PLOS ONE RESEARCH ARTICLE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio expression profiles in porcine skeletal muscle Yron Joseph Yabut Manaig ID1,2,3*, Lourdes Criado-Mesas2, Anna Esteve-Codina ID4, Emilio Mármol-Sánchez ID5,6, Anna Castelló1,2, Armand Sánchez1,2, Josep M. Folch1,2 1 Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, a1111111111 Spain, 2 Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA- a1111111111 UAB-UB Consortium, Bellaterra, Barcelona, Spain, 3 Department of Veterinary Medicine and Animal a1111111111 Sciences, Università degli Studi di Milano, Lodi, Italy, 4 CNAG-CRG, Centre for Genomic Regulation (CRG), a1111111111 Barcelona Institute of Science and Technology (BIST), Barcelona, Spain, 5 Department of Molecular a1111111111 Biosciences, Science for Life Laboratory, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden, 6 Centre for Palaeogenetics, Stockholm, Sweden * yronjosephmanaig@gmail.com OPEN ACCESS Citation: Manaig YJY, Criado-Mesas L, Esteve- Abstract Codina A, Mármol-Sánchez E, Castelló A, Sánchez A, et al. (2023) Identifying miRNA-mRNA Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) are essential fatty regulatory networks on extreme n-6/n-3 acids with antagonistic inflammatory functions that play vital roles in metabolic health and polyunsaturated fatty acid ratio expression profiles immune response. Current commercial swine diets tend to over-supplement with n-6 in porcine skeletal muscle. PLoS ONE 18(5): PUFAs, which may increase the likelihood of developing inflammatory diseases and affect e0283231. https://doi.org/10.1371/journal. pone.0283231 the overall well-being of the animals. However, it is still poorly understood how n-6/n-3 PUFA ratios affect the porcine transcriptome expression and how messenger RNAs (mRNAs) and Editor: Atsushi Asakura, University of Minnesota Medical School, UNITED STATES microRNAs (miRNAs) might regulate biological processes related to PUFA metabolism. On account of this, we selected a total of 20 Iberian × Duroc crossbred pigs with extreme values Received: October 13, 2022 for n-6/n-3 FA ratio (10 high vs 10 low), and longissimus dorsi muscle samples were used to Accepted: March 6, 2023 identify differentially expressed mRNAs and miRNAs. The observed differentially expressed Published: May 4, 2023 mRNAs were associated to biological pathways related to muscle growth and immunomodu- Copyright: © 2023 Manaig et al. This is an open lation, while the differentially expressed microRNAs (ssc-miR-30a-3p, ssc-miR-30e-3p, ssc- access article distributed under the terms of the miR-15b and ssc-miR-7142-3p) were correlated to adipogenesis and immunity. Relevant Creative Commons Attribution License, which miRNA-to-mRNA regulatory networks were also predicted (i.e., mir15b to ARRDC3; mir- permits unrestricted use, distribution, and reproduction in any medium, provided the original 7142-3p to METTL21C), and linked to lipolysis, obesity, myogenesis, and protein degrada- author and source are credited. tion. The n-6/n-3 PUFA ratio differences in pig skeletal muscle revealed genes, miRNAs and Data Availability Statement: All RNA- and miRNA- enriched pathways involved in lipid metabolism, cell proliferation and inflammation. Seq data are both available at NCBI Sequence Read Archive (SRA) under Accession PRJNA882638. Funding: This work was supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) and the Fondo Europeo de Desarrollo Regional (FEDER) Introduction with project references: AGL2017-82641-R and PID2020-112677RB-C22. YJYM is financially Dietary concentration of polyunsaturated fatty acids (PUFAs) can potentially affect and supported by the European Union Horizon 2020 change the gene expression profile of key tissues such as skeletal muscle or fat compartments, PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 1 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs Research and Innovation programme H2020- with relevant implications for their commercial transformation and consumption [1]. These MSCA-ITN-2017- EJD: Marie Skłodowska-Curie alterations, due to nutritional interventions, may rewire multiple regulatory networks in nutri- Innovative Training Networks (European Joint ent metabolism, thus affecting messenger RNA (mRNA) transcription, splicing, trafficking Doctorate) – Grant agreement nº: 765423 – MANNA. LCM was funded with a FPI grant from and further synthesis of derived proteins [2,3]. Gene expression regulation can also be medi- the AGL2014-56369-C2 project. EMS was funded ated through post-transcriptional regulation, of which microRNAs (miRNAs) are key effec- with a PhD fellowship FPU15/01733 awarded by tors. miRNAs are small non-coding RNAs of ~22 nucleotides long that are able to bind to the Spanish Ministerio de Educación, Cultura y specific sequences of the 3’ untranslated regions (3’ UTRs) of targeted mRNAs and trigger Deporte (MECD). We also acknowledge the their degradation and/or inhibit their translation [4]. Thousands of miRNAs can be found on support of the Spanish Ministerio de Economı́a y Competitividad for the “Severo Ochoa” Programme online databases such as miRBase, a searchable collection of published miRNA sequences and for Centres of Excellence in R&D (Project No. their annotation. As of its release v22.1, there are 38,589 miRNA entries published over 271 CEX2019-000902-S) to the Centre for Research in species, including 408 precursors and 457 mature miRNAs for Sus scrofa (pig) [5]. Besides, the Agricultural Genomics (CRAG) and to the development of next generation sequencing (NGS) technologies has provided a better under- programmes of Centres de Recerca de Catalunya standing of the genome organization, structure, function, and evolution in livestock animals. (CERCA). The funders had no role in the design of Nowadays, it is commonly used to study complex traits to improve livestock production effi- the study; in the collection, analyses, or interpretation of data; in the writing of the ciency and reproductive health [6]. manuscript, or in the decision to publish the Pigs are one of the most important agricultural livestock animals for meat production, results. accounting to a total of 122.5 million tons globally in 2021. Food and Agriculture Organization Competing interests: The authors have declared (FAO) also highlighted that the recent expansion of world meat output was mainly driven by that no competing interests exist. the increase in pork output [7]. Porcine fatness or leanness are considered as relevant target traits for selection since they could impact productive and reproductive performance, as well as meat quality [8]. Specific porcine breeds, such as Landrace, have been extensively selected to increase lean meat production and reduce fat deposition [9]. Although this may improve over- all pig production efficiency, such breeding programs may negatively affect meat quality traits like juiciness, tenderness, flavor and overall sensory quality of pork [10]. For a more efficient production, fats and oils are supplemented on diets, as they contain 2.25 times more energy than cereal grains, which further increases energy density and reduces feed intake [11]. Addi- tionally, the fatty acid content of the carcass is directly influenced by the dietary fats ingested by pigs, mimicking the fatty acid composition of the diet [12]. However, there is still limited knowledge on how mRNAs and miRNAs interact to regulate fatty acid metabolism pathways, or how diet and fatty acid content might influence their expression profiles, especially on Ibe- rian pigs [13]. Moreover, as the excessive supplementation of omega-6 (n-6) PUFAs becomes more prevalent on commercial pig diets, this pro-inflammatory PUFAs can impose risk of developing inflammatory diseases such as cardiovascular diseases, diabetes or obesity [14,15]. Regulation of n-6 PUFA-derived metabolites can be done through balancing the ratio between these and omega-3 (n-3) PUFAs, which counteract pro-inflammatory responses elicited by the excess of n-6 PUFAs [16]. In order to better understand putative regulatory relationships between mRNAs and miR- NAs related to changes in the n-6/n-3 PUFAs muscle composition in pigs, we identified differ- entially expressed (DE) mRNAs and miRNAs in skeletal muscle tissue from a population of Iberian × Duroc pigs with high and low values of n-6/n-3 PUFAs ratio. Additionally, we char- acterized putative mRNA-miRNA transcriptomic interactions using computational prediction and regulatory network analyses. Materials and methods Animal material A total of 20 longissimus dorsi (LD) skeletal muscle samples were obtained from an experimen- tal backcross population of Iberian and Duroc pigs, as previously described by Martinez-Mon- tes et al. [17]. Pigs were housed following standard intensive system according to European PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 2 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs directives on animal welfare, and were fed ad libitum with a cereal-based commercial diet. Muscle samples were collected immediately after slaughter, snap-frozen in liquid nitrogen and stored at -80ºC until further use. Fatty acids profiling was performed by using gas chromatog- raphy of methyl esters protocol on 200 g of LD muscle. Sampled animals were selected based on their analyzed values for n-6/n-3 PUFAs ratio and a total of 10 with highest (H) and 10 with lowest (L) n-6/n-3 ratio values were kept for further analyses [18]. A similar number of males and females were present in each group (S1 Table) and the use of siblings within each group was avoided. A summary of the measured phenotypes in the selected animals is available at S1 Table. Ethics statement. All animal procedures were performed according to the Spanish Policy for Animal Protection RD1201/05, which meets the European Union Directive 86/609 about the protection of animals used in experimentation. The protocol was approved by the Com- mittee on the Ethics of Animal Experiments of the Instituto Nacional de Investigación y Tec- nologı́a Agraria y Alimentaria CEEA (Permit Number: 2014/026). RNA isolation, library preparation, and sequencing of total and small RNAs Total RNA. The LD skeletal muscle samples were submerged in liquid nitrogen, pulver- ized using a mortar and pestle, and subsequently homogenized in 1 ml of TRI Reagent (Thermo Fisher Scientific, Barcelona, Spain). The RiboPure kit (Ambion, Austin, Texas, USA) was used to isolate the total RNA fraction, and its concentration and purity were determined with a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Barcelona, Spain). RNA integrity was assessed with a Bioanalyzer-2100 equipment (Agilent Technologies Inc., Santa Clara, California, USA), using the Agilent RNA 6000 Nano Kit (Agilent Technologies, Inc., Santa Clara, California, USA). Libraries were prepared with the TruSeq SBS Kit v3-HS (Illumina Inc., California, USA) and a minimum of 30 million hits of 75 bp-length paired-end reads were acquired per sample using an Illumina HiSeq 3000/4000 equipment (CNAG-CRG, Barcelona, Centro Nacional de Análisis Genómico; https://www.cnag.crg.eu). Small RNA. The extraction of total RNA, including miRNA and small RNA, was per- formed with the same muscle tissue material employed for total RNA sequencing and using the miRNeasy Kit (QIAGEN, Germantown, Maryland, USA) following manufacturer’s spe- cifications. More specifically, approximately 50 mg of tissue per sample was disrupted and homogenized in 700 μl of QIAzol Lysis Reagent (QIAGEN, Germantown, Maryland, USA) using 2ml Lysing matrix D tubes (MP Biomedicals, Santa Ana, CA) and a Precellys 24 instru- ment (Bertin Technologies, Rockville, MD). After RNA isolation following the miRNeasy pro- tocol, the extracted RNA molecules were eluted in 30 μl of water. The concentration, purity, and RNA integrity were assessed as per aforementioned for total RNAs. A minimum of 10 mil- lion hits of 50 bp-length single-end read were acquired per sample using the same sequencing equipment used for mRNA libraries. RNA-Seq and miRNA-Seq data processing Raw mRNA and miRNA sequences were subjected to quality control through the FastQC tool [19]. In order to remove the Illumina adapters used during library preparation and sequenc- ing, reads were trimmed using the Cutadapt software v0.9.5 [20]. RNA-Seq data sequence alignment was performed against the reference pig genome (Sscrofa11.1) by using the STAR [21] aligner with default parameters. Sequences were then quantified with the RSEM software [22]. On the other hand, for miRNA-Seq data, sequence alignment was performed against the reference pig genome (Sscrofa11.1 and miRBase 22.1) by using Bowtie23 aligner and the PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 3 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs following specifications for aligning short miRNA reads were taken into consideration: 1) allowing no mismatches in the alignment, 2) removing reads with more than 20 putative map- ping sites, and 3) reporting first single best stratum alignment (bowtie -n 0 -l 25 -m 20 -k 1— best—strata) [23,24]. Quantification of aligned miRNA reads were performed using HTSEQ software [25]. Only mRNAs and miRNAs with an overall expression across all samples higher than 20 counts were considered for subsequent differential expression analyses [26]. Differential gene expression analyses between the H and L groups from both RNA-Seq and miRNA-Seq data were performed with the DESeq2 software [26], including sex and batch effects as covariates in the linear model (S1 Table). Both mRNAs and miRNAs from differen- tial expression analyses were considered significant at an absolute fold-change (FC) > 1.5 and adjusted p-value < 0.05. We considered the H group as reference, meaning that any gene upre- gulation would imply its overexpression in the L group, resulting in a positive fold change, and vice versa. Gene ontology and pathway enrichment