Quantitative proteomics and multi-omics analysis identifies potential biomarkers and the underlying pathological molecular networks in Chinese patients with multiple sclerosis.
Differentially expressed protein
Gut microbiome
Immunoinflammatory response
Metabolic profile
Multi-omics interaction networks
Multiple sclerosis
Potential biomarker
Proteomics
Journal
BMC neurology
ISSN: 1471-2377
Titre abrégé: BMC Neurol
Pays: England
ID NLM: 100968555
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
28
06
2024
accepted:
21
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Multiple sclerosis (MS) is an autoimmune disorder caused by chronic inflammatory reactions in the central nervous system. Currently, little is known about the changes of plasma proteomic profiles in Chinese patients with MS (CpwMS) and its relationship with the altered profiles of multi-omics such as metabolomics and gut microbiome, as well as potential molecular networks that underlie the etiology of MS. To uncover the characteristics of proteomics landscape and potential multi-omics interaction networks in CpwMS, Plasma samples were collected from 22 CpwMS and 22 healthy controls (HCs) and analyzed using a Tandem Mass Tag (TMT)-based quantitative proteomics approach. Our results showed that the plasma proteomics pattern was significantly different in CpwMS compared to HCs. A total of 90 differentially expressed proteins (DEPs), such as LAMP1 and FCG2A, were identified in CpwMS plasma comparing to HCs. Furthermore, we also observed extensive and significant correlations between the altered proteomic profiles and the changes of metabolome, gut microbiome, as well as altered immunoinflammatory responses in MS-affected patients. For instance, the level of LAMP1 and ERN1 were significantly and positively correlated with the concentrations of metabolite L-glutamic acid and pro-inflammatory factor IL-17 (Padj < 0.05). However, they were negatively correlated with the amounts of other metabolites such as L-tyrosine and sphingosine 1-phosphate, as well as the concentrations of IL-8 and MIP-1α. This study outlined the underlying multi-omics integrated mechanisms that might regulate peripheral immunoinflammatory responses and MS progression. These findings are potentially helpful for developing new assisting diagnostic biomarker and therapeutic strategies for MS.
Identifiants
pubmed: 39478468
doi: 10.1186/s12883-024-03926-3
pii: 10.1186/s12883-024-03926-3
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
423Subventions
Organisme : Basic Research Program of Guizhou Province
ID : QianKeHe Basics-ZK [2023] General 583
Organisme : Project of Development Center for Medical Science & Technology, National Health Commission of the PRC
ID : WKZX2022JG0105
Organisme : S&T Major Project of Lishui City
ID : 2017ZDYF15
Informations de copyright
© 2024. The Author(s).
Références
Reich DS, Lucchinetti CF, Calabresi PA. Multiple Scler N Engl J Med. 2018;378(2):169–80.
doi: 10.1056/NEJMra1401483
Yong H, Chartier G, Quandt J. Modulating inflammation and neuroprotection in multiple sclerosis. J Neurosci Res. 2018;96(6):927–50.
pubmed: 28580582
doi: 10.1002/jnr.24090
Sospedra M, Martin R. Immunology of multiple sclerosis. Annu Rev Immunol. 2005;23:683–747.
pubmed: 15771584
doi: 10.1146/annurev.immunol.23.021704.115707
Walton C, et al. Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, third edition. Mult Scler. 2020;26(14):1816–21.
pubmed: 33174475
pmcid: 7720355
doi: 10.1177/1352458520970841
das Neves SP, et al. Altered astrocytic function in experimental neuroinflammation and multiple sclerosis. Glia. 2021;69(6):1341–68.
pubmed: 33247866
doi: 10.1002/glia.23940
International Multiple Sclerosis Genetics. C., Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science, 2019. 365(6460).
