An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dietary Intervention and Sample Collection
2.2. Gene Expression and miRNA Analysis
2.2.1. Microarray Gene Expression Analysis
2.2.2. Small RNA Sequencing (Small RNA-Seq) Analysis
2.3. Bioinformatic and Statistical Analysis
2.3.1. Gene Differential Expression between Biological Conditions
2.3.2. Testing Gene Signature Performances with Partial Least-Squares Discriminant Analysis and Hierarchical Clustering
2.3.3. Application of MicroRNA Master Regulator Analysis (MMRA)
- MMRA step1: MiRNA differential expression analysis
- MMRA step2: target genes enrichment analysis
- MMRA step3: network analysis
- MMRA step4: Step-wise Linear Regression (SLR) analysis
2.4. Pathways Analysis
2.4.1. Molecular Pathway Analysis with GeneTrail2
2.4.2. Metabolic Pathways Analysis with MetExplore
2.5. Logical Modeling of Gene Regulatory and Metabolic Networks
2.6. Validation of Gene Expression
3. Results
3.1. Effect of High Fat Diet and Transgenerational Supplementation with EPA in Gene and miRNAs Expression
3.2. MiRNAs Significantly Contribute to the Expression of Phenotype Signature Gene
3.3. Identification of Differentially Regulated Cellular and Signaling Pathways
3.4. Differential Enrichment of Metabolic Pathways in HFepa and HFoleic Groups
3.5. MiRNAs Regulated Genes Involved in the Integration of Insulin Signaling, PPAR Signaling, Glucose Metabolism, and FA Metabolism
3.6. Computational Modeling Predicts that Dynamism in Genes and miRNAs Expression Leads to Specific Cell Metabolic Phenotypes
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- de Ferranti, S.; Mozaffarian, D. The perfect storm: Obesity, adipocyte dysfunction, and metabolic consequences. Clin. Chem. 2008, 54, 945–955. [Google Scholar] [CrossRef] [Green Version]
- Hatting, M.; Tavares, C.D.J.; Sharabi, K.; Rines, A.K.; Puigserver, P. Insulin regulation of gluconeogenesis. Ann. N.Y. Acad. Sci. 2018, 1411, 21–35. [Google Scholar] [CrossRef] [PubMed]
- Samuel, V.T.; Shulman, G.I. The pathogenesis of insulin resistance: Integrating signaling pathways and substrate flux. J. Clin. Investig. 2016, 126, 12–22. [Google Scholar] [CrossRef] [Green Version]
- Albracht-Schulte, K.; Kalupahana, N.S.; Ramalingam, L.; Wang, S.; Rahman, S.M.; Robert-McComb, J.; Moustaid-Moussa, N. Omega-3 fatty acids in obesity and metabolic syndrome: A mechanistic update. J. Nutr. Biochem. 2018, 58, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Martinez-Fernandez, L.; Laiglesia, L.M.; Huerta, A.E.; Martinez, J.A.; Moreno-Aliaga, M.J. Omega-3 fatty acids and adipose tissue function in obesity and metabolic syndrome. Prostaglandins Other Lipid Mediat. 2015, 121, 24–41. [Google Scholar] [CrossRef] [PubMed]
- Pinel, A.; Morio-Liondore, B.; Capel, F. n-3 Polyunsaturated fatty acids modulate metabolism of insulin-sensitive tissues: Implication for the prevention of type 2 diabetes. J. Physiol. Biochem. 2014, 70, 647–658. [Google Scholar] [CrossRef] [PubMed]
