Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model
Abstract
:1. Introduction
2. Results
2.1. Cognitive Impairment in db/db Mice
2.2. Transcriptomic Analysis
2.3. Metabolomic Analysis
2.4. Gut Microbiota Analysis
2.5. Correlation Analysis
2.6. Integrated Multi-Omics Analysis
3. Discussion
4. Materials and Methods
4.1. Animals
4.2. Morris Water Maze Test
4.3. Sample Preparation
4.4. Transcriptomic Analysis of Brain Samples
4.5. Metabolomic Analysis of Serum Samples
4.6. Fecal Microbiota Analysis
4.7. Correlation Analysis
4.8. Integrated Multi-Omics Analysis
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
References
- Galicia-Garcia, U.; Benito-Vicente, A.; Jebari, S.; Larrea-Sebal, A.; Siddiqi, H.; Uribe, K.B.; Ostolaza, H.; Martín, C. Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci. 2020, 21, 6275. [Google Scholar] [CrossRef] [PubMed]
- Roden, M.; Shulman, G.I. The integrative biology of type 2 diabetes. Nature 2019, 576, 51–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Srikanth, V.; Sinclair, A.J.; Hill-Briggs, F.; Moran, C.; Biessels, G.J. Type 2 diabetes and cognitive dysfunction—towards effective management of both comorbidities. Lancet Diabetes Endocrinol. 2020, 8, 535–545. [Google Scholar] [CrossRef]
- Mollon, J.; Curran, J.E.; Mathias, S.R.; Knowles, E.E.; Carlisle, P.; Fox, P.T.; Olvera, R.L.; Göring, H.H.; Rodrigue, A.; Almasy, L. Neurocognitive impairment in type 2 diabetes: Evidence for shared genetic aetiology. Diabetologia 2020, 63, 977–986. [Google Scholar] [CrossRef]
- Sharma, G.; Parihar, A.; Talaiya, T.; Dubey, K.; Porwal, B.; Parihar, M.S. Cognitive impairments in type 2 diabetes, risk factors and preventive strategies. J. Basic Clin. Physiol. Pharmacol. 2020, 31, 20190105. [Google Scholar] [CrossRef]
- Biessels, G.J.; Strachan, M.W.; Visseren, F.L.; Kappelle, L.J.; Whitmer, R.A. Dementia and cognitive decline in type 2 diabetes and prediabetic stages: Towards targeted interventions. Lancet Diabetes Endocrinol. 2014, 2, 246–255. [Google Scholar] [CrossRef]
- Palta, P.; Schneider, A.L.; Biessels, G.J.; Touradji, P.; Hill-Briggs, F. Magnitude of cognitive dysfunction in adults with type 2 diabetes: A meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. J. Int. Neuropsychol. Soc. 2014, 20, 278–291. [Google Scholar] [CrossRef] [Green Version]
- Masters, C.L.; Bateman, R.; Blennow, K.; Rowe, C.C.; Sperling, R.A.; Cummings, J.L. Alzheimer’s disease. Nat. Rev. Dis. Primers 2015, 1, 15056. [Google Scholar] [CrossRef]
- Biessels, G.J.; Staekenborg, S.; Brunner, E.; Brayne, C.; Scheltens, P. Risk of dementia in diabetes mellitus: A systematic review. Lancet Neurol. 2006, 5, 64–74. [Google Scholar] [CrossRef]
- Steen, E.; Terry, B.M.; Rivera, E.J.; Cannon, J.L.; Neely, T.R.; Tavares, R.; Xu, X.J.; Wands, J.R.; de la Monte, S.M. Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease–is this type 3 diabetes? J. Alzheimer’s Dis. 2005, 7, 63–80. [Google Scholar] [CrossRef] [Green Version]