Genetics IMS. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476(7359):214–9.
doi: 10.1038/nature10251
Genetics IMS. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med. 2007;357(9):851–62.
doi: 10.1056/NEJMoa073493
Ramanujam R, et al. Effect of Smoking Cessation on multiple sclerosis prognosis. JAMA Neurol. 2015;72(10):1117–23.
pubmed: 26348720
doi: 10.1001/jamaneurol.2015.1788
Ascherio A, et al. Vitamin D as an early predictor of multiple sclerosis activity and progression. JAMA Neurol. 2014;71(3):306–14.
pubmed: 24445558
pmcid: 4000029
doi: 10.1001/jamaneurol.2013.5993
Olsson T, Barcellos LF, Alfredsson L. Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nat Rev Neurol. 2017;13(1):25–36.
pubmed: 27934854
doi: 10.1038/nrneurol.2016.187
Marrie RA. Environmental risk factors in multiple sclerosis aetiology. Lancet Neurol. 2004;3(12):709–18.
pubmed: 15556803
doi: 10.1016/S1474-4422(04)00933-0
Kaufmann M, et al. Identification of early neurodegenerative pathways in progressive multiple sclerosis. Nat Neurosci. 2022;25(7):944–55.
pubmed: 35726057
doi: 10.1038/s41593-022-01097-3
Sen MK et al. Proteomics of multiple sclerosis: inherent issues in defining the pathoetiology and identifying (early) biomarkers. Int J Mol Sci, 2021. 22(14).
De Masi R, et al. The clinical potential of blood-proteomics in multiple sclerosis. BMC Neurol. 2013;13:45.
pubmed: 23692923
pmcid: 3674984
doi: 10.1186/1471-2377-13-45
Cappelletti C, et al. Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls. Clin Proteom. 2022;19(1):23.
doi: 10.1186/s12014-022-09361-1
Avsar T, et al. CSF proteomics identifies Specific and Shared pathways for multiple sclerosis clinical subtypes. PLoS ONE. 2015;10(5):e0122045.
pubmed: 25942430
pmcid: 4420287
doi: 10.1371/journal.pone.0122045
Singh V, Tripathi A, Dutta R. Proteomic approaches to Decipher mechanisms underlying pathogenesis in multiple sclerosis patients. Proteomics. 2019;19(16):e1800335.
pubmed: 31119864
pmcid: 6690771
doi: 10.1002/pmic.201800335
Fiorini A, et al. Involvement of oxidative stress in occurrence of relapses in multiple sclerosis: the spectrum of oxidatively modified serum proteins detected by proteomics and redox proteomics analysis. PLoS ONE. 2013;8(6):e65184.
pubmed: 23762311
pmcid: 3676399
doi: 10.1371/journal.pone.0065184
Mosleth EF, et al. Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis. Sci Rep. 2021;11(1):4087.
pubmed: 33602999
pmcid: 7892850
doi: 10.1038/s41598-021-82388-w
Elkabes S, Li H. Proteomic strategies in multiple sclerosis and its animal models. Proteom Clin Appl. 2007;1(11):1393–405.
doi: 10.1002/prca.200700315
Satoh JI, Tabunoki H, Yamamura T. Molecular network of the comprehensive multiple sclerosis brain-lesion proteome. Mult Scler. 2009;15(5):531–41.
pubmed: 19389748
doi: 10.1177/1352458508101943
Rai NK, et al. Comparative proteomic profiling identifies reciprocal expression of mitochondrial proteins between White and Gray Matter lesions from multiple sclerosis brains. Front Neurol. 2021;12:779003.
pubmed: 35002930
pmcid: 8740228
doi: 10.3389/fneur.2021.779003
Wallin MT, et al. Serum proteomic analysis of a pre-symptomatic multiple sclerosis cohort. Eur J Neurol. 2015;22(3):591–9.
pubmed: 25104396
doi: 10.1111/ene.12534
Han MH, et al. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature. 2008;451(7182):1076–81.
pubmed: 18278032
doi: 10.1038/nature06559
Magliozzi R, et al. Iron homeostasis, complement, and coagulation cascade as CSF signature of cortical lesions in early multiple sclerosis. Ann Clin Transl Neurol. 2019;6(11):2150–63.
