Neuron-specific transcriptomic signatures indicate neuroinflammation and altered neuronal activity in ASD temporal cortex.

Autism spectrum disorder (ASD) is a highly heterogeneous disorder, yet transcriptomic profiling of bulk brain tissue has identified substantial convergence among dysregulated genes and pathways in ASD. However, this approach lacks cell-specific resolution. We performed comprehensive transcriptomic analyses on bulk tissue and laser-capture microdissected (LCM) neurons from 59 postmortem human brains (27 ASD and 32 controls) in the superior temporal gyrus (STG) of individuals ranging from 2 to 73 years of age. In bulk tissue, synaptic signaling, heat shock protein-related pathways, and RNA splicing were significantly altered in ASD. There was age-dependent dysregulation of genes involved in gamma aminobutyric acid (GABA) (GAD1 and GAD2) and glutamate (SLC38A1) signaling pathways. In LCM neurons, AP-1-mediated neuroinflammation and insulin/IGF-1 signaling pathways were upregulated in ASD, while mitochondrial function, ribosome, and spliceosome components were downregulated. GABA synthesizing enzymes GAD1 and GAD2 were both downregulated in ASD neurons. Mechanistic modeling suggested a direct link between inflammation and ASD in neurons, and prioritized inflammation-associated genes for future study. Alterations in small nucleolar RNAs (snoRNAs) associated with splicing events suggested interplay between snoRNA dysregulation and splicing disruption in neurons of individuals with ASD. Our findings supported the fundamental hypothesis of altered neuronal communication in ASD, demonstrated that inflammation was elevated at least in part in ASD neurons, and may reveal windows of opportunity for biotherapeutics to target the trajectory of gene expression and clinical manifestation of ASD throughout the human lifespan.

Ischemic Stroke with Comorbid Cancer Has Specific miRNA-mRNA Networks in Blood That Vary by Ischemic Stroke Mechanism.

OBJECTIVE: Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. METHODS: We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor-matched controls (n = 30). mRNA-Seq and miRNA-Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA-mRNA regulatory pairs. Network-level analyses identified stroke etiology-specific responses in CS. RESULTS: A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA-mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15-gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15-gene panel correctly identified etiologies for 13 of 13 CS-cardioembolic and 11 of 11 CS-noncardioembolic participants upon cross-validation; 11 of 16 CS-cryptogenic participants were predicted cardioembolic. INTERPRETATION: We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024.

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke.

BACKGROUND: After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy. METHODS: The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke. RESULTS: Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood. CONCLUSIONS: Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

Early peripheral blood gene expression associated with good and poor 90-day ischemic stroke outcomes.

BACKGROUND: This study identified early immune gene responses in peripheral blood associated with 90-day ischemic stroke (IS) outcomes. METHODS: Peripheral blood samples from the CLEAR trial IS patients at ≤ 3 h, 5 h, and 24 h after stroke were compared to vascular risk factor matched controls. Whole-transcriptome analyses identified genes and networks associated with 90-day IS outcome assessed using the modified Rankin Scale (mRS) and the NIH Stroke Scale (NIHSS). RESULTS: The expression of 467, 526, and 571 genes measured at ≤ 3, 5 and 24 h after IS, respectively, were associated with poor 90-day mRS outcome (mRS ≥ 3), while 49, 100 and 35 genes at ≤ 3, 5 and 24 h after IS were associated with good mRS 90-day outcome (mRS ≤ 2). Poor outcomes were associated with up-regulated genes or pathways such as IL-6, IL-7, IL-1, STAT3, S100A12, acute phase response, P38/MAPK, FGF, TGFA, MMP9, NF-kB, Toll-like receptor, iNOS, and PI3K/AKT. There were 94 probe sets shared for poor outcomes vs. controls at all three time-points that correlated with 90-day mRS; 13 probe sets were shared for good outcomes vs. controls at all three time-points; and 46 probe sets were shared for poor vs. good outcomes at all three time-points that correlated with 90-day mRS. Weighted Gene Co-Expression Network Analysis (WGCNA) revealed modules significantly associated with 90-day outcome for mRS and NIHSS. Poor outcome modules were enriched with up-regulated neutrophil genes and with down-regulated T cell, B cell and monocyte-specific genes; and good outcome modules were associated with erythroblasts and megakaryocytes. Finally, genes identified by genome-wide association studies (GWAS) to contain significant stroke risk loci or loci associated with stroke outcome including ATP2B, GRK5, SH3PXD2A, CENPQ, HOXC4, HDAC9, BNC2, PTPN11, PIK3CG, CDK6, and PDE4DIP were significantly differentially expressed as a function of stroke outcome in the current study. CONCLUSIONS: This study suggests the immune response after stroke may impact functional outcomes and that some of the early post-stroke gene expression markers associated with outcome could be useful for predicting outcomes and could be targets for improving outcomes.

