Computational discovery of plasma microRNA profiles as biomarkers of temporal lobe epilepsy

<div>Presented at ISMB/ECCB 2015 - July 10 – 14, 2015, Dublin, Ireland</div><div><br></div><div><br></div>Epilepsy is a common neurological disorder affecting approximately 1% of the population and is characterised by recurrent unprovoked seizures. The lack of a clinically accepted biomarker for epilepsy diagnosis as well as the incomplete and vague history often provided by the patient is responsible for up to 30% misdiagnosis. MicroRNAs are a class of small non-coding RNA that regulate gene expression at a post-transcriptional level. MicroRNAs are important contributors to brain function and emerging animal and human data suggest microRNAs control multiple pathways in epilepsy. MicroRNAs are also detectable in various body fluids and their stability as well as link to disease mechanism makes them potentially ideal molecular biomarkers of epilepsy. We determined plasma levels of over 800 microRNAs collected from 20 healthy volunteers and 20 epilepsy patients using highthroughput real-time quantitative reverse transcription PCR. Computational analysis included normalisation, clustering, differential expression analysis, target prediction and pathway analysis. A number of significantly differentially expressed microRNAs were identified between control and epilepsy samples including known brain-expressed microRNAs implicated in epilepsy. Furthermore, we applied feature selection with machine learning algorithms, including support vector machines and bidirectional recurrent neural networks, to build a microRNAs-based predictor of epilepsy, validated on an independent test set. This analyse showed that these classifiers may be useful in supporting the existence of a set of microRNAs implicated in disease pathogenesis that may be biomarkers of human epilepsy

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Objectives<p>The German socio-demographic estimation scale was developed by Jahn et al. (1) to quickly predict premorbid global cognitive functioning in patients. So far, it has been validated in healthy adults and has shown a good correlation with the full and verbal IQ of the Wechsler Adult Intelligence Scale (WAIS) in this group. However, there are no data regarding its use as a bedside test in epilepsy patients.</p>Methods<p>Forty native German speaking adult patients with refractory epilepsy were included. They completed a neuropsychological assessment, including a nine scale short form of the German version of the WAIS-III and the German socio-demographic estimation scale by Jahn et al. (1) during their presurgical diagnostic stay in our center. We calculated means, correlations, and the rate of concordance (range ±5 and ±7.5 IQ score points) between these two measures for the whole group, and a subsample of 19 patients with a global cognitive functioning level within 1 SD of the mean (IQ score range 85–115) and who had completed their formal education before epilepsy onset.</p>Results<p>The German demographic estimation scale by Jahn et al. (1) showed a significant mean overestimation of the global cognitive functioning level of eight points in the epilepsy patient sample compared with the short form WAIS-III score. The accuracy within a range of ±5 or ±7.5 IQ score points for each patient was similar to that of the healthy controls reported by Jahn et al. (1) in our subsample, but not in our whole sample.</p>Conclusion<p>Our results show that the socio-demographic scale by Jahn et al. (1) is not sufficiently reliable as an estimation tool of global cognitive functioning in epilepsy patients. It can be used to estimate global cognitive functioning in a subset of patients with a normal global cognitive functioning level who have completed their formal education before epilepsy onset, but it does not reliably predict global cognitive functioning in epilepsy patients in general, who often do not fulfill these criteria. It is therefore not a useful tool to be applied in the general neuropsychological presurgical evaluation of epilepsy patients.</p

"TORNADO" - Theranostic One-Step RNA Detector; microfluidic disc for the direct detection of microRNA-134 in plasma and cerebrospinal fluid.