analysis Differentially expressed mRNA genes analyzed between H and L groups were subjected to Gene ontology (GO) and pathway enrichment analyses using Cytoscape v3.7.1 software with the ClueGO v.2.5.4 plug-in application to determine enriched Biological Process terms [27,28]. Identification of enriched terms was done using a one-sided hypergeometric test of significance, with a false discovery rate approach for multiple testing correction [29]. Co-expression network analysis between mRNAs and miRNAs A co-expression network between mRNA and miRNA expression profiles was built according to the established pipeline as previously reported by Mármol-Sánchez et al. [24]. The Partial Correlation with Information Theory (PCIT) network inference algorithm was used to recog- nize meaningful gene-to-gene interactions by employing first-order partial correlation coeffi- cients obtained for each trio of genes in conjunction with an information theory technique [30,31]. To do so, we calculated the Pearson pairwise correlation coefficients (r) for each expressed miRNA and DE mRNA between H and L groups. Assuming that miRNAs can bio- logically suppress mRNA expression, we reported only those co-expressed miRNA-to-mRNA pairs showing an r value < -0.50. To further retain only relevant miRNA-to-mRNA correla- tions with biological meaning, the seed of the annotated porcine mature miRNAs (7mer-m8, from 2nd to 8th 5’ nucleotides) were reverse-complemented and interrogated along the anno- tated 3’ UTRs (Sscrofa 11.1; http://www.ensembl.org./biomart) of porcine mRNA genes, by making use of the SeqKit toolkit [32]. We also investigated whether the mRNAs predicted to interact with miRNAs showed meaningful expression correlations with other mRNA-encod- ing genes. We only kept the mRNA pairs with |r| > 0.7 as determined by the PCIT algorithm. The more stringent threshold imposed for mRNA-to-mRNA expression associations as com- pared to miRNA-to-mRNA predicted interactions (r < -0.50) was motivated by the fact that the expression correlation between mRNA pairs is commonly of great magnitude than that of miRNA-to-mRNA interactions [33]. Results Differentially expressed genes and miRNAs Out of 11,521 porcine mRNAs detected as sufficiently expressed, a total of 432 differentially expressed genes (DEGs) were obtained between H and L pigs according to their n-6/n-3 PUFA ratio (S2 Table), with 157 and 275 mRNAs being upregulated and downregulated in L PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 4 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs Fig 1. Volcano plot showing differentially expressed mRNA genes (DEGs) with an absolute fold change > 1.5 and adjusted p- value < 0.05 after comparing pigs with High (H) and Low (L) profiles of n-6/n-3 PUFAs ratio in longissimus dorsi skeletal muscle. Upregulated genes (in green) correspond to genes overexpressed in L pigs, and vice versa. https://doi.org/10.1371/journal.pone.0283231.g001 pigs with respect to H pigs, respectively (Fig 1). From the 457 annotated porcine miRNAs, a | FC| > 1.5 threshold for changes in expression between H and L pigs showed no DE miRNAs for the PUFA ratio trait. When a less stringent FC threshold was considered (|FC| > 1.2), 4 DE miRNAs were identified: ssc-miR-15b, ssc-mir30a-3p, ssc-miR-30e-3p and ssc-miR-7142-3p (S3 Table). Functional analysis and pathway enrichment of DEGs A total of 80 significant unique GO terms (adjusted p-value < 0.05) were detected for DEGs related to H and L pigs for the n-6/n-3 PUFA ratio trait. A full list of enriched GO terms is shown in S4 Table. The significant biological processes highlighted were related to muscle PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 5 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs Fig 2. Enriched gene ontologies using DEGs after comparing RNA-Seq gene expression profiles of H and L pigs and their related biological processes. Significant unique GO terms (adjusted p-value < 0.05) are obtained from the ClueGO plug-in embedded on the Cytoscape software. https://doi.org/10.1371/journal.pone.0283231.g002 structure development (GO:0061061), positive regulation of skeletal muscle cell differentiation (GO:2001016), SREBP signaling pathway (GO:0032933) and adrenergic receptor signaling pathway (GO:0071875), among others, as shown in Fig 2. mRNA-miRNA co-expression regulatory network A total of 196 miRNAs were detected as significantly co-expressed (r < -0.50) with the differ- entially expressed mRNA genes for n-6/n-3 PUFAs ratio (S5 Table). Among the 196 detected miRNAs, we focused on the 4 DE miRNAs previously highlighted (see Results above, S3 Table) and used them for predicting binding sites of their mature miRNA seeds to the 3’ UTR region of putative DE mRNA targets (S6–S8 Tables). Almost half (214 out of 432, 49.54%) of the DEGs showed putative binding sites in their 3’ UTRs for the seed region of the 4 DE miR- NAs (Table 1 and S3 Table). Further combining relevant miRNA-mRNA expression correlations according to the PCIT algorithm (r < -0.50) and 3’ UTR region seed matching, 2 out of the 4 DE miRNAs showed meaningful co-expression with two DEGs: ssc-miR-15b was predicted to bind to the 3’ UTR of the arresting domain containing 3 (ARRDC3) gene, while ssc-miR-7142-3p was predicted to bind the 3’ UTR of the methyltransferase-like 21C (METTL21C) gene (S9 Table). PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 6 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs Table 1. Number of putative targeted DE mRNAs (DEGS) with predicted binding sites for DE miRNAs between pigs with High (H) and Low (L) n-6/n-3 PUFAs ratio in longissimus dorsi skeletal muscle. DE miRNAs Number of targeted DEGsa % over total DEGs ssc-mir-15b 125 28.94% ssc-miR-30a-3p 130 30.09% ssc-miR-30e-3p ssc-miR-7142-3p 54 12.50% a Differentially expressed genes (DEGs) = 432 in total; The ssc-miR-30a-3p and ssc-miR-30e-3p have the same mature miRNA seed (7mer-m8, from 2nd to 8th 5’ nucleotides). https://doi.org/10.1371/journal.pone.0283231.t001 Several other genes were also significantly associated with the expression of these two DEGs (ARRDC3 and METL21C, S10 and S11 Tables). As shown in Fig 3, the ARRDC3 gene showed meaningful correlation with 41 differentially expressed mRNAs (S10 Table), whereas the METTL21C gene was significantly correlated with 5 DEGs (S11 Table). The related functions and associations to lipid metabolism, immunity, and/or inflammation of these DEGs aresum- marized in S12 Table. Discussion DEGs and their relationship to lipid-mediated expression and immunomodulation Our results after GO enrichment analyses of DEGs (S4 Table) showed GO terms mostly related to muscle growth and differentiation, glucose and lipid metabolism. Some of the genes related to muscle tissue and structure development pathways were also reported in human, mice and ruminants (i.e. cattle, sheep, lamb). The aryl hydrocarbon receptor nuclear translocator like Fig 3. Meaningful co-expression network