid and, therefore, the opposite situation reveals a relationship with the low groups. For the TF selection, 740 TF were considered, which were expressed among the FA groups and were deposited in the Animal Transcription Factor Database (AnimalTFDB) v.3.0 [99]. Only the TF which had their scores with ±2.58 standard deviation (99%) in relation to the mean were selected for functional and network analysis.

4.8. Gene Co-Expresion Analysis: Weighted Gene Co-Expression Network Analysis (WGCNA)

For the differential co-expression analysis (DCO), the weighted gene co-expression network analysis (WGCNA) package implemented in R software [15] was used (DCO-WGCNA). The two specific EFA groups (LA and ALA) that included the highest (n = 15) and lowest (n = 15) samples were analyzed. As in PCIT analyses, the raw gene counts were normalized (cpm) and had their variance stabilized (variance stabilizing transformation) as well as filtered. These groups were analyzed individually, and the connectivity values were calculated among all the genes in the network. The connectivity measure represents the degree of connection between each pair of genes in the network, and it is obtained through the sum of Pearson’s correlations raised to the soft power threshold (β). It should be noted that β is individual for each analysis, and it should approach the criterion of scale-free topology (R2 ≥ 0.8) [100]. The values of β = 9 and 8 were used for LA-H and ALA-H and β = 13 and 10 for LA-L and ALA-L, respectively. For the gene clustering and module formation, the unsigned step-by-step method was used, implemented in the hierarchical clustering algorithm (dynamic tree-cutting) package [15]. A minimum size of 30 genes per module and a dissimilarity of >0.20 among the modules were applied [15,101].
Each module has an eigengene measurement (ME), which corresponds to the representation of the first main component of the expression profile of the entire module [15]. This measure was correlated with the evaluated traits in the study, and modules that showed correlations of ≥|0.4| as significant (p ≤ 0.1) were selected for further analysis.

4.9. Functional Enrichment

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 tool [102,103] was used to identify overrepresented gene ontology (GO) terms and KEGG pathways using the list of genes from the differential analyses and the Bos taurus annotation file as a background. The list of genes identified in the DEG and both DCO (PCIT and WGCNA) analyses was subjected separately to functional enrichment. The p-values were adjusted by False Discovery Rate (FDR) [104], and significant GO:Terms—biological process (BP), molecular function (MF), and component cellular (CC)—and KEGG pathways were considered when p-adj ≤ 0.05.

5. Conclusions

The DEG and DCO analyses combined allowed us to point out potential unexplored genes/transcription factors and biological processes underlying the EFA (LA and ALA) profile in the Longissimus thoracis muscle of Nellore cattle finished in the feedlot. Among them, we can highlight the differentially expressed genes ECHS1 and IVD, as well as the hubbing genes ASB5 and ERLIN1, and the TF NFIA, since these genes displayed an outstanding capacity to strongly impact both EFA, linoleic and alpha-linolenic acids. Furthermore, the TF NFYA, NFYB, FASN, and PPARG and FADS2 genes associated with the LA, as well as the TF RORA and ELOVL5 gene with ALA, were able to directly impact the phenotypes.
Our findings contribute to the pinpointing of the potential biomarkers for this complex, arduous, and late measuring trait. Moreover, our results provide information that would help us to better understand the genetic and physiological mechanisms related to the synthesis of essential fatty acids in cattle. Furthermore, they can support -omics studies, especially genomic-wide association studies and genomic selection, since this information could be used to weight some makers located on transcriptional factors or nearby regulatory genes, which can regulate gene expression and related pathways. Therefore, this also could assist in the early selection of animals with a healthier essential fatty acid profile.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/metabo12050471/s1, Table S1: List of differentially expressed genes for fatty acid phenotypes; Table S2: List of GO:Terms and KEGG pathways enriched in differentially expressed genes for fatty acid phenotypes by functional enrichment analysis on DAVID web-tool; Table S3: List of differential hubbing genes—Linoleic acid; Table S4: List of differential hubbing genes—Alpha-linolenic acid; Table S5: List of GO:Terms and KEGG pathways enriched in differential hubbing genes by functional enrichment analysis on DAVID web-tool—Linoleic acid; Table S6: List of GO:Terms and KEGG pathways enriched in differential hubbing genes by functional enrichment analysis on DAVID web-tool—Alpha-linolenic acid; Table S7: List of transcription factors retrieved by regulatory impact factor analysis (PCIT)—Linoleic acid; Table S8: List of transcription factors retrieved by regulatory impact factor analysis (PCIT)—Alpha-linolenic acid; Table S9: List of GO:Terms and KEGG pathways enriched in transcription factors identified for fatty acid phenotypes by functional enrichment analysis on DAVID web-tool; Table S10: List of genes inserted in significant co-expression modules (WGCNA)—Linoleic acid (HIGH); Table S11: List of genes inserted in significant co-expression modules (WGCNA)—Linoleic acid (LOW); Table S12: List of genes inserted in significant co-expression modules (WGCNA)—Alpha-linolenic acid (HIGH); Table S13: List of genes inserted in significant co-expression modules (WGCNA)—Alpha-linolenic acid (LOW); Table S14: List of GO:Terms and KEGG pathways enriched in differentially co-expressed genes for fatty acid phenotypes by functional enrichment analysis on DAVID web-tool. Figure S1: Principal component analysis (PCA) plot of 44 animals evaluated for essential fatty acids (LA and ALA); Figure S2: Hierarchical clustering between the samples in EFA groups; Figure S3: Module–trait relationships (C18:2 n-6—LA-H); Figure S4: Module–trait relationships (C18:2 n-6—LA-L); Figure S5: Module–trait relationships (C18:3 n-3—ALA-H); Figure S6: Module–trait relationships (C18:3 n-3—ALA-L).