pubmed: 31675181
pmcid: 6856609
doi: 10.1002/acn3.50893
Lourenco AS, et al. Proteomics-based technologies in the discovery of biomarkers for multiple sclerosis in the cerebrospinal fluid. Curr Mol Med. 2011;11(4):326–49.
pubmed: 21506919
doi: 10.2174/156652411795677981
Singh V, et al. Proteomics technologies for biomarker discovery in multiple sclerosis. J Neuroimmunol. 2012;248(1–2):40–7.
pubmed: 22129845
doi: 10.1016/j.jneuroim.2011.11.004
Kroksveen AC, et al. Cerebrospinal fluid proteomics in multiple sclerosis. Biochim Biophys Acta. 2015;1854(7):746–56.
pubmed: 25526888
doi: 10.1016/j.bbapap.2014.12.013
Huang J, et al. Inflammation-related plasma and CSF biomarkers for multiple sclerosis. Proc Natl Acad Sci U S A. 2020;117(23):12952–60.
pubmed: 32457139
pmcid: 7293699
doi: 10.1073/pnas.1912839117
Dagley LF, Emili A, Purcell AW. Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis. Proteom Clin Appl. 2013;7(1–2):91–108.
doi: 10.1002/prca.201200104
Tremlett H, et al. Serum proteomics in multiple sclerosis disease progression. J Proteom. 2015;118:2–11.
doi: 10.1016/j.jprot.2015.02.018
Malekzadeh A, et al. Plasma proteome in multiple sclerosis disease progression. Ann Clin Transl Neurol. 2019;6(9):1582–94.
pubmed: 31364818
pmcid: 7651845
doi: 10.1002/acn3.771
Fitzner B, Hecker M, Zettl UK. Molecular biomarkers in cerebrospinal fluid of multiple sclerosis patients. Autoimmun Rev. 2015;14(10):903–13.
pubmed: 26071103
doi: 10.1016/j.autrev.2015.06.001
Stoop MP, et al. Proteomics comparison of cerebrospinal fluid of relapsing remitting and primary progressive multiple sclerosis. PLoS ONE. 2010;5(8):e12442.
pubmed: 20805994
pmcid: 2929207
doi: 10.1371/journal.pone.0012442
Chase Huizar C, Raphael I, Forsthuber TG. Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis. Cell Immunol. 2020;358:104219.
pubmed: 33039896
doi: 10.1016/j.cellimm.2020.104219
Bedri SK, et al. Plasma protein profiling reveals candidate biomarkers for multiple sclerosis treatment. PLoS ONE. 2019;14(5):e0217208.
pubmed: 31141529
pmcid: 6541274
doi: 10.1371/journal.pone.0217208
Salazar IL, et al. Posttranslational modifications of proteins are key features in the identification of CSF biomarkers of multiple sclerosis. J Neuroinflammation. 2022;19(1):44.
pubmed: 35135578
pmcid: 8822857
doi: 10.1186/s12974-022-02404-2
O’Connor KC, et al. Comprehensive phenotyping in multiple sclerosis: discovery based proteomics and the current understanding of putative biomarkers. Dis Markers. 2006;22(4):213–25.
pubmed: 17124343
pmcid: 3851054
doi: 10.1155/2006/670439
Harris VK, Sadiq SA. Disease biomarkers in multiple sclerosis: potential for use in therapeutic decision making. Mol Diagn Ther. 2009;13(4):225–44.
pubmed: 19712003
doi: 10.1007/BF03256329
Amin B, et al. New poteintial serum biomarkers in multiple sclerosis identified by proteomic strategies. Curr Med Chem. 2014;21(13):1544–56.
pubmed: 24180278
doi: 10.2174/09298673113206660311
Liguori M, et al. Proteomic profiling in multiple sclerosis clinical courses reveals potential biomarkers of neurodegeneration. PLoS ONE. 2014;9(8):e103984.