Progression of cerebral white matter hyperintensities is related to leucocyte gene expression.

Cerebral white matter hyperintensities are an important contributor to ageing brain pathology. Progression in white matter hyperintensity volume is associated with cognitive decline and gait impairment. Understanding the factors associated with white matter hyperintensity progression provides insight into pathogenesis and may identify novel treatment targets to improve cognitive health. We postulated that the immune system interaction with cerebral vessels and tissue may be associated with disease progression, and thus evaluated the relationship of blood leucocyte gene expression to progression of cerebral white matter hyperintensities. A brain MRI was obtained at baseline in 166 patients assessed for a cognitive complaint, and then repeated at regular intervals over a median of 5.9 years (interquartile range 3.5-8.2 years). White matter hyperintensity volumes were measured by semi-automated segmentation and percentage change in white matter hyperintensity per year calculated. A venous blood sample obtained at baseline was used to measure whole-genome expression by RNA sequencing. The relationship between change in white matter hyperintensity volumes over time and baseline leucocyte gene expression was analysed. The mean age was 77.8 (SD 7.5) years and 60.2% of participants were female. The median white matter hyperintensity volume was 13.4 ml (SD 17.4 ml). The mean change in white matter hyperintensity volume was 12% per year. Patients were divided in quartiles by percentage change in white matter hyperintensity volume, which was: -3.5% per year in quartile 1, 7.4% per year in quartile 2, 11.7% in quartile 3 and 33.6% per year in quartile 4. There were 148 genes associated with changing white matter hyperintensity volumes over time (P < 0.05 r > |0.2|). Genes and pathways identified have roles in endothelial dysfunction, extracellular matrix remodelling, altered remyelination, inflammation and response to ischaemia. ADAM8, CFD, EPHB4, FPR2, Wnt-B-catenin, focal adhesion kinase and SIGLEC1 were among the identified genes. The progression of white matter hyperintensity volumes over time is associated with genes involved in endothelial dysfunction, extracellular matrix remodelling, altered remyelination, inflammation and response to ischaemia. Further studies are needed to evaluate the role of peripheral inflammation in relation to rate of white matter hyperintensity progression and the contribution to cognitive decline.

Aging Immune System in Acute Ischemic Stroke: A Transcriptomic Analysis.

[Figure: see text].

mRNA Expression Profiles from Whole Blood Associated with Vasospasm in Patients with Subarachnoid Hemorrhage.

