Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population

Our group recently employed genome-wide transcriptional profiling in tandem with machine-learning based analysis to identify a ten-gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publicly available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 h post-symptom onset, as well from 23 cardiovascular disease controls, were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) were extracted, compared between groups, and evaluated for their discriminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in an independent patient population, and further suggest that it is temporally stable over the first 24 h of stroke pathology

The Ohio State University Dream Team: Innovation for Well-being fellowship and coaching program

Abstract Background: Nearly 70% of faculty experience very high levels of stress. Integrative Nurse Coaching (INC) can help by assisting clients in establishing goals and embarking on new lifestyle behaviors that help to decrease perceived stress, achieve work life integration, and enhance life satisfaction. Our goal was to evaluate a faculty coaching and fellowship program to support faculty well-being while developing innovation competency. Methods: We employed an INC paradigm to coach five faculty to build confidence and competence in innovation and enhance well-being. We offered monthly group and individual coaching and used a qualitative research thematic analysis to determine themes important for the fellow and group experiences, identify outcomes, and create recommendations for the future. Results: We identified the following themes as outcomes for our program: (1) enhanced connection, comradery, and support; (2) increased confidence and competence in navigating academia; (3) shift from a fixed mindset to an innovation mindset; and (4) increased ability to identify and manage stress and burnout. Fellows also experienced a shift from focusing on individual needs to addressing the needs of the community at the college. Conclusion: Nurse coaching is an effective strategy to address faculty stress and burnout. Additional research is needed to evaluate the Innovation for Well-being faculty fellowship program and its impact on the academic community

Monocyte-lymphocyte cross-communication via soluble CD163 directly links innate immune system activation and adaptive immune system suppression following ischemic stroke

CD163 is a scavenger receptor expressed on innate immune cell populations which can be shed from the plasma membrane via the metalloprotease ADAM17 to generate a soluble peptide with lympho-inhibitory properties. The purpose of this study was to investigate CD163 as a possible effector of stroke-induced adaptive immune system suppression. Liquid biopsies were collected from ischemic stroke patients (n = 39), neurologically asymptomatic controls (n = 20), and stroke mimics (n = 20) within 24 hours of symptom onset. Peripheral blood ADAM17 activity and soluble CD163 levels were elevated in stroke patients relative to non-stroke control groups, and negatively associated with post-stroke lymphocyte counts. Subsequent in vitro experiments suggested that this stroke-induced elevation in circulating soluble CD163 likely originates from activated monocytic cells, as serum from stroke patients stimulated ADAM17-dependant CD163 shedding from healthy donor-derived monocytes. Additional in vitro experiments demonstrated that stroke-induced elevations in circulating soluble CD163 can elicit direct suppressive effects on the adaptive immune system, as serum from stroke patients inhibited the proliferation of healthy donor-derived lymphocytes, an effect which was attenuated following serum CD163 depletion. Collectively, these observations provide novel evidence that the innate immune system employs protective mechanisms aimed at mitigating the risk of post-stroke autoimmune complications driven by adaptive immune system overactivation, and that CD163 is key mediator of this phenomenon

Machine-Learning Approach Identifies a Pattern of Gene Expression in Peripheral Blood that can Accurately Detect Ischaemic Stroke

Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population

Our group recently employed genome-wide transcriptional profiling in tandem with machine-learning based analysis to identify a ten-gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publicly available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 h post-symptom onset, as well from 23 cardiovascular disease controls, were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) were extracted, compared between groups, and evaluated for their dis- criminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in an in- dependent patient population, and further suggest that it is temporally stable over the first 24 h of stroke pa- thology

Rapid mitochondrial dysfunction mediates TNF-alpha-induced neurotoxicity

Tumor necrosis factor alpha (TNF-α) is known to exacerbate ischemic brain injury; however, the mechanism is unknown. Previous studies have evaluated the effects of TNF-α on neurons with long exposures to high doses of TNF-α, which is not pathophysiologically relevant. We characterized the rapid effects of TNF-α on basal respiration, ATP production, and maximal respiration using pathophysiologically relevant, post-stroke concentrations of TNF-α. We observed a reduction in mitochondrial function as early as 1.5 h after exposure to low doses of TNF-α, followed by a decrease in cell viability in HT-22 cells and primary neurons. Subsequently, we used the HT-22 cell line to determine the mechanism by which TNF-α causes a rapid and profound reduction in mitochondrial function. Pre-treating with TNF-R1 antibody, but not TNF-R2 antibody, ameliorated the neurotoxic effects of TNF-α, indicating that TNF-α exerts its neurotoxic effects through TNF-R1. We observed an increase in caspase 8 activity and a decrease in mitochondrial membrane potential after exposure to TNF-α which resulted in a release of cytochrome c from the mitochondria into the cytosol. These novel findings indicate for the first time that an acute exposure to pathophysiologically relevant concentrations of TNF-α has neurotoxic effects mediated by a rapid impairment of mitochondrial function

Nursing genetics and genomics: The International Society of Nurses in Genetics (ISONG) survey

Background: The International Society of Nursing in Genetics (ISONG) fosters scienti fi c and professional de- velopment in the discovery, interpretation, and application of genomic information in nursing research, edu- cation, and clinical practice. Objectives: Assess genomic-related activities of ISONG members in research, education and practice, and com- petencies to serve as global leaders in genomics. Design: Cross-sectional survey (21-items) assessing genomic-related training, knowledge, and practice. Settings: An email invitation included a link to the anonymous online survey. Participants: All ISONG members ( n = 350 globally) were invited to partake. Methods: Descriptive statistics and Wilcoxon Rank Sum Test for between-group comparisons. Results: Respondents ( n = 231, 66%), were mostly Caucasian, female, with a master's degree or higher. Approximately 70% wanted to incorporate genomics in research, teaching, and practice. More than half reported high genomic competency, and over 95% reported that genomics is relevant the next 5 years. Conclusions: Findings provide a foundation for developing additional educational programs for an international nursing workforce in genomic

Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood