Abstract
Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets.
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Data availability
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022850. Microarrays raw data can be accessed through the Gene Expression Omnibus (GEO) data repository with the accession number GSE162955.
Abbreviations
- BCA:
-
Bicinchoninic acid
- CL:
-
Corresponding contralateral brain area
- CV:
-
Coefficient of variation
- FDR:
-
False discovery rate
- GEO:
-
Gene Expression Omnibus
- GIS:
-
Gene influential score
- GPF:
-
Gas phase fractionation
- GSS:
-
Gene set scores
- IC:
-
Infarct core
- LC–ESI–MS/MS:
-
Liquid chromatography coupled to electrospray ionization – tandem mass spectrometry
- LogFC:
-
Logarithmic base twofold-change
- LTQ:
-
Linear trap quadrupole
- MCAO:
-
Middle cerebral artery occlusion
- moGSA:
-
Multiple omics gene set analysis
- mRS:
-
Modified Rankin Scale
- NIHSS:
-
National Institutes of Health stroke scale
- RQ:
-
Relative quantification
- rt-PA:
-
Recombinant tissue plasminogen activator
References
Benjamin EJ, Virani SS, Callaway CW et al (2018) Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation 137:e67–e492. https://doi.org/10.1161/CIR.0000000000000558
Lo EH, Dalkara T, Moskowitz MA (2003) Neurological diseases: mechanisms, challenges and opportunities in stroke. Nat Rev Neurosci 4:399–414. https://doi.org/10.1038/nrn1106
Karsy M, Brock A, Guan J et al (2017) Neuroprotective strategies and the underlying molecular basis of cerebrovascular stroke. Neurosurg Focus 42:E3. https://doi.org/10.3171/2017.1.FOCUS16522
O’Collins VE, Macleod MR, Donnan GA et al (2006) 1026 Experimental treatments in acute stroke. Ann Neurol. https://doi.org/10.1002/ana.20741
Yew KS, Cheng EM (2015) Diagnosis of acute stroke. Am Fam Physician 91:528–536. http://www.aafp.org/afp/2015/0415/p528.html
Montaner J, Ramiro L, Simats A et al (2020) Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol 16:247–264. https://doi.org/10.1038/s41582-020-0350-6
Cuadrado E, Rosell A, Colomé N et al (2010) The proteome of human brain after ischemic stroke. J Neuropathol Exp Neurol 69:1105–1115. https://doi.org/10.1097/NEN.0b013e3181f8c539
Malik R, Chauhan G, Traylor M et al (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. https://doi.org/10.1038/s41588-018-0058-3
Shin TH, Lee DY, Basith S, Manavalan B, Paik MJ, Rybinnik I, Mouradian MM, Ahn JH, Lee G (2020) Metabolome Changes in Cerebral Ischemia. Cells 9:1630
Ludhiadch A, Vasudeva K, Munshi A (2020) Establishing molecular signatures of stroke focusing on omic approaches: a narrative review. Int J Neurosci 130:1250–1266. https://doi.org/10.1080/00207454.2020.1732964
Cuadrado E, Rosell A, Penalba A et al (2009) Vascular MMP-9/TIMP-2 and neuronal MMP-10 up-regulation in human brain after stroke: a combined laser microdissection and protein array study. J Proteome Res 8:3191–3197. https://doi.org/10.1021/pr801012x
Yuan D, Liu C, Hu B (2018) Dysfunction of membrane trafficking leads to ischemia-reperfusion injury after transient cerebral ischemia. Transl Stroke Res 9:215–222. https://doi.org/10.1007/s12975-017-0572-0
Wang L, Wei C, Deng L et al (2018) The accuracy of serum matrix metalloproteinase-9 for predicting hemorrhagic transformation after acute ischemic stroke: a systematic review and meta-analysis. J Stroke Cerebrovasc Dis. https://doi.org/10.1016/j.jstrokecerebrovasdis.2018.01.023
