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Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

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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

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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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    Authors and Affiliations

    1. Neurovascular Research Laboratory, Vall D’Hebron Institute of Research (VHIR), Hospital Vall D’Hebron, Universitat Autònoma de Barcelona, Pg. Vall d’Hebron 119-129, 08035, Barcelona, Spain

      Laura Ramiro, Teresa García-Berrocoso, Leire Goicoechea, Alba Simats, Víctor Llombart & Joan Montaner

    2. CSIC/UAB Proteomics Laboratory, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC/IDIBAPS), Barcelona, Spain

      Teresa García-Berrocoso

    3. Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Barcelona, Spain

      Ferran Briansó

    4. Statistics and Bioinformatics Unit, Vall D’Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain

      Ricardo Gonzalo & Alex Sánchez-Pla

    5. High Technology Unit, Vall D’Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain

      Ricardo Gonzalo

    6. Proteomics Core Facility, Faculty of Medicine, University Medical Center, University of Geneva, Geneva, Switzerland

      Alexandre Hainard

    7. Neuropathology, Pathology Department, Hospital Vall D’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain

      Elena Martínez-Saez

    8. Proteomics Laboratory, Vall d’Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain

      Francesc Canals

    9. Translational Biomarker Group, Internal Medicine Department, University Medical Center, University of Geneva, Geneva, Switzerland

      Jean-Charles Sanchez

    10. Institute de Biomedicine of Seville, IBiS/Hospital Universitario Virgen del Rocío/CSIC/University of Seville & Department of Neurology, Hospital Universitario Virgen Macarena, Seville, Spain

      Joan Montaner

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    1. Laura Ramiro
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    Correspondence to Teresa García-Berrocoso.

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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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    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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