- Rorbach-Dolata, A.; Piwowar, A. Neurometabolic evidence supporting the hypothesis of increased incidence of type 3 diabetes mellitus in the 21st century. BioMed Res. Int. 2019, 2019, 1435276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nguyen, T.T.; Ta, Q.T.H.; Nguyen, T.K.O.; Nguyen, T.T.D.; Van Giau, V. Type 3 diabetes and its role implications in Alzheimer’s disease. Int. J. Mol. Sci. 2020, 21, 3165. [Google Scholar] [CrossRef] [PubMed]
- De Felice, F.G.; Ferreira, S.T. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer disease. Diabetes 2014, 63, 2262–2272. [Google Scholar] [CrossRef] [Green Version]
- Alzheimer’s Disease International. World Alzheimer Report 2019: Attitudes to Dementia; Alzheimer’s Disease International: London, UK, 2019. [Google Scholar]
- Prince, M.; Bryce, R.; Albanese, E.; Wimo, A.; Ribeiro, W.; Ferri, C.P. The global prevalence of dementia: A systematic review and metaanalysis. Alzheimer’s Dement. 2013, 9, 63–75.e2. [Google Scholar] [CrossRef] [PubMed]
- Karahalil, B. Overview of systems biology and omics technologies. Curr. Med. Chem. 2016, 23, 4221–4230. [Google Scholar] [CrossRef]
- Tavassoly, I.; Goldfarb, J.; Iyengar, R. Systems biology primer: The basic methods and approaches. Essays Biochem. 2018, 62, 487–500. [Google Scholar] [CrossRef] [PubMed]
- Kavanagh, K.; Day, S.M.; Pait, M.C.; Mortiz, W.R.; Newgard, C.B.; Ilkayeva, O.; Mcclain, D.A.; Macauley, S.L. Type-2-diabetes alters CSF but not plasma metabolomic and AD risk profiles in vervet monkeys. Front. Neurosci. 2019, 13, 843. [Google Scholar] [CrossRef] [Green Version]
- Gurung, M.; Li, Z.; You, H.; Rodrigues, R.; Jump, D.B.; Morgun, A.; Shulzhenko, N. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 2020, 51, 102590. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; He, Y.; Ma, J.; Huang, P.; Du, J.; Cao, L.; Wang, Y.; Xiao, Q.; Tang, H.; Chen, S. Mild cognitive impairment has similar alterations as Alzheimer’s disease in gut microbiota. Alzheimer’s Dement. 2019, 15, 1357–1366. [Google Scholar] [CrossRef]
- Sun, M.F.; Shen, Y.Q. Dysbiosis of gut microbiota and microbial metabolites in Parkinson’s Disease. Ageing Res. Rev. 2018, 45, 53–61. [Google Scholar] [CrossRef]
- Carranza-Naval, M.J.; Vargas-Soria, M.; Hierro-Bujalance, C.; Baena-Nieto, G.; Garcia-Alloza, M.; Infante-Garcia, C.; Del Marco, A. Alzheimer’s disease and diabetes: Role of diet, microbiota and inflammation in preclinical models. Biomolecules 2021, 11, 262. [Google Scholar] [CrossRef] [PubMed]
- Qin, J.; Li, Y.; Cai, Z.; Li, S.; Zhu, J.; Zhang, F.; Liang, S.; Zhang, W.; Guan, Y.; Shen, D. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012, 490, 55–60. [Google Scholar] [CrossRef] [PubMed]
- McKay, E.C.; Beck, J.S.; Khoo, S.K.; Dykema, K.J.; Cottingham, S.L.; Winn, M.E.; Paulson, H.L.; Lieberman, A.P.; Counts, S.E. Peri-infarct upregulation of the oxytocin receptor in vascular dementia. J. Neuropathol. Exp. Neurol. 2019, 78, 436–452. [Google Scholar] [CrossRef] [PubMed]
- Zhuang, Z.; Yang, R.; Wang, W.; Qi, L.; Huang, T. Associations between gut microbiota and Alzheimer’s disease, major depressive disorder, and schizophrenia. J. Neuroinflamm. 2020, 17, 1–9. [Google Scholar] [CrossRef]
