Abstract

The diagnosis of epilepsy and recent seizure remain important clinical challenges. Blood-based molecules represent potential circulating biomarkers that could provide simple, cheap, quick tests to support diagnosis, prognosis, monitoring, and decisions on choice and effectiveness of therapy. There have been recent and significant advances in identifying candidate molecules. A number of proteins hold promise, including HMGB1, matrix proteins, and neuron-enriched markers such as UCHL1. Small noncoding RNAs called microRNAs have more recently emerged, with interest stemming from their tissue-specific expression, suitability for rapid and quantitative assay, and link to mechanisms of disease and potential as treatments. Here we review why blood-based biomarkers are needed, the properties sought, and the quality and quantity of evidence obtained from clinical and experimental studies. We finish by identifying the necessary studies in the future that could bring these valuable supports to the clinic.

Introduction

Why Should Circulating Biofluids Contain Molecular Biomarkers of Epilepsy?

Why should a biofluid such as blood contain molecules of diagnostic value for epilepsy? After all, the brain is separated from the circulation by the blood–brain barrier (BBB) (Daneman and Prat, 2015). Research has increasingly shown that there is exchange of molecules between brain and blood under physiological conditions and that this increases during disease states. Neurovascular coupling also provides a mechanism by which changes in neuronal activity prompt exchanges of nutrients and waste via local microvascular responses (Kaplan et al., 2020). Cells of the immune system continuously patrol the meningeal spaces (Alves de Lima et al., 2020). During this process, immune cells engage with cells of the BBB through receptor mechanisms, bringing about changes in both target and recipient cells. Thus, even under physiological conditions, there is material transfer between the brain and the circulation. Whenever there is damage to the brain or a breach in the BBB, these exchanges increase, as immune cells directly enter the brain tissue and expand their surveillance (Obermeier et al., 2013; Daneman and Prat, 2015). The characterization of the glymphatic system, a transcellular route by which brain-generated metabolic by-products and other factors are evicted from the parenchyma, provides a further route for exchange between brain and the periphery (Rasmussen et al., 2021). Brain activity states influence the relative flow and drainage of fluids and the molecules within. Alterations in this system affect trafficking of molecules out of the brain and are associated with neurological diseases (Daneman and Prat, 2015).

The integrity and function of the BBB may itself be impaired in epilepsy (Marchi et al., 2012). Brain imaging studies in patients who have had recent seizures reveal the transient and often widespread opening of the BBB (Ruber et al., 2018). Prolonged seizures likely cause more pronounced or prolonged opening (Gorter et al., 2015). This provides both controlled and uncontrolled routes by which molecules and other material may transit from the epileptogenic tissue into the circulation where they could be detected. Studies have also shown that circulating immune cells generate microvesicles that, upon neuronal activation, are taken up by neurons and glia (Kur et al., 2020). Microvesicles contain genetic material such as RNAs which can influence the transcriptional landscape in recipient cells, suggesting an additional mechanism by which brain and circulation communicate (Valadi et al., 2007; Jeppesen et al., 2019). Together, there are multiple mechanisms by which molecules can transit from the brain into the blood. If a particular set of these molecules reflects pathophysiological processes specifically associated with epilepsy, it would have the potential to serve as mechanistic biomarkers.

Other Criteria That Must Be Met for Circulating Molecular Biomarkers

The utility of a molecule as a biomarker depends on more than its mechanistic basis and presence in the circulation. Other conditions need to be met. The level of the molecule in a patient needs to have as wide a gap as possible from whatever the comparison group is (which may be a healthy control or another neurological condition in the event of using a molecule to differentiate between epilepsy and psychogenic nonepileptic seizures [PNES]). Ideally, the molecule can be measured and quantified in absolute terms so that it does not need to be measured alongside standards or other samples. A molecular biomarker needs to be stable upon entry to the circulation. Labile metabolites and many other molecules are rapidly degraded upon entry to the circulation. There is less practical use of a molecule that must be assayed within minutes of an event. This has previously compromised the value of promising epilepsy biomarkers, such as prolactin (Chen et al., 2005). Additionally, they will ideally be assayable using a convenient point-of-care test. Again, molecules that are impractical, costly, or require lengthy preparatory times for measurement and quantitation will be unlikely to have practical use in clinical medicine. Finally, a link to a therapeutic may also be an advantage. The co-development of drugs with a companion biomarker is generally seen as optimal for new drug development (Loscher et al., 2013). Recent examples in clinical trials of new epilepsy therapies illustrate this concept. Soluble vascular cell adhesion molecule (VCAM) has been used as a pharmacodynamic marker to monitor natalizumab dosing (an α4-integrin inhibitor) (French et al., 2021), and plasma 24-hydroxycholesterol has been used to monitor cholesterol 24-hydroxylase inhibition by soticlestat in trials for Dravet and Lennox-Gastaut syndromes (NCT03650452) (Hawkins et al., 2021).

What Type of Molecules Should We Be Looking for?