- Pinel, A.; Pitois, E.; Rigaudiere, J.P.; Jouve, C.; De Saint-Vincent, S.; Laillet, B.; Montaurier, C.; Huertas, A.; Morio, B.; Capel, F. EPA prevents fat mass expansion and metabolic disturbances in mice fed with a Western diet. J. Lipid Res. 2016, 57, 1382–1397. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Massiera, F.; Barbry, P.; Guesnet, P.; Joly, A.; Luquet, S.; Moreilhon-Brest, C.; Mohsen-Kanson, T.; Amri, E.Z.; Ailhaud, G. A Western-like fat diet is sufficient to induce a gradual enhancement in fat mass over generations. J. Lipid Res. 2010, 51, 2352–2361. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dao, M.C.; Sokolovska, N.; Brazeilles, R.; Affeldt, S.; Pelloux, V.; Prifti, E.; Chilloux, J.; Verger, E.O.; Kayser, B.D.; Aron-Wisnewsky, J.; et al. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front. Physiol. 2018, 9, 1958. [Google Scholar] [CrossRef]
- Yang, Q.; Vijayakumar, A.; Kahn, B.B. Metabolites as regulators of insulin sensitivity and metabolism. Nat. Rev. Mol. Cell Biol. 2018, 19, 654–672. [Google Scholar] [CrossRef]
- Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef] [PubMed]
- Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207–210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, C.J.; Servant, N.; Toedling, J.; Sarazin, A.; Marchais, A.; Duvernois-Berthet, E.; Cognat, V.; Colot, V.; Voinnet, O.; Heard, E.; et al. ncPRO-seq: A tool for annotation and profiling of ncRNAs in sRNA-seq data. Bioinformatics 2012, 28, 3147–3149. [Google Scholar] [CrossRef] [Green Version]
- Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B (Methodol.) 1995, 57, 289–300. [Google Scholar] [CrossRef]
- Rohart, F.; Gautier, B.; Singh, A.; Le Cao, K.A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 2017, 13, e1005752. [Google Scholar] [CrossRef] [Green Version]
- Cantini, L.; Isella, C.; Petti, C.; Picco, G.; Chiola, S.; Ficarra, E.; Caselle, M.; Medico, E. MicroRNA-mRNA interactions underlying colorectal cancer molecular subtypes. Nat. Commun. 2015, 6, 9878. [Google Scholar] [CrossRef] [Green Version]
- Wang, L.; Feng, Z.; Wang, X.; Zhang, X. DEGseq: An R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 2010, 26, 136–138. [Google Scholar] [CrossRef]
- Hsu, S.D.; Lin, F.M.; Wu, W.Y.; Liang, C.; Huang, W.C.; Chan, W.L.; Tsai, W.T.; Chen, G.Z.; Lee, C.J.; Chiu, C.M.; et al. miRTarBase: A database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011, 39, D163–D169. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Wang, X. miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020, 48, D127–D131. [Google Scholar] [CrossRef] [Green Version]
- Agarwal, V.; Bell, G.W.; Nam, J.W.; Bartel, D.P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015, 4. [Google Scholar] [CrossRef] [PubMed]
- Stockel, D.; Kehl, T.; Trampert, P.; Schneider, L.; Backes, C.; Ludwig, N.; Gerasch, A.; Kaufmann, M.; Gessler, M.; Graf, N.; et al. Multi-omics enrichment analysis using the GeneTrail2 web service. Bioinformatics 2016, 32, 1502–1508. [Google Scholar] [CrossRef] [PubMed]
- Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cottret, L.; Wildridge, D.; Vinson, F.; Barrett, M.P.; Charles, H.; Sagot, M.F.; Jourdan, F. MetExplore: A web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res. 2010, 38, W132–W137. [Google Scholar] [CrossRef]
- Heinken, A.; Sahoo, S.; Fleming, R.M.; Thiele, I. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 2013, 4, 28–40. [Google Scholar] [CrossRef] [Green Version]
