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Inflammatory, Regulatory, and Autophagy Co-Expression Modules

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Inflammatory, Regulatory, and Autophagy Co-Expression Modules Durocher et al. Journal of Neuroinflammation (2019) 16:56 https://doi.org/10.1186/s12974-019-1433-4 RESEARCH Open Access Inflammatory, regulatory, and autophagy co-expression modules and hub genes underlie the peripheral immune response to human intracerebral hemorrhage Marc Durocher1, Bradley P. Ander1, Glen Jickling1, Farah Hamade1, Heather Hull1, Bodie Knepp1, Da Zhi Liu1, Xinhua Zhan1, Anh Tran1, Xiyuan Cheng1, Kwan Ng1, Alan Yee1, Frank R. Sharp1 and Boryana Stamova1,2* Abstract Background: Intracerebral hemorrhage (ICH) has a high morbidity and mortality. The peripheral immune system and cross-talk between peripheral blood and brain have been implicated in the ICH immune response. Thus, we delineated the gene networks associated with human ICH in the peripheral blood transcriptome. We also compared the differentially expressed genes in blood following ICH to a prior human study of perihematomal brain tissue. Methods: We performed peripheral blood whole-transcriptome analysis of ICH and matched vascular risk factor control subjects (n = 66). Gene co-expression network analysis identified groups of co-expressed genes (modules) associated with ICH and their most interconnected genes (hubs). Mixed-effects regression identified differentially expressed genes in ICH compared to controls. Results: Of seven ICH-associated modules, six were enriched with cell-specific genes: one neutrophil module, one neutrophil plus monocyte module, one T cell module, one Natural Killer cell module, and two erythroblast modules. The neutrophil/monocyte modules were enriched in inflammatory/immune pathways; the T cell module in T cell receptor signaling genes; and the Natural Killer cell module in genes regulating alternative splicing, epigenetic, and post-translational modifications. One erythroblast module was enriched in autophagy pathways implicated in experimental ICH, and NRF2 signaling implicated in hematoma clearance. Many hub genes or module members, such as IARS, mTOR, S1PR1, LCK, FYN, SKAP1, ITK, AMBRA1, NLRC4, IL6R, IL17RA, GAB2, MXD1, PIK3CD, NUMB, MAPK14, DDX24, EVL, TDP1, ATG3, WDFY3, GSK3B, STAT3, STX3, CSF3R, PIP4K2A, ANXA3, DGAT2, LRP10, FLOT2, ANK1, CR1, SLC4A1, and DYSF, have been implicated in neuroinflammation, cell death, transcriptional regulation, and some as experimental ICH therapeutic targets. Gene-level analysis revealed 1225 genes (FDR p < 0.05, fold-change > |1.2|) have altered expression in ICH in peripheral blood. There was significant overlap of the 1225 genes with dysregulated genes in human perihematomal brain tissue (p =7×10−3). Overlapping genes were enriched for neutrophil-specific genes (p =6.4×10−08) involved in interleukin, neuroinflammation, apoptosis, and PPAR signaling. (Continued on next page) * Correspondence: [email protected] 1Department of Neurology, University of California Davis School of Medicine, Sacramento, CA 95817, USA 2MIND Institute Biosciences Building, 2805 50th Street, Sacramento, CA 95817, USA © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Durocher et al. Journal of Neuroinflammation (2019) 16:56 Page 2 of 21 (Continued from previous page) Conclusions: This study delineates key processes underlying ICH pathophysiology, complements experimental ICH findings, and the hub genes significantly expand the list of novel ICH therapeutic targets. The overlap between blood and brain gene responses underscores the importance of examining blood-brain interactions in human ICH. Keywords: Intracerebral hemorrhage, ICH, NRF2, Autophagy, Hematoma, Hematoma clearance, Src kinase inhibitors, Gene expression, Co-expression networks, Gene networks Introduction animal ICH models. The findings also highlight candidate Approximately 795,000 strokes occur in the USA each genes that influence major processes underlying the re- year [1]. Stroke remains a leading cause of death and sponse to ICH and parallel responding genes/pathways in disability [2, 3]. Although primary non-traumatic intra- blood and brain, underscoring the importance of further cerebral hemorrhage (ICH) only accounts for 10–15% of studies of the peripheral blood-brain immune strokes [1, 4], its mortality rate of 59% at 1 year [4]is communication. much higher than that for ischemic stroke (IS) (14%) [5]. Many pre-clinical and a few clinical studies implicate Materials and methods neuroinflammation as contributing to neurological injury Study subjects produced by