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Irlnc: a Novel Functional Noncoding RNA Contributes to Intramuscular Fat

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Irlnc: a Novel Functional Noncoding RNA Contributes to Intramuscular Fat Wang et al. BMC Genomics (2021) 22:95 https://doi.org/10.1186/s12864-020-07349-5 RESEARCH ARTICLE Open Access IRLnc: a novel functional noncoding RNA contributes to intramuscular fat deposition Ligang Wang1, Zhong-Yin Zhou2*, Tian Zhang1,3, Longchao Zhang1, Xinhua Hou1, Hua Yan1 and Lixian Wang1* Abstract Background: Intramuscular fat (IMF) is associated with meat quality and insulin resistance in animals. Research on genetic mechanism of IMF decomposition has positive meaning to pork quality and diseases such as obesity and type 2 diabetes treatment. In this study, an IMF trait segregation population was used to perform RNA sequencing and to analyze the joint or independent effects of genes and long intergenic non-coding RNAs (lincRNAs) on IMF. Results: A total of 26 genes including six lincRNA genes show significantly different expression between high- and low-IMF pigs. Interesting, one lincRNA gene, named IMF related lincRNA (IRLnc) not only has a 292-bp conserved region in 100 vertebrates but also has conserved up and down stream genes (< 10 kb) in pig and humans. Real- time quantitative polymerase chain reaction (RT-qPCR) validation study indicated that nuclear receptor subfamily 4 group A member 3 (NR4A3) which located at the downstream of IRLnc has similar expression pattern with IRLnc. RNAi-mediated loss of function screens identified that IRLnc silencing could inhibit both of the RNA and protein expression of NR4A3. And the in-situ hybridization co-expression experiment indicates that IRLnc may directly binding to NR4A3. As the NR4A3 could regulate the catecholamine catabolism, which could affect insulin sensitivity, we inferred that IRLnc influence IMF decomposition by regulating the expression of NR4A3. Conclusions: In conclusion, a novel functional noncoding variation named IRLnc has been found contribute to IMF by regulating the expression of NR4A3. These findings suggest novel mechanistic approach for treatment of insulin resistance in human beings and meat quality improvement in animal. Keywords: Intramuscular fat, Long non-coding RNA, Insulin resistance, Pig Background acids (∼10–15% of total fatty acids) than beef and lamb Intramuscular fat (IMF) refers to the amount of fat lo- [3]. Long-chain polyunsaturated fatty acids (LC-PUFA) cated in skeletal muscle fibers [1]. Excess accumulation such as omega-3 PUFA, eicosapentaenoic (EPA, 20:5n- of IMF has been reported to be associated with diseases, 3), and docosahexaenoic (DHA, 22:6n-3) acids are well such as type 2 diabetes and insulin resistance in humans accepted having beneficial effects on human brain devel- [2]. In animals, as an important determinant of meat opment and cardiovascular disease [4, 5]. Both extremely quality, IMF content directly influences flavor and juici- high and extremely low IMF content is undesirable in ness and indirectly influences tenderness and meat color consumed meat [1]. Thus, IMF is an important factor [1]. Moreover, pork IMF contains more unsaturated fatty for human health. It is generally accepted that IMF is a complex trait that * Correspondence: [email protected]; [email protected] is influenced by multiple genes or quantitative trait loci 2State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China (QTLs). To date, a total of 709 QTLs have been reported 1Key Laboratory of Farm Animal Genetic Resources and Germplasm to be associated with pig IMF content (PigQTLdb, Innovation of Ministry of Agriculture of China, Institute of Animal Sciences, http://www.animalgenome.org/cgi-bin/QTLdb/SS/index, Chinese Academy of Agricultural Sciences, Beijing 100193, China Full list of author information is available at the end of the article released at April 26, 2020) [6]. However, the locations of © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. BMC Genomics (2021) 22:95 Page 2 of 12 these QTLs are not accurate due to the limited density identified using a paired