Author Contributions

Conceptualization, G.P.S., E.P., W.B.d.S., F.B. and R.A.C.; methodology, G.P.S., P.A.A., W.B.d.S., A.S.C.P., L.G.d.A., F.B. and R.A.C.; software, G.P.S. and P.A.A.; validation, G.P.S., E.P., P.A.A., F.B. and R.A.C.; formal analysis, G.P.S.; investigation, G.P.S., E.P., W.B.d.S., F.B. and R.A.C.; resources, A.S.C.P., L.G.d.A., F.B. and R.A.C.; data curation, G.P.S., E.P. and W.B.d.S.; writing—original draft preparation, G.P.S.; writing—review and editing, E.P., P.A.A., W.B.d.S., A.S.C.P., L.G.d.A., F.B. and R.A.C.; visualization, G.P.S., E.P. and W.B.d.S.; supervision, F.B. and R.A.C.; project administration, F.B. and R.A.C.; funding acquisition, L.G.d.A., F.B. and R.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

The characterization of the meat fatty acid profile in the Nellore cattle and the RNA sequencing were partially funded by São Paulo Research Foundation—FAPESP through the projects “Study of the genetic variability of meat fatty acid profile in Nellore cattle finished in feedlot” (FAPESP grant #2011/2141-0), and “Genetic aspects of quality, efficiency, and sustainability of meat production in Nellore breed animals” (FAPESP grants #2017/10630-2 and #2018/20026-8).

Institutional Review Board Statement

The study was conducted in line with the Animal Use Ethics Committee of the Faculty of Agricultural and Veterinary Sciences (FCAV), Unesp, Jaboticabal/SP (certificate number 18340/16).

Data Availability Statement

The RNA-seq data were submitted to the Sequence Read Archive (SRA) database and are available under accession number: PRJNA780472.