- Sun, Y.V.; Hu, Y.-J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet. 2016, 93, 147–190. [Google Scholar]
- Gan, W.Z.; Ramachandran, V.; Lim, C.S.Y.; Koh, R.Y. Omics-based biomarkers in the diagnosis of diabetes. J. Basic Clin. Physiol. Pharmacol. 2020, 31, 20190120. [Google Scholar] [CrossRef]
- Kerscher, S.; Dröse, S.; Zickermann, V.; Brandt, U. The three families of respiratory NADH dehydrogenases. Bioenergetics 2007, 45, 185–222. [Google Scholar]
- Rich, P.R. Mitochondrial cytochrome c oxidase: Catalysis, coupling and controversies. Biochem. Soc. Trans. 2017, 45, 813–829. [Google Scholar] [CrossRef]
- Patti, M.-E.; Corvera, S. The role of mitochondria in the pathogenesis of type 2 diabetes. Endocr. Rev. 2010, 31, 364–395. [Google Scholar] [CrossRef] [Green Version]
- Chen, R.; Lai, U.H.; Zhu, L.; Singh, A.; Ahmed, M.; Forsyth, N.R. Reactive oxygen species formation in the brain at different oxygen levels: The role of hypoxia inducible factors. Front. Cell Dev. Biol. 2018, 6, 132. [Google Scholar] [CrossRef] [Green Version]
- Sickmann, H.M.; Waagepetersen, H.S.; Schousboe, A.; Benie, A.J.; Bouman, S.D. Obesity and type 2 diabetes in rats are associated with altered brain glycogen and amino-acid homeostasis. J. Cereb. Blood Flow Metab. 2010, 30, 1527–1537. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xu, J.; Zheng, Y.; Lv, S.; Kang, J.; Yu, Y.; Hou, K.; Li, Y.; Chi, G. Lactate promotes reactive astrogliosis and confers axon guidance potential to astrocytes under oxygen-glucose deprivation. Neuroscience 2020, 442, 54–68. [Google Scholar] [CrossRef] [PubMed]
- Liguori, C.; Stefani, A.; Sancesario, G.; Sancesario, G.; Marciani, M.; Pierantozzi, M. CSF lactate levels, τ proteins, cognitive decline: A dynamic relationship in Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 2015, 86, 655–659. [Google Scholar] [CrossRef] [PubMed]
- Pugliese, M.; Carrasco, J.L.; Andrade, C.; Mas, E.; Mascort, J.; Mahy, N. Severe cognitive impairment correlates with higher cerebrospinal fluid levels of lactate and pyruvate in a canine model of senile dementia. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2005, 29, 603–610. [Google Scholar] [CrossRef]
- Zheng, H.; Zheng, Y.; Zhao, L.; Chen, M.; Bai, G.; Hu, Y.; Hu, W.; Yan, Z.; Gao, H. Cognitive decline in type 2 diabetic db/db mice may be associated with brain region-specific metabolic disorders. Biochim. Biophys. Acta (BBA)-Mol. Basis Dis. 2017, 1863, 266–273. [Google Scholar] [CrossRef]
- Chiang, J.Y.; Ferrell, J.M. Bile acids as metabolic regulators and nutrient sensors. Annu. Rev. Nutr. 2019, 39, 175–200. [Google Scholar] [CrossRef]
- Wahlström, A.; Sayin, S.I.; Marschall, H.-U.; Bäckhed, F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 2016, 24, 41–50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Monteiro-Cardoso, V.F.; Corlianò, M.; Singaraja, R.R. Bile Acids: A Communication Channel in the Gut-Brain Axis. NeuroMol. Med. 2021, 23, 99–117. [Google Scholar] [CrossRef]
