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Current Neurovascular Research

Editor-in-Chief

ISSN (Print): 1567-2026
ISSN (Online): 1875-5739

Research Article

Decoding the Transcriptional Response to Ischemic Stroke in Obese and Non-obese Mice Brain

Author(s): Jing Liang*, Ruiyao Hu*, Xin Wang, Xinjing Liu, Lulu Pei, Mengke Tian, Wenxian Sun, Luyang Zhang, Lan Ding, Yuying Wang, Yuming Xu and Bo Song

Volume 18, Issue 2, 2021

Published on: 19 July, 2021

Page: [211 - 218] Pages: 8

DOI: 10.2174/1567202618666210719150845

Price: $65

Abstract

Background: Ischemic Stroke (IS) is a serious cerebrovascular disease, which leads to irreversible damage or death of brain cells. Effective control of stroke risk factors can effectively reduce the incidence of IS. However, there was an “obesity paradox” about the relationship between obesity and the prognosis of IS, in which obesity would not bring worse outcomes than non-obese IS patients.

Objective: Herein, we aimed to investigate the transcriptional response to IS in obese and nonobese mice brain via RNA-Seq technology. The datasets of obese and non-obese mice with/without IS were obtained from the Gene Expression Omnibus (GEO) database.

Methods: Differentially expressed genes (DEGs) between Control and Obesity (DEGsObesity) and between Obesity and Obese-Stroke (DEGsObese-Stroke) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Protein-Protein Interaction (PPI) network analysis were performed to predict the function of DEGs. 28 and 109 DEGs were screened in DEGsObesity and DEGsObese-Stroke, respectively.

Results: Significantly, in the top 10 key-genes of DEGsObese-Stroke (Tnf, Lgals3, Serpinb2, Ly6c2, Chil3, Clec4e, Mmp3, Mefv, Spn, Tlr8), Tnf and Mefv were involved in the NOD-like receptor signaling pathway, which was consistent with KEGG pathway enrichment results. And Chil3, as a mononuclear cell marker, was significantly elevated in Obese-Stroke compared with Stroke, suggesting mononuclear cell, rather than other peripheral immune cells, infiltrated into the brain of Obese-stroke.

Conclusion: Hence, we concluded that obesity could affect the brain microenvironment at the transcriptome level and Stroke after obesity could lead to more changes in NOD-like receptor signaling pathway and monocyte infiltration, compared with non-obese Stroke.

Keywords: Stroke, obesity, inflammation, NOD-like receptor, monocytes, KEGG.