Acknowledgments

To the post-graduate program in Genetics and Animal Breeding from the Faculty of Agricultural and Veterinary Sciences—São Paulo State (FCAV—UNESP) in conjunction with the Coordination Office for Advancement of University-Level Personnel (CAPES), which provided all the support to carry out the analyses.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Biesalski, H.K. Meat as a Component of a Healthy Diet-Are There Any Risks or Benefits If Meat is Avoided in the Diet? Meat Sci. 2005, 70, 509–524. [Google Scholar] [CrossRef] [PubMed]
  2. Eilander, A.; Harika, R.K.; Zock, P.L. Intake and Sources of Dietary Fatty Acids in Europe: Are Current Population Intakes of Fats Aligned with Dietary Recommendations? Eur. J. Lipid Sci. Technol. 2015, 117, 1370–1377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Costa, M.; Alves, S.P.; Cappucci, A.; Cook, S.R.; Duarte, A.; Caldeira, R.M.; McAllister, T.A.; Bessa, R.J.B. Effects of Condensed and Hydrolyzable Tannins on Rumen Metabolism with Emphasis on the Biohydrogenation of Unsaturated Fatty Acids. J. Agric. Food Chem. 2018, 66, 3367–3377. [Google Scholar] [CrossRef] [PubMed]
  4. da Costa, A.S.; Pires, V.M.; Fontes, C.M.; Prates, J.A.M. Expression of Genes Controlling Fat Deposition in Two Genetically Diverse Beef Cattle Breeds Fed High or Low Silage Diets. BMC Vet. Res. 2013, 9, 118. [Google Scholar] [CrossRef] [Green Version]
  5. Fiorentini, G.; Lage, J.F.; Carvalho, I.P.C.; Messana, J.D.; Canesin, R.C.; Reis, R.A.; Berchielli, T.T. Lipid Sources with Different Fatty Acid Profile Alters the Fatty Acid Profile and Quality of Beef from Confined Nellore Steers. Asian-Australas. J. Anim. Sci. 2015, 28, 976–986. [Google Scholar] [CrossRef] [Green Version]
  6. Osorio, J.S.; Moisa, S.J. Gene Regulation in Ruminants: A Nutritional Perspective. In Gene Expression and Control; IntechOpen: London, UK, 2019; pp. 1–27. [Google Scholar]
  7. Aboujaoude, C.; Pereira, A.S.C.; Feitosa, F.L.B.; De Lemos, M.V.A.; Chiaia, H.L.J.; Berton, M.P.; Peripolli, E.; Silva, R.M.D.O.; Ferrinho, A.M.; Mueller, L.F.; et al. Genetic Parameters for Fatty Acids in Intramuscular Fat from Feedlot-Finished Nelore Carcasses. Anim. Prod. Sci. 2016, 58, 234–243. [Google Scholar] [CrossRef] [Green Version]
  8. Chiaia, H.L.J.; Peripoli, E.; de Oliveira Silva, R.M.; Aboujaoude, C.; Feitosa, F.L.B.; de Lemos, M.V.A.; Berton, M.P.; Olivieri, B.F.; Espigolan, R.; Tonussi, R.L.; et al. Genomic Prediction for Beef Fatty Acid Profile in Nellore Cattle. Meat Sci. 2017, 128, 60–67. [Google Scholar] [CrossRef] [Green Version]
  9. Cesar, A.S.M.; Regitano, L.C.A.; Mourão, G.B.; Tullio, R.R.; Lanna, D.P.D.; Nassu, R.T.; Mudado, M.A.; Oliveira, P.S.N.; do Nascimento, M.L.; Chaves, A.S.; et al. Genome-Wide Association Study for Intramuscular Fat Deposition and Composition in Nellore Cattle. BMC Genet. 2014, 15, 39. [Google Scholar] [CrossRef] [Green Version]
  10. Fonseca, L.F.S.; dos Santos Silva, D.B.; Gimenez, D.F.J.; Baldi, F.; Ferro, J.A.; Chardulo, L.A.L.; de Albuquerque, L.G. Gene Expression Profiling and Identification of Hub Genes in Nellore Cattle with Different Marbling Score Levels. Genomics 2020, 112, 873–879. [Google Scholar] [CrossRef]
  11. Lemos, M.V.A.; Chiaia, H.L.J.; Berton, M.P.; Feitosa, F.L.B.; Aboujaoud, C.; Camargo, G.M.F.; Pereira, A.S.C.; Albuquerque, L.G.; Ferrinho, A.M.; Mueller, L.F.; et al. Genome-Wide Association between Single Nucleotide Polymorphisms with Beef Fatty Acid Profile in Nellore Cattle Using the Single Step Procedure. BMC Genom. 2016, 17, 213. [Google Scholar] [CrossRef] [Green Version]
  12. Hudson, N.J.; Dalrymple, B.P.; Reverter, A. Beyond Differential Expression: The Quest for Causal Mutations and Effector Molecules Keywords Why Skeletal Muscle ? BMC Genom. 2012, 13, 356. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Chowdhury, H.A.; Bhattacharyya, D.K.; Kalita, J.K. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM Trans. Comput. Biol. Bioinform. 2019, 17, 1154–1173. [Google Scholar] [CrossRef]
  14. Singh, A.J.; Ramsey, S.A.; Filtz, T.M.; Kioussi, C. Differential Gene Regulatory Networks in Development and Disease. Cell. Mol. Life Sci. 2018, 75, 1013–1025. [Google Scholar] [CrossRef] [PubMed]
  15. Langfelder, P.; Horvath, S. WGCNA: An R Package for Weighted Correlation Network Analysis Peter. BMC Bioinform. 2008, 9, 559. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Reverter, A.; Chan, E.K.F. Combining Partial Correlation and an Information Theory Approach to the Reversed Engineering of Gene Co-Expression Networks. Bioinformatics 2008, 24, 2491–2497. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Deng, T.; Liang, A.; Liang, S.; Ma, X.; Lu, X.; Duan, A.; Pang, C.; Hua, G.; Liu, S.; Campanile, G.; et al. Integrative Analysis of Transcriptome and GWAS Data to Identify the Hub Genes Associated with Milk Yield Trait in Buffalo. Front. Genet. 2019, 10, 36. [Google Scholar] [CrossRef] [Green Version]