- Chen, C.; Xia, S.; He, J.; Lu, G.; Xie, Z.; Han, H. Roles of taurine in cognitive function of physiology, pathologies and toxication. Life Sci. 2019, 231, 116584. [Google Scholar] [CrossRef]
- Chouraki, V.; Preis, S.R.; Yang, Q.; Beiser, A.; Li, S.; Larson, M.G.; Weinstein, G.; Wang, T.J.; Gerszten, R.E.; Vasan, R.S. Association of amine biomarkers with incident dementia and Alzheimer’s disease in the Framingham Study. Alzheimer’s Dement. 2017, 13, 1327–1336. [Google Scholar] [CrossRef]
- Reimann, F.; Gribble, F.M. G protein-coupled receptors as new therapeutic targets for type 2 diabetes. Diabetologia 2016, 59, 229–233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Egerod, K.L.; Petersen, N.; Timshel, P.N.; Rekling, J.C.; Wang, Y.; Liu, Q.; Schwartz, T.W.; Gautron, L. Profiling of G protein-coupled receptors in vagal afferents reveals novel gut-to-brain sensing mechanisms. Mol. Metab. 2018, 12, 62–75. [Google Scholar] [CrossRef] [PubMed]
- Thomas, C.; Gioiello, A.; Noriega, L.; Strehle, A.; Oury, J.; Rizzo, G.; Macchiarulo, A.; Yamamoto, H.; Mataki, C.; Pruzanski, M. TGR5-mediated bile acid sensing controls glucose homeostasis. Cell Metab. 2009, 10, 167–177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, K.-S.; Seeley, R.J.; Sandoval, D.A. Signalling from the periphery to the brain that regulates energy homeostasis. Nat. Rev. Neurosci. 2018, 19, 185–196. [Google Scholar] [CrossRef]
- Keitel, V.; Görg, B.; Bidmon, H.J.; Zemtsova, I.; Spomer, L.; Zilles, K.; Häussinger, D. The bile acid receptor TGR5 (Gpbar-1) acts as a neurosteroid receptor in brain. Glia 2010, 58, 1794–1805. [Google Scholar] [CrossRef]
- McMillin, M.; Frampton, G.; Tobin, R.; Dusio, G.; Smith, J.; Shin, H.; Newell-Rogers, K.; Grant, S.; DeMorrow, S. TGR 5 signaling reduces neuroinflammation during hepatic encephalopathy. J. Neurochem. 2015, 135, 565–576. [Google Scholar] [CrossRef]
- Dionísio, P.A.; Amaral, J.D.; Ribeiro, M.F.; Lo, A.C.; D’Hooge, R.; Rodrigues, C.M. Amyloid-β pathology is attenuated by tauroursodeoxycholic acid treatment in APP/PS1 mice after disease onset. Neurobiol. Aging 2015, 36, 228–240. [Google Scholar] [CrossRef] [Green Version]
- Yanguas-Casás, N.; Barreda-Manso, M.A.; Nieto-Sampedro, M.; Romero-Ramírez, L. TUDCA: An agonist of the bile acid receptor GPBAR1/TGR5 with anti-inflammatory effects in microglial cells. J. Cell. Physiol. 2017, 232, 2231–2245. [Google Scholar] [CrossRef]
- Ramalho, R.M.; Viana, R.J.; Low, W.C.; Steer, C.J.; Rodrigues, C.M. Bile acids and apoptosis modulation: An emerging role in experimental Alzheimer’s disease. Trends Mol. Med. 2008, 14, 54–62. [Google Scholar] [CrossRef]
- Nunes, A.F.; Amaral, J.D.; Lo, A.C.; Fonseca, M.B.; Viana, R.J.; Callaerts-Vegh, Z.; D’Hooge, R.; Rodrigues, C.M. TUDCA, a bile acid, attenuates amyloid precursor protein processing and amyloid-β deposition in APP/PS1 mice. Mol. Neurobiol. 2012, 45, 440–454. [Google Scholar] [CrossRef]