[1]
Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden and mortality: Estimates from monitoring, surveillance, and modelling. Lancet Neurol 2009; 8(4): 345-54.
[http://dx.doi.org/10.1016/S1474-4422(09)70023-7] [PMID: 19233730]
[2]
Seshadri S, Wolf PA. Lifetime risk of stroke and dementia: Current concepts, and estimates from the Framingham Study. Lancet Neurol 2007; 6(12): 1106-14.
[http://dx.doi.org/10.1016/S1474-4422(07)70291-0] [PMID: 18031707]
[3]
George MG, Tong X, Kuklina EV, Labarthe DR. Trends in stroke hospitalizations and associated risk factors among children and young adults, 1995-2008. Ann Neurol 2011; 70(5): 713-21.
[http://dx.doi.org/10.1002/ana.22539] [PMID: 21898534]
[4]
Kissela BM, Khoury JC, Alwell K, et al. Age at stroke: Temporal trends in stroke incidence in a large, biracial population. Neurology 2012; 79(17): 1781-7.
[http://dx.doi.org/10.1212/WNL.0b013e318270401d] [PMID: 23054237]
[5]
Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: A policy statement from the American Heart Association. Circulation 2011; 123(8): 933-44.
[http://dx.doi.org/10.1161/CIR.0b013e31820a55f5] [PMID: 21262990]
[6]
Page KA, Seo D, Belfort-DeAguiar R, et al. Circulating glucose levels modulate neural control of desire for high-calorie foods in humans. J Clin Invest 2011; 121(10): 4161-9.
[http://dx.doi.org/10.1172/JCI57873] [PMID: 21926468]
[7]
Kernan WN, Inzucchi SE, Sawan C, Macko RF, Furie KL. Obesity: A stubbornly obvious target for stroke prevention. Stroke 2013; 44(1): 278-86.
[http://dx.doi.org/10.1161/STROKEAHA.111.639922] [PMID: 23111440]
[8]
Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384(9945): 766-81.
[http://dx.doi.org/10.1016/S0140-6736(14)60460-8] [PMID: 24880830]
[9]
Tang XN, Liebeskind DS, Towfighi A. The role of diabetes, obesity, and metabolic syndrome in stroke. Semin Neurol 2017; 37(3): 267-73.
[http://dx.doi.org/10.1055/s-0037-1603753] [PMID: 28759908]
[10]
Lumeng CN, Saltiel AR. Inflammatory links between obesity and metabolic disease. J Clin Invest 2011; 121(6): 2111-7.
[http://dx.doi.org/10.1172/JCI57132] [PMID: 21633179]
[11]
Zhou BF. Effect of body mass index on all-cause mortality and incidence of cardiovascular diseases- report for meta-analysis of prospective studies open optimal cut-off points of body mass index in Chinese adults. Biomed Environ Sci 2002; 15(3): 245-52.
[PMID: 12500665]
[12]
Rodríguez-Castro E, Rodríguez-Yáñez M, Arias-Rivas S, et al. Obesity Paradox in Ischemic Stroke: Clinical and Molecular Insights. Transl Stroke Res 2019; 10(6): 639-49.
[http://dx.doi.org/10.1007/s12975-019-00695-x] [PMID: 30980283]
[13]
Kim BJ, Lee SH, Jung KH, Yu KH, Lee BC, Roh JK. Dynamics of obesity paradox after stroke, related to time from onset, age, and causes of death. Neurology 2012; 79(9): 856-63.
[http://dx.doi.org/10.1212/WNL.0b013e318266fad1] [PMID: 22895584]
[14]
Kazemi-Bajestani SM, Ghayour-Mobarhan M, Thrift AG, et al. Obesity paradox versus frailty syndrome in first-ever ischemic stroke survivors. Official J Int Stroke Society 2015; 10(7): 12567.
[http://dx.doi.org/10.1111/ijs.12567]
[15]
Jang SY, Shin YI, Kim DY, et al. Effect of obesity on functional outcomes at 6 months post-stroke among elderly Koreans: A prospective multicentre study. BMJ Open 2015; 5(12): e008712.
[http://dx.doi.org/10.1136/bmjopen-2015-008712] [PMID: 26685024]
[16]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[17]
Varet H, Brillet-Guéguen L, Coppée JY, Dillies MA. SARTools: A DESeq2- and EdgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data. PLoS One 2016; 11(6): e0157022.
[http://dx.doi.org/10.1371/journal.pone.0157022] [PMID: 27280887]
[18]
Dennis G Jr, Sherman BT, Hosack DA, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003; 4(5): 3.
[http://dx.doi.org/10.1186/gb-2003-4-5-p3] [PMID: 12734009]
[19]
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. Cytohubba: Identifying hub objects and sub-networks from complex interactome. BMC systems biology 2014; (8 Suppl): S11.
[20]
Androvic P, Kirdajova D, Tureckova J, et al. Decoding the transcriptional response to ischemic stroke in young and aged mouse brain. Cell Rep 2020; 31(11): 107777.
[http://dx.doi.org/10.1016/j.celrep.2020.107777] [PMID: 32553170]