  18. Yan, Z.; Huang, H.; Freebern, E.; Santos, D.J.A.; Dai, D.; Si, J.; Ma, C.; Cao, J.; Guo, G.; Liu, G.E.; et al. Integrating RNA-Seq with GWAS Reveals Novel Insights into the Molecular Mechanism Underpinning Ketosis in Cattle. BMC Genom. 2020, 21, 489. [Google Scholar] [CrossRef]
  19. Lim, D.; Chai, H.-H.; Lee, S.-H.; Cho, Y.-M.; Choi, J.-W.; Kim, N.-K. Gene Expression Patterns Associated with Peroxisome Proliferator-Activated Receptor (PPAR) Signaling in the Longissimus Dorsi of Hanwoo (Korean Cattle). Asian-Australas. J. Anim. Sci. 2015, 28, 1075–1083. [Google Scholar] [CrossRef] [Green Version]
  20. Houten, S.M.; Wanders, R.J.A. A General Introduction to the Biochemistry of Mitochondrial Fatty Acid β-Oxidation. J. Inherit. Metab. Dis. 2010, 33, 469–477. [Google Scholar] [CrossRef] [Green Version]
  21. Schulz, H. Oxidation of Fatty Acids in Eukaryotes. In Biochemistry of Lipids, Lipoproteins and Membranes; Elsevier: Amsterdam, The Netherlands, 2002; pp. 131–154. ISBN 9780444532190. [Google Scholar]
  22. Rutter, J.; Winge, D.R.; Schiffman, J.D. Succinate Dehydrogenase—Assembly, Regulation and Role in Human Disease. Mitochondrion 2010, 10, 393–401. [Google Scholar] [CrossRef] [Green Version]
  23. Jeong, J.; Bong, J.; Kim, G.D.; Joo, S.T.; Lee, H.-J.; Baik, M. Transcriptome Changes Favoring Intramuscular Fat Deposition in the Longissimus Muscle Following Castration of Bulls1. J. Anim. Sci. 2013, 91, 4692–4704. [Google Scholar] [CrossRef] [PubMed]
  24. Rakhshandehroo, M.; Knoch, B.; Müller, M.; Kersten, S. Peroxisome Proliferator-Activated Receptor Alpha Target Genes. PPAR Res. 2010, 2010, 612089. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Yan, S.; Xue-Feng, Y.; Hao-Lei, L.; Nian, F.; Yan, O.; Kai, Q. Long-Chain Acyl-CoA Synthetase in Fatty Acid Metabolism Involved in Liver and Other Diseases: An Update. World J. Gastroenterol. 2015, 21, 3492. [Google Scholar] [CrossRef] [PubMed]
  26. Gross, D.A.; Zhan, C.; Silver, D.L. Direct Binding of Triglyceride to Fat Storage-Inducing Transmembrane Proteins 1 and 2 Is Important for Lipid Droplet Formation. Proc. Natl. Acad. Sci. USA 2011, 108, 19581–19586. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Kadereit, B.; Kumar, P.; Wang, W.-J.; Miranda, D.; Snapp, E.L.; Severina, N.; Torregroza, I.; Evans, T.; Silver, D.L. Evolutionarily Conserved Gene Family Important for Fat Storage. Proc. Natl. Acad. Sci. USA 2008, 105, 94–99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Bi, X.; Xu, Y.; Li, T.; Li, X.; Li, W.; Shao, W.; Wang, K.; Zhan, G.; Wu, Z.; Liu, W.; et al. RNA Targets Ribogenesis Factor WDR43 to Chromatin for Transcription and Pluripotency Control. Mol. Cell 2019, 75, 102–116.e9. [Google Scholar] [CrossRef]
  29. Oliveira, P.S.N.; Coutinho, L.L.; Cesar, A.S.M.; Da Silva Diniz, W.J.; De Souza, M.M.; Andrade, B.G.; Koltes, J.E.; Mourão, G.B.; Zerlotini, A.; Reecy, J.M.; et al. Co-Expression Networks Reveal Potential Regulatory Roles of MiRNAs in Fatty Acid Composition of Nelore Cattle. Front. Genet. 2019, 10, 651. [Google Scholar] [CrossRef] [Green Version]
  30. Lim, D.; Lee, S.H.; Kim, N.K.; Cho, Y.M.; Chai, H.H.; Seong, H.H.; Kim, H. Gene Co-Expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle. Asian-Australas. J. Anim. Sci. 2013, 26, 19–29. [Google Scholar] [CrossRef]
  31. Jensen, J.H.; Conley, L.N.; Hedegaard, J.; Nielsen, M.; Young, J.F.; Oksbjerg, N.; Hornshøj, H.; Bendixen, C.; Thomsen, B. Gene Expression Profiling of Porcine Skeletal Muscle in the Early Recovery Phase Following Acute Physical Activity. Exp. Physiol. 2012, 97, 833–848. [Google Scholar] [CrossRef]
  32. Wang, Y.H.; Bower, N.I.; Reverter, A.; Tan, S.H.; De Jager, N.; Wang, R.; McWilliam, S.M.; Cafe, L.M.; Greenwood, P.L.; Lehnert, S.A. Gene Expression Patterns during Intramuscular Fat Development in Cattle. J. Anim. Sci. 2009, 87, 119–130. [Google Scholar] [CrossRef]
  33. Muñoz, M.; García-Casco, J.M.; Caraballo, C.; Fernández-Barroso, M.Á.; Sánchez-Esquiliche, F.; Gómez, F.; del Carmen Rodríguez, M.; Silió, L. Identification of Candidate Genes and Regulatory Factors Underlying Intramuscular Fat Content Through Longissimus Dorsi Transcriptome Analyses in Heavy Iberian Pigs. Front. Genet. 2018, 9, 608. [Google Scholar] [CrossRef] [PubMed]
  34. Howe, V.; Sharpe, L.J.; Alexopoulos, S.J.; Kunze, S.V.; Chua, N.K.; Li, D.; Brown, A.J. Cholesterol Homeostasis: How Do Cells Sense Sterol Excess? Chem. Phys. Lipids 2016, 199, 170–178. [Google Scholar] [CrossRef]
  35. Huber, M.D.; Vesely, P.W.; Datta, K.; Gerace, L. Erlins Restrict SREBP Activation in the ER and Regulate Cellular Cholesterol Homeostasis. J. Cell Biol. 2013, 203, 427–436. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Bell, S.P. The Origin Recognition Complex: From Simple Origins to Complex Functions. Genes Dev. 2002, 16, 659–672. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Fluge, Ø.; Bruland, O.; Akslen, L.A.; Varhaug, J.E.; Lillehaug, J.R. NATH, a Novel Gene Overexpressed in Papillary Thyroid Carcinomas. Oncogene 2002, 21, 5056–5068. [Google Scholar] [CrossRef] [Green Version]