BACKGROUND: Though there are many biomarker studies of plasma and serum in patients with aneurysmal subarachnoid hemorrhage (SAH), few have examined blood cells that might contribute to vasospasm. In this study, we evaluated inflammatory and prothrombotic pathways by examining mRNA expression in whole blood of SAH patients with and without vasospasm. METHODS: Adult SAH patients with vasospasm (n = 29) and without vasospasm (n = 21) were matched for sex, race/ethnicity, and aneurysm treatment method. Diagnosis of vasospasm was made by angiography. mRNA expression was measured by Affymetrix Human Exon 1.0 ST Arrays. SAH patients with vasospasm were compared to those without vasospasm by ANCOVA to identify differential gene, exon, and alternatively spliced transcript expression. Analyses were adjusted for age, batch, and time of blood draw after SAH. RESULTS: At the gene level, there were 259 differentially expressed genes between SAH patients with vasospasm compared to patients without (false discovery rate < 0.05, |fold change| ≥ 1.2). At the exon level, 1210 exons representing 1093 genes were differentially regulated between the two groups (P < 0.005, ≥ 1.2 |fold change|). Principal components analysis segregated SAH patients with and without vasospasm. Signaling pathways for the 1093 vasospasm-related genes included adrenergic, P2Y, ET-1, NO, sildenafil, renin-angiotensin, thrombin, CCR3, CXCR4, MIF, fMLP, PKA, PKC, CRH, PPARα/RXRα, and calcium. Genes predicted to be alternatively spliced included IL23A, RSU1, PAQR6, and TRIP6. CONCLUSIONS: This is the first study to demonstrate that mRNA expression in whole blood distinguishes SAH patients with vasospasm from those without vasospasm and supports a role of coagulation and immune systems in vasospasm.

Cancer-Related Ischemic Stroke Has a Distinct Blood mRNA Expression Profile.

Background and Purpose- Comorbid cancer is common in patients with acute ischemic stroke (AIS). As blood mRNA profiles can distinguish AIS mechanisms, we hypothesized that cancer-related AIS would have a distinctive gene expression profile. Methods- We evaluated 4 groups of 10 subjects prospectively enrolled at 3 centers from 2009 to 2018. This included the group of interest with active solid tumor cancer and AIS and 3 control groups with active cancer only, AIS only, or vascular risk factors only. Subjects in the AIS-only and cancer-only groups were matched to subjects in the cancer-stroke group by age, sex, and cancer type (if applicable). Subjects in the vascular risk factor group were matched to subjects in the cancer-stroke and stroke-only groups by age, sex, and vascular risk factors. Blood was drawn 72 to 120 hours after stroke. Total RNA was processed using 3' mRNA sequencing. ANOVA and Fisher least significant difference contrast methods were used to estimate differential gene expression between groups. Results- In the cancer-stroke group, 50% of strokes were cryptogenic. All groups had differentially expressed genes that could distinguish among them. Comparing the cancer-stroke group to the stroke-only group and after accounting for cancer-only genes, 438 genes were differentially expressed, including upregulation of multiple genes/pathways implicated in autophagy signaling, immunity/inflammation, and gene regulation, including IL (interleukin)-1, interferon, relaxin, mammalian target of rapamycin signaling, SQSTMI1 (sequestosome-1), and CREB1 (cAMP response element binding protein-1). Conclusions- This study provides evidence for a distinctive molecular signature in blood mRNA expression profiles of patients with cancer-related AIS. Future studies should evaluate whether blood mRNA can predict detection of occult cancer in patients with AIS. Clinical Trial Registration- URL: https://clinicaltrials.gov. Unique identifier: NCT02604667.

Inflammatory, regulatory, and autophagy co-expression modules and hub genes underlie the peripheral immune response to human intracerebral hemorrhage.

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. 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.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis.

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

Altered Expression of Long Noncoding RNAs in Blood After Ischemic Stroke and Proximity to Putative Stroke Risk Loci.