between miRNA-to-mRNA and mRNA-to-mRNA genes. r = Pearson correlation value; lines in red for miRNA-to-mRNA interactions denote a negative correlation. https://doi.org/10.1371/journal.pone.0283231.g003 PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 7 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs (ARNTL), a gene that regulates the circadian release of PUFAs and modulates feeding behavior in mice, alongside with forkhead box N2 (FOXN2), are associated with obesity [34,35]. Another interesting gene was the diaphanous related formin 1 (DIAPH1), which is regulated upon nutritional intervention with long chain PUFAs (n-6 and n-3) and it is reported to be involved in lipid metabolism in cattle [36]. Supplementation of flaxseed or fish oil can increase the expression of the guanidinoacetate N-methyltransferase (GAMT) gene, which is involved in folate-homocysteine metabolism in embryos and liver of pregnant mice [37]. The absence of n-3 PUFA in rodents (i.e. DHA) has been reported to affect cognitive brain function and a few of its synaptomes including homer scaffold protein 1 (HOMER1) [38]. The Kruppel like factor 5 (KLF5) gene regulates muscle differentiation in myoblasts and controls lipid metabo- lism in mature skeletal muscle in mice [39,40]. In addition, GWAS analyses in human meta- bolic syndrome discovered the association of the strawberry notch homolog 1 (SBNO1) gene on plasma high-density lipoprotein cholesterol concentration, whereas the vestigial like family member 2 (VGLL2) gene was linked to the fatty acids profile in sheep [41,42]. The T- box transcription factor 1 (TBX1) gene, together with miR-193a-3p/TGF-β2, was found to drive iron-dependent cell death ferroptosis through the accumulation of lipid peroxides in neonates [43,44]. Abundancy on n-3 PUFA in cattle is reported to increase the gene expression of insulin-like growth factors such as the insulin-like growth factor binding protein 5 (IGFBP- 5) and further influence reproductive performance [45]. Other genes like the glycerophospho- choline phosphodiesterase 1 (GPCPD1), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), or phosphoglucomutase 5 (PGM5), are related to glycophospholipid formation, cholesterol synthesis and glycolysis [46–49]. Another relevant GO term that can be highlighted from our results is the SREBP signaling pathway. Sterol regulatory-element binding proteins (SREBPs) are transcription factors that regulate the expression profiles of genes that are involved in lipid synthesis, energy storage and cholesterol regulation. When these proteins are activated, they can trigger lipid-mediated cel- lular stress that can cause metabolic diseases such as obesity, atherosclerosis, diabetes mellitus, inflammation, and organ fibrosis [50,51]. On the other hand, our enrichment analyses also emphasized the adenylate cyclase-activat- ing adrenergic receptor signaling pathway based on the DEGs involved. Adrenergic receptors play a vital role in mediating stress-induced signals, in immunomodulation and in stress- related behavioral changes [52,53]. This pathway also triggers the formation of cyclic-adeno- sine 30 ,50 -monophosphate (cAMP), which regulates intracellular metabolism and it is linked to glycolysis [54,55]. Stimulation of both SREBP signaling pathway and adrenergic receptor sig- naling pathway could also be related to the pro-inflammatory role of n-6 PUFA. Addition of n-6 PUFA increased the β-adrenergic receptor binding and adenylate cyclase activity in pig adipocyte plasma membrane [56]. Furthermore, it was also reported that over supplementa- tion of n-6 PUFAs in swine diets can stimulate the innate immune response and acute inflam- matory response [57]. Association of differentially expressed porcine miRNAs to adipogenesis and inflammation We obtained a total of 4 DE miRNAs (ssc-miR-30a-3p, ssc-miR-30e-3p, ssc-miR-15b and ssc- miR-7142-3p) between high and low n-6/n-3 PUFA ratio contrast on porcine skeletal muscle. The expression of miR-30a in pigs has been associated to adipocyte formation, fat deposition, myogenic differentiation and immune system [58–62]. miR-30a may also be related to cellular response to infection, immune modulation and pathological processes since it was detected on multiple pig-related viral studies concerning porcine parvovirus, porcine reproductive and PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 8 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs respiratory syndrome virus or H1N1 swine influenza A virus [63–65]. A study on a minipig obesity model also demonstrated how miR-30a could regulate the expression of genes related to adipogenesis and low-grade chronic inflammation in obesity [58,66,67]. This was in accor- dance to a previous report by our team, in which we predicted that miR-30a could potentially bind to and regulate the mRNA of porcine ELOVL fatty acid elongase 6 (ELOVL6) gene, which is responsible of the elongation of PUFAs and de novo lipogenesis [68]. However, the ELOVL6 gene was not among the detected DEGs in the current study (S2 Table), thus suggesting that differences in miR-30a expression are not affecting the ELOVL6 mRNA levels, but might inhibit its translation. As a member of miR-30 family, ssc-miR-30e also targets mRNA genes that are related to skeletal muscle growth, energy metabolism and increased feed efficiency in swine [69,70]. A few reports on pigs have also elucidated the role of miR-30e on binding to mRNA transcripts from genes related to pathogenesis, virus-host interactions and immune response [71–73]. On the other hand, mir-15b is mainly associated to blood vessel formation (angiogenesis), tumor growth and cellular ATP level modulation. Metabolites obtained from n-6 PUFAs could promote angiogenesis by increasing expression of transcription growth factors (i.e. TGF-β), whereas n-3-PUFA-derived substances contain anti-angiogenic, anti-inflammatory and antitumor properties [74–76]. Besides, ssc-mir-7142-3p is a mirtron located in the intronic fraction of the microtubule affinity regulating kinase 2 (MARK2) gene. This miRNA has been detected in lung tissue infected with Actinobacillus pleuropneumoniae and its differential expression has been associated to the overexpression of the retinol binding protein 4 (RBP4) gene [77,78]. RBP4, mainly secreted by the liver and adipocytes, is a transporter of vitamin A and it is involved in various pathophysiological processes, such as obesity, insulin resistance and cardiovascular diseases, demonstrating a strong association of this mirtron to inflamma- tory-related processes [78]. Meaningful miRNA-to-mRNA regulatory networks affected by changes in n-6/n-3 ratio Co-expression network analyses between DE miRNAs and DEGs highlighted 2 miRNAs that could potentially bind to and inhibit the expression of 2 DE mRNAs when comparing pigs with high and low n-6/n-3 PUFA ratio in skeletal muscle. The upregulated DE miRNA ssc- miR-15b was predicted to bind to the 3’ UTR of the arrestin