  38. Carmeli, E.; Moas, M.; Reznick, A.Z.; Coleman, R. Matrix Metalloproteinases and Skeletal Muscle: A Brief Review. Muscle Nerve 2004, 29, 191–197. [Google Scholar] [CrossRef]
  39. Urs, S.; Smith, C.; Campbell, B.; Saxton, A.M.; Taylor, J.; Zhang, B.; Snoddy, J.; Voy, B.J.; Moustaid-Moussa, N. Gene Expression Profiling in Human Preadipocytes and Adipocytes by Microarray Analysis. J. Nutr. 2004, 134, 762–770. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. dos Santos Silva, D.B.; Fonseca, L.F.S.; Magalhães, A.F.B.; Muniz, M.M.M.; Baldi, F.; Ferro, J.A.; Chardulo, L.A.L.; Pinheiro, D.G.; de Albuquerque, L.G. Transcriptome Profiling of Muscle in Nelore Cattle Phenotypically Divergent for the Ribeye Muscle Area. Genomics 2020, 112, 1257–1263. [Google Scholar] [CrossRef]
  41. Flanagan, J.N.; Linder, K.; Mejhert, N.; Dungner, E.; Wahlen, K.; Decaunes, P.; Rydén, M.; Bjoörklund, P.; Arver, S.; Bhasin, S.; et al. Role of Follistatin in Promoting Adipogenesis in Women. J. Clin. Endocrinol. Metab. 2009, 94, 3003–3009. [Google Scholar] [CrossRef] [Green Version]
  42. Tao, W.; Moore, R.; Meng, Y.; Yeasky, T.M.; Smith, E.R.; Xu, X.-X. Disabled-2 Determines Commitment of a Pre-Adipocyte Population in Juvenile Mice. Sci. Rep. 2016, 6, 35947. [Google Scholar] [CrossRef] [Green Version]
  43. Terrand, J.; Bruban, V.; Zhou, L.; Gong, W.; El Asmar, Z.; May, P.; Zurhove, K.; Haffner, P.; Philippe, C.; Woldt, E.; et al. LRP1 Controls Intracellular Cholesterol Storage and Fatty Acid Synthesis through Modulation of Wnt Signaling. J. Biol. Chem. 2009, 284, 381–388. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Tao, W.; Moore, R.; Meng, Y.; Smith, E.R.; Xu, X.-X. Endocytic Adaptors Arh and Dab2 Control Homeostasis of Circulatory Cholesterol. J. Lipid Res. 2016, 57, 809–817. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Masson, O.; Chavey, C.; Dray, C.; Meulle, A.; Daviaud, D.; Quilliot, D.; Muller, C.; Valet, P.; Liaudet-Coopman, E. LRP1 Receptor Controls Adipogenesis and is Up-Regulated In Human and Mouse Obese Adipose Tissue. PLoS ONE 2009, 4, e7422. [Google Scholar] [CrossRef] [Green Version]
  46. Yi, H.-C.; Liu, Y.-L.; You, P.; Pan, J.-S.; Zhou, J.-Y.; Liu, Z.-J.; Zhang, Z.-Y. Overexpression of DEK Gene Is Correlated with Poor Prognosis in Hepatocellular Carcinoma. Mol. Med. Rep. 2015, 11, 1318–1323. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Zhang, X.; Novera, W.; Zhang, Y.; Deng, L.-W. MLL5 (KMT2E): Structure, Function, and Clinical Relevance. Cell. Mol. Life Sci. 2017, 74, 2333–2344. [Google Scholar] [CrossRef] [PubMed]
  48. Daguenet, E.; Dujardin, G.; Valcárcel, J. The Pathogenicity of Splicing Defects: Mechanistic Insights into Pre-mRNA Processing Inform Novel Therapeutic Approaches. EMBO Rep. 2015, 16, 1640–1655. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Lakkaraju, A.K.K.; Thankappan, R.; Mary, C.; Garrison, J.L.; Taunton, J.; Strub, K. Efficient Secretion of Small Proteins in Mammalian Cells Relies on Sec62-Dependent Posttranslational Translocation. Mol. Biol. Cell 2012, 23, 2712–2722. [Google Scholar] [CrossRef]
  50. Niendorf, S.; Oksche, A.; Kisser, A.; Loöhler, J.; Prinz, M.; Schorle, H.; Feller, S.; Lewitzky, M.; Horak, I.; Knobeloch, K.-P. Essential Role of Ubiquitin-Specific Protease 8 for Receptor Tyrosine Kinase Stability and Endocytic Trafficking In Vivo. Mol. Cell. Biol. 2007, 27, 5029–5039. [Google Scholar] [CrossRef] [Green Version]
  51. Waki, H.; Nakamura, M.; Yamauchi, T.; Wakabayashi, K.; Yu, J.; Hirose-Yotsuya, L.; Take, K.; Sun, W.; Iwabu, M.; Okada-Iwabu, M.; et al. Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-Seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation. PLoS Genet. 2011, 7, e1002311. [Google Scholar] [CrossRef]
  52. Hiraike, Y.; Waki, H.; Yu, J.; Nakamura, M.; Miyake, K.; Nagano, G.; Nakaki, R.; Suzuki, K.; Kobayashi, H.; Yamamoto, S.; et al. NFIA Co-Localizes with PPARγ and Transcriptionally Controls the Brown Fat Gene Program. Nat. Cell Biol. 2017, 19, 1081–1092. [Google Scholar] [CrossRef] [Green Version]
  53. Ramayo-Caldas, Y.; Mármol-Sánchez, E.; Ballester, M.; Sánchez, J.P.; González-Prendes, R.; Amills, M.; Quintanilla, R. Integrating Genome-Wide Co-Association and Gene Expression to Identify Putative Regulators and Predictors of Feed Efficiency in Pigs. Genet. Sel. Evol. 2019, 51, 48. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Alexandre, P.A.; Kogelman, L.J.A.; Santana, M.H.A.; Passarelli, D.; Pulz, L.H.; Fantinato-Neto, P.; Silva, P.L.; Leme, P.R.; Strefezzi, R.F.; Coutinho, L.L.; et al. Liver Transcriptomic Networks Reveal Main Biological Processes Associated with Feed Efficiency in Beef Cattle. BMC Genom. 2015, 16, 1073. [Google Scholar] [CrossRef] [Green Version]
  55. Brunes, L.C.; Baldi, F.; Lopes, F.B.; Lôbo, R.B.; Espigolan, R.; Costa, M.F.O.; Stafuzza, N.B.; Magnabosco, C.U. Weighted Single-step Genome-wide Association Study and Pathway Analyses for Feed Efficiency Traits in Nellore Cattle. J. Anim. Breed. Genet. 2020, 138, 23–44. [Google Scholar] [CrossRef] [PubMed]