- Gonzalez, A.G.; Naldi, A.; Sanchez, L.; Thieffry, D.; Chaouiya, C. GINsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks. Bio Syst. 2006, 84, 91–100. [Google Scholar] [CrossRef]
- Yugi, K.; Kubota, H.; Hatano, A.; Kuroda, S. Trans-Omics: How to Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers. Trends Biotechnol. 2016, 34, 276–290. [Google Scholar] [CrossRef] [Green Version]
- Miskov-Zivanov, N.; Turner, M.S.; Kane, L.P.; Morel, P.A.; Faeder, J.R. The duration of T cell stimulation is a critical determinant of cell fate and plasticity. Sci. Signal. 2013, 6, ra97. [Google Scholar] [CrossRef] [Green Version]
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
- Besse-Patin, A.; Jeromson, S.; Levesque-Damphousse, P.; Secco, B.; Laplante, M.; Estall, J.L. PGC1A regulates the IRS1:IRS2 ratio during fasting to influence hepatic metabolism downstream of insulin. Proc. Natl. Acad. Sci. USA 2019, 116, 4285–4290. [Google Scholar] [CrossRef] [Green Version]
- Tocher, D.R.; Betancor, M.B.; Sprague, M.; Olsen, R.E.; Napier, J.A. Omega-3 Long-Chain Polyunsaturated Fatty Acids, EPA and DHA: Bridging the Gap between Supply and Demand. Nutrients 2019, 11, 89. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rodriguez-Cruz, M.; Serna, D.S. Nutrigenomics of omega-3 fatty acids: Regulators of the master transcription factors. Nutrition 2017, 41, 90–96. [Google Scholar] [CrossRef] [PubMed]
- Pinel, A.; Rigaudiere, J.P.; Jouve, C.; Montaurier, C.; Jousse, C.; LHomme, M.; Morio, B.; Capel, F. Transgenerational supplementation with eicosapentaenoic acid reduced the metabolic consequences on the whole body and skeletal muscle in mice receiving an obesogenic diet. Eur. J. Nutr. 2020. under review. [Google Scholar]
- Rodriguez Melendez, R. Importance of biotin metabolism. Rev. Investig. Clin. 2000, 52, 194–199. [Google Scholar]
- Huergo, L.F.; Dixon, R. The Emergence of 2-Oxoglutarate as a Master Regulator Metabolite. Microbiol. Mol. Biol. Rev. Mmbr. 2015, 79, 419–435. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hardwick, J.P.; Osei-Hyiaman, D.; Wiland, H.; Abdelmegeed, M.A.; Song, B.J. PPAR/RXR Regulation of Fatty Acid Metabolism and Fatty Acid omega-Hydroxylase (CYP4) Isozymes: Implications for Prevention of Lipotoxicity in Fatty Liver Disease. PPAR Res. 2009, 2009, 952734. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dubois, V.; Eeckhoute, J.; Lefebvre, P.; Staels, B. Distinct but complementary contributions of PPAR isotypes to energy homeostasis. J. Clin. Investig. 2017, 127, 1202–1214. [Google Scholar] [CrossRef] [Green Version]
- Booth, A.D.; Magnuson, A.M.; Cox-York, K.A.; Wei, Y.; Wang, D.; Pagliassotti, M.J.; Foster, M.T. Inhibition of adipose tissue PPARgamma prevents increased adipocyte expansion after lipectomy and exacerbates a glucose-intolerant phenotype. Cell Prolif. 2017, 50. [Google Scholar] [CrossRef]
- Jones, J.R.; Barrick, C.; Kim, K.A.; Lindner, J.; Blondeau, B.; Fujimoto, Y.; Shiota, M.; Kesterson, R.A.; Kahn, B.B.; Magnuson, M.A. Deletion of PPARgamma in adipose tissues of mice protects against high fat diet-induced obesity and insulin resistance. Proc. Natl. Acad. Sci. USA 2005, 102, 6207–6212. [Google Scholar] [CrossRef] [Green Version]