pubmed: 25098164
pmcid: 4123901
doi: 10.1371/journal.pone.0103984
Komori M, et al. Proteomic pattern analysis discriminates among multiple sclerosis-related disorders. Ann Neurol. 2012;71(5):614–23.
pubmed: 22522477
doi: 10.1002/ana.22633
Bystrom S, et al. Affinity proteomic profiling of plasma, cerebrospinal fluid, and brain tissue within multiple sclerosis. J Proteome Res. 2014;13(11):4607–19.
pubmed: 25231264
doi: 10.1021/pr500609e
Sakurai T, et al. Identification of antibodies as biological markers in serum from multiple sclerosis patients by immunoproteomic approach. J Neuroimmunol. 2011;233(1–2):175–80.
pubmed: 21131059
doi: 10.1016/j.jneuroim.2010.11.003
Colomba P, et al. Identification of biomarkers in cerebrospinal fluid and serum of multiple sclerosis patients by immunoproteomics approach. Int J Mol Sci. 2014;15(12):23269–82.
pubmed: 25517032
pmcid: 4284765
doi: 10.3390/ijms151223269
Probert F, et al. Integrative biochemical, proteomics and metabolomics cerebrospinal fluid biomarkers predict clinical conversion to multiple sclerosis. Brain Commun. 2021;3(2):fcab084.
pubmed: 33997784
pmcid: 8111065
doi: 10.1093/braincomms/fcab084
Miedema A, et al. High-resolution transcriptomic and proteomic profiling of heterogeneity of brain-derived Microglia in multiple sclerosis. Front Mol Neurosci. 2020;13:583811.
pubmed: 33192299
pmcid: 7654237
doi: 10.3389/fnmol.2020.583811
Kotelnikova E, et al. MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis. Proc Natl Acad Sci U S A. 2019;116(19):9671–6.
pubmed: 31004050
pmcid: 6510997
doi: 10.1073/pnas.1818347116
Cicalini I et al. Integrated Lipidomics and Metabolomics Analysis of Tears in multiple sclerosis: an insight into diagnostic potential of Lacrimal Fluid. Int J Mol Sci, 2019. 20(6).
Qendro V et al. Integrative proteomics, genomics, and translational immunology approaches reveal mutated forms of Proteolipid Protein 1 (PLP1) and mutant-specific immune response in multiple sclerosis. Proteomics, 2017. 17(6).
Del Boccio P, et al. Integration of metabolomics and proteomics in multiple sclerosis: from biomarkers discovery to personalized medicine. Proteom Clin Appl. 2016;10(4):470–84.
doi: 10.1002/prca.201500083
Villoslada P, Baranzini S. Data integration and systems biology approaches for biomarker discovery: challenges and opportunities for multiple sclerosis. J Neuroimmunol. 2012;248(1–2):58–65.
pubmed: 22281286
doi: 10.1016/j.jneuroim.2012.01.001
Abbott NJ, et al. Structure and function of the blood-brain barrier. Neurobiol Dis. 2010;37(1):13–25.
pubmed: 19664713
doi: 10.1016/j.nbd.2009.07.030
Campos-Bedolla P, et al. Role of the blood-brain barrier in the nutrition of the central nervous system. Arch Med Res. 2014;45(8):610–38.
pubmed: 25481827
doi: 10.1016/j.arcmed.2014.11.018
Correale J, Fiol M. Chitinase effects on immune cell response in neuromyelitis optica and multiple sclerosis. Mult Scler. 2011;17(5):521–31.
pubmed: 21159721
doi: 10.1177/1352458510392619
Lycke J, Zetterberg H. The role of blood and CSF biomarkers in the evaluation of new treatments against multiple sclerosis. Expert Rev Clin Immunol. 2017;13(12):1143–53.
pubmed: 29090607
doi: 10.1080/1744666X.2017.1400380
Ottervald J, et al. Multiple sclerosis: identification and clinical evaluation of novel CSF biomarkers. J Proteom. 2010;73(6):1117–32.