[21]
Friedman BA, Srinivasan K, Ayalon G, et al. Diverse brain myeloid expression profiles reveal distinct microglial activation states and aspects of alzheimer’s disease not evident in mouse models. Cell Rep 2018; 22(3): 832-47.
[http://dx.doi.org/10.1016/j.celrep.2017.12.066] [PMID: 29346778]
[22]
Hammond TR, Dufort C, Dissing-Olesen L, et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 2019; 50(1): 253-271.e6.
[http://dx.doi.org/10.1016/j.immuni.2018.11.004] [PMID: 30471926]
[23]
Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by microwell-Seq. Cell 2018; 172(5): 1091-1107.e17.
[http://dx.doi.org/10.1016/j.cell.2018.02.001] [PMID: 29474909]
[24]
Jordão MJC, Sankowski R, Brendecke SM, et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 2019; 363(6425): eaat7554.
[http://dx.doi.org/10.1126/science.aat7554] [PMID: 30679343]
[25]
Ay H, Furie KL, Singhal A, Smith WS, Sorensen AG, Koroshetz WJ. An evidence-based causative classification system for acute ischemic stroke. Ann Neurol 2005; 58(5): 688-97.
[http://dx.doi.org/10.1002/ana.20617] [PMID: 16240340]
[26]
Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993; 24(1): 35-41.
[http://dx.doi.org/10.1161/01.STR.24.1.35] [PMID: 7678184]
[27]
Amarenco P, Bogousslavsky J, Caplan LR, Donnan GA, Hennerici MG. New approach to stroke subtyping: The A-S-C-O (phenotypic) classification of stroke. Cerebrovasc Dis 2009; 27(5): 502-8.
[http://dx.doi.org/10.1159/000210433] [PMID: 19342826]
[28]
Marques BL, Oliveira-Lima OC, Carvalho GA, et al. Neurobiology of glycine transporters: From molecules to behavior. Neurosci Biobehav Rev 2020; 118: 97-110.
[http://dx.doi.org/10.1016/j.neubiorev.2020.07.025] [PMID: 32712279]
[29]
Kogelman LJ, Fu J, Franke L, et al. Inter-tissue gene co-expression networks between metabolically healthy and unhealthy obese individuals. PLoS One 2016; 11(12): e0167519.
[http://dx.doi.org/10.1371/journal.pone.0167519] [PMID: 27907186]
[30]
Deng Y, Chen D, Gao F, et al. Exosomes derived from microRNA-138-5p-overexpressing bone marrow-derived mesenchymal stem cells confer neuroprotection to astrocytes following ischemic stroke via inhibition of LCN2. J Biol Eng 2019; 13: 71.
[http://dx.doi.org/10.1186/s13036-019-0193-0] [PMID: 31485266]
[31]
Fann DY, Lim YA, Cheng YL, et al. Evidence that NF-κB and MAPK signaling promotes nlrp inflammasome activation in neurons following ischemic stroke. Mol Neurobiol 2018; 55(2): 1082-96.
[http://dx.doi.org/10.1007/s12035-017-0394-9] [PMID: 28092085]
[32]
Saber S, Youssef ME, Sharaf H, et al. BBG enhances OLT1177-induced NLRP3 inflammasome inactivation by targeting P2X7R/NLRP3 and MyD88/NF-κB signaling in DSS-induced colitis in rats. Life Sci 2021; 270: 119123.
[http://dx.doi.org/10.1016/j.lfs.2021.119123] [PMID: 33548287]
[33]
Madrigal-Matute J, Lindholt JS, Fernandez-Garcia CE, et al. Galectin-3, a biomarker linking oxidative stress and inflammation with the clinical outcomes of patients with atherothrombosis. J Am Heart Assoc 2014; 3(4): e000785.
[http://dx.doi.org/10.1161/JAHA.114.000785] [PMID: 25095870]
[34]
Wang A, Zhong C, Zhu Z, et al. Serum Galectin-3 and poor outcomes among patients with acute ischemic stroke. Stroke 2018; 49(1): 211-4.
[http://dx.doi.org/10.1161/STROKEAHA.117.019084] [PMID: 29229724]
[35]
Schroder WA, Hirata TD, Le TT, et al. SerpinB2 inhibits migration and promotes a resolution phase signature in large peritoneal macrophages. Sci Rep 2019; 9(1): 12421.
[http://dx.doi.org/10.1038/s41598-019-48741-w] [PMID: 31455834]
[36]
Li L, Liu X, Sanders KL, et al. TLR8-Mediated metabolic control of human treg function: A mechanistic target for cancer immunotherapy. Cell Metab 2019; 29(1): 103-123.e5.
[http://dx.doi.org/10.1016/j.cmet.2018.09.020] [PMID: 30344014]
[37]
Meås HZ, Haug M, Beckwith MS, et al. Sensing of HIV-1 by TLR8 activates human T cells and reverses latency. Nat Commun 2020; 11(1): 147.
[http://dx.doi.org/10.1038/s41467-019-13837-4] [PMID: 31919342]
[38]
Kim E, Cho S. CNS and peripheral immunity in cerebral ischemia: Partition and interaction. Exp Neurol 2021; 335: 113508.
[http://dx.doi.org/10.1016/j.expneurol.2020.113508] [PMID: 33065078]

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