domain containing 3 (ARRDC3) gene. Arrestins are a small family of multi-faceted protein trafficking adaptors that bind to membrane proteins, which regulate signal transduction at G protein-coupled receptors (GCPR) and promote endocytosis. ARRDC3 is a known α-arrestin and its activation could be due to nutrient excess or cellular stressors [79,80]. Our results showed that this gene was involved in a few biological processes such as adrenergic receptor signaling pathway, negative regulation of G protein-coupled receptor signaling pathway, negative regulation of behavior and regulation of ubiquitin-protein transferase activity. ARRDC3 was reported to co-immu- noprecipitate and interact with β2-adrenergic receptors and facilitate its ubiquitination and degradation [81–83]. In addition, this gene is also involved in obesity development, insulin resistance, body mass regulation, glucose metabolism, adiposity and energy expendi- ture [84–86]. Meanwhile, the mirtron ssc-miR-7142-3p might target the methyltransferase-like 21c (METTL21C) mRNA transcripts, which encode for a protein-lysine methyltransferase involved in regulation of myogenesis, muscle function and protein catabolism [87,88]. From our results, there is a strong positive association between high n-3 PUFA concentration and METTL21C expression. A decreased expression of this gene was also reported after long-term PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 9 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs exercise, in which elevated levels of inflammatory cytokines, oxidative stress, and leukocytosis could be observed [89,90]. From our results, we might hypothesize that the upregulation of ssc-miR-15b and downregulation of ssc-miR-1472-3p, together with ARRDC3 downregulation and METTL21C upregulation could be linked to low n-6/n-3 PUFA ratio and the production of anti-inflammatory metabolites, stimulating receptors related to stress and immunity. How- ever, further validation among these predicted regulatory networks should be done in order to verify their biological importance in terms of porcine growth and immune response. Putative mRNA-to-mRNA correlations highlight genes related to immunity and metabolic stress Potential correlation and interaction between the two possible target genes of DE miRNAs, ARRDC3 and METTL21C, and DEGs were further investigated. The phosphodiesterase 4D (PDE4D), a gene that is associated with the regulation of interleukin production and cAMP- mediated signaling, belongs to the same adrenergic receptor signaling pathway as ARRDC3 (S4 Table). The tumor inhibition properties of ARRDC3 are presumably facilitated by linking target substrates such as β-adrenergic receptor and integrin β4 to E3 ligase, in which these tar- get substrates become ubiquintinated and degraded by the proteasome [91]. Both the Hes related family bHLH transcription factor with YRPW motif like (HEYL) and EvC ciliary complex subunit 1 (EVC) genes were correlated with METTL21C, and associated with muscle organ and structure development (S4 Table). One study looked into the changes of gene expression on some signaling pathways that could be affected by the specific knock- down of METTL21C, including HEYL-targeted Notch pathway. Although it did not affect the expression of HEYL gene, they reported that METTL21C is a critical component for bone and muscle homeostasis [92]. Our results also showed that both EVC and METTL21C were upre- gulated in L pigs. In contrast, an upregulation EVC and downregulation METT21C, among other differentially expressed genes, were observed in BRAF-mutant cell lines, in response to metabolic stress through glucose withdrawal [93]. Conclusion The high and low values of n-6/n-3 PUFA ratio on porcine skeletal muscle influence the expression profiles, related biological pathways and transcriptomic correlations and interac- tions between differentially expressed mRNAs and miRNAs. Predicted co-expression regulatory networks among mRNAs and miRNAs may be attributed to the pro- and anti- inflammatory functions of n-6 and n-3 PUFAs, respectively. Our findings highlighted mRNA genes, miRNAs and enriched pathways that were related to lipids metabolism, cell growth and inflammation, according to differences in muscle n-6/n-3 PUFA ratio. Supporting information S1 Table. Phenotypic values of n-6/n-3 PUFA ratio, sex classification, and batch grouping recorded in 20 Iberian x Duroc pigs. (XLSX) S2 Table. Differentially expressed genes (DEGs) according to n-6/n-3 PUFA ratio. (XLSX) S3 Table. Differentially expressed miRNAs (DEmiRNAs) according to n-6/n-3 PUFA ratio. (XLSX) PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 10 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs S4 Table. List of Gene Ontology (GO) terms related to DEGs according to n-6/n-3 PUFA ratio. (XLSX) S5 Table. Predicted porcine mature miRNAs co-expression with DEGs. (XLSX) S6 Table. miR-15b seed binding interrogation on the 3’ UTR of DEGs. (XLSX) S7 Table. miR-30a and miR-30e seed binding interrogation on the 3’ UTR of DEGs. (XLSX) S8 Table. miR-7142 seed binding interrogation on the 3’ UTR of DEGs. (XLSX) S9 Table. Meaningful co-expression network between negatively correlated DEGs and DE miRNAs according to n-6/n-3 PUFA ratio. (XLSX) S10 Table. Meaningful mRNA-mRNA co-expression of ARRDC3 gene and other DEGs according to n-6/n-3 PUFA ratio. (XLSX) S11 Table. Meaningful mRNA-mRNA co-expression of METTL21C gene and other DEGs according to n-6/n-3 PUFA ratio. (XLSX) S12 Table. List of genes significantly correlated with ARRDC3 and METTL21C and their related functions. (XLSX) Acknowledgments We thank all of the members of the INIA, IRTA, and UAB institutions which contributed to the generation of the animal material used in the current study and Betlem Cabrera of CRAG for the assistance in genomics laboratory. Author Contributions Conceptualization: Yron Joseph Yabut Manaig, Armand Sánchez, Josep M. Folch. Data curation: Yron Joseph Yabut Manaig, Lourdes Criado-Mesas, Anna Esteve-Codina, Emilio Mármol-Sánchez, Josep M. Folch. Formal analysis: Yron Joseph Yabut Manaig, Anna Esteve-Codina, Josep M. Folch. Funding acquisition: Armand Sánchez, Josep M. Folch. Investigation: Yron Joseph Yabut Manaig, Lourdes Criado-Mesas, Anna Esteve-Codina, Anna Castelló, Josep M. Folch. Methodology: Yron Joseph Yabut Manaig, Lourdes Criado-Mesas, Emilio Mármol-Sánchez, Anna Castelló, Josep M. Folch. Project administration: Armand Sánchez, Josep M. Folch. Resources: Armand Sánchez. PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 11 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs Software: Yron Joseph Yabut Manaig, Lourdes Criado-Mesas, Emilio Mármol-Sánchez, Josep M. Folch. Supervision: Armand Sánchez, Josep M. Folch. Visualization: Yron Joseph Yabut Manaig. Writing – original draft: Yron Joseph Yabut Manaig. Writing – review & editing: Yron Joseph Yabut Manaig, Lourdes Criado-Mesas, Emilio Már- mol-Sánchez, Armand Sánchez, Josep M. Folch. References 1. Butler G. Manipulating dietary PUFA in animal feed: implications for human health. Proc Nutr Soc. 2014 Feb 5; 73(1):87–95. https://doi.org/10.1017/S0029665113003790 PMID: 24308374 2. Berná G, Oliveras-López MJ, Jurado-Ruı́z E, Tejedo J, Bedoya F, Soria B, et al. Nutrigenetics and nutri- genomics insights into diabetes etiopathogenesis. Nutrients. 