doi: 10.1016/j.jprot.2010.01.004
Kroksveen AC, et al. Quantitative proteomics suggests decrease in the secretogranin-1 cerebrospinal fluid levels during the disease course of multiple sclerosis. Proteomics. 2015;15(19):3361–9.
pubmed: 26152395
doi: 10.1002/pmic.201400142
Stoop MP, et al. Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced mass spectrometry. Proteomics. 2008;8(8):1576–85.
pubmed: 18351689
doi: 10.1002/pmic.200700446
Comabella M et al. CSF chitinase 3-Like 2 is Associated with Long-Term disability progression in patients with Progressive multiple sclerosis. Neurol Neuroimmunol Neuroinflamm, 2021. 8(6).
Liu H, et al. Label-free quantitative proteomic analysis of Cerebrospinal Fluid and serum in patients with relapse-remitting multiple sclerosis. Front Genet. 2022;13:892491.
pubmed: 35571066
pmcid: 9092947
doi: 10.3389/fgene.2022.892491
Dumont D, et al. Proteomic analysis of cerebrospinal fluid from multiple sclerosis patients. Proteomics. 2004;4(7):2117–24.
pubmed: 15221773
doi: 10.1002/pmic.200300715
Kroksveen AC, Guldbrandsen A, Vedeler C, Myhr KM, Opsahl JA, Berven FS. Cerebrospinal fluid proteome comparison between multiple sclerosis patients and controls. Acta Neurol Scand Suppl. 2012;126(Suppl. 195):90–6.
Lehmensiek V, et al. Cerebrospinal fluid proteome profile in multiple sclerosis. Mult Scler. 2007;13(7):840–9.
pubmed: 17881397
doi: 10.1177/1352458507076406
Stoop MP, et al. Decreased neuro-axonal proteins in CSF at First Attack of suspected multiple sclerosis. Proteom Clin Appl. 2017;11:11–2.
doi: 10.1002/prca.201700005
Hammack BN, et al. Proteomic analysis of multiple sclerosis cerebrospinal fluid. Mult Scler. 2004;10(3):245–60.
pubmed: 15222687
doi: 10.1191/1352458504ms1023oa
Jankovska E, et al. Quantitative proteomic analysis of cerebrospinal fluid of women newly diagnosed with multiple sclerosis. Int J Neurosci. 2022;132(7):724–34.
pubmed: 33059501
doi: 10.1080/00207454.2020.1837801
Chiasserini D, et al. CSF proteome analysis in multiple sclerosis patients by two-dimensional electrophoresis. Eur J Neurol. 2008;15(9):998–1001.
pubmed: 18637954
doi: 10.1111/j.1468-1331.2008.02239.x
Hinsinger G, et al. Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis. Mult Scler. 2015;21(10):1251–61.
pubmed: 25698171
doi: 10.1177/1352458514561906
Harris VK, et al. Bri2-23 is a potential cerebrospinal fluid biomarker in multiple sclerosis. Neurobiol Dis. 2010;40(1):331–9.
pubmed: 20600910
doi: 10.1016/j.nbd.2010.06.007
Kroksveen AC, et al. Discovery and initial verification of differentially abundant proteins between multiple sclerosis patients and controls using iTRAQ and SID-SRM. J Proteom. 2013;78:312–25.
doi: 10.1016/j.jprot.2012.09.037
Awad A, et al. Analyses of cerebrospinal fluid in the diagnosis and monitoring of multiple sclerosis. J Neuroimmunol. 2010;219(1–2):1–7.
pubmed: 19782408
doi: 10.1016/j.jneuroim.2009.09.002
Khademi M, et al. Intense inflammation and nerve damage in early multiple sclerosis subsides at older age: a reflection by cerebrospinal fluid biomarkers. PLoS ONE. 2013;8(5):e63172.