BACKGROUND AND PURPOSE: Although peripheral blood mRNA and micro-RNA change after ischemic stroke, any role for long noncoding RNA (lncRNA), which comprise most of the genome and have been implicated in various diseases, is unknown. Thus, we hypothesized that lncRNA expression also changes after stroke. METHODS: lncRNA expression was assessed in 266 whole-blood RNA samples drawn once per individual from patients with ischemic stroke and matched with vascular risk factor controls. Differential lncRNA expression was assessed by ANCOVA (P<0.005; fold change>|1.2|), principal components analysis, and hierarchical clustering on a derivation set (n=176) and confirmed on a validation set (n=90). Poststroke temporal lncRNA expression changes were assessed using ANCOVA with confounding factor correction (P<0.005; partial correlation with time since event >|0.4|). Because sexual dimorphism exists in stroke, analyses were performed for each sex separately. RESULTS: A total of 299 lncRNAs were differentially expressed between stroke and control males, whereas 97 lncRNAs were differentially expressed between stroke and control females. Significant changes of lncRNA expression with time after stroke were detected for 49 lncRNAs in men and 31 lncRNAs in women. Some differentially expressed lncRNAs mapped close to genomic locations of previously identified putative stroke-risk genes, including lipoprotein, lipoprotein(a)-like 2, ABO (transferase A, α1-3-N-acetylgalactosaminyltransferase; transferase B, α1-3-galactosyltransferase) blood group, prostaglandin 12 synthase, and α-adducins. CONCLUSIONS: This study provides evidence of altered and sexually dimorphic lncRNA expression in peripheral blood of patients with stroke compared with that of controls and suggests that lncRNAs have potential for stroke biomarker development. Some regulated lncRNA could regulate some previously identified putative stroke-risk genes.

RNA in blood is altered prior to hemorrhagic transformation in ischemic stroke.

OBJECTIVE: Hemorrhagic transformation (HT) is a major complication of ischemic stroke that worsens outcomes and increases mortality. Disruption of the blood-brain barrier is a central feature of HT pathogenesis, and leukocytes may contribute to this process. We sought to determine whether ischemic strokes that develop HT have differences in RNA expression in blood within 3 hours of stroke onset prior to treatment with thrombolytic therapy. METHODS: Stroke patient blood samples were obtained prior to treatment with thrombolysis, and leukocyte RNA was assessed by microarray analysis. Strokes that developed HT (n = 11) were compared to strokes without HT (n = 33) and controls (n = 14). Genes were identified (corrected p < 0.05, fold change ≥|1.2|), and functional analysis was performed. RNA prediction of HT in stroke was evaluated using cross-validation, and in a second stroke cohort (n = 52). RESULTS: Ischemic strokes that developed HT had differential expression of 29 genes in circulating leukocytes prior to treatment with thrombolytic therapy. A panel of 6 genes could predict strokes that later developed HT with 80% sensitivity and 70.2% specificity. Key pathways involved in HT of human stroke are described, including amphiregulin, a growth factor that regulates matrix metalloproteinase-9; a shift in transforming growth factor-ß signaling involving SMAD4, INPP5D, and IRAK3; and a disruption of coagulation factors V and VIII. INTERPRETATION: Identified genes correspond to differences in inflammation and coagulation that may predispose to HT in ischemic stroke. Given the adverse impact of HT on stroke outcomes, further evaluation of the identified genes and pathways is warranted to determine their potential as therapeutic targets to reduce HT and as markers of HT risk.

Distinctive RNA expression profiles in blood associated with Alzheimer disease after accounting for white matter hyperintensities.

BACKGROUND: Defining the RNA transcriptome in Alzheimer Disease (AD) will help understand the disease mechanisms and provide biomarkers. Though the AD blood transcriptome has been studied, effects of white matter hyperintensities (WMH) were not considered. This study investigated the AD blood transcriptome and accounted for WMH. METHODS: RNA from whole blood was processed on whole-genome microarrays. RESULTS: A total of 293 probe sets were differentially expressed in AD versus controls, 5 of which were significant for WMH status. The 288 AD-specific probe sets classified subjects with 87.5% sensitivity and 90.5% specificity. They represented 188 genes of which 29 have been reported in prior AD blood and 89 in AD brain studies. Regulated blood genes included MMP9, MME (Neprilysin), TGFß1, CA4, OCLN, ATM, TGM3, IGFR2, NOV, RNF213, BMX, LRRN1, CAMK2G, INSR, CTSD, SORCS1, SORL1, and TANC2. CONCLUSIONS: RNA expression is altered in AD blood irrespective of WMH status. Some genes are shared with AD brain.