2014; 6(11):5338–69. https://doi.org/10. 3390/nu6115338 PMID: 25421534 3. Marı́n-Garcı́a PJ, Llobat L. How does protein nutrition affect the epigenetic changes in pig? A review. Animals. 2021; 11(2):1–17. https://doi.org/10.3390/ani11020544 PMID: 33669864 4. Bartel DP. Metazoan MicroRNAs. Cell. 2018; 173(1):20–51. https://doi.org/10.1016/j.cell.2018.03.006 PMID: 29570994 5. Kozomara A, Birgaoanu M, Griffiths-Jones S. MiRBase: From microRNA sequences to function. Nucleic Acids Res. 2019; 47(D1):D155–62. https://doi.org/10.1093/nar/gky1141 PMID: 30423142 6. Sharma A, Park JE, Chai HH, Jang GW, Lee SH, Lim D. Next generation sequencing in livestock spe- cies- A Review. J Anim Breed Genomics. 2017; 1(1). 7. Food and Agriculture Organization (FAO). Meat Market Review—Overview of market and policy devel- opments in 2021. Rome; 2022. https://www.fao.org/3/cc0984en/cc0984en.pdf. 8. Xing K, Zhao X, Ao H, Chen S, Yang T, Tan Z, et al. Transcriptome analysis of miRNA and mRNA in the livers of pigs with highly diverged backfat thickness. Sci Rep. 2019; 9(1):1–12. 9. Martinsen KH, Ødegård J, Olsen D, Meuwissen THE. Genetic variation in efficiency to deposit fat and lean meat in Norwegian Landrace and Duroc pigs1. J Anim Sci. 2015 Aug 1; 93(8):3794–800. 10. Rauw WM, Kanis E, Noordhuizen-Stassen EN, Grommers FJ. Undesirable side effects of selection for high production efficiency in farm animals: A review. Livest Prod Sci. 1998; 56(1):15–33. 11. Patience JF. Strategies to maximize wean-to-finish net income: Focus on feed and other technical issues. In: Ball RO, editor. Advances in Pork Production. Edmonton, AB: University of Alberta; 2010. p. 75–85. 12. National Research Council. Nutrient Requirements of Swine. 11th ed. Nutrient Requirements of Swine. Washington, D.C.: National Academies Press; 2012. 13. Bai L, Liang R, Yang Y, Hou X, Wang Z, Zhu S, et al. MicroRNA-21 regulates PI3K/Akt/mTOR signaling by targeting TGFβI during skeletal muscle development in pigs. PLoS One. 2015; 10(5). 14. Sakayori N, Katakura M, Hamazaki K, Higuchi O, Fujii K, Fukabori R, et al. Maternal dietary imbalance between omega-6 and omega-3 fatty acids triggers the offspring’s overeating in mice. Commun Biol. 2020 Dec 28; 3(1):473. https://doi.org/10.1038/s42003-020-01209-4 PMID: 32859990 15. Zivkovic AM, Telis N, German JB, Hammock BD. Dietary omega-3 fatty acids aid in the modulation of inflammation and metabolic health. Calif Agric. 2011 Jul; 65(3):106–11. https://doi.org/10.3733/ca. v065n03p106 PMID: 24860193 16. Dennis EA, Norris PC. Eicosanoid storm in infection and inflammation. Nat Rev Immunol. 2015 Aug 3; 15(8):511–23. https://doi.org/10.1038/nri3859 PMID: 26139350 17. Martı́nez-Montes ÁM, Fernández A, Muñoz M, Noguera JL, Folch JM, Fernández AI. Using genome wide association studies to identify common QTL regions in three different genetic backgrounds based on Iberian pig breed. PLoS One. 2018; 13(3). https://doi.org/10.1371/journal.pone.0190184 PMID: 29522525 18. Crespo-Piazuelo D, Criado-Mesas L, Revilla M, Castelló A, Noguera JL, Fernández AI, et al. Identifica- tion of strong candidate genes for backfat and intramuscular fatty acid composition in three crosses based on the Iberian pig. Sci Rep. 2020; 10(1):1–17. 19. Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. http://www. bioinformatics.babraham.ac.uk./projects/fastqc/. PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 12 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs 20. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.jour- nal. 2011 May 2; 17(1):10. 21. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA- seq aligner. Bioinformatics. 2013 Jan; 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 PMID: 23104886 22. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a refer- ence genome. BMC Bioinformatics. 2011 Dec 4; 12(1):323. 23. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009; 10(3):R25. https://doi.org/10.1186/gb-2009-10- 3-r25 PMID: 19261174 24. Mármol-Sánchez E, Ramayo-Caldas Y, Quintanilla R, Cardoso TF, González-Prendes R, Tibau J, et al. Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skele- tal muscle to nutrient supply. J Anim Sci Biotechnol. 2020 Dec 17; 11(1):10. https://doi.org/10.1186/ s40104-019-0412-z PMID: 31969983 25. Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015 Jan 15; 31(2):166–9. https://doi.org/10.1093/bioinformatics/btu638 PMID: 25260700 26. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 Dec 5; 15(12):550. https://doi.org/10.1186/s13059-014-0550-8 PMID: 25516281 27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environ- ment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov; 13 (11):2498–504. https://doi.org/10.1101/gr.1239303 PMID: 14597658 28. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformat- ics. 2009 Apr 15; 25(8):1091–3. https://doi.org/10.1093/bioinformatics/btp101 PMID: 19237447 29. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to mul- tiple testing. J R Stat Soc Ser B. 1995 Jan; 57(1):289–300. 30. Reverter A, Chan EKF. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics. 2008 Nov; 24(21):2491–7. https://doi.org/10.1093/bioinformatics/btn482 PMID: 18784117 31. Watson-Haigh NS, Kadarmideen HN, Reverter A. PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches. Bioinformatics. 2010 Feb; 26 (3):411–3. https://doi.org/10.1093/bioinformatics/btp674 PMID: 20007253 32. Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One. 2016 Oct; 11(10):e0163962. https://doi.org/10.1371/journal.pone.0163962 PMID: 27706213 33. Mayya VK, Duchaine TF. On the availability of microRNA-induced silencing complexes, saturation of microRNA-binding sites and stoichiometry. Nucleic Acids Res. 2015 Sep; 43(15):7556–65. https://doi. org/10.1093/nar/gkv720 PMID: 26227970 34. Paschos GK, Ibrahim S, Song W-L, Kunieda T, Grant G, Reyes TM, et al. Obesity in mice with adipo- cyte-specific deletion of clock component Arntl. Nat Med. 2012 Dec 11; 18(12):1768–77. https://doi.org/ 10.1038/nm.2979 PMID: 23142819 35. Laufer BI, Hasegawa Y, Zhang Z, Hogrefe CE, Del Rosso LA, Haapanan L, et al. Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models. Nat Com- mun 13, 5538 (2022). https://doi.org/10.1038/s41467-022-33162-7 PMID: 36130949 36. Liu R, Liu X, Bai X, Xiao C, Dong Y. Different expression of lipid metabolism-related genes in Shandong black cattle and Luxi cattle based on transcriptome analysis. Sci Rep. 2020 Dec 14; 10(1):21915. https://doi.org/10.1038/s41598-020-79086-4 PMID: 33318614 37. Li K, Li J, Gu J, Guo X, Gao T, Li D. The protective effect of polyunsaturated fatty acid intake during pregnancy against embryotoxicity of sodium valproate in mice. Food Funct. 