Circulating MicroRNAs From Plasma Small Extracellular Vesicles as Potential Diagnostic Biomarkers in Pediatric Epilepsy and Drug-Resistant Epilepsy

Extracellular Vesicles: The Next Generation of Biomarkers and Treatment for Central Nervous System Diseases

It has been widely established that the characterization of extracellular vesicles (EVs), particularly small EVs (sEVs), shed by different cell types into biofluids, helps to identify biomarkers and therapeutic targets in neurological and neurodegenerative diseases. Recent studies are also exploring the efficacy of mesenchymal stem cell-derived extracellular vesicles naturally enriched with therapeutic microRNAs and proteins for treating various diseases. In addition, EVs released by various neural cells play a crucial function in the modulation of signal transmission in the brain in physiological conditions. However, in pathological conditions, such EVs can facilitate the spread of pathological proteins from one brain region to the other. On the other hand, the analysis of EVs in biofluids can identify sensitive biomarkers for diagnosis, prognosis, and disease progression. This review discusses the potential therapeutic use of stem cell-derived EVs in several central nervous system diseases. It lists their differences and similarities and confers various studies exploring EVs as biomarkers. Further advances in EV research in the coming years will likely lead to the routine use of EVs in therapeutic settings.

Extracellular vesicles: Function, resilience, biomarker, bioengineering, and clinical implications

Extracellular vesicles (EVs) have emerged as key players in intercellular communication, disease pathology, and therapeutic innovation. Initially overlooked as cellular debris, EVs are now recognized as vital mediators of cell-to-cell communication, ferrying a cargo of proteins, nucleic acids, and lipids, providing cellular resilience in response to stresses. This review provides a comprehensive overview of EVs, focusing on their role as biomarkers in disease diagnosis, their functional significance in physiological and pathological processes, and the potential of bioengineering for therapeutic applications. EVs offer a promising avenue for noninvasive disease diagnosis and monitoring, reflecting the physiological state of originating cells. Their diagnostic potential spans a spectrum of diseases, including cancer, cardiovascular disorders, neurodegenerative diseases, and infectious diseases. Moreover, their presence in bodily fluids such as blood, urine, and cerebrospinal fluid enhances their diagnostic utility, presenting advantages over traditional methods. Beyond diagnostics, EVs mediate crucial roles in intercellular communication, facilitating the transfer of bioactive molecules between cells. This communication modulates various physiological processes such as tissue regeneration, immune modulation, and neuronal communication. Dysregulation of EV-mediated communication is implicated in diseases such as cancer, immune disorders, and neurodegenerative diseases, highlighting their therapeutic potential. Bioengineering techniques offer avenues for manipulating EVs for therapeutic applications, from isolation and purification to engineering cargo and targeted delivery systems. These approaches hold promise for developing novel therapeutics tailored to specific diseases, revolutionizing personalized medicine. However, challenges such as standardization, scalability, and regulatory approval need addressing for successful clinical translation. Overall, EVs represent a dynamic frontier in biomedical research with vast potential for diagnostics, therapeutics, and personalized medicine.

Insights into epileptogenesis from post-traumatic epilepsy

Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.

Smart Nanoscale Extracellular Vesicles in the Brain: Unveiling their Biology, Diagnostic Potential, and Therapeutic Applications

Information exchange is essential for the brain, where it communicates the physiological and pathological signals to the periphery and vice versa. Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound cellular informants actively transferring informative calls to and from the brain via lipids, proteins, and nucleic acid cargos. In recent years, EVs have also been widely used to understand brain function, given their "cell-like" properties. On the one hand, the presence of neuron and astrocyte-derived EVs in biological fluids have been exploited as biomarkers to understand the mechanisms and progression of multiple neurological disorders; on the other, EVs have been used in designing targeted therapies due to their potential to cross the blood-brain-barrier (BBB). Despite the expanding literature on EVs in the context of central nervous system (CNS) physiology and related disorders, a comprehensive compilation of the existing knowledge still needs to be made available. In the current review, we provide a detailed insight into the multifaceted role of brain-derived extracellular vesicles (BDEVs) in the intricate regulation of brain physiology. Our focus extends to the significance of these EVs in a spectrum of disorders, including brain tumors, neurodegenerative conditions, neuropsychiatric diseases, autoimmune disorders, and others. Throughout the review, parallels are drawn for using EVs as biomarkers for various disorders, evaluating their utility in early detection and monitoring. Additionally, we discuss the promising prospects of utilizing EVs in targeted therapy while acknowledging the existing limitations and challenges associated with their applications in clinical scenarios. A foundational comprehension of the current state-of-the-art in EV research is essential for informing the design of future studies.