  56. Li, H.; Liu, J. The Novel Function of HINFP as a Co-Activator in Sterol-Regulated Transcription of PCSK9 in HepG2 Cells. Biochem. J. 2012, 443, 757–768. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Nishi-Tatsumi, M.; Yahagi, N.; Takeuchi, Y.; Toya, N.; Takarada, A.; Murayama, Y.; Aita, Y.; Sawada, Y.; Piao, X.; Oya, Y.; et al. A Key Role of Nuclear Factor Y in the Refeeding Response of Fatty Acid Synthase in Adipocytes. FEBS Lett. 2017, 591, 965–978. [Google Scholar] [CrossRef] [Green Version]
  58. Lee, H.H.; An, S.M.; Ye, B.J.; Lee, J.H.; Yoo, E.J.; Jeong, G.W.; Kang, H.J.; Alfadda, A.A.; Lim, S.W.; Park, J.; et al. TonEBP/NFAT5 Promotes Obesity and Insulin Resistance by Epigenetic Suppression of White Adipose Tissue Beiging. Nat. Commun. 2019, 10, 3536. [Google Scholar] [CrossRef] [PubMed]
  59. Ma, X.; Zhang, H.; Yuan, L.; Jing, H.; Thacker, P.; Li, D. CREBL2, Interacting with CREB, Induces Adipogenesis in 3T3-L1 Adipocytes. Biochem. J. 2011, 439, 27–38. [Google Scholar] [CrossRef] [PubMed]
  60. Nishizuka, M.; Tsuchiya, T.; Nishihara, T.; Imagawa, M. Induction of Bach1 and ARA70 Gene Expression at an Early Stage of Adipocyte Differentiation of Mouse 3T3-L1 Cells. Biochem. J. 2002, 361, 629. [Google Scholar] [CrossRef]
  61. Laity, J.H.; Lee, B.M.; Wright, P.E. Zinc Finger Proteins: New Insights into Structural and Functional Diversity. Curr. Opin. Struct. Biol. 2001, 11, 39–46. [Google Scholar] [CrossRef]
  62. Vivas-García, Y.; Falletta, P.; Liebing, J.; Louphrasitthiphol, P.; Feng, Y.; Chauhan, J.; Scott, D.A.; Glodde, N.; Chocarro-Calvo, A.; Bonham, S.; et al. Lineage-Restricted Regulation of SCD and Fatty Acid Saturation by MITF Controls Melanoma Phenotypic Article Lineage-Restricted Regulation of SCD and Fatty Acid Saturation by MITF Controls Melanoma Phenotypic Plasticity. Mol. Cell 2020, 77, 120–137. [Google Scholar] [CrossRef]
  63. Lowe, C.E.; Dennis, R.J.; Obi, U.; O’Rahilly, S.; Rochford, J.J. Investigating the Involvement of the ATF6α Pathway of the Unfolded Protein Response in Adipogenesis. Int. J. Obes. 2012, 36, 1248–1251. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Jin, W.; Takagi, T.; Kanesashi, S.; Kurahashi, T.; Nomura, T.; Harada, J.; Ishii, S. Schnurri-2 Controls BMP-Dependent Adipogenesis via Interaction with Smad Proteins. Dev. Cell 2006, 10, 461–471. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Lau, P.; Nixon, S.J.; Parton, R.G.; Muscat, G.E.O. RORa Regulates the Expression of Genes Involved in Lipid Homeostasis in Skeletal Muscle Cells. J. Biol. Chem. 2004, 279, 36828–36840. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Reith, W.; Durand, B.; Barras, E.; Mach, B. Function of Major Histocompatibility Complex Class II Promoters Requires Cooperative Binding between Factors RFX and NF-Y. Proc. Natl. Acad. Sci. USA 1994, 91, 554–558. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Júnior, G.A.F.; Costa, R.B.; de Camargo, G.M.F.; Carvalheiro, R.; Rosa, G.J.M.; Baldi, F.; Garcia, D.A.; Gordo, D.G.M.; Espigolan, R.; Takada, L.; et al. Genome Scan for Postmortem Carcass Traits in Nellore Cattle1. J. Anim. Sci. 2016, 94, 4087–4095. [Google Scholar] [CrossRef]
  68. Huang, L.; Yu, Z.; Zhang, T.; Zhao, X.; Huang, G. HSP40 Interacts with Pyruvate Kinase M2 and Regulates Glycolysis and Cell Proliferation in Tumor Cells. PLoS ONE 2014, 9, e92949. [Google Scholar] [CrossRef]
  69. Liang, F.; Li, Q.; Li, X.; Li, Z.; Gong, Z.; Deng, H.; Xiang, B.; Zhou, M.; Li, X.; Li, G.; et al. TSC22D2 Interacts with PKM2 and Inhibits Cell Growth in Colorectal Cancer. Int. J. Oncol. 2016, 49, 1046–1056. [Google Scholar] [CrossRef] [Green Version]
  70. Lu, Y.-B.; Shi, C.; Yang, B.; Lu, Z.-F.; Wu, Y.-L.; Zhang, R.-Y.; He, X.; Li, L.-M.; Hu, B.; Hu, Y.-W.; et al. Long Noncoding RNA ZNF800 Suppresses Proliferation and Migration of Vascular Smooth Muscle Cells by Upregulating PTEN and Inhibiting AKT/MTOR/HIF-1α Signaling. Atherosclerosis 2020, 312, 43–53. [Google Scholar] [CrossRef]
  71. Lei, D.; Hu, G.; Chen, Y.; Hao, T.; Gao, Y.; Luo, F. Forkhead Box S1 Inhibits the Progression of Hepatocellular Carcinoma. OncoTargets Ther. 2020, 13, 11839–11848. [Google Scholar] [CrossRef]
  72. Dambara, A.; Morinaga, T.; Fukuda, N.; Yamakawa, Y.; Kato, T.; Enomoto, A.; Asai, N.; Murakumo, Y.; Matsuo, S.; Takahashi, M. Nucleolin Modulates the Subcellular Localization of GDNF-Inducible Zinc Finger Protein 1 and Its Roles in Transcription and Cell Proliferation. Exp. Cell Res. 2007, 313, 3755–3766. [Google Scholar] [CrossRef]
  73. Borges, J.; TorÍo-Padrón, N.; Momeni, A.; Mueller, M.C.; Tegtmeier, F.T.; Stark, B.G. Adipose Precursor Cells (Preadipocytes) Induce Formation of New Vessels in Fibrin Glue on the Newly Developed Cylinder Chorioallantoic Membrane Model (CAM). Minim. Invasive Ther. Allied Technol. 2006, 15, 246–252. [Google Scholar] [CrossRef] [PubMed]
  74. Kotarba, G.; Krzywinska, E.; Grabowska, A.I.; Taracha, A.; Wilanowski, T. TFCP2/TFCP2L1/UBP1 Transcription Factors in Cancer. Cancer Lett. 2018, 420, 72–79. [Google Scholar] [CrossRef] [PubMed]