Eng LF, Ghirnikar RS, Lee YL (2000) Glial fibrillary acidic protein: GFAP-thirty-one years (1969–2000). Neurochem Res 25:1439–1451. https://doi.org/10.1023/a:1007677003387
Perry LA, Lucarelli T, Penny-Dimri JC et al (2018) Glial fibrillary acidic protein for the early diagnosis of intracerebral hemorrhage: systematic review and meta-analysis of diagnostic test accuracy. Int J Stroke. https://doi.org/10.1177/1747493018806167
Moore DF, Li H, Jeffries N et al (2005) Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation. https://doi.org/10.1161/01.CIR.0000152105.79665.C6
Stamova B, Xu H, Jickling G et al (2010) Gene expression profiling of blood for the prediction of ischemic stroke. Stroke. https://doi.org/10.1161/STROKEAHA.110.588335
Stamova B, Ander BP, Jickling G et al (2018) The intracerebral hemorrhage blood transcriptome in humans differs from the ischemic stroke and vascular risk factor control blood transcriptomes. J Cereb Blood Flow Metab 39:1818–1835. https://doi.org/10.1177/0271678X18769513
Haas R, Zelezniak A, Iacovacci J et al (2017) Designing and interpreting “multi-omic” experiments that may change our understanding of biology. Curr Opin Syst Biol 6:37–45. https://doi.org/10.1016/j.coisb.2017.08.009
Subramanian I, Verma S, Kumar S et al (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 14:1177932219899051. https://doi.org/10.1177/1177932219899051
de Tayrac M, Lê S, Aubry M et al (2009) Simultaneous analysis of distinct omics data sets with integration of biological knowledge: multiple factor analysis approach. BMC Genomics 10:32. https://doi.org/10.1186/1471-2164-10-32
Simats A, Ramiro L, García-Berrocoso T et al (2020) A mouse brain-based multi-omics integrative approach reveals potential blood biomarkers for ischemic stroke. Mol Cell Proteomics. https://doi.org/10.1074/mcp.RA120.002283
Brott T, Adams HP, Olinger CP et al (1989) Measurements of acute cerebral infarction: a clinical examination scale. Stroke. https://doi.org/10.1161/01.STR.20.7.864
van Swieten JC, Koudstaal PJ, Visser MC et al (1988) Interobserver agreement for the assessment of handicap in stroke patients. Stroke 19:604–607. https://doi.org/10.1161/01.str.19.5.604
Adams H, Adams H, Bendixen B et al (1993) Classification of subtype of acute ischemic stroke. Stroke 23:35–41. https://doi.org/10.1161/01.STR.24.1.35
Riba-Llena I, Jarca CI, Mundet X et al (2013) Investigating silent strokes in hypertensives: a magnetic resonance imaging study (ISSYS): rationale and protocol design. BMC Neurol 13:130–137. https://doi.org/10.1186/1471-2377-13-130
García-Berrocoso T, Llombart V, Colàs-Campàs L et al (2018) Single cell immuno-laser microdissection coupled to label-free proteomics to reveal the proteotypes of human brain cells after ischemia. Mol Cell Proteomics 17:175–189. https://doi.org/10.1074/mcp.ra117.000419
Kauffmann A, Gentleman R, Huber W (2009) arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25:415–416. https://doi.org/10.1093/bioinformatics/btn647
Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264. https://doi.org/10.1093/biostatistics/4.2.249
Meng C, Kuster B, Culhane AC, Gholami AM (2014) A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15:162. https://doi.org/10.1186/1471-2105-15-162
González I, Déjean S, Martin P, Baccini A (2008) CCA: an R package to extend canonical correlation analysis. J Stat Softw 23(12):1–14. https://doi.org/10.18637/jss.v023.i12
Meng C, Basunia A, Peters B, Gholami AM, Kuster B, Culhane AC (2019) MOGSA: integrative single sample gene-set analysis of multiple omics data. Mol Cell Prot 18(8):S153–S168. https://doi.org/10.1074/mcp.TIR118.001251