Revisiting the concept of drug-resistant epilepsy: A TASK1 report of the ILAE/AES Joint Translational Task Force

Despite progress in the development of anti-seizure medications (ASMs), one third of people with epilepsy have drug-resistant epilepsy (DRE). The working definition of DRE, proposed by the International League Against Epilepsy (ILAE) in 2010, helped identify individuals who might benefit from presurgical evaluation early on. As the incidence of DRE remains high, the TASK1 workgroup on DRE of the ILAE/American Epilepsy Society (AES) Joint Translational Task Force discussed the heterogeneity and complexity of its presentation and mechanisms, the confounders in drawing mechanistic insights when testing treatment responses, and barriers in modeling DRE across the lifespan and translating across species. We propose that it is necessary to revisit the current definition of DRE, in order to transform the preclinical and clinical research of mechanisms and biomarkers, to identify novel, effective, precise, pharmacologic treatments, allowing for earlier recognition of drug resistance and individualized therapies.

MicroRNAs as Potential Biomarkers of Post-Traumatic Epileptogenesis: A Systematic Review

Structural or post-traumatic epilepsy often develops after brain tissue damage caused by traumatic brain injury, stroke, infectious diseases of the brain, etc. Most often, between the initiating event and epilepsy, there is a period without seizures-a latent period. At this time, the process of restructuring of neural networks begins, leading to the formation of epileptiform activity, called epileptogenesis. The prediction of the development of the epileptogenic process is currently an urgent and difficult task. MicroRNAs are inexpensive and minimally invasive biomarkers of biological and pathological processes. The aim of this study is to evaluate the predictive ability of microRNAs to detect the risk of epileptogenesis. In this study, we conducted a systematic search on the MDPI, PubMed, ScienceDirect, and Web of Science platforms. We analyzed publications that studied the aberrant expression of circulating microRNAs in epilepsy, traumatic brain injury, and ischemic stroke in order to search for microRNAs-potential biomarkers for predicting epileptogenesis. Thus, 31 manuscripts examining biomarkers of epilepsy, 19 manuscripts examining biomarkers of traumatic brain injury, and 48 manuscripts examining biomarkers of ischemic stroke based on circulating miRNAs were analyzed. Three miRNAs were studied: miR-21, miR-181a, and miR-155. The findings showed that miR-21 and miR-155 are associated with cell proliferation and apoptosis, and miR-181a is associated with protein modifications. These miRNAs are not strictly specific, but they are involved in processes that may be indirectly associated with epileptogenesis. Also, these microRNAs may be of interest when they are studied in a cohort with each other and with other microRNAs. To further study the microRNA-based biomarkers of epileptogenesis, many factors must be taken into account: the time of sampling, the type of biological fluid, and other nuances. Currently, there is a need for more in-depth and prolonged studies of epileptogenesis.

Identification of miRNAs in extracellular vesicles as potential diagnostic markers for pediatric epilepsy and drug-resistant epilepsy via bioinformatics analysis

Background

Pediatric epilepsy (PE) is a common neurological disease. However, many challenges regarding the clinical diagnosis and treatment of PE and drug-resistant epilepsy (DRE) remain unsettled. Our study aimed to identify potential miRNA biomarkers in children with epilepsy and drug-resistant epilepsy by scrutinizing differential miRNA expression profiles.

Methods

In this study, miRNA expression profiles in plasma extracellular vesicles (EV) of normal controls, children with drug-effective epilepsy (DEE), and children with DRE were obtained. In addition, differential analysis, transcription factor (TF) enrichment analysis, Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and target gene prediction were used to identify specifically expressed miRNAs and their potential mechanisms of action. Potential diagnostic markers for DRE were identified using machine learning algorithms, and their diagnostic efficiency was assessed by the receiver operating characteristic curve (ROC).

Results

The hsa-miR-1307-3p, hsa-miR-196a-5p, hsa-miR-199a-3p, and hsa-miR-21-5p were identified as diagnostic markers for PE, with values of area under curve (AUC) 0.780, 0.840, 0.832, and 0.816, respectively. In addition, the logistic regression model incorporating these four miRNAs had an AUC value of 0.940, and its target gene enrichment analysis highlighted that these miRNAs were primarily enriched in the PI3K-Akt, MAPK signaling pathways, and cell cycle. Furthermore, hsa-miR-99a-5p, hsa-miR-532-5p, hsa-miR-181d-5p, and hsa-miR-181a-5p showed good performance in differentiating children with DRE from those with DEE, with AUC values of 0.737 (0.534-0.940), 0.737 (0.523-0.952), 0.788 (0.592-0.985), and 0.788 (0.603-0.974), respectively.

Conclusion

This study characterized the expression profile of miRNAs in plasma EVs of children with epilepsy and identified miRNAs that can be used for the diagnosis of DRE.

Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity

People with diabetes can wear a device that measures blood glucose and delivers just the amount of insulin needed to return the glucose level to within bounds. Currently, people with epilepsy do not have access to an equivalent wearable device that measures a systemic indicator of an impending seizure and delivers a rapidly acting medication or other intervention (e.g., an electrical stimulus) to terminate or prevent a seizure. Given that seizure susceptibility is reliably increased in systemic inflammatory states, we propose a novel closed-loop device where release of a fast-acting therapy is governed by sensors that quantify the magnitude of systemic inflammation. Here, we review the evidence that patients with epilepsy have raised levels of systemic indicators of inflammation than controls, and that some anti-inflammatory drugs have reduced seizure occurrence in animals and humans. We then consider the options of what might be incorporated into a responsive anti-seizure system.

Micro-RNAs from Plasma-Derived Small Extracellular Vesicles as Potential Biomarkers for Tic Disorders Diagnosis

Tic disorders (TDs) are a series of childhood neuropsychiatric disorders characterized by involuntary motor and/or vocal tics and commonly comorbid with several other psychopathological and/or behavioral disorders (e.g., attention deficit hyperactivity disorder and obsessive–compulsive disorder), which indeed aggravate clinical symptoms and complicate diagnosis and treatment. Micro-RNAs (miRNAs) derived from small extracellular vesicles (sEVs) have been recognized as novel circulating biomarkers of disease. To identify specific miRNAs derived from plasma sEVs for TDs’ diagnosis and prognosis, we used official EV isolation and purification methods to characterize the plasma-derived EV miRNAs from children with different types of TDs. Nanoparticle tracking analysis, transmission electron microscopy, and immunoblot analysis of EV surface markers were applied to confirm the features and quality of sEVs. The RNA sequencing (RNA-seq) approach was adapted to identify novel circulating sEVs-derived miRNAs with altered expression levels in paired comparisons of TDs versus healthy controls (HCs), transient tic disorder (TTD) versus chronic motor or vocal tic disorder (CTD), and TTD versus Tourette Syndrome (TS). GO term and KEGG pathway were performed for functional analysis and the receiver operator curve analysis was followed to test the diagnosis efficacy of differentially expressed miRNAs (DEMs) derived from plasma sEVs among paired groups, namely, TDs versus HCs, TTD versus CTD, and TTD versus TS. As a result, 10 miRNAs (hsa-let-7a, hsa-let-7b, hsa-let-7c, hsa-let-7e, hsa-let-7f, hsa-miR-25-3p, hsa-miR-29a-3p, hsa-miR-30b-5p, hsa-miR-125b-5p, and hsa-miR-1469) have demonstrated a significantly different expression signature in the TDs group compared to HCs with excellent area under curve (AUC) values of 0.99, 0.973, 0.997, 1, 0.99, 0.997, 0.987, 0.993, 0.977, and 0.997, respectively, and the diagnostic efficacy of miRNAs was also estimated for discriminating TTD from CTD or TS. In our research, we finally obtained several potential sEVs-derived miRNA biomarkers to assess the diagnosis and prognosis of TDs.

Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

BACKGROUND AND PURPOSE

This study aimed to effectively identify children with drug-resistant epilepsy (DRE) in the early stage of epilepsy, and take personalized interventions, to improve patients' prognosis, reduce serious comorbidity, and save social resources. Herein, we developed and validated a nomogram prediction model for children with DRE.

METHODS

The training set was patients with epilepsy who visited the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) between January 2015 and December 2017. The independent risk factors for DRE were screened by univariate and multivariate logistic regression analyses using SPSS21 software. The nomogram was designed according to the regression coefficient. The nomogram was validated in the training and validation sets. Internal validation was conducted using bootstrapping analyses. We also externally validated this instrument in patients with epilepsy from the Children's Hospital of Soochow University (Gusu District, Jiangsu Province, China) and Yancheng Maternal and Child Health Hospital between January 2018 and December 2018. The nomogram's performance was assessed by concordance (C-index), calibration curves, as well as GiViTI calibration belts.

RESULTS

Multivariate logistic regression analysis of 679 children with epilepsy from the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) showed that onset age<1, status epilepticus (SE), focal seizure, > 20 pre-treatment seizures, clear etiology (caused by genetic, structural, metabolic, or infectious), development and epileptic encephalopathy (DEE), and neurological abnormalities were all independent risk factors for DRE. The AUC of 0.92 for the training set compared to that of 0.91 for the validation set suggested a good discrimination ability of the prediction model. The C-index was 0.92 and 0.91 in the training and validation sets. Additionally, both good calibration curves and GiViTI calibration belts (P-value: 0.849 and 0.291, respectively) demonstrated that the predicted risks had strong consistency with the observed outcomes, suggesting that the prediction model in both groups was perfectly calibrated.

CONCLUSION

A nomogram prediction model for DRE was developed, with good discrimination and calibration in the training set and the validation set. Furthermore, the model demonstrated great accuracy, consistency, and prediction ability. Therefore, the nomogram prediction model can aid in the timely identification of DRE in children.