| Literature DB >> 33066304 |
Ciro De Luca1, Assunta Virtuoso1,2, Nicola Maggio3,4, Sara Izzo5, Michele Papa1,6, Anna Maria Colangelo6,7.
Abstract
Stroke is a major challenge in modern medicine and understanding the role of the neuronal extracellular matrix (NECM) in its pathophysiology is fundamental for promoting brain repair. Currently, stroke research is focused on the neurovascular unit (NVU). Impairment of the NVU leads to neuronal loss through post-ischemic and reperfusion injuries, as well as coagulatory and inflammatory processes. The ictal core is produced in a few minutes by the high metabolic demand of the central nervous system. Uncontrolled or prolonged inflammatory response is characterized by leukocyte infiltration of the injured site that is limited by astroglial reaction. The metabolic failure reshapes the NECM through matrix metalloproteinases (MMPs) and novel deposition of structural proteins continues within months of the acute event. These maladaptive reparative processes are responsible for the neurological clinical phenotype. In this review, we aim to provide a systems biology approach to stroke pathophysiology, relating the injury to the NVU with the pervasive metabolic failure, inflammatory response and modifications of the NECM. The available data will be used to build a protein-protein interaction (PPI) map starting with 38 proteins involved in stroke pathophysiology, taking into account the timeline of damage and the co-expression scores of their RNA patterns The application of the proposed network could lead to a more accurate design of translational experiments aiming at improving both the therapy and the rehabilitation processes.Entities:
Keywords: maladaptive plasticity; matrix metalloproteinases; neuronal extracellular matrix; neurovascular unit; stroke; systems biology
Mesh:
Substances:
Year: 2020 PMID: 33066304 PMCID: PMC7589675 DOI: 10.3390/ijms21207554
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1Extracellular/intracellular network analysis within the damaged tissue (string-db.org platform) for protein–protein interactions (PPIs). The proteins related to stroke pathophysiology are clustered and PPIs are defined. This map is difficult to use for the design of further experiments since the interference with any node will apply to every edge at the same time, an atypical phenomenon in biological systems. The map is based on a critical analysis of recent literature (see Table 1 for selected experimental studies). The database query with 38 proteins showed 203 edges with an average node degree of 10.7 (expected number of edges 35, average local clustering coefficient 0.575; PPI enrichment p-value < 10−16). For further details, the map is accessible at this link: [37].
Figure 2Bidimensional matrix of RNA expression patterns and protein co-regulation. The 38 proteins proposed in the network map of Figure 1 are listed in a bidimensional matrix to show the co-expression scores (from 0 to 1 on the color visual scale) based on RNA expression patterns and protein co-regulation (string-db.org platform). The regulatory functions are subjected to a precise time-coupled expression. For further details, the matrix is accessible at this link: [38].
Figure 3Protein–protein interaction (PPI) maps generated considering the timescale of stroke. Clustering is dependent on time-specific activation, avoiding a direct interaction analysis (presented in Figure 1) and considering the collected literature data and co-expression matrix presented in Figure 2. (A) Alarmins are released and constitutive matrix metalloproteinase 2 (MMP2) and purinergic receptors are promptly activated following stroke. (B) The inflammasome activates the neuro-immune pathway of cytokines, adhesion molecules, protease receptors and inducible MMPs. (C) Leukocytes arrive hours after stroke, act with their enzymes and remain for several days and, while glial activation proceeds, a novel extracellular matrix (NECM) is secreted and reactive gliosis regulates the neurotrophin concentration and receptors.
Synoptic table of the main representative experimental studies analyzed to select 38 proteins used as input for the string database (Figure 1 and Figure 2). NIH: neuro-immune hemostasis, MMPs: matrix metalloproteinases, ADAMTS: a disintegrin and metalloproteinase with thrombospondin motifs, TIMP: tissue inhibitor of metalloproteinases, CSPGs: chondroitin sulfate proteoglycan, HSPGs: heparan sulfate proteoglycan, TnC: tenascin-C, TnR: tenascin-R, tPA: tissue plasminogen activator, GFAP: glial fibrillary acidic protein, ITGAM: integrin subunit alpha M, HMGB1: high-mobility group box 1 protein, TLR: Toll-like receptor, NFκB: nuclear factor-κB.
| Research Paper | Analyzed Pathways | Selected Proteins | Model |
|---|---|---|---|
| Qiu J et al., 2010 [ | Alarmins—MMPs—NIH | HMGB1, TLR4, MMP9 | Mouse, in vivo and in vitro |
| Gu BJ and Wiley JS, 2006 [ | Alarmins—MMPs—NIH | MMP9, P2X7R, TIMP-1 | Human, in vitro |
| Gao, H et al., 2011 [ | Alarmins—NIH | HMGB1, ITGAM, NFkB | Mouse, in vitro |
| Choi MS et al., 2010 [ | Alarmins—MMPs—NIH | MMP9, P2YR | Rat, in vitro |
| Manaenko A et al., 2010 [ | Alarmins—NIH | HSP70, IL1, TNFα, collagen | Mouse, in vivo |
| Malik R et al., 2018 [ | NIH—MMPs | NOS, COL4A | Human, clinical |
| Clausen BH et al., 2008 [ | NIH—Glial activation | IL1, TNFα, ITGAM | Mouse, in vivo |
| Botchkina GI et al., 1997 [ | NIH—Neurotrophins | TNFα, p75NTR | Rat, in vivo |
| Atangana E et al., 2017 [ | NIH—Glial activation | P-selectin, Iba1, ITGAM | Mice, in vivo |
| Weisenburger-Lile D et al., 2019 [ | NIH—Glial activation | Neutrophil elastase, ITGAM | Human, clinical |
| Stubbe T et al., 2013 [ | NIH—Glial activation | ITGAM, Iba1 | Mouse, in vivo |
| Choucry AM et al., 2019 [ | NIH—Neurotrophins | p75NTR, TrkA | Rat, in vivo |
| Candelario-jalil E et al., 2011 [ | NIH—MMPs | MMP2, MMP9 | Human, clinical |
| Cheng T et al., 2006 [ | NIH—MMPs | MMP2, MMP9, PAR1, Thrombin, tPA, NFkB | Mouse in vivo, Human in vitro |
| del Zoppo GJ et al., 2012 [ | NIH—MMPs—Glial activation | MMP2, MMP9, GFAP, ITGAM, COL4A, HSPGs | NonHuman Primate in vivo, mouse in vitro |
| Mishiro K et al., 2012 [ | NIH—MMPs | MMP9, tPA | Mouse, in vivo |
| Chen W et al., 2009 [ | MMPs—Glial activation | MMP2, MMP9, TIMP1, TIMP2, GFAP | Rat, in vivo |
| Ye H et al., 2015 [ | NIH—Glial activation | S100B | Human, meta-analysis |
| Maysami S et al., 2015 [ | NIH—Glial activation | CXCL1, CCL3, Iba1 | Mice, in vivo |
| Quattromani MJ et al., 2018a,b [ | NIH—MMPs | MMP2, MMP9, ADAMTS4, TIMP1, tPA, CSPGs, TnC, TnR | Rat and Mouse, in vivo |
| Matsumoto H et al., 2008 [ | NIH—Glial activation | Iba1, NG2, ITGAM | Rat, in vivo |
| Carmichael ST et al., 2005 [ | MMPs | CSPGs | Rat, in vivo |