Prediction of cardioembolic, arterial, and lacunar causes of cryptogenic stroke by gene expression and infarct location.

BACKGROUND AND PURPOSE: The cause of ischemic stroke remains unclear, or cryptogenic, in as many as 35% of patients with stroke. Not knowing the cause of stroke restricts optimal implementation of prevention therapy and limits stroke research. We demonstrate how gene expression profiles in blood can be used in conjunction with a measure of infarct location on neuroimaging to predict a probable cause in cryptogenic stroke. METHODS: The cause of cryptogenic stroke was predicted using previously described profiles of differentially expressed genes characteristic of patients with cardioembolic, arterial, and lacunar stroke. RNA was isolated from peripheral blood of 131 cryptogenic strokes and compared with profiles derived from 149 strokes of known cause. Each sample was run on Affymetrix U133 Plus 2.0 microarrays. Cause of cryptogenic stroke was predicted using gene expression in blood and infarct location. RESULTS: Cryptogenic strokes were predicted to be 58% cardioembolic, 18% arterial, 12% lacunar, and 12% unclear etiology. Cryptogenic stroke of predicted cardioembolic etiology had more prior myocardial infarction and higher CHA(2)DS(2)-VASc scores compared with stroke of predicted arterial etiology. Predicted lacunar strokes had higher systolic and diastolic blood pressures and lower National Institutes of Health Stroke Scale compared with predicted arterial and cardioembolic strokes. Cryptogenic strokes of unclear predicted etiology were less likely to have a prior transient ischemic attack or ischemic stroke. CONCLUSIONS: Gene expression in conjunction with a measure of infarct location can predict a probable cause in cryptogenic strokes. Predicted groups require further evaluation to determine whether relevant clinical, imaging, or therapeutic differences exist for each group.

Ischemic transient neurological events identified by immune response to cerebral ischemia.

BACKGROUND AND PURPOSE: Deciphering whether a transient neurological event (TNE) is of ischemic or nonischemic etiology can be challenging. Ischemia of cerebral tissue elicits an immune response in stroke and transient ischemic attack (TIA). This response, as detected by RNA expressed in immune cells, could potentially distinguish ischemic from nonischemic TNE. METHODS: Analysis of 208 TIAs, ischemic strokes, controls, and TNE was performed. RNA from blood was processed on microarrays. TIAs (n=26) and ischemic strokes (n=94) were compared with controls (n=44) to identify differentially expressed genes (false discovery rate <0.05, fold change ≥1.2). Genes common to TIA and stroke were used predict ischemia in TIA diffusion-weighted imaging-positive/minor stroke (n=17), nonischemic TNE (n=13), and TNE of unclear etiology (n=14). RESULTS: Seventy-four genes expressed in TIA were common to those in ischemic stroke. Functional pathways common to TIA and stroke related to activation of innate and adaptive immune systems, involving granulocytes and B cells. A prediction model using 26 of the 74 ischemia genes distinguished TIA and stroke subjects from control subjects with 89% sensitivity and specificity. In the validation cohort, 17 of 17 TIA diffusion-weighted imaging-positive/minor strokes were predicted to be ischemic, and 10 of 13 nonischemic TNE were predicted to be nonischemic. In TNE of unclear etiology, 71% were predicted to be ischemic. These subjects had higher ABCD(2) scores. CONCLUSIONS: A common molecular response to ischemia in TIA and stroke was identified, relating to activation of innate and adaptive immune systems. TNE of ischemic etiology was identified based on gene profiles that may be of clinical use once validated.