  75. Jack, B.H.A.; Crossley, M. GATA Proteins Work Together with Friend of GATA (FOG) and C-Terminal Binding Protein (CTBP) Co-Regulators to Control Adipogenesis. J. Biol. Chem. 2010, 285, 32405–32414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Cesar, A.S.M.; Regitano, L.C.A.; Koltes, J.E.; Fritz-Waters, E.R.; Lanna, D.P.D.; Gasparin, G.; Mourão, G.B.; Oliveira, P.S.N.; Reecy, J.M.; Coutinho, L.L. Putative Regulatory Factors Associated with Intramuscular Fat Content. PLoS ONE 2015, 10, e0128350. [Google Scholar] [CrossRef] [Green Version]
  77. Lin, S.-J.; Yang, D.-R.; Yang, G.; Lin, C.-Y.; Chang, H.-C.; Li, G.; Chang, C. TR2 and TR4 Orphan Nuclear Receptors. In Current Topics in Developmental Biology; Elsevier Inc.: Amsterdam, The Netherlands, 2017; Volume 125, pp. 357–373. [Google Scholar]
  78. Wenzel, J.J.; Piehler, A.; Kaminski, W.E. ABC A-Subclass Proteins: Gatekeepers of Cellular Phospho- and Sphingolipid Transport. Front. Biosci. 2007, 12, 3177. [Google Scholar] [CrossRef]
  79. Lefterova, M.I.; Lazar, M.A. New Developments in Adipogenesis. Trends Endocrinol. Metab. 2009, 20, 107–114. [Google Scholar] [CrossRef]
  80. Roh, S.G.; Hishikawa, D.; Hong, Y.H.; Sasaki, S. Control of Adipogenesis in Ruminants. Anim. Sci. J. 2006, 77, 472–477. [Google Scholar] [CrossRef]
  81. Sevane, N.; Armstrong, E.; Cortés, O.; Wiener, P.; Wong, R.P.; Dunner, S. Association of Bovine Meat Quality Traits with Genes Included in the PPARG and PPARGC1A Networks. Meat Sci. 2013, 94, 328–335. [Google Scholar] [CrossRef]
  82. Jeong, J.; Kwon, E.G.; Im, S.K.; Seo, K.S.; Baik, M. Expression of Fat Deposition and Fat Removal Genes Is Associated with Intramuscular Fat Content in Longissimus Dorsi Muscle of Korean Cattle Steers1. J. Anim. Sci. 2012, 90, 2044–2053. [Google Scholar] [CrossRef]
  83. Chen, L.; Ekine-Dzivenu, C.; Vinsky, M.; Basarab, J.; Aalhus, J.; Dugan, M.E.R.; Fitzsimmons, C.; Stothard, P.; Li, C. Genome-Wide Association and Genomic Prediction of Breeding Values for Fatty Acid Composition in Subcutaneous Adipose and Longissimus Lumborum Muscle of Beef Cattle. BMC Genet. 2015, 16, 135. [Google Scholar] [CrossRef] [Green Version]
  84. Ralston, J.C.; Matravadia, S.; Gaudio, N.; Holloway, G.P.; Mutch, D.M. Polyunsaturated Fatty Acid Regulation of Adipocyte FADS1 and FADS2 Expression and Function. Obesity 2015, 23, 725–728. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Matsumoto, H.; Nogi, T.; Tabuchi, I.; Oyama, K. The SNPs in the Promoter Regions of the Bovine FADS2 and FABP4 Genes Are Associated with Beef Quality Traits. Livest. Sci. 2014, 163, 34–40. [Google Scholar] [CrossRef]
  86. Leonard, A.E.; Bobik, E.G.; Dorado, J.; Kroeger, P.E.; Chuang, L.-T.; Thurmond, J.M.; Parker-Barnes, J.M.; Das, T.; Huang, Y.-S.; Mukerji, P. Cloning of a Human CDNA Encoding a Novel Enzyme Involved in the Elongation of Long-Chain Polyunsaturated Fatty Acids. Biochem. J. 2000, 350, 765. [Google Scholar] [CrossRef] [PubMed]
  87. Wakil, S.J.; Abu-Elheiga, L.A. Fatty Acid Metabolism: Target for Metabolic Syndrome. J. Lipid Res. 2009, 50, S138–S143. [Google Scholar] [CrossRef] [Green Version]
  88. Cotter, D.G.; Schugar, R.C.; Crawford, P.A. Ketone Body Metabolism and Cardiovascular Disease. Am. J. Physiol. Circ. Physiol. 2013, 304, H1060–H1076. [Google Scholar] [CrossRef] [Green Version]
  89. Bligh, E.G.; Dyer, W.J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 1959, 37, 911–917. [Google Scholar] [CrossRef]
  90. Folch, J.; Less, M.; Stanley, G.H.S. A Simple Method for the Isolation and Purification of Total Lipides from Animal Tissues. J. Biol. Chem. 1957, 226, 497–509. [Google Scholar] [CrossRef]
  91. Kramer, J.K.G.; Fellner, V.; Dugan, M.E.R.; Sauer, F.D.; Mossoba, M.M.; Yurawecz, M.P. Evaluating Acid and Base Catalysts in the Methylation of Milk and Rumen Fatty Acids with Special Emphasis on Conjugated Dienes and Total Trans Fatty Acids. Lipids 1997, 32, 1219–1228. [Google Scholar] [CrossRef]
  92. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
  93. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef]
  94. Anders, S.; Pyl, P.T.; Huber, W. HTSeq—A Python Framework to Work with High-Throughput Sequencing Data. Bioinformatics 2015, 31, 166–169. [Google Scholar] [CrossRef] [PubMed]
  95. Zabell, S.L. On Student’s 1908 Article “The Probable Error of a Mean”. J. Am. Stat. Assoc. 2008, 103, 1–7. [Google Scholar] [CrossRef]
  96. Love, M.I.; Huber, W.; Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 1995, 57, 289–300. [Google Scholar] [CrossRef]
  98. Watson-Haigh, N.S.; Kadarmideen, H.N.; Reverter, A. PCIT: An R Package for Weighted Gene Co-Expression Networks Based on Partial Correlation and Information Theory Approaches. Bioinformatics 2010, 26, 411–413. [Google Scholar] [CrossRef] [Green Version]
  99. Reverter, A.; Hudson, N.J.; Nagaraj, S.H.; Pérez-Enciso, M.; Dalrymple, B.P. Regulatory Impact Factors: Unraveling the Transcriptional Regulation of Complex Traits from Expression Data. Bioinformatics 2010, 26, 896–904. [Google Scholar] [CrossRef]