- Sona, C.; Kumar, A.; Dogra, S.; Kumar, B.A.; Umrao, D.; Yadav, P.N. Docosahexaenoic acid modulates brain-derived neurotrophic factor via GPR40 in the brain and alleviates diabesity-associated learning and memory deficits in mice. Neurobiol. Dis. 2018, 118, 94–107. [Google Scholar] [CrossRef] [PubMed]
- Loeffen, J.; Smeets, R.; Smeitink, J.; Triepels, R.; Sengers, R.; Trijbels, F.; van den Heuvel, L. The human NADH: Ubiquinone oxidoreductase NDUFS5 (15kDa) subunit: cDNA cloning, chromosomal localization, tissue distribution and the absence of mutations in isolated complex I-deficient patients. J. Inherit. Metab. Dis. 1999, 22, 19–28. [Google Scholar] [CrossRef] [PubMed]
- Devarajan, A.; Shih, D.; Reddy, S.T. Inflammation, infection, cancer and all that… the role of paraoxonases. In Oxidative Stress and Inflammation in Non-Communicable Diseases-Molecular Mechanisms and Perspectives in Therapeutics; Springer: Cham, Switzerland, 2014; pp. 33–41. [Google Scholar]
- Tykvart, J.; Bařinka, C.; Svoboda, M.; Navrátil, V.; Souček, R.; Hubálek, M.; Hradilek, M.; Šácha, P.; Lubkowski, J.; Konvalinka, J. Structural and biochemical characterization of a novel aminopeptidase from human intestine. J. Biol. Chem. 2015, 290, 11321–11336. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, W.S.; Weickert, C.S.; Garner, B. Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J. Neurochem. 2008, 104, 1145–1166. [Google Scholar] [CrossRef]
- Hu, Y.H.; Zhang, Y.; Jiang, L.Q.; Wang, S.; Lei, C.Q.; Sun, M.S.; Shu, H.B.; Liu, Y. WDFY 1 mediates TLR 3/4 signaling by recruiting TRIF. EMBO Rep. 2015, 16, 447–455. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.-T.; Yang, N.-S.; Lin, Y.-C.; Ho, S.-T.; Li, K.-Y.; Lin, J.-S.; Liu, J.-R.; Chen, M.-J. A combination of Lactobacillus mali APS1 and dieting improved the efficacy of obesity treatment via manipulating gut microbiome in mice. Sci. Rep. 2018, 8, 6153. [Google Scholar] [CrossRef]
- Lyon, P.; Strippoli, V.; Fang, B.; Cimmino, L. B Vitamins and One-Carbon Metabolism: Implications in Human Health and Disease. Nutrients 2020, 12, 2867. [Google Scholar] [CrossRef]
- Ostrakhovitch, E.; Tabibzadeh, S. Homocysteine and age-associated disorders. Ageing Res. Rev. 2019, 49, 144–164. [Google Scholar] [CrossRef]
- Roager, H.M.; Licht, T.R. Microbial tryptophan catabolites in health and disease. Nat. Commun. 2018, 9, 3294. [Google Scholar] [CrossRef] [Green Version]
- Chyan, Y.-J.; Poeggeler, B.; Omar, R.A.; Chain, D.G.; Frangione, B.; Ghiso, J.; Pappolla, M.A. Potent neuroprotective properties against the Alzheimer β-amyloid by an endogenous melatonin-related indole structure, indole-3-propionic acid. J. Biol. Chem. 1999, 274, 21937–21942. [Google Scholar] [CrossRef] [Green Version]
- Trammell, S.A.; Weidemann, B.J.; Chadda, A.; Yorek, M.S.; Holmes, A.; Coppey, L.J.; Obrosov, A.; Kardon, R.H.; Yorek, M.A.; Brenner, C. Nicotinamide riboside opposes type 2 diabetes and neuropathy in mice. Sci. Rep. 2016, 6, 26933. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pertea, M.; Kim, D.; Pertea, G.M.; Leek, J.T.; Salzberg, S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016, 11, 1650–1667. [Google Scholar] [CrossRef] [PubMed]
- Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Canzler, S.; Hackermuller, J. multiGSEA: A GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinform. 2020, 21, 561. [Google Scholar] [CrossRef]
- Jassal, B.; Matthews, L.; Viteri, G.; Gong, C.; Lorente, P.; Fabregat, A.; Sidiropoulos, K.; Cook, J.; Gillespie, M.; Haw, R. The reactome pathway knowledgebase. Nucleic Acids Res. 2020, 48, D498–D503. [Google Scholar] [CrossRef]
- Chambers, M.C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D.L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30, 918–920. [Google Scholar] [CrossRef]
- Smith, C.A.; Want, E.J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78, 779–787. [Google Scholar] [CrossRef]
- Pang, Z.; Chong, J.; Zhou, G.; de Lima Morais, D.A.; Chang, L.; Barrette, M.; Gauthier, C.; Jacques, P.-É.; Li, S.; Xia, J. MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021, 49, W388–W396. [Google Scholar] [CrossRef]
- Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef]
- Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef] [PubMed]
- Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, 1–18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Siddiqui, J.K.; Baskin, E.; Liu, M.; Cantemir-Stone, C.Z.; Zhang, B.; Bonneville, R.; McElroy, J.P.; Coombes, K.R.; Mathe, E.A. IntLIM: Integration using linear models of metabolomics and gene expression data. BMC Bioinform. 2018, 19, 81. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: Augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016, 44, D380–D384. [Google Scholar] [CrossRef]
- Korotkevich, G.; Sukhov, V.; Budin, N.; Shpak, B.; Artyomov, M.N.; Sergushichev, A. Fast gene set enrichment analysis. bioRxiv 2021, 060012. [Google Scholar] [CrossRef] [Green Version]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
- Wickham, H. ggplot2. In Wiley Interdisciplinary Reviews: Computational Statistics; Wiley: Hoboken, NJ, USA, 2011; Volume 3, pp. 180–185. [Google Scholar]
- Kolde, R. Pheatmap: Pretty heatmaps. R Package Version 2012, 1, 726. [Google Scholar]
- Pedersen, T.L. Package “ggraph”. R package Version 1.0.0. CRAN, 2017. [Google Scholar]
- Thévenot, E.A.; Roux, A.; Xu, Y.; Ezan, E.; Junot, C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J. Proteome Res. 2015, 14, 3322–3335. [Google Scholar] [CrossRef]
- Kanai, M.; Maeda, Y.; Okada, Y. Grimon: Graphical interface to visualize multi-omics networks. Bioinformatics 2018, 34, 3934–3936. [Google Scholar] [CrossRef] [Green Version]
- Shi, L.; Westerhuis, J.A.; Rosén, J.; Landberg, R.; Brunius, C. Variable selection and validation in multivariate modelling. Bioinformatics 2019, 35, 972–980. [Google Scholar] [CrossRef] [PubMed]
- Singh, A.; Shannon, C.P.; Gautier, B.; Rohart, F.; Vacher, M.; Tebbutt, S.J.; Lê Cao, K.-A. DIABLO: From multi-omics assays to biomarker discovery, an integrative approach. bioRxiv 2018, 067611. [Google Scholar] [CrossRef]
Pathway Name | p-Value | NES | Size |
---|---|---|---|