samples model in edgeR [16]. of microsatellite markers. Long-term fine-mapping ex- This included 5 novel protein-coding genes, 15 known periments are needed to refine these loci and investigate protein-coding genes and 6 lincRNAs (Table 1 and causative variants [7]. Furthermore, most of the single Fig. 1). Six of the 20 protein-coding genes were upregu- nucleotide polymorphisms (SNPs) associated with IMF lated with more than 2-fold changes (FC) in the pigs in genome-wide association studies only explain a small with low IMF content. And these six genes were N- part of the total genetic variance. Studies identifying Acetyl-Alpha-Glucosaminidase (NAGLU), novel gene 1 genetic variation that explains this “missing heritability” (Novel1), mitochondrially encoded NADH: ubiquinone of IMF are urgently needed [8]. oxidoreductase core subunit 6 (ND6), sushi domain con- Since they reside in regulatory elements of the gen- taining 3 (SUSD3), novel gene 4 (Novel4), and leucine ome, noncoding genomic variants located in intronic re- gions of protein-coding genes or in intergenic regions Table 1 Description of significantly DE transcripts between IMF- may have functional roles in the expression of specific differential pigs phenotypes or traits. In pigs, long intergenic non-coding Gene symbol logFC logCPM PValue FDR Chromosome RNAs (lincRNAs) have been reported to be associated NAGLU 10.55 0.22 2.48E-31 7.73E-27 12 with permanent molars, adipose and muscle develop- Novel1 10.49 0.18 2.56E-18 4.00E-14 AEMK02000407.1 ment, and energy metabolism [9–11]. Although several ACBD7 −2.78 1.97 5.52E-13 5.75E-09 10 lincRNAs have been reported associated with pork and poultry IMF [12–15], little is known about the mechan- PPARGC1 −1.80 8.55 8.39E-11 6.55E-07 8 ism of lincRNA gene regulation in pig IMF.. The objec- IRLnc −2.29 2.11 8.48E-09 5.30E-05 1 tives of this work were to perform RNA sequencing Novel2 −8.04 −2.25 2.99E-08 1.56E-04 18 analysis using an IMF character segregation population GADD45A −1.12 4.15 5.62E-07 2.48E-03 6 which construct using high IMF pigs (Min pig) and low IRLnc2 7.95 −2.12 6.35E-07 2.48E-03 9 IMF pigs (Large white pigs) crossbred and to analyze the IRLnc3 7.68 −2.44 2.13E-06 7.40E-03 2 joint or independent effects of lincRNAs on IMF. More- over, we aimed to identify genetic markers that may be NR4A3 −1.94 5.82 2.44E-06 7.62E-03 1 suitable for inclusion in animal genetic improvement SRXN1 −1.23 2.50 2.69E-06 7.64E-03 17 programs and provide new targets for the treatment of IRLnc4 −1.26 2.98 3.15E-06 8.21E-03 10 insulin resistance in humans. LEP −2.05 −0.51 4.85E-06 1.16E-02 18 SLC20A1 −1.06 5.60 5.38E-06 1.16E-02 3 Results FASN −1.21 4.18 5.57E-06 1.16E-02 12 RNA sequencing, data mapping, and transcript identification PRKAG2 −1.31 4.79 6.90E-06 1.35E-02 18 RNA sequencing of longissimus dorsi muscle tissue has ND6 4.67 −0.44 1.09E-05 1.89E-02 MT been done first in our research. After filtering, a total of Novel3 5.01 −1.94 1.09E-05 1.89E-02 AEMK02000635.1 579.53 million clean reads (97.22% of the raw data) were IRLnc5 7.75 −2.38 1.19E-05 1.95E-02 17 obtained. More than 75% of the clean data could be IRLnc6 4.73 −1.37 1.38E-05 2.11E-02 11 mapped to the reference genome (v11.1 ftp://ftp. ADIPOQ −1.19 5.05 1.42E-05 2.11E-02 13 ensembl.org/pub/release-102/fasta/sus_scrofa/dna/). A total of 26,276 transcript units were identified, including SUSD3 7.44 −2.68 1.57E-05 2.23E-02 3 4671 lincRNAs. Among these 26,276 units, 59.7% Novel4 3.77 −1.18 2.58E-05 3.50E-02 AEMK02000297.1 encoded proteins, 3.4% were miscellaneous RNAs, 2.2% C2CD3 −1.18 3.94 2.73E-05 3.55E-02 9 were microRNAs (miRNAs), 0.6% were mitochondrial Novel5 −1.52 2.65 3.57E-05 4.35E-02 3 ribosomal RNAs (rRNAs), 0.2% were small nuclear LRRC66 8.01 −1.96 3.62E-05 4.35E-02 8 RNAs (snRNAs), and the remaining 33.6% were pseudo- DE differential expression. IMF intramuscular fat. Gene symbol names of the genes and processed transcripts. The clean data have genes. FC fold change (low - IMF vs. high - IMF). CPM counts per million. FDR been submitted to the Genome Sequence Archive, with false discovery rate.
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