ICH [3, 6, 7]. Following ICH, a complex cas- Sixty-six male (M) and female (F) subjects with intrace- cade of local and systemic immune responses occurs lead- rebral hemorrhage (ICH, n = 33, 24 M/9F) and vascular ing to blood-brain barrier disruption, cerebral edema, and risk factor (VRF)-matched control subjects (CTRL, n = cell damage/death followed by hematoma removal and 33, 24 M/9F) were recruited from the Universities of brain repair [7–11]. Since there is communication between California at Davis and San Francisco as well as the Uni- the peripheral immune system and the central nervous sys- versity of Alberta, Canada. Procedures were approved by tem through afferent and efferent trafficking of cells and the IRBs at participating universities and written in- molecules, the peripheral immune system is a key driver of formed consent was obtained from participants or their damage and repair following ICH [7, 12, 13]. Peripheral proxy. ICH was diagnosed by board-certified neurolo- leukocytic infiltration into the brain is seen early following gists based upon histories, exams, and magnetic reson- ICH [7]. This involves chemokines, cytokines, matrix me- ance imaging (MRI) and/or computed tomographic (CT) talloproteinases (MMPs), and interactions between circulat- brain scan [16]. Subjects were matched for age, race, sex, ing leukocytes and vascular endothelial cells, with different and VRFs, which included hypertension, diabetes melli- cell types employing common as well as unique pathways tus, hyperlipidemia, and smoking status. Exclusion cri- [14]. Thus, it is important to study both the local and sys- teria were previous stroke (for CTRLs) and ischemic temic immune response to human ICH. stroke with hemorrhagic transformation. We examined the systemic peripheral immune response to human ICH and how it compares to the local human Blood collection, RNA isolation perihematomal brain tissue response. We investigated the Blood was collected in PAXgene tubes and RNA isolated peripheral blood gene co-expression networks and their as previously described [17, 18]. There was a single most interconnected genes (hubs) following ICH, identi- blood draw per subject. Time after symptom onset for fied differential gene-level RNA expression and the poten- ICH varied from 4.2 to 124.3 h. tially affected pathways, and compared the peripheral blood response to the one in previously published human Arrays and processing peri-hematoma ICH brain [15]. The hub genes we identi- RNA was processed on the GeneChip® Human Tran- fied have been implicated as major regulators of the im- scriptome Array (HTA) 2.0 (Affymetrix, Santa Clara, mune response including the inflammasome, autophagy, CA) which examines the coding and non-coding tran- and transcriptional and epigenetic regulation. Some of the scriptome. Raw expression values for each gene were hub genes have also been successfully tested in animal saved in Affymetrix.CEL and Affymetrix.DAT files. Using ICH models. Notably, there was a significant overlap be- Affymetrix Power Tools (APT), the. CEL transformer tween genes differentially expressed in this human ICH applied GC correction (GCCN) and single space trans- peripheral blood study compared to a prior human study formation (SST) to the HTA.CEL files. GCCN-SST of perihematomal brain tissue [15]. These findings will transformations were conducted through a command further our understanding of the biological processes oc- line using APT 1.18.0 in “batch” mode. GCCN-SST curring following ICH, as well as provide human data con- transformed .CEL files were uploaded into Partek Flow firming targets that have been tested or will be tested in software (Partek Inc., St. Louis, MO) and probe sets Durocher et al. Journal of Neuroinflammation (2019) 16:56 Page 3 of 21 mapped to the Human Genome hg19 using the STAR (false-discovery rate) p value < 0.05 and a fold change 2.4.1d aligner. Nominal read coverage depth was defined (FC) > |1.2| for ICH vs CTRL were considered as 30 million and default mapping parameters were significant. used. RPKM normalization was performed. Cell-specific gene involvement Weighted gene co-expression network construction and To identify modules of co-expressed genes enriched with analysis blood cell type-specific genes, we overlapped the gene The 56,638 genes (mRNA and ncRNA) present in ICH list of each module with lists of blood cell type-specific and CTRL data were filtered by removing genes with a genes [24, 25]. We calculated significant overlaps of maximum expression across all samples of < 5, leaving genes using hypergeometric
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