Liberzon A, Birger C, Thorvaldsdóttir H et al (2015) The molecular signatures database hallmark gene set collection. Cell Syst. https://doi.org/10.1016/j.cels.2015.12.004
Romero-Calvo I, Ocón B, Martínez-Moya P et al (2010) Reversible Ponceau staining as a loading control alternative to actin in Western blots. Anal Biochem 401:318–320. https://doi.org/10.1016/j.ab.2010.02.036
Fanara P, Wong PYA, Husted KH et al (2012) Cerebrospinal fluid-based kinetic biomarkers of axonal transport in monitoring neurodegeneration. J Clin Invest. https://doi.org/10.1172/JCI64575
Lam MPY, Wang D, Lau E et al (2014) Protein kinetic signatures of the remodeling heart following isoproterenol stimulation. J Clin Invest. https://doi.org/10.1172/JCI73787
Fagan A, Culhane AC, Higgins DG (2007) A multivariate analysis approach to the integration of proteomic and gene expression data. Proteomics. https://doi.org/10.1002/pmic.200600898
Simats A, García-Berrocoso T, Montaner J (2016) Neuroinflammatory biomarkers: from stroke diagnosis and prognosis to therapy. BiochimBiophysActa - Mol Basis Dis 1862:411–424. https://doi.org/10.1016/j.bbadis.2015.10.025
González I, Cao KAL, Davis MJ, Déjean S (2012) Visualising associations between paired “omics” data sets. BioData Min. https://doi.org/10.1186/1756-0381-5-19
Amantea D, Bagetta G (2017) Excitatory and inhibitory amino acid neurotransmitters in stroke: from neurotoxicity to ischemic tolerance. Curr Opin Pharmacol 35:111–119. https://doi.org/10.1016/j.coph.2017.07.014
Puig B, Brenna S, Magnus T (2018) Molecular communication of a dying neuron in stroke. Int J Mol Sci 19:2834. https://doi.org/10.3390/ijms19092834
Baeten KM, Akassoglou K (2011) Extracellular matrix and matrix receptors in blood-brain barrier formation and stroke. Dev Neurobiol. https://doi.org/10.1002/dneu.20954
Gould DB, Phalan FC, Van Mil SE et al (2006) Role of COL4A1 in small-vessel disease and hemorrhagic stroke. N Engl J Med. https://doi.org/10.1056/NEJMoa053727
Ellison JA, Barone FC, Feuerstein GZ (1999) Matrix remodeling after stroke: de novo expression of matrix proteins and integrin receptors. Ann New York Acad Sci 890(1):204–222. https://doi.org/10.1111/j.1749-6632.1999.tb07996.x
Frank S, Gaume B, Bergmann-Leitner ES et al (2001) The role of dynamin-related protein 1, a mediator of mitochondrial fission, in Apoptosis. Dev Cell 1:515–525. https://doi.org/10.1016/S1534-5807(01)00055-7
Jang A, Koh P (2016) Ischemic brain injury decreases dynamin-like protein 1 expression in a middle cerebral artery occlusion animal model and glutamate-exposed HT22 cells. Lab Anim Res 6055:194–199. https://doi.org/10.5625/lar.2016.32.4.194
Grohm J, Kim SW, Mamrak U et al (2012) Inhibition of Drp1 provides neuroprotection in vitro and in vivo. Cell Death Differ. https://doi.org/10.1038/cdd.2012.18
Zhang N, Wang S, Li Y et al (2013) A selective inhibitor of Drp1, mdivi-1, acts against cerebral ischemia/reperfusion injury via an anti-apoptotic pathway in rats. Neurosci Lett. https://doi.org/10.1016/j.neulet.2012.12.049
Chiang Y, Schneiderman MH, Vishwanatha JK (1993) Annexin II expression is regulated during mammalian cell cycle. Cancer Res 53:6017–21
Sharma MC (2019) Annexin A2 (ANX A2): an emerging biomarker and potential therapeutic target for aggressive cancers. Int J Cancer 144:2074–2081. https://doi.org/10.1002/ijc.31817
Kim J, Hajjar KA (2002) Annexin II: a plasminogen-plasminogen activator co-receptor. Front Biosci 7(2):341–348. https://doi.org/10.2741/kim