pubmed: 23667585
pmcid: 3646751
doi: 10.1371/journal.pone.0063172
Stangel M, et al. The utility of cerebrospinal fluid analysis in patients with multiple sclerosis. Nat Rev Neurol. 2013;9(5):267–76.
pubmed: 23528543
doi: 10.1038/nrneurol.2013.41
Noben JP, et al. Lumbar cerebrospinal fluid proteome in multiple sclerosis: characterization by ultrafiltration, liquid chromatography, and mass spectrometry. J Proteome Res. 2006;5(7):1647–57.
pubmed: 16823972
doi: 10.1021/pr0504788
Liu S, et al. Quantitative proteomic analysis of the cerebrospinal fluid of patients with multiple sclerosis. J Cell Mol Med. 2009;13(8A):1586–603.
pubmed: 19602050
pmcid: 3828869
doi: 10.1111/j.1582-4934.2009.00850.x
Meinl E, Krumbholz M, Hohlfeld R. B lineage cells in the inflammatory central nervous system environment: migration, maintenance, local antibody production, and therapeutic modulation. Ann Neurol. 2006;59(6):880–92.
pubmed: 16718690
doi: 10.1002/ana.20890
Filippi M, et al. Multiple sclerosis. Nat Rev Dis Primers. 2018;4(1):43.
pubmed: 30410033
doi: 10.1038/s41572-018-0041-4
Polman CH, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the McDonald Criteria. Ann Neurol. 2005;58(6):840–6.
pubmed: 16283615
doi: 10.1002/ana.20703
Ludwig KR, Schroll MM, Hummon AB. Comparison of In-Solution, FASP, and S-Trap based digestion methods for Bottom-Up proteomic studies. J Proteome Res. 2018;17(7):2480–90.
pubmed: 29754492
pmcid: 9319029
doi: 10.1021/acs.jproteome.8b00235
Perez-Riverol Y, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543–52.
pubmed: 34723319
doi: 10.1093/nar/gkab1038
Ritchie ME, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Gatto L, et al. Visualization of proteomics data using R and bioconductor. Proteomics. 2015;15(8):1375–89.
pubmed: 25690415
pmcid: 4510819
doi: 10.1002/pmic.201400392
Wu T, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2(3):100141.
Kanehisa M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.
pubmed: 27899662
doi: 10.1093/nar/gkw1092
Yu G, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.
pubmed: 22455463
doi: 10.1089/omi.2011.0118
Yu G, et al. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 2015;31(4):608–9.
pubmed: 25677125
doi: 10.1093/bioinformatics/btu684
Szklarczyk D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13.
pubmed: 30476243
doi: 10.1093/nar/gky1131
Jain A, Tuteja G. TissueEnrich: tissue-specific gene enrichment analysis. Bioinformatics. 2019;35(11):1966–7.
pubmed: 30346488
doi: 10.1093/bioinformatics/bty890
Robin X, et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
pubmed: 21414208
pmcid: 3068975
doi: 10.1186/1471-2105-12-77
Yang F, et al. Altered plasma metabolic profiles in Chinese patients with multiple sclerosis. Front Immunol. 2021;12:792711.
pubmed: 34975894
pmcid: 8715987
doi: 10.3389/fimmu.2021.792711
Ling Z, et al. Alterations of the fecal microbiota in Chinese patients with multiple sclerosis. Front Immunol. 2020;11:590783.
pubmed: 33391265
pmcid: 7772405
doi: 10.3389/fimmu.2020.590783
Porozhan Y et al. Defective integrator activity shapes the transcriptome of patients with multiple sclerosis. Life Sci Alliance, 2024. 7(10).