Gene expression (Transcription) | 3.42 × 10−19 | 1.909039 | 916 |
Metabolism of RNA | 3.14 × 10−18 | 2.090669 | 526 |
RNA Polymerase II Transcription | 2.50 × 10−17 | 1.883699 | 820 |
Processing of Capped Intron-Containing Pre-mRNA | 3.31 × 10−13 | 2.227814 | 223 |
Cell Cycle | 4.68 × 10−12 | 1.87915 | 503 |
Generic Transcription Pathway | 1.19 × 10−11 | 1.750073 | 705 |
mRNA Splicing | 4.21 × 10−11 | 2.190093 | 174 |
pre-mRNA splicing | 1.59 × 10−10 | 2.178913 | 168 |
Cell Cycle, Mitotic | 3.26 × 10−10 | 1.870694 | 423 |
Cell Cycle Checkpoints | 1.22 × 10−7 | 1.903855 | 230 |
M Phase | 2.68 × 10−7 | 1.754935 | 315 |
S Phase | 5.32 × 10−7 | 1.958874 | 131 |
G1/S Transition | 1.52 × 10−6 | 2.018423 | 99 |
Extracellular matrix organization | 4.83 × 10−6 | −2.067058 | 185 |
Respiratory electron transport | 6.76 × 10−6 | −2.37502 | 85 |
Mitotic G1 phase and G1/S transition | 6.94 × 10−6 | 1.900258 | 121 |
Metabolism | 1.24 × 10−5 | −1.39958 | 1391 |
Mitotic Metaphase and Anaphase | 1.68 × 10−5 | 1.707057 | 199 |
Mitotic Anaphase | 1.95 × 10−5 | 1.701565 | 198 |
DNA Replication | 2.58 × 10−5 | 1.851959 | 114 |
Transport of Mature mRNA derived from an Intron-Containing Transcript | 2.91 × 10−5 | 1.982446 | 67 |
DNA Repair | 5.03 × 10−5 | 1.643199 | 240 |
Synthesis of DNA | 6.43 × 10−5 | 1.816823 | 106 |
SUMOylation | 6.54 × 10−5 | 1.731646 | 138 |
Transport of Mature Transcript to Cytoplasm | 7.11 × 10−5 | 1.927207 | 75 |
DNA Replication Pre-Initiation | 8.43 × 10−5 | 1.856579 | 77 |
G1/S DNA Damage Checkpoints | 8.51 × 10−5 | 1.900279 | 63 |
G2/M Checkpoints | 8.74 × 10−5 | 1.780989 | 121 |
Complex I biogenesis | 9.29 × 10−5 | −2.392202 | 52 |
RNA Polymerase II Transcription Termination | 9.81 × 10−5 | 1.912934 | 60 |
Separation of Sister Chromatids | 0.000126 | 1.702185 | 152 |
SUMO E3 ligases SUMOylate target proteins | 0.000133 | 1.716686 | 133 |
Mitotic Prometaphase | 0.000142 | 1.679872 | 163 |
Epigenetic regulation of gene expression | 0.000157 | 1.997729 | 47 |
RNA Polymerase II Pre-transcription Events | 0.00021 | 1.855255 | 75 |
The citric acid (TCA) cycle and respiratory electron transport | 0.000253 | −1.974008 | 156 |
Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins. | 0.000285 | −2.125326 | 109 |
p53-Dependent G1 DNA Damage Response | 0.000305 | 1.921084 | 61 |
p53-Dependent G1/S DNA damage checkpoint | 0.000305 | 1.921084 | 61 |
Syndecan interactions | 0.000318 | −2.137732 | 14 |
Chondroitin sulfate biosynthesis | 0.00032 | −2.018888 | 18 |
ECM proteoglycans | 0.000339 | −1.985636 | 22 |
mRNA 3′-end processing | 0.00035 | 1.874325 | 51 |
Transcriptional Regulation by TP53 | 0.000365 | 1.513781 | 268 |
DNA Double-Strand Break Repair | 0.000366 | 1.682974 | 114 |
TP53 Regulates Transcription of Cell Cycle Genes | 0.00044 | 1.955699 | 29 |
Non-integrin membrane-ECM interactions | 0.000441 | −1.994996 | 18 |
Cross-presentation of soluble exogenous antigens (endosomes) | 0.000448 | 1.858045 | 41 |
MET activates PTK2 signaling | 0.000453 | −2.076443 | 14 |