Diagnosis of seizure disorders such as epilepsy currently relies on clinical examination and electroencephalogram recordings and is associated with substantial mis-diagnosis. The miRNA, miR-134 (MIR134 in humans), has been found to be elevated in brain tissue after experimental status epilepticus and in human epilepsy cells and their detection in biofluids may serve as unique biomarkers. miRNAs from unprocessed human plasma and human cerebrospinal fluid samples were used in a novel electrochemical detection based on electrocatalytic platinum nanoparticles inside a centrifugal microfluidic device where the sandwich assay is formed using an event triggered release system, suitable for the rapid point-of-care detection of low abundance biomarkers of disease. The device has the advantage of controlling the rotation speed of the centrifugal device to pump nanoliter volumes of fluid at a set time and manipulate the transfer of liquids within the device. The centrifugal platform improves reaction rates and yields by proposing efficient mixing strategies to overcome diffusion-limited processes and improve mass transport rates, resulting in reduced hybridization times with a limit of detection of 1 pM target concentration. Plasma and cerebrospinal fluid samples (unprocessed) from patients with epilepsy or who experienced status epilepticus were tested and the catalytic response obtained was in range of the calibration plot. This study demonstrates a rapid and simple detection for epilepsy biomarkers in biofluid.</p

Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy

Epilepsy diagnosis is complex, requires a team of specialists and relies on in-depth patient and family history, MRI-imaging and EEG monitoring. There is therefore an unmet clinical need for a non-invasive, molecular-based, biomarker to either predict the development of epilepsy or diagnose a patient with epilepsy who may not have had a witnessed seizure. Recent studies have demonstrated a role for microRNAs in the pathogenesis of epilepsy. MicroRNAs are short non-coding RNA molecules which negatively regulate gene expression, exerting profound influence on target pathways and cellular processes. The presence of microRNAs in biofluids, ease of detection, resistance to degradation and functional role in epilepsy render them excellent candidate biomarkers. Here we performed the first multi-model, genome-wide profiling of plasma microRNAs during epileptogenesis and in chronic temporal lobe epilepsy animals. From video-EEG monitored rats and mice we serially sampled blood samples and identified a set of dysregulated microRNAs comprising increased miR-93-5p, miR-142-5p, miR-182-5p, miR-199a-3p and decreased miR-574-3p during one or both phases. Validation studies found miR-93-5p, miR-199a-3p and miR-574-3p were also dysregulated in plasma from patients with intractable temporal lobe epilepsy. Treatment of mice with common anti-epileptic drugs did not alter the expression levels of any of the five miRNAs identified, however administration of an anti-epileptogenic microRNA treatment prevented dysregulation of several of these miRNAs. The miRNAs were detected within the Argonuate2-RISC complex from both neurons and microglia indicating these miRNA biomarker candidates can likely be traced back to specific brain cell types. The current studies identify additional circulating microRNA biomarkers of experimental and human epilepsy which may support diagnosis of temporal lobe epilepsy via a quick, cost-effective rapid molecular-based test

A systems approach delivers a functional microRNA catalog and expanded targets for seizure suppression in temporal lobe epilepsy.

Temporal lobe epilepsy is the most common drug-resistant form of epilepsy in adults. The reorganization of neural networks and the gene expression landscape underlying pathophysiologic network behavior in brain structures such as the hippocampus has been suggested to be controlled, in part, by microRNAs. To systematically assess their significance, we sequenced Argonaute-loaded microRNAs to define functionally engaged microRNAs in the hippocampus of three different animal models in two species and at six time points between the initial precipitating insult through to the establishment of chronic epilepsy. We then selected commonly up-regulated microRNAs for a functional in vivo therapeutic screen using oligonucleotide inhibitors. Argonaute sequencing generated 1.44 billion small RNA reads of which up to 82% were microRNAs, with over 400 unique microRNAs detected per model. Approximately half of the detected microRNAs were dysregulated in each epilepsy model. We prioritized commonly up-regulated microRNAs that were fully conserved in humans and designed custom antisense oligonucleotides for these candidate targets. Antiseizure phenotypes were observed upon knockdown of miR-10a-5p, miR-21a-5p, and miR-142a-5p and electrophysiological analyses indicated broad safety of this approach. Combined inhibition of these three microRNAs reduced spontaneous seizures in epileptic mice. Proteomic data, RNA sequencing, and pathway analysis on predicted and validated targets of these microRNAs implicated derepressed TGF-β signaling as a shared seizure-modifying mechanism. Correspondingly, inhibition of TGF-β signaling occluded the antiseizure effects of the antagomirs. Together, these results identify shared, dysregulated, and functionally active microRNAs during the pathogenesis of epilepsy which represent therapeutic antiseizure targets.</p

Functional gene enrichment and network analysis.