2018; 9(5):2634–43. https://doi.org/10.1039/C7FO01604B PMID: 29561026 38. Hopiavuori BR, Agbaga MP, Brush RS, Sullivan MT, Sonntag WE, Anderson RE. Regional changes in CNS and retinal glycerophospholipid profiles with age: a molecular blueprint. J Lipid Res. 2017 Apr; 58 (4):668–80. https://doi.org/10.1194/jlr.M070714 PMID: 28202633 39. Oishi Y, Manabe I, Tobe K, Ohsugi M, Kubota T, Fujiu K, et al. SUMOylation of Krüppel-like transcription factor 5 acts as a molecular switch in transcriptional programs of lipid metabolism involving PPAR-δ. Nat Med. 2008 Jun 25; 14(6):656–66. PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 13 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs 40. Hayashi S, Manabe I, Suzuki Y, Relaix F, Oishi Y. Klf5 regulates muscle differentiation by directly tar- geting muscle-specific genes in cooperation with MyoD in mice. Elife. 2016 Oct 15; 5. https://doi.org/10. 7554/eLife.17462 PMID: 27743478 41. Lanktree MB, Joy TR, Hegele RA. The metabolic syndrome. In: genomic and personalized medicine. Elsevier; 2013. p. 1006–16. 42. Rovadoscki GA, Pertile SFN, Alvarenga AB, Cesar ASM, Pértille F, Petrini J, et al. Estimates of geno- mic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep. BMC Genomics. 2018 Dec 21; 19(1):375. 43. Zhong L, Yang H, Zhu B, Zhao X, Xie M, Cao M, et al. The TBX1/miR-193a-3p/TGF-β2 axis mediates CHD by promoting ferroptosis. Oxid Med Cell Longev. 2022 Jan 7; 2022:1–13. 44. Yang WS, Kim KJ, Gaschler MM, Patel M, Shchepinov MS, Stockwell BR. Peroxidation of polyunsatu- rated fatty acids by lipoxygenases drives ferroptosis. Proc Natl Acad Sci. 2016 Aug 23; 113(34). https:// doi.org/10.1073/pnas.1603244113 PMID: 27506793 45. Coyne GS, Kenny DA, Waters SM. Effect of dietary n-3 polyunsaturated fatty acid supplementation on bovine uterine endometrial and hepatic gene expression of the insulin-like growth factor system. Therio- genology. 2011 Feb; 75(3):500–12. https://doi.org/10.1016/j.theriogenology.2010.09.018 PMID: 21111466 46. Trivedi PC, Bartlett JJ, Mercer A, Slade L, Surette M, Ballabio A, et al. Loss of function of transcription factor EB remodels lipid metabolism and cell death pathways in the cardiomyocyte. Biochim Biophys Acta—Mol Basis Dis. 2020 Oct; 1866(10):165832. https://doi.org/10.1016/j.bbadis.2020.165832 PMID: 32437957 47. Jonscher KR, Alfonso-Garcia A, Suhalim JL, Orlicky DJ, Potma EO, Ferguson VL, et al. Spaceflight activates lipotoxic pathways in mouse liver. PLoS One. 2016 Apr 20; 11(4):e0152877. https://doi.org/ 10.1371/journal.pone.0152877 PMID: 27097220 48. Würtz P, Wang Q, Soininen P, Kangas AJ, Fatemifar G, Tynkkynen T, et al. Metabolomic profiling of statin use and genetic inhibition of HMG-CoA reductase. J Am Coll Cardiol. 2016 Mar; 67(10):1200–10. https://doi.org/10.1016/j.jacc.2015.12.060 PMID: 26965542 49. Karanth S, Tran VM, Kuberan B, Schlegel A. Polyunsaturated fatty acyl-coenzyme As are inhibitors of cholesterol biosynthesis in zebrafish and mice. Dis Model Mech. 2013 Jan 1. https://doi.org/10.1242/ dmm.013425 PMID: 24057001 50. Shimano H, Sato R. SREBP-regulated lipid metabolism: convergent physiology—divergent pathophysi- ology. Nat Rev Endocrinol. 2017 Dec 29; 13(12):710–30. https://doi.org/10.1038/nrendo.2017.91 PMID: 28849786 51. DeBose-Boyd RA, Ye J. SREBPs in lipid metabolism, insulin signaling, and beyond. Trends Biochem Sci. 2018 May; 43(5):358–68. https://doi.org/10.1016/j.tibs.2018.01.005 PMID: 29500098 52. Kolmus K, Tavernier J, Gerlo S. β2-Adrenergic receptors in immunity and inflammation: stressing NF- κB. Brain Behav Immun. 2015 Mar; 45:297–310. 53. Gorman AL, Dunn AJ. β-adrenergic receptors are involved in stress-related behavioral changes. Phar- macol Biochem Behav. 1993 May; 45(1):1–7. 54. Gao M, Ping P, Post S, Insel PA, Tang R, Hammond HK. Increased expression of adenylylcyclase type VI proportionately increases β-adrenergic receptor-stimulated production of cAMP in neonatal rat car- diac myocytes. Proc Natl Acad Sci. 1998 Feb 3; 95(3):1038–43. 55. Depre C, Ponchaut S, Deprez J, Maisin L, Hue L. Cyclic AMP suppresses the inhibition of glycolysis by alternative oxidizable substrates in the heart. J Clin Invest. 1998 Jan 15; 101(2):390–7. https://doi.org/ 10.1172/JCI1168 PMID: 9435311 56. Nicolas C, Lacasa D, Giudicelli Y, Demarne Y, Agli B, Lecourtier M-J, et al. Dietary (n-6) polyunsatu- rated fatty acids affect β-Adrenergic receptor binding and adenylate cyclase activity in pig adipocyte plasma membrane. J Nutr. 1991 Aug 1; 121(8):1179–86. 57. Manaig Y, Sandrini S, Panseri S, Tedeschi G, Folch J, Sánchez A, et al. Low n-6/n-3 gestation and lac- tation diets influence early performance, muscle and adipose polyunsaturated fatty acid content and deposition, and relative abundance of proteins in suckling piglets. Molecules. 2022 May 4; 27(9):2925. https://doi.org/10.3390/molecules27092925 PMID: 35566276 58. Mentzel CMJ, Alkan F, Keinicke H, Jacobsen MJ, Gorodkin J, Fredholm M, et al. Joint profiling of miR- NAs and mRNAs reveals miRNA mediated gene regulation in the Göttingen minipig obesity model. PLoS One. 2016 Nov 30; 11(11):e0167285. 59. Peng J, Zhao JS, Shen YF, Mao HG, Xu NY. MicroRNA expression profiling of lactating mammary gland in divergent phenotype swine breeds. Int J Mol Sci. 2015 Jan 8; 16(1):1448–65. https://doi.org/10. 3390/ijms16011448 PMID: 25580536 PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 14 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs 60. Bai Y, Huang JM, Liu G, Zhang JB, Wang JY, Liu CK, et al. A comprehensive microRNA expression pro- file of the backfat tissue from castrated and intact full-sib pair male pigs. BMC Genomics. 2014 Dec 20; 15(1):47. https://doi.org/10.1186/1471-2164-15-47 PMID: 24443800 61. Wang J, Hua L, Chen J, Zhang J, Bai X, Gao B, et al. Identification and characterization of long non-cod- ing RNAs in subcutaneous adipose tissue from castrated and intact full-sib pair Huainan male pigs. BMC Genomics. 2017 Dec 19; 18(1):542. https://doi.org/10.1186/s12864-017-3907-z PMID: 28724410 62. Sarkar S, Dey BK, Dutta A. MiR-322/424 and -503 are induced during muscle differentiation and pro- mote cell cycle quiescence and differentiation by down-regulation of Cdc25A. Mol Biol Cell. 2010 Jul; 21 (13):2138–49. https://doi.org/10.1091/mbc.e10-01-0062 PMID: 20462953 63. Li X, Zhu L, Liu X, Sun X, Zhou Y, Lang Q, et al. Differential expression of microRNAs in porcine parvovi- rus infected porcine cell line. Virol J. 2015 Dec 20; 12(1):128. https://doi.org/10.1186/s12985-015-0359- 4 PMID: 26290078 64. Hicks JA., Yoo D, Liu HC. Characterization of the microRNAome in porcine reproductive and respiratory syndrome virus infected macrophages. PLoS One. 2013 Dec 5; 8(12):e82054. https://doi.org/10.1371/ journal.pone.0082054 PMID: 24339989 65. Jiang P, Zhou N, Chen X, Zhao X, Li D, Wang F, et al. Integrative analysis of differentially expressed microRNAs of pulmonary alveolar macrophages from piglets during H1N1 swine influenza A virus infec- tion. Sci Rep. 2015 Jul 2; 5(1):8167. https://doi.org/10.1038/srep08167 PMID: 25639204 66. Iikuni N, Kwan Lam Q, Lu L, Matarese G, Cava A. Leptin and inflammation. Curr Immunol Rev. 2008 May 1; 4(2):70–9. https://doi.org/10.2174/157339508784325046 PMID: 20198122 67. Santoro N, Kursawe R, D’Adamo E, Dykas DJ, Zhang CK, Bale AE, et al. A common variant in the pata- tin-like phospholipase 3 gene (PNPLA3) is associated with fatty liver disease in obese children and ado- lescents. Hepatology. 