Cellular responses to external stimuli | 0.000508 | 1.458568 | 323 |
Pathway Name | p-Value | NES | Size |
---|---|---|---|
Adaptive Immune System | 0.039127 | −1.399622 | 2 |
Aflatoxin activation and detoxification | 0.046795 | 1.316706 | 1 |
Aspartate and asparagine metabolism | 0.033776 | 1.490742 | 4 |
Biosynthesis of DHA-derived sulfido conjugates | 0.046795 | 1.316706 | 1 |
Biosynthesis of maresin conjugates in tissue regeneration (MCTR) | 0.046795 | 1.316706 | 1 |
Biosynthesis of protectin and resolvin conjugates in tissue regeneration (PCTR and RCTR) | 0.046795 | 1.316706 | 1 |
C-type lectin receptors (CLRs) | 0.039127 | −1.399622 | 2 |
CLEC7A (Dectin-1) signaling | 0.039127 | −1.399622 | 2 |
Cytokine Signaling in Immune system | 0.007981 | −1.639997 | 4 |
DAG and IP3 signaling | 0.039127 | −1.399622 | 2 |
ERK1/ERK2 pathway | 0.014242 | −1.575384 | 3 |
Fc epsilon receptor (FCERI) mediated NF-κB activation | 0.039127 | −1.399622 | 2 |
FLT3 Signaling | 0.007981 | −1.639997 | 4 |
FCERI signaling | 0.039127 | −1.399622 | 2 |
Gamma carboxylation, hypusine formation, and arylsulfatase activation | 0.034649 | −1.505648 | 4 |
Glycerophospholipid biosynthesis | 0.027534 | 1.563377 | 11 |
Histidine catabolism | 0.018443 | −1.610865 | 5 |
MAPK family signaling cascades | 0.014242 | −1.575384 | 3 |
Metabolism | 0.015173 | −1.465771 | 109 |
Nucleobase catabolism | 0.004696 | −1.825806 | 22 |
Nucleotide metabolism | 0.007471 | −1.804005 | 24 |
Nucleotide salvage | 0.000174 | −2.036357 | 17 |
Phenylalanine and tyrosine metabolism | 0.009249 | 1.590928 | 4 |
Purine salvage | 0.001607 | −1.851134 | 8 |
Pyrimidine catabolism | 0.014483 | −1.661637 | 11 |
Pyrimidine salvage | 0.004541 | −1.800858 | 9 |
Pyruvate metabolism | 0.019494 | 1.606548 | 5 |
Pyruvate metabolism and Citric Acid (TCA) cycle | 0.012356 | 1.706404 | 6 |
RAF/MAP kinase cascade | 0.014242 | −1.575384 | 3 |
RAS processing | 0.014242 | −1.575384 | 3 |
SLC-mediated transmembrane transport | 0.036751 | −1.512534 | 40 |
Synthesis of diphthamide-EEF2 | 0.034649 | −1.505648 | 4 |
The citric acid (TCA) cycle and respiratory electron transport | 0.004119 | 1.757392 | 7 |
Transport of nucleosides and free purine and pyrimidine bases across the plasma membrane | 0.002303 | −1.831755 | 11 |
Transport of small molecules | 0.025099 | −1.574603 | 45 |
Transport of vitamins, nucleosides, and related molecules | 0.010163 | −1.765573 | 24 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Song, X.; Zhu, Z.; Qian, X.; Liu, X.; Chen, S.; Tang, H. Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model. Molecules 2022, 27, 1904. https://doi.org/10.3390/molecules27061904
Song X, Zhu Z, Qian X, Liu X, Chen S, Tang H. Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model. Molecules. 2022; 27(6):1904. https://doi.org/10.3390/molecules27061904
Chicago/Turabian StyleSong, Xiaoxuan, Zeyu Zhu, Xiaohang Qian, Xiaoli Liu, Shengdi Chen, and Huidong Tang. 2022. "Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model" Molecules 27, no. 6: 1904. https://doi.org/10.3390/molecules27061904