Huichun Xu

Brain and blood microRNA expression profiling of ischemic stroke, intracerebral hemorrhage, and kainate seizures

MicroRNAs (miRNAs) regulate gene expression and have a critical role in many biologic and pathologic processes. We hypothesized that miRNA expression profiles in injured brain (hippocampus) would show common as well as unique profiles when compared with those of blood. Adult, untouched, control rats were compared with rats with sham surgeries, ischemic strokes, brain hemorrhage (lysed blood, fresh blood, or thrombin), and kainate-induced seizures. Brain and whole-blood miRNA expression profiles were assessed 24 h later using TaqMan rodent miRNA arrays. MicroRNA response profiles were different for each condition. Many miRNAs changed more than 1.5-fold in brain and blood after each experimental manipulation, and several miRNAs were upregulated or downregulated in both brain and blood after a given injury. A few miRNAs (e.g., miR-298, miR-155, and miR-362-3p) were upregulated or downregulated more than twofold in both brain and blood after several different injuries. The results show the possible use of blood miRNAs as biomarkers for brain injury; that selected blood miRNAs may correlate with miRNA changes in the brain; and that many of the mRNAs, previously shown to be regulated in brain and blood after brain injury, are likely accounted for by changes in miRNA expression.

Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study.

Ischemic brain and peripheral white blood cells release cytokines, chemokines and other molecules that activate the peripheral white blood cells after stroke. To assess gene expression in these peripheral white blood cells, whole blood was examined using oligonucleotide microarrays in 15 patients at 2.4 ± 0.5, 5 and 24 h after onset of ischemic stroke and compared with control blood samples. The 2.4 h blood samples were drawn before patients were treated either with tissue-type plasminogen activator (tPA) alone or with tPA plus Eptifibatide (the Combination approach to Lysis utilizing Eptifibatide And Recombinant tPA trial). Most genes induced in whole blood at 2 to 3 h were also induced at 5 and 24 h. Separate studies showed that the genes induced at 2 to 24 h after stroke were expressed mainly by polymorphonuclear leukocytes and to a lesser degree by monocytes. These genes included: matrix metalloproteinase 9; S100 calcium-binding proteins P, A12 and A9; coagulation factor V; arginase I; carbonic anhydrase IV; lymphocyte antigen 96 (cluster of differentiation (CD)96); monocarboxylic acid transporter (6); ets-2 (erythroblastosis virus E26 oncogene homolog 2); homeobox gene Hox 1.11; cytoskeleton-associated protein 4; N-formylpeptide receptor; ribonuclease-2; N-acetylneuraminate pyruvate lyase; BCL6; glycogen phosphorylase. The fold change of these genes varied from 1.6 to 6.8 and these 18 genes correctly classified 10/15 patients at 2.4 h, 13/15 patients at 5h and 15/15 patients at 24 h after stroke. These data provide insights into the inflammatory responses after stroke in humans, and should be helpful in diagnosis, understanding etiology and pathogenesis, and guiding acute treatment and development of new treatments for stroke.

Hypoxia preconditioning in the brain

Exposure to moderate hypoxia alone does not cause neuronal death as long as blood pressure and cerebral blood flow are maintained in mammals. In neonatal and adult mammals including rats and mice, carotid occlusion in combination with hypoxia produces neuronal death and brain infarction. However, preexposure to 8% oxygen for 3 h protects the brain and likely other organs of neonatal and adult rats against combined hypoxia-ischemia 24 h later. In this paper, the possible mechanisms of this so-called hypoxia-induced tolerance to ischemia is discussed. One mechanism likely involves hypoxia-inducible factor-1α (HIF-1α). HIF-1α is a transcription factor that – during hypoxia – binds with a second protein (HIF-1β) in the nucleus to promoter elements in hypoxia-responsive target genes. This causes upregulation of HIF target genes including VEGF, erythropoietin, iNOS, glucose transporter-1, glycolytic enzymes, and many other genes to protect the brain against ischemia 24 h later. In addition, non-HIF pathways including MTF-1, Egr-1 and others act directly or indirectly on other target genes to also promote hypoxia-induced preconditioning. Hypoxia preconditioning can be mimicked by iron chelators like desferrioxamine and transition metals like cobalt chloride that inhibit prolyl hydroxylases, increase HIF-1α levels in the brain, and produce protection of the brain against combined hypoxia-ischemia 24 h later. This hypoxia preconditioning has potential clinical usefulness in protecting high-risk newborns or to provide protection prior to surgery.

Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood

BackgroundGene expression studies require appropriate normalization methods. One such method uses stably expressed reference genes. Since suitable reference genes appear to be unique for each tissue, we have identified an optimal set of the most stably expressed genes in human blood that can be used for normalization.MethodsWhole-genome Affymetrix Human 2.0 Plus arrays were examined from 526 samples of males and females ages 2 to 78, including control subjects and patients with Tourette syndrome, stroke, migraine, muscular dystrophy, and autism. The top 100 most stably expressed genes with a broad range of expression levels were identified. To validate the best candidate genes, we performed quantitative RT-PCR on a subset of 10 genes (TRAP1, DECR1, FPGS, FARP1, MAPRE2, PEX16, GINS2, CRY2, CSNK1G2 and A4GALT), 4 commonly employed reference genes (GAPDH, ACTB, B2M and HMBS) and PPIB, previously reported to be stably expressed in blood. Expression stability and ranking analysis were performed using GeNorm and NormFinder algorithms.ResultsReference genes were ranked based on their expression stability and the minimum number of genes needed for nomalization as calculated using GeNorm showed that the fewest, most stably expressed genes needed for acurate normalization in RNA expression studies of human whole blood is a combination of TRAP1, FPGS, DECR1 and PPIB. We confirmed the ranking of the best candidate control genes by using an alternative algorithm (NormFinder).ConclusionThe reference genes identified in this study are stably expressed in whole blood of humans of both genders with multiple disease conditions and ages 2 to 78. Importantly, they also have different functions within cells and thus should be expressed independently of each other. These genes should be useful as normalization genes for microarray and RT-PCR whole blood studies of human physiology, metabolism and disease.