Jiang Y, Fan X, Yu Z et al (2015) Low dose tPA plus annexin A2 combination attenuates tPA delayed treatment-associated hemorrhage and improves recovery in rat embolic focal stroke. Neurosci Lett. https://doi.org/10.1016/j.neulet.2015.06.050
Wang X, Wang X, Fan X et al (2014) Effects of tissue plasminogen activator and annexin A2 combination therapy on long-term neurological outcomes of rat focal embolic stroke. Stroke. https://doi.org/10.1161/STROKEAHA.113.003823
Jiang Y, Fan X, Yu Z et al (2015) Combination low-dose tissue-type plasminogen activator plus annexin A2 for improving thrombolytic stroke therapy. Front Cell Neurosci 9:397. https://doi.org/10.3389/fncel.2015.00397
Fan X, Jiang Y, Yu Z et al (2017) Annexin A2 plus low-dose tissue plasminogen activator combination attenuates cerebrovascular dysfunction after focal embolic stroke of rats. Transl Stroke Res. https://doi.org/10.1007/s12975-017-0542-6
Onwuekwe I, Ezeala-Adikaibe B (2012) Ischemic stroke and neuroprotection. Ann Med Health Sci Res. https://doi.org/10.4103/2141-9248.105669
Bartosik-Psujek H, Belniak E, Stelmasiak Z (2003) Markers of inflammation in cerebral ischemia. NeurolSci 24:279–280. https://doi.org/10.1007/s10072-003-0156-5
Irmak S, Oliveira-Ferrer L, Singer BB et al (2009) Pro-angiogenic properties of orosomucoid (ORM). Exp Cell Res 315:3201–3209. https://doi.org/10.1016/j.yexcr.2009.07.024
Kandregula CAB, Smilin Bell Aseervatham G, Bentley GT, Kandasamy R (2016) Alpha-1 antitrypsin: associated diseases and therapeutic uses. Clin Chim Acta 459:109–116. https://doi.org/10.1016/j.cca.2016.05.028
Acknowledgements
Under a collaborative agreement, Affymetrix – ThermoFisher Scientific kindly supplied the gene expression microarrays used in this study without being involved in any part of the study development.
We thank Dr. Pilar Delgado for providing blood samples from the ISSYS cohort.
Microarray sample processing and hybridization were carried out at the High Technology Unit (UAT) from VHIR. We acknowledge the Molecular Diagnosis Platform staff for their contribution to this project’s development. Bioinformatics analysis has been carried out in the Statistics and Bioinformatics Unit (UEB) at Vall d’Hebron Research Institute (VHIR)
Funding
This work has been funded by Instituto de Salud Carlos III (PI15/00354, PI18/00804), MINECO (MTM2015-64465-C2-1R) and GRBIO (2014-SGR-464) and co-financed by the European Regional Development Fund (FEDER). Neurovascular Research Laboratory takes part in the Spanish stroke research network INVICTUS + (RD16/0019/0021). L.R is supported by a pre-doctoral fellowship from the Instituto de Salud Carlos III (IFI17/00012).
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The whole study was approved by the Ethics Committee of Vall d’Hebron Hospital (PR[HG]85/04, PR[HG]89/03 and PR[IR]87/10). Written informed consent was acquired from all participants or relatives in agreement with the Declaration of Helsinki.
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Laura Ramiro and Teresa García-Berrocoso contributed equally to this work.
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Ramiro, L., García-Berrocoso, T., Briansó, F. et al. Integrative Multi-omics Analysis to Characterize Human Brain Ischemia. Mol Neurobiol 58, 4107–4121 (2021). https://doi.org/10.1007/s12035-021-02401-1
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DOI: https://doi.org/10.1007/s12035-021-02401-1