The X-chromosome has a different pattern of gene expression in women compared with men with ischemic stroke.

BACKGROUND AND PURPOSE: Differences in ischemic stroke between men and women have been mainly attributed to hormonal effects. However, sex differences in immune response to ischemia may exist. We hypothesized that differential expression of X-chromosome genes in blood immune cells contribute to differences between men and women with ischemic stroke. METHODS: RNA levels of 683 X-chromosome genes were measured on Affymetrix U133 Plus2.0 microarrays. Blood samples from patients with ischemic stroke were obtained at ≤ 3 hours, 5 hours, and 24 hours (n=61; 183 samples) after onset and compared with control subjects without symptomatic vascular diseases (n=109). Sex difference in X-chromosome gene expression was determined using analysis of covariance (false discovery rate ≤ 0.05, fold change ≥ 1.2). RESULTS: At ≤ 3, 5, and 24 hours after stroke, there were 37, 140, and 61 X-chromosome genes, respectively, that changed in women; and 23, 18, and 31 X-chromosome genes that changed in men. Female-specific genes were associated with post-translational modification, small-molecule biochemistry, and cell-cell signaling. Male-specific genes were associated with cellular movement, development, cell-trafficking, and cell death. Altered sex specific X-chromosome gene expression occurred in 2 genes known to be associated with human stroke, including galactosidase A and IDS, mutations of which result in Fabry disease and Hunter syndrome, respectively. CONCLUSIONS: There are differences in X-chromosome gene expression between men and women with ischemic stroke. Future studies are needed to decipher whether these differences are associated with sexually dimorphic immune response, repair or other mechanisms after stroke, or whether some of them represent risk determinants.

Profiles of lacunar and nonlacunar stroke.

OBJECTIVE: Determining which small deep infarcts (SDIs) are of lacunar, arterial, or cardioembolic etiology is challenging, but important in delivering optimal stroke prevention therapy. We sought to distinguish lacunar from nonlacunar causes of SDIs using a gene expression profile. METHODS: A total of 184 ischemic strokes were analyzed. Lacunar stroke was defined as a lacunar syndrome with infarction <15mm in a region supplied by penetrating arteries. RNA from blood was processed on whole genome microarrays. Genes differentially expressed between lacunar (n = 30) and nonlacunar strokes (n = 86) were identified (false discovery rate ≤ 0.05, fold change >|1.5|) and used to develop a prediction model. The model was evaluated by cross-validation and in a second test cohort (n = 36). The etiology of SDIs of unclear cause (SDIs ≥ 15mm or SDIs with potential embolic source) (n = 32) was predicted using the derived model. RESULTS: A 41-gene profile discriminated lacunar from nonlacunar stroke with >90% sensitivity and specificity. Of the 32 SDIs of unclear cause, 15 were predicted to be lacunar, and 17 were predicted to be nonlacunar. The identified profile represents differences in immune response between lacunar and nonlacunar stroke. INTERPRETATION: Profiles of differentially expressed genes can distinguish lacunar from nonlacunar stroke. SDIs of unclear cause were frequently predicted to be of nonlacunar etiology, suggesting that comprehensive workup of SDIs is important to identify potential cardioembolic and arterial causes. Further study is required to evaluate the gene profile in an independent cohort and determine the clinical and treatment implications of SDIs of predicted nonlacunar etiology.

Gene expression changes implicate specific peripheral immune responses to Deep and Lobar Intracerebral Hemorrhages in humans.