Blood–brain barrier breakdown and repair by Src after thrombin‐induced injury

Thrombin mediates the life‐threatening cerebral edema that occurs after intracerebral hemorrhage. Therefore, we examined the mechanisms of thrombin‐induced injury to the blood–brain barrier (BBB) and subsequent mechanisms of BBB repair.

Gene Expression Profiling of Blood for the Prediction of Ischemic Stroke

Background and Purpose— A blood-based biomarker of acute ischemic stroke would be of significant value in clinical practice. This study aimed to (1) replicate in a larger cohort our previous study using gene expression profiling to predict ischemic stroke; and (2) refine prediction of ischemic stroke by including control groups relevant to ischemic stroke. Methods— Patients with ischemic stroke (n=70, 199 samples) were compared with control subjects who were healthy (n=38), had vascular risk factors (n=52), and who had myocardial infarction (n=17). Whole blood was drawn ≤3 hours, 5 hours, and 24 hours after stroke onset and from control subjects. RNA was processed on whole genome microarrays. Genes differentially expressed in ischemic stroke were identified and analyzed for predictive ability to discriminate stroke from control subjects. Results— The 29 probe sets previously reported predicted a new set of ischemic strokes with 93.5% sensitivity and 89.5% specificity. Sixty- and 46-probe sets differentiated control groups from 3-hour and 24-hour ischemic stroke samples, respectively. A 97-probe set correctly classified 86% of ischemic strokes (3 hour+24 hour), 84% of healthy subjects, 96% of vascular risk factor subjects, and 75% with myocardial infarction. Conclusions— This study replicated our previously reported gene expression profile in a larger cohort and identified additional genes that discriminate ischemic stroke from relevant control groups. This multigene approach shows potential for a point-of-care test in acute ischemic stroke.

Gene Expression in Peripheral Blood Differs after Cardioembolic Compared with Large-Vessel Atherosclerotic Stroke: Biomarkers for the Etiology of Ischemic Stroke

There are no biomarkers that differentiate cardioembolic from large-vessel atherosclerotic stroke, although the treatments differ for each and ~30% of strokes and transient ischemic attacks have undetermined etiologies using current clinical criteria. We aimed to define gene expression profiles in blood that differentiate cardioembolic from large-vessel atherosclerotic stroke. Peripheral blood samples were obtained from healthy controls and acute ischemic stroke patients (< 3, 5, and 24 h). RNA was purified, labeled, and applied to Affymetrix Human U133 Plus 2.0 Arrays. Expression profiles in the blood of cardioembolic stroke patients are distinctive from those of large-vessel atherosclerotic stroke patients. Seventy-seven genes differ at least 1.5-fold between them, and a minimum number of 23 genes differentiate the two types of stroke with at least 95.2% specificity and 95.2% sensitivity for each. Genes regulated in large-vessel atherosclerotic stroke are expressed in platelets and monocytes and modulate hemostasis. Genes regulated in cardioembolic stroke are expressed in neutrophils and modulate immune responses to infectious stimuli. This new method can be used to predict whether a stroke of unknown etiology was because of cardioembolism or large-vessel atherosclerosis that would lead to different therapy. These results have wide ranging implications for similar disorders.

Signatures of cardioembolic and large-vessel ischemic stroke.

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.

Profiles of lacunar and nonlacunar stroke

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.

Distinctive RNA Expression Profiles in Blood Associated With White Matter Hyperintensities in Brain

Background and Purpose— White matter hyperintensities (WMH) are areas of high signal detected by T2 and fluid-attenuated inversion recovery sequences on brain MRI. Although associated with aging, cerebrovascular risk factors, and cognitive impairment, the pathogenesis of WMH remains unclear. Thus, RNA expression was assessed in the blood of individuals with and without extensive WMH to search for evidence of oxidative stress, inflammation, and other abnormalities described in WMH lesions in brain. Methods— Subjects included 20 with extensive WMH (WMH+), 45% of whom had Alzheimer disease, and 18 with minimal WMH (WMH−), 44% of whom had Alzheimer disease. All subjects were clinically evaluated and underwent quantitative MRI. Total RNA from whole blood was processed on human whole genome Affymetrix HU133 Plus 2.0 microarrays. RNA expression was analyzed using an analysis of covariance. Results— Two hundred forty-one genes were differentially regulated at ±1.2-fold difference (P<0.005) in subjects with WMH+ as compared to WMH−, regardless of cognitive status and 50 genes were differentially regulated with ±1.5-fold difference (P<0.005). Cluster and principal components analyses showed that the expression profiles for these genes distinguished WMH+ from WMH− subjects. Function analyses suggested that WMH-specific genes were associated with oxidative stress, inflammation, detoxification, and hormone signaling, and included genes associated with oligodendrocyte proliferation, axon repair, long-term potentiation, and neurotransmission. Conclusions— The unique RNA expression profile in blood associated with WMH is consistent with roles of systemic oxidative stress and inflammation, as well as other potential processes in the pathogenesis or consequences of WMH.