- Sethi, S.; Ziouzenkova, O.; Ni, H.; Wagner, D.D.; Plutzky, J.; Mayadas, T.N. Oxidized omega-3 fatty acids in fish oil inhibit leukocyte-endothelial interactions through activation of PPAR alpha. Blood 2002, 100, 1340–1346. [Google Scholar] [CrossRef] [Green Version]
- Zuniga, J.; Cancino, M.; Medina, F.; Varela, P.; Vargas, R.; Tapia, G.; Videla, L.A.; Fernandez, V. N-3 PUFA supplementation triggers PPAR-alpha activation and PPAR-alpha/NF-kappaB interaction: Anti-inflammatory implications in liver ischemia-reperfusion injury. PLoS ONE 2011, 6, e28502. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Puigserver, P.; Wu, Z.; Park, C.W.; Graves, R.; Wright, M.; Spiegelman, B.M. A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell 1998, 92, 829–839. [Google Scholar] [CrossRef] [Green Version]
- Yoon, J.C.; Puigserver, P.; Chen, G.; Donovan, J.; Wu, Z.; Rhee, J.; Adelmant, G.; Stafford, J.; Kahn, C.R.; Granner, D.K.; et al. Control of hepatic gluconeogenesis through the transcriptional coactivator PGC-1. Nature 2001, 413, 131–138. [Google Scholar] [CrossRef]
- Koo, S.H.; Satoh, H.; Herzig, S.; Lee, C.H.; Hedrick, S.; Kulkarni, R.; Evans, R.M.; Olefsky, J.; Montminy, M. PGC-1 promotes insulin resistance in liver through PPAR-alpha-dependent induction of TRB-3. Nat. Med. 2004, 10, 530–534. [Google Scholar] [CrossRef]
- Du, K.; Herzig, S.; Kulkarni, R.N.; Montminy, M. TRB3: A tribbles homolog that inhibits Akt/PKB activation by insulin in liver. Science 2003, 300, 1574–1577. [Google Scholar] [CrossRef] [Green Version]
- Choi, C.S.; Befroy, D.E.; Codella, R.; Kim, S.; Reznick, R.M.; Hwang, Y.J.; Liu, Z.X.; Lee, H.Y.; Distefano, A.; Samuel, V.T.; et al. Paradoxical effects of increased expression of PGC-1alpha on muscle mitochondrial function and insulin-stimulated muscle glucose metabolism. Proc. Natl. Acad. Sci. USA 2008, 105, 19926–19931. [Google Scholar] [CrossRef] [Green Version]
- Gu, L.; Ding, X.; Wang, Y.; Gu, M.; Zhang, J.; Yan, S.; Li, N.; Song, Z.; Yin, J.; Lu, L.; et al. Spexin alleviates insulin resistance and inhibits hepatic gluconeogenesis via the FoxO1/PGC-1alpha pathway in high-fat-diet-induced rats and insulin resistant cells. Int. J. Biol. Sci. 2019, 15, 2815–2829. [Google Scholar] [CrossRef] [Green Version]
- White, M.F. IRS proteins and the common path to diabetes. Am. J. Physiol. Endocrinol. Metab. 2002, 283, E413–E422. [Google Scholar] [CrossRef] [Green Version]
- Honma, M.; Sawada, S.; Ueno, Y.; Murakami, K.; Yamada, T.; Gao, J.; Kodama, S.; Izumi, T.; Takahashi, K.; Tsukita, S.; et al. Selective insulin resistance with differential expressions of IRS-1 and IRS-2 in human NAFLD livers. Int. J. Obes. 2018, 42, 1544–1555. [Google Scholar] [CrossRef] [Green Version]
- Nandi, A.; Kitamura, Y.; Kahn, C.R.; Accili, D. Mouse models of insulin resistance. Physiol. Rev. 2004, 84, 623–647. [Google Scholar] [CrossRef]
- Valverde, A.M.; Burks, D.J.; Fabregat, I.; Fisher, T.L.; Carretero, J.; White, M.F.; Benito, M. Molecular mechanisms of insulin resistance in IRS-2-deficient hepatocytes. Diabetes 2003, 52, 2239–2248. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, J.; Ou, J.; Bashmakov, Y.; Horton, J.D.; Brown, M.S.; Goldstein, J.L. Insulin inhibits transcription of IRS-2 gene in rat liver through an insulin response element (IRE) that resembles IREs of other insulin-repressed genes. Proc. Natl. Acad. Sci. USA 2001, 98, 3756–3761. [Google Scholar] [CrossRef] [Green Version]