The peripheral immune system response to Intracerebral Hemorrhage (ICH) may differ with ICH in different brain locations. Thus, we investigated peripheral blood mRNA expression of Deep ICH, Lobar ICH, and vascular risk factor-matched control subjects (n = 59). Deep ICH subjects usually had hypertension. Some Lobar ICH subjects had cerebral amyloid angiopathy (CAA). Genes and gene networks in Deep ICH and Lobar ICH were compared to controls. We found 774 differentially expressed genes (DEGs) and 2 co-expressed gene modules associated with Deep ICH, and 441 DEGs and 5 modules associated with Lobar ICH. Pathway enrichment showed some common immune/inflammatory responses between locations including Autophagy, T Cell Receptor, Inflammasome, and Neuroinflammation Signaling. Th2, Interferon, GP6, and BEX2 Signaling were unique to Deep ICH. Necroptosis Signaling, Protein Ubiquitination, Amyloid Processing, and various RNA Processing terms were unique to Lobar ICH. Finding amyloid processing pathways in blood of Lobar ICH patients suggests peripheral immune cells may participate in processes leading to perivascular/vascular amyloid in CAA vessels and/or are involved in its removal. This study identifies distinct peripheral blood transcriptome architectures in Deep and Lobar ICH, emphasizes the need for considering location in ICH studies/clinical trials, and presents potential location-specific treatment targets.

Signatures of cardioembolic and large-vessel ischemic stroke.

OBJECTIVE: The cause of stroke remains unknown or cryptogenic in many patients. We sought to determine whether gene expression signatures in blood can distinguish between cardioembolic and large-vessel causes of stroke, and whether these profiles can predict stroke etiology in the cryptogenic group. METHODS: A total of 194 samples from 76 acute ischemic stroke patients were analyzed. RNA was isolated from blood and run on Affymetrix U133 Plus2.0 microarrays. Genes that distinguish large-vessel from cardioembolic stroke were determined at 3, 5, and 24 hours following stroke onset. Predictors were evaluated using cross-validation and a separate set of patients with known stroke subtype. The cause of cryptogenic stroke was predicted based on a model developed from strokes of known cause and identified predictors. RESULTS: A 40-gene profile differentiated cardioembolic stroke from large-vessel stroke with >95% sensitivity and specificity. A separate 37-gene profile differentiated cardioembolic stroke due to atrial fibrillation from nonatrial fibrillation causes with >90% sensitivity and specificity. The identified genes elucidate differences in inflammation between stroke subtypes. When applied to patients with cryptogenic stroke, 17% are predicted to be large-vessel and 41% to be cardioembolic stroke. Of the cryptogenic strokes predicted to be cardioembolic, 27% were predicted to have atrial fibrillation. INTERPRETATION: Gene expression signatures distinguish cardioembolic from large-vessel causes of ischemic stroke. These gene profiles may add valuable diagnostic information in the management of patients with stroke of unknown etiology though they need to be validated in future independent, large studies.

Bacterial lipopolysaccharide is associated with stroke.

We aimed to determine if plasma levels of bacterial lipopolysaccharide (LPS) and lipoteichoic acid (LTA) are associated with different causes of stroke and correlate with C-reactive protein (CRP), LPS-binding protein (LBP), and the NIH stroke scale (NIHSS). Ischemic stroke (cardioembolic (CE), large artery atherosclerosis (LAA), small vessel occlusion (SVO)), intracerebral hemorrhage (ICH), transient ischemic attack (TIA) and control subjects were compared (n = 205). Plasma LPS, LTA, CRP, and LBP levels were quantified by ELISA. LPS and CRP levels were elevated in ischemic strokes (CE, LAA, SVO) and ICH compared to controls. LBP levels were elevated in ischemic strokes (CE, LAA) and ICH. LTA levels were increased in SVO stroke compared to TIA but not controls. LPS levels correlated with CRP and LBP levels in stroke and TIA. LPS, LBP and CRP levels positively correlated with the NIHSS and WBC count but negatively correlated with total cholesterol. Plasma LPS and LBP associate with major causes of ischemic stroke and with ICH, whereas LPS/LBP do not associate with TIAs. LTA only associated with SVO stroke. LPS positively correlated with CRP, LBP, and WBC but negatively correlated with cholesterol. Higher LPS levels were associated with worse stroke outcomes.