Salehi Z, et al. RNA sequencing of CD4(+) T cells in relapsing-remitting multiple sclerosis patients at Relapse: deciphering the involvement of novel genes and pathways. J Mol Neurosci. 2021;71(12):2628–45.
pubmed: 34286457
doi: 10.1007/s12031-021-01878-8
Martin-Gutierrez L, et al. Dysregulated lipid metabolism networks modulate T-cell function in people with relapsing-remitting multiple sclerosis. Clin Exp Immunol. 2024;217(2):204–18.
pubmed: 38625017
pmcid: 11239565
doi: 10.1093/cei/uxae032
Shang Z, et al. Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells. PeerJ. 2020;8:e8357.
pubmed: 32117605
pmcid: 7003695
doi: 10.7717/peerj.8357
Yeh WZ, et al. Transcriptomics identifies blunted immunomodulatory effects of vitamin D in people with multiple sclerosis. Sci Rep. 2024;14(1):1436.
pubmed: 38228657
pmcid: 10792011
doi: 10.1038/s41598-024-51779-0
Lindsey JW, Agarwal SK, Tan FK. Gene expression changes in multiple sclerosis relapse suggest activation of T and non-T cells. Mol Med. 2011;17(1–2):95–102.
pubmed: 20882258
doi: 10.2119/molmed.2010.00071
Malhotra S, et al. NLRP3 inflammasome as prognostic factor and therapeutic target in primary progressive multiple sclerosis patients. Brain. 2020;143(5):1414–30.
pubmed: 32282893
doi: 10.1093/brain/awaa084
Shi Y, et al. Discovery of Novel biomarkers for Diagnosing and Predicting the Progression of multiple sclerosis using TMT-Based quantitative proteomics. Front Immunol. 2021;12:700031.
pubmed: 34489947
pmcid: 8417809
doi: 10.3389/fimmu.2021.700031
Stoop MP, et al. Effects of natalizumab treatment on the cerebrospinal fluid proteome of multiple sclerosis patients. J Proteome Res. 2013;12(3):1101–7.
pubmed: 23339689
doi: 10.1021/pr3012107
Nystad AE, et al. Fingolimod downregulates brain sphingosine-1-phosphate receptor 1 levels but does not promote remyelination or neuroprotection in the cuprizone model. J Neuroimmunol. 2020;339:577091.
pubmed: 31739156
doi: 10.1016/j.jneuroim.2019.577091
Bruijstens AL et al. Neurodegeneration and humoral response proteins in cerebrospinal fluid associate with pediatric-onset multiple sclerosis and not monophasic demyelinating syndromes in childhood. Mult Scler, 2022: p. 13524585221125369.
Farrell RA, et al. Humoral immune response to EBV in multiple sclerosis is associated with disease activity on MRI. Neurology. 2009;73(1):32–8.
pubmed: 19458321
pmcid: 2848585
doi: 10.1212/WNL.0b013e3181aa29fe
Keegan M, et al. Relation between humoral pathological changes in multiple sclerosis and response to therapeutic plasma exchange. Lancet. 2005;366(9485):579–82.
pubmed: 16099294
doi: 10.1016/S0140-6736(05)67102-4
Lunemann JD, et al. Broadened and elevated humoral immune response to EBNA1 in pediatric multiple sclerosis. Neurology. 2008;71(13):1033–5.
pubmed: 18809840
pmcid: 2676958
doi: 10.1212/01.wnl.0000326576.91097.87
Ziemssen T, Ziemssen F. The role of the humoral immune system in multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE). Autoimmun Rev. 2005;4(7):460–7.
pubmed: 16137612
doi: 10.1016/j.autrev.2005.03.005
Wheeler MA, et al. Environmental Control of Astrocyte Pathogenic Activities in CNS inflammation. Cell. 2019;176(3):581–e59618.
pubmed: 30661753
pmcid: 6440749
doi: 10.1016/j.cell.2018.12.012
Saraswat D, et al. Heparanome-mediated rescue of oligodendrocyte progenitor quiescence following inflammatory demyelination. J Neurosci. 2021;41(10):2245–63.
pubmed: 33472827
pmcid: 8018763
doi: 10.1523/JNEUROSCI.0580-20.2021
Teixeira F, Gotte M. Involvement of Syndecan-1 and Heparanase in Cancer and inflammation. Adv Exp Med Biol. 2020;1221:97–135.