  100. Hu, H.; Miao, Y.-R.; Jia, L.-H.; Yu, Q.-Y.; Zhang, Q.; Guo, A.-Y. AnimalTFDB 3.0: A Comprehensive Resource for Annotation and Prediction of Animal Transcription Factors. Nucleic Acids Res. 2019, 47, D33–D38. [Google Scholar] [CrossRef]
  101. Zhang, B.; Horvath, S. A General Framework for Weighted Gene Co-Expression Network Analysis. Stat. Appl. Genet. Mol. Biol. 2005, 4, 17. [Google Scholar] [CrossRef]
  102. Yip, A.M.; Horvath, S. Gene Network Interconnectedness and the Generalized Topological Overlap Measure. BMC Bioinform. 2007, 8, 22. [Google Scholar] [CrossRef] [Green Version]
  103. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nat. Protoc. 2009, 4, 44–57. [Google Scholar] [CrossRef]
  104. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics Enrichment Tools: Paths toward the Comprehensive Functional Analysis of Large Gene Lists. Nucleic Acids Res. 2009, 37, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Metabolites 12 00471 g001 550
Figure 1. Sample dendrogram and trait heatmap for each essential fatty acid (linoleic and α-linolenic acid). The range of colors represents the intensity of the trait based on its phenotype.
Figure 1. Sample dendrogram and trait heatmap for each essential fatty acid (linoleic and α-linolenic acid). The range of colors represents the intensity of the trait based on its phenotype.
Metabolites 12 00471 g001
Table
Table 1. List of extreme differential hubbing genes for the linoleic acid (LA—C18:2) and alpha-linolenic (ALA—C18:3) fatty acids.
Table 1. List of extreme differential hubbing genes for the linoleic acid (LA—C18:2) and alpha-linolenic (ALA—C18:3) fatty acids.
GroupsGene SymbolDHGroupsGene SymbolDH
WDR4368 ASB540
ASB563 TRAFD135
LA-HORC460ALA-HWDR4331
ERLIN157 NAA1530
TRAFD150 ERLIN130
FSTL1−70 DEK−91
FN1−66 PRPF38B−89
LA-LDAB2−63ALA-LKMT2E−77
LRP1−62 USP8−77
MMP14−60 SEC62−75
LA-H: Samples with the 15 highest values/phenotypes for the linoleic acid; LA-L: Samples with the 15 lowest values/phenotypes for the linoleic acid; ALA-H: Samples with the 15 highest values/phenotypes for the alpha-linoleic acid; ALA-L: Samples with the 15 lowest values/phenotypes for the alpha-linoleic acid.
Table
Table 2. List of genes with the highest and lowest regulatory impact factor (RIF) scores (RIF1 and RIF2) between the contrasting linoleic acid (LA—C18:2) groups.
Table 2. List of genes with the highest and lowest regulatory impact factor (RIF) scores (RIF1 and RIF2) between the contrasting linoleic acid (LA—C18:2) groups.
GroupsGene SymbolRIF1RIF2
RIF1 (+)ZNF1344.91−1.01
NFIA4.50−0.39
NFYA4.07−0.84
NFAT54.020.78
ZNF5843.540.07
RIF2 (+)ZBTB43−0.462.95
ZNF473−0.092.83
SLC2A4RG−0.342.77
BACH1−0.172.73
NFYB2.872.68
RIF2 (−)HINFP1.02−2.70
WIZ−0.63−2.65
Table
Table 3. List of genes with the highest and lowest regulatory impact factor (RIF) scores (RIF1 and RIF2) between the contrasting alpha-linoleic acid (ALA—C18:3) groups.
Table 3. List of genes with the highest and lowest regulatory impact factor (RIF) scores (RIF1 and RIF2) between the contrasting alpha-linoleic acid (ALA—C18:3) groups.
GroupsGene SymbolRIF1RIF2
RIF1 (+)NFIA7.43−1.05
DNAJC15.110.67
RORA4.940.46
NFX14.802.27
MITF4.751.14
RIF1 (−)FOXS1−3.180.15
UBP1−2.810.14
GZF1−2.680.73
RIF2 (+)TSC22D2−0.462.95
ZNF800−0.092.83
ATF6−0.342.75
HIVEP2−0.172.73
ZNF4732.872.68
RIF2 (−)NR2C10.16−3.57
HINFP−0.32−2.68
ZFPM10.19−2.65
Table
Table 4. Descriptive statistics of the phenotypic data evaluated in the two contrasting groups and means comparison.
Table 4. Descriptive statistics of the phenotypic data evaluated in the two contrasting groups and means comparison.
Fatty AcidLow Group (n = 15)High Group (n = 15)
MinMaxMeanSDMinMaxMeanSDp-Value
LA2.475.794.570.898.0011.839.391.19*
ALA0.230.550.460.080.781.210.940.14*
* p-value < 0.0001.
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Schettini, G.P.; Peripolli, E.; Alexandre, P.A.; dos Santos, W.B.; Pereira, A.S.C.; de Albuquerque, L.G.; Baldi, F.; Curi, R.A. Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle. Metabolites 2022, 12, 471. https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12050471

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Schettini GP, Peripolli E, Alexandre PA, dos Santos WB, Pereira ASC, de Albuquerque LG, Baldi F, Curi RA. Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle. Metabolites. 2022; 12(5):471. https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12050471

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Schettini, Gustavo Pimenta, Elisa Peripolli, Pâmela Almeida Alexandre, Wellington Bizarria dos Santos, Angélica Simone Cravo Pereira, Lúcia Galvão de Albuquerque, Fernando Baldi, and Rogério Abdallah Curi. 2022. "Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle" Metabolites 12, no. 5: 471. https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12050471

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