- Ide, T.; Shimano, H.; Yahagi, N.; Matsuzaka, T.; Nakakuki, M.; Yamamoto, T.; Nakagawa, Y.; Takahashi, A.; Suzuki, H.; Sone, H.; et al. SREBPs suppress IRS-2-mediated insulin signalling in the liver. Nat. Cell Biol. 2004, 6, 351–357. [Google Scholar] [CrossRef]
- Nakagawa, Y.; Shimano, H.; Yoshikawa, T.; Ide, T.; Tamura, M.; Furusawa, M.; Yamamoto, T.; Inoue, N.; Matsuzaka, T.; Takahashi, A.; et al. TFE3 transcriptionally activates hepatic IRS-2, participates in insulin signaling and ameliorates diabetes. Nat. Med. 2006, 12, 107–113. [Google Scholar] [CrossRef] [PubMed]
- Tao, H.; Wang, M.M.; Zhang, M.; Zhang, S.P.; Wang, C.H.; Yuan, W.J.; Sun, T.; He, L.J.; Hu, Q.K. MiR-126 Suppresses the Glucose-Stimulated Proliferation via IRS-2 in INS-1 beta Cells. PLoS ONE 2016, 11, e0149954. [Google Scholar] [CrossRef] [PubMed]
- Maniyadath, B.; Chattopadhyay, T.; Verma, S.; Kumari, S.; Kulkarni, P.; Banerjee, K.; Lazarus, A.; Kokane, S.S.; Shetty, T.; Anamika, K.; et al. Loss of Hepatic Oscillatory Fed microRNAs Abrogates Refed Transition and Causes Liver Dysfunctions. Cell Rep. 2019, 26, 2212–2226. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, L.; He, X.; Lim, L.P.; de Stanchina, E.; Xuan, Z.; Liang, Y.; Xue, W.; Zender, L.; Magnus, J.; Ridzon, D.; et al. A microRNA component of the p53 tumour suppressor network. Nature 2007, 447, 1130–1134. [Google Scholar] [CrossRef] [Green Version]
- Chakraborty, C.; Doss, C.G.; Bandyopadhyay, S.; Agoramoorthy, G. Influence of miRNA in insulin signaling pathway and insulin resistance: Micro-molecules with a major role in type-2 diabetes. Wiley Interdiscip. Rev. RNA 2014, 5, 697–712. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.; Padhye, A.; Sharma, A.; Song, G.; Miao, J.; Mo, Y.Y.; Wang, L.; Kemper, J.K. A pathway involving farnesoid X receptor and small heterodimer partner positively regulates hepatic sirtuin 1 levels via microRNA-34a inhibition. J. Biol. Chem. 2010, 285, 12604–12611. [Google Scholar] [CrossRef] [Green Version]
- Cheung, O.; Puri, P.; Eicken, C.; Contos, M.J.; Mirshahi, F.; Maher, J.W.; Kellum, J.M.; Min, H.; Luketic, V.A.; Sanyal, A.J. Nonalcoholic steatohepatitis is associated with altered hepatic MicroRNA expression. Hepatology 2008, 48, 1810–1820. [Google Scholar] [CrossRef] [Green Version]
- Kong, L.; Zhu, J.; Han, W.; Jiang, X.; Xu, M.; Zhao, Y.; Dong, Q.; Pang, Z.; Guan, Q.; Gao, L.; et al. Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: A clinical study. Acta Diabetol. 2011, 48, 61–69. [Google Scholar] [CrossRef] [PubMed]
Parameter- | Reference | HFoleic | HFepa | ||||||
---|---|---|---|---|---|---|---|---|---|
Body Weight (g) | 26.7 | ± | 0.9 | 36.1 | ± | 1 *** | 33.8 | ± | 2.7 ** |
Fat % | 16 | ± | 1 | 32 | ± | 1 *** | 24 | ± | 3 ** † |
Lean % | 79 | ± | 1 | 64 | ± | 1 *** | 72 | ± | 3 ** †† |
Liver (gram) | 0.87 | ± | 0.04 | 1.11 | ± | 0.04 * | 0.99 | ± | 0.09 |
AUCglucose (a.u.) | 171 | ± | 6 | 207 | ± | 11 * | 196 | ± | 8 |
Glucose (mg/dL) | 195.6 | ± | 13.1 | 274.2 | ± | 13.1 ** | 228.1 | ± | 14.0 † |