pubmed: 32274708
doi: 10.1007/978-3-030-34521-1_4
Changyaleket B, et al. Heparanase: potential roles in multiple sclerosis. J Neuroimmunol. 2017;310:72–81.
pubmed: 28778449
doi: 10.1016/j.jneuroim.2017.07.001
Bitan M, et al. Heparanase upregulates Th2 cytokines, ameliorating experimental autoimmune encephalitis. Mol Immunol. 2010;47(10):1890–8.
pubmed: 20399501
pmcid: 2865651
doi: 10.1016/j.molimm.2010.03.014
de Mestre AM, et al. Expression of the heparan sulfate-degrading enzyme heparanase is induced in infiltrating CD4(+) T cells in experimental autoimmune encephalomyelitis and regulated at the level of transcription by early growth response gene. J Leukoc Biol. 2007;82(5):1289–300.
pubmed: 29350861
doi: 10.1189/jlb.0507315
Heyman B, Yang Y. Mechanisms of heparanase inhibitors in cancer therapy. Exp Hematol. 2016;44(11):1002–12.
pubmed: 27576132
pmcid: 5083136
doi: 10.1016/j.exphem.2016.08.006
Sanderson RD, et al. Heparanase regulation of cancer, autophagy and inflammation: new mechanisms and targets for therapy. FEBS J. 2017;284(1):42–55.
pubmed: 27758044
doi: 10.1111/febs.13932
Zhang GL, et al. Significance of host heparanase in promoting tumor growth and metastasis. Matrix Biol. 2020;93:25–42.
pubmed: 32534153
pmcid: 7704762
doi: 10.1016/j.matbio.2020.06.001
Higashi N, Irimura T, Nakajima M. Heparanase is involved in Leukocyte Migration. Adv Exp Med Biol. 2020;1221:435–44.
pubmed: 32274720
doi: 10.1007/978-3-030-34521-1_16
Caruana I, et al. Heparanase promotes tumor infiltration and antitumor activity of CAR-redirected T lymphocytes. Nat Med. 2015;21(5):524–9.
pubmed: 25849134
pmcid: 4425589
doi: 10.1038/nm.3833
Li JP, Vlodavsky I. Heparin, heparan sulfate and heparanase in inflammatory reactions. Thromb Haemost. 2009;102(5):823–8.
pubmed: 19888515
Meirovitz A, et al. Heparanase in inflammation and inflammation-associated cancer. FEBS J. 2013;280(10):2307–19.
pubmed: 23398975
pmcid: 3651782
doi: 10.1111/febs.12184
Goldberg R, et al. Versatile role of heparanase in inflammation. Matrix Biol. 2013;32(5):234–40.
pubmed: 23499528
pmcid: 4061494
doi: 10.1016/j.matbio.2013.02.008
Vlodavsky I, Iozzo RV, Sanderson RD. Heparanase: multiple functions in inflammation, diabetes and atherosclerosis. Matrix Biol. 2013;32(5):220–2.
pubmed: 23499526
doi: 10.1016/j.matbio.2013.03.001
Takahashi H, et al. Involvement of heparanase in migration of microglial cells. Biochim Biophys Acta. 2008;1780(4):709–15.
pubmed: 18222122
doi: 10.1016/j.bbagen.2007.12.014
Lively S, Schlichter LC. The microglial activation state regulates migration and roles of matrix-dissolving enzymes for invasion. J Neuroinflammation. 2013;10:75.
pubmed: 23786632
pmcid: 3693964
doi: 10.1186/1742-2094-10-75
Takahashi H, et al. Expression of heparanase in nestin-positive reactive astrocytes in ischemic lesions of rat brain after transient middle cerebral artery occlusion. Neurosci Lett. 2007;417(3):250–4.
pubmed: 17368723
doi: 10.1016/j.neulet.2007.02.075