<p>Significant gene-set enrichments on 329 genes deleted in GGE patients revealed an enrichment of GRIN2B interacting proteins, genes of the MGI abnormal emotion/affect behaviour annotation and of the GO cognition annotation. Segmental clusters of genes belonging to a gene family were removed. Positional clustering of genes physically linked on a microdeletion is indicated by a slash between the gene symbols.</p><p>Functional gene enrichment and network analysis.</p

Gene-disrupting microdeletions found only in patients with genetic generalised epilepsy.

<p>GGE, genetic generalised epilepsy; CTR: population control; Chr: chromosome, start/end: genomic start and end point of the deleted segment, hg19; ^<i>P</i>-value: type-1 error rate for a χ2-test with df = 1; OR, 95%-CI, odds ratio with 95% confidence interval. Disease phenotype: ASD: autism spectrum disorder, ADHD: attention deficit hyperactivity disorder, AN: anorexia nervosa, AUT: autism, BPD: bipolar disorder, EE: epileptic encephalopathy, EPI: epilepsy, ID: intellectual disability, MCP: microcephaly, SCZ: schizophrenia; GGE syndromes: CAE: childhood absence epilepsy, JAE: juvenile absence epilepsy, JME: juvenile myoclonic epilepsy, EGMA: epilepsy with generalised tonic-clonic seizures alone predominantly on awakening, EGTCS: epilepsy with generalised tonic-clonic seizures alone, gsw: generalised spike and wave discharges on the electroencephalogram, number/: age-at-onset of afebrile generalised seizures. # previously published in [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005226#pgen.1005226.ref026" target="_blank">26</a>] and * [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005226#pgen.1005226.ref027" target="_blank">27</a>]. Bold gene symbols indicate genes previously implicated in epileptogenesis.</p><p>Gene-disrupting microdeletions found only in patients with genetic generalised epilepsy.</p

Could the 2017 ILAE and the four-dimensional epilepsy classifications be merged to a new “Integrated Epilepsy Classification”?

Over the last few decades the ILAE classifications for seizures and epilepsies (ILAE-EC) have been updated repeatedly to reflect the substantial progress that has been made in diagnosis and understanding of the etiology of epilepsies and seizures and to correct some of the shortcomings of the terminology used by the original taxonomy from the 1980s. However, these proposals have not been universally accepted or used in routine clinical practice. During the same period, a separate classification known as the “Four-dimensional epilepsy classification” (4D-EC) was developed which includes a seizure classification based exclusively on ictal symptomatology, which has been tested and adapted over the years. The extensive arguments for and against these two classification systems made in the past have mainly focused on the shortcomings of each system, presuming that they are incompatible. As a further more detailed discussion of the differences seemed relatively unproductive, we here review and assess the concordance between these two approaches that has evolved over time, to consider whether a classification incorporating the best aspects of the two approaches is feasible. To facilitate further discussion in this direction we outline a concrete proposal showing how such a compromise could be accomplished, the “Integrated Epilepsy Classification”. This consists of five categories derived to different degrees from both of the classification systems: 1) a “Headline” summarizing localization and etiology for the less specialized users, 2) “Seizure type(s)”, 3) “Epilepsy type” (focal, generalized or unknown allowing to add the epilepsy syndrome if available), 4) “Etiology”, and 5) “Comorbidities & patient preferences”