Insulin (pg/mL) | 68.6 | ± | 8.3 | 182.8 | ± | 18.8 *** | 109.4 | ± | 25.9 † |
TAG (g/L) | 0.310 | ± | 0.013 | 0.366 | ± | 0.024 | 0.378 | ± | 0.039 |
T Cholesterol (g/L) | 0.926 | ± | 0.034 | 1.116 | ± | 0.050 * | 1.166 | ± | 0.068 * |
Glycerol (µM) | 213.1 | ± | 11.9 | 237.2 | ± | 19.0 | 257.1 | ± | 24.0 |
Nefa (mM) | 0.350 | ± | 0.038 | 0.228 | ± | 0.029 | 0.404 | ± | 0.081 |
Gene | Associated Signature in HFepa | Associated Signature in HFoleic | miRNA Regulation | microRNA Regulation in HFepa | microRNA Regulation in HFoleic |
---|---|---|---|---|---|
Sigmar1 | Down | mmu.miR.32.5p | up | ||
Xpa | Down | Down | mmu.miR.150.5p | down | |
Xpa | Down | Down | mmu.miR.335.3p | up | up |
Xpa | Down | Down | mmu.miR.1948.3p | up | up |
Rai1 | Down | mmu.miR.7052.3p | down | ||
Zfp777 | Down | Down | mmu.miR.335.3p | up | up |
Txn2 | Down | Down | mmu.miR.18a.3p | up | |
Txn2 | Down | Down | mmu.miR.98.3p | down | |
Trpt1 | Down | Down | mmu.miR.148a.3p | up | |
Agpat6 | Down | Down | mmu.miR.195a.3p | down | down |
Leng9 | Down | Down | mmu.miR.7052.3p | down | |
Plekhf1 | up | up | mmu.miR.150.5p | down | |
Plekhf1 | up | up | mmu.miR.335.3p | up | up |
Stard4 | up | up | mmu.miR.32.5p | up | |
Stard4 | up | up | mmu.miR.7052.3p | down | |
Dnajb1 | up | mmu.miR.19a.3p | down | ||
Dnajb1 | up | mmu.miR.7068.3p | down | down | |
Hspb1 | up | up | mmu.miR.128.3p | down | down |
Hspb1 | up | up | mmu.miR.150.5p | down | |
Hspb1 | up | up | mmu.miR.7068.3p | down | down |
Pcp4l1 | up | up | mmu.miR.1948.3p | up | up |
A_55_P2525368 | Down | miRNA regulation not significant | |||
Ptpmt1 | Down | Down | miRNA regulation not significant | ||
Magohb | Down | miRNA regulation not significant | |||
A130022F02Rik | Down | Down | miRNA regulation not significant | ||
Gm13547 | Down | Down | no regulated by a miRNA | ||
Lrrfip1 | Down | Down | no regulated by a miRNA | ||
Rabl3 | Down | Down | no regulated by a miRNA | ||
Kansl1l | up | up | miRNA regulation not significant | ||
P4ha1 | up | miRNA regulation not significant | |||
Inhbb | up | miRNA regulation not significant | |||
Mtss1 | up | up | miRNA regulation not significant | ||
Hyou1 | up | up | miRNA regulation not significant | ||
Gbe1 | up | up | miRNA regulation not significant | ||
Slc5a3 | up | up | miRNA regulation not significant | ||
Ints6 | up | up | miRNA regulation not significant | ||
0610031O16Rik | up | up | no regulated by a miRNA |
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Corral-Jara, K.F.; Cantini, L.; Poupin, N.; Ye, T.; Rigaudière, J.P.; Vincent, S.D.S.; Pinel, A.; Morio, B.; Capel, F. An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids. Nutrients 2020, 12, 3864. https://doi.org/10.3390/nu12123864
Corral-Jara KF, Cantini L, Poupin N, Ye T, Rigaudière JP, Vincent SDS, Pinel A, Morio B, Capel F. An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids. Nutrients. 2020; 12(12):3864. https://doi.org/10.3390/nu12123864
Chicago/Turabian StyleCorral-Jara, Karla Fabiola, Laura Cantini, Nathalie Poupin, Tao Ye, Jean Paul Rigaudière, Sarah De Saint Vincent, Alexandre Pinel, Béatrice Morio, and Frédéric Capel. 2020. "An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids" Nutrients 12, no. 12: 3864. https://doi.org/10.3390/nu12123864