2010 Aug 27; 52(4):1281–90. https://doi.org/10.1002/hep.23832 PMID: 20803499 68. Corominas J, Marchesi JA, Puig-Oliveras A, Revilla M, Estellé J, Alves E, et al. Epigenetic regulation of the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. Genet Sel Evol. 2015 Dec 25; 47(1):20. https://doi.org/10.1186/s12711-015-0111-y PMID: 25887840 69. Jia H, Zhao Y, Li T, Zhang Y, Zhu D. miR-30e is negatively regulated by myostatin in skeletal muscle and is functionally related to fiber-type composition. Acta Biochim Biophys Sin (Shanghai). 2017 May; 49(5):392–9. https://doi.org/10.1093/abbs/gmx019 PMID: 28338991 70. Zaragosi LE, Wdziekonski B, Le Brigand K, Villageois P, Mari B, Waldmann R, et al. Small RNA sequencing reveals miR-642a-3p as a novel adipocyte-specific microRNA and miR-30 as a key regula- tor of human adipogenesis. Genome Biol. 2011; 12(7):R64. https://doi.org/10.1186/gb-2011-12-7-r64 PMID: 21767385 71. Núñez-Hernández F, Pérez LJ, Muñoz M, Vera G, Accensi F, Sánchez A, et al. Differential expression of porcine microRNAs in African swine fever virus infected pigs: a proof-of-concept study. Virol J. 2017 Dec 17; 14(1):198. https://doi.org/10.1186/s12985-017-0864-8 PMID: 29041944 72. Ye L, Su X, Wu Z, Zheng X, Wang J, Zi C, et al. Analysis of differential miRNA expression in the duode- num of Escherichia coli F18-sensitive and -resistant weaned piglets. PLoS One. 2012 Aug 24; 7(8): e43741. 73. Bao H, Kommadath A, Liang G, Sun X, Arantes AS, Tuggle CK, et al. Genome-wide whole blood micro- RNAome and transcriptome analyses reveal miRNA-mRNA regulated host response to foodborne path- ogen Salmonella infection in swine. Sci Rep. 2015 Oct 31; 5(1):12620. 74. Kang JX, Liu A. The role of the tissue omega-6/omega-3 fatty acid ratio in regulating tumor angiogene- sis. Cancer Metastasis Rev. 2013 Jun 23; 32(1–2):201–10. https://doi.org/10.1007/s10555-012-9401-9 PMID: 23090260 75. Kubiczkova L, Sedlarikova L, Hajek R, Sevcikova S. TGF-β–an excellent servant but a bad master. J Transl Med. 2012 Dec 3; 10(1):183. 76. Wang W, Zhu J, Lyu F, Panigrahy D, Ferrara KW, Hammock B, et al. ω-3 polyunsaturated fatty acids- derived lipid metabolites on angiogenesis, inflammation and cancer. Prostaglandins Other Lipid Mediat. 2014 Oct; 113–115:13–20. 77. Podolska A, Anthon C, Bak M, Tommerup N, Skovgaard K, Heegaard PM, et al. Profiling microRNAs in lung tissue from pigs infected with Actinobacillus pleuropneumoniae. BMC Genomics. 2012 Dec 6; 13 (1):459. https://doi.org/10.1186/1471-2164-13-459 PMID: 22953717 78. Zhao Y, Rao J, Qiu T, Li C, Zhou X. The Effect of RBP4 on microRNA expression profiles in porcine gran- ulosa cells. Animals. 2021 May 13; 11(5):1391. https://doi.org/10.3390/ani11051391 PMID: 34068244 79. Moore CAC, Milano SK, Benovic JL. Regulation of receptor trafficking by GRKs and arrestins. Annu Rev Physiol. 2007 Mar 1; 69(1):451–82. https://doi.org/10.1146/annurev.physiol.69.022405.154712 PMID: 17037978 PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 15 / 16 PLOS ONE Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio in pigs 80. Weinberg ZY, Puthenveedu MA. Regulation of G protein-coupled receptor signaling by plasma mem- brane organization and endocytosis. Traffic. 2019 Feb 11; 20(2):121–9. https://doi.org/10.1111/tra. 12628 PMID: 30536564 81. Nabhan JF, Pan H, Lu Q. Arrestin domain-containing protein 3 recruits the NEDD4 E3 ligase to mediate ubiquitination of the β2-adrenergic receptor. EMBO Rep. 2010 Aug 18; 11(8):605–11. 82. Kang DS, Tian X, Benovic JL. Role of β-arrestins and arrestin domain-containing proteins in G protein- coupled receptor trafficking. Curr Opin Cell Biol. 2014 Apr; 27:63–71. 83. Shea FF, Rowell JL, Li Y, Chang T-H, Alvarez CE. Mammalian alpha arrestins link activated seven transmembrane receptors to Nedd4 family E3 ubiquitin ligases and interact with beta arrestins. PLoS One. 2012 Dec 7; 7(12):e50557. 84. Patwari P, Emilsson V, Schadt EE, Chutkow WA, Lee S, Marsili A, et al. The arrestin domain-containing 3 protein regulates body mass and energy expenditure. Cell Metab. 2011 Nov; 14(5):671–83. https:// doi.org/10.1016/j.cmet.2011.08.011 PMID: 21982743 85. Ogawa M, Kanda T, Higuchi T, Takahashi H, Kaneko T, Matsumoto N, et al. Possible association of arrestin domain-containing protein 3 and progression of non-alcoholic fatty liver disease. Int J Med Sci. 2019; 16(7):909–21. https://doi.org/10.7150/ijms.34245 PMID: 31341404 86. Batista TM, Dagdeviren S, Carroll SH, Cai W, Melnik VY, Noh HL, et al. Arrestin domain-containing 3 (Arrdc3) modulates insulin action and glucose metabolism in liver. Proc Natl Acad Sci. 2020 Mar 24; 117(12):6733–40. https://doi.org/10.1073/pnas.1922370117 PMID: 32156724 87. Wang C, Arrington J, Ratliff AC, Chen J, Horton HE, Nie Y, et al. Methyltransferase-like 21c methylates and stabilizes the heat shock protein Hspa8 in type I myofibers in mice. J Biol Chem. 2019 Sep; 294 (37):13718–28. https://doi.org/10.1074/jbc.RA119.008430 PMID: 31346037 88. Wiederstein JL, Nolte H, Günther S, Piller T, Baraldo M, Kostin S, et al. Skeletal muscle-specific methyl- transferase METTL21C trimethylates p97 and regulates autophagy-associated protein breakdown. Cell Rep. 2018 May; 23(5):1342–56. https://doi.org/10.1016/j.celrep.2018.03.136 PMID: 29719249 89. Olsen T, Lee S, Vinknes K, Norheim F, Hjorth M, Turner C, et al. Tissue expression of methyltrans- ferases in response to acute and long-term exercise in sedentary men. Research Square [Preprint]; 2021 [cited 2022 June 25]. Available from: https://www.researchsquare.com/article/rs-143201/v1. 90. Wilund KR. Is the anti-inflammatory effect of regular exercise responsible for reduced cardiovascular disease? Clin Sci. 2007 Jun 1; 112(11):543–55. https://doi.org/10.1042/CS20060368 PMID: 17459004 91. Soung YH, Pruitt K, Chung J. Epigenetic silencing of ARRDC3 expression in basal-like breast cancer cells. Sci Rep. 2015 May 24; 4(1):3846. 92. Huang J, Hsu YH, Mo C, Abreu E, Kiel DP, Bonewald LF, et al. METTL21C is a potential pleiotropic gene for osteoporosis and sarcopenia acting through the modulation of the NF-κB signaling pathway. J Bone Miner Res. 2014 Jul; 29(7):1531–40. 93. Fernández KAMG. Targeting NRAS mutant melanomas through metabolic stress. Ph. D. Thesis. Universitat Autònoma de Barcelona; 2021. https://ddd.uab.cat/pub/tesis/2021/hdl_10803_673108/ kamgf1de1.pdf. PLOS ONE | https://doi.org/10.1371/journal.pone.0283231 May 4, 2023 16 / 16