Various molecules have been measured in blood samples from patients with epilepsy and animal models. To date, none have been found to be sufficiently sensitive and specific as biomarkers. However, interest in this strategy has never been higher, and recent discoveries highlight novel classes of molecules which have favorable biochemical properties and links to brain function in health and disease. In addition, some of these molecules seem to be plausible therapeutic targets in their own right. In the present chapter we review blood-based biomarkers of epilepsy, focusing on the most promising categories, namely microRNAs (miRNA) and proteins. These molecules are relatively stable, can be assayed using routine techniques such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), and offer the possibility of absolute quantification. There are mechanistic links to pathologic processes and therapies, and we can be reasonably confident of their cellular origin, for example, because the molecule is expressed only in neurons. Here, we focus on the evidence of their biomarker value paying particular attention to studies which include an assessment of performance using receiver operating characteristic (ROC) analysis, a standard method to determine sensitivity and specificity of a putative biomarker (Pitkanen et al., 2018). A value close to 1.0 indicates high potential as a biomarker. However, there is no recognized value for ROC, and a decision may be made to continue exploring a biomarker with values as low as 0.8 when alternatives are limited.

How Would a Molecular Biomarker Be Used?

On a practical level, and assuming a sensitive and specific biomarker is identified for epilepsy or seizures, a variety of settings can be envisaged where a blood-based biomarker might be measured. For example, it might be measured in an emergency department (ED) setting to assess whether a patient has had a seizure or to support a decision on whether and when to give or readminister an anticonvulsant (rescue medication). Or it might be measured in a diagnostic setting, where other investigations are perhaps not conclusive, a biomarker might support a decision on whether seizures are epileptic or psychogenic in origin. Finally, it might be measured in a home setting, where a patient might monitor a biomarker that tracks or predicts risk of seizure. This could be helpful during adjustment of medication and could support people with epilepsy to make judgements about their activities.

In contrast with other biomarker categories, based, for example, on imaging or EEG, molecular biomarkers in the peripheral blood would be much less costly and much easier to use. As such, they may be employed for screening purposes, provided that they ensure a high negative predictive value (NPV, the probability that a patient does not have the disease when the test result is negative), but not necessarily a high positive predictive value (PPV, the probability that a patient has the disease when the test result is positive). In other words, staged use of different biomarkers may be envisaged, depending on the context of use, that is, the manner and purpose of use for the biomarker. For example, a noninvasive, low-cost blood-based monitoring biomarker could provide a tool to assess patients serially to verify development of epileptogenesis. Such a tool might not be intended as diagnostic but could serve as a screen for when to use higher cost or more invasive confirmatory diagnostic procedures.

What technology would be needed for testing such biomarkers? If the molecule is a protein, it could be measured using mass spectrometry, although this is relatively expensive and time-consuming. It is more likely that a protein biomarker would be tested using an ELISA-type plate assay, which can provide quantitative data within a few hours or overnight. However, this would be less practical in an emergency setting when a rapid test is needed. For molecules such as miRNA, a PCR-type assay could be performed. As with ELISA tests, most PCR reactions require time to perform, generally involving extraction of RNA, reverse transcription, and then primer-based amplification and detection. This makes it suitable if the test is performed as part of a diagnostic workup where the sample can be processed overnight, perhaps via a service provider. However, a capacity to test rapidly would be required for emergency department-based situations where results are needed immediately. Technology is being developed to solve some of these issues. In the case of miRNAs, non-amplification-based electrochemical direct detection is possible and prototype devices have emerged (McArdle et al., 2017). Enzymatic-based biosensors have recently been developed for certain labile metabolites. This includes purines such as adenosine which increase in the blood following seizures and may be elevated in epilepsy patients. Technology to detect and quantify purines in a drop of blood has recently been reported (Beamer et al., 2021). In summary, with the development and validation of blood-based biomarkers for epilepsy and seizure, there will need to be ongoing technological developments to ensure that they can be used in a clinical setting. Figure 37–1 presents an overview of the potential use of circulating molecules for diagnosis of epilepsy and seizure.

Figure 37–1.

Overview of circulating protein and miRNA biomarkers of epilepsy. Cartoon shows hypothesis behind how and why circulating molecular biomarkers of epilepsy may come about, how they might be detected and analyzed, and some examples of leading candidate (more...)

miRNAs as Epilepsy Biomarkers

Presence of miRNAs in Blood in Epilepsy—Clinical Findings

Table 37–1 provides a summary of the published findings on miRNAs in blood samples from patients with epilepsy. Study design varies, with some incorporating a discovery phase where miRNAs were first profiled in an unbiased manner before validation of lead candidates. Some studies include separate discovery and validation cohorts, the preferred design, whereas others used a single study population. Here, we omitted studies that did not feature a ROC analysis to assess the sensitivity and specificity of the miRNA to discriminate one group from another (e.g., patients compared to healthy controls).

Table 37–1

Clinical Studies on Circulating, Blood-Based miRNA Biomarkers of Epilepsy.

The first study to report differences in circulating miRNA levels in the blood of patients with epilepsy was by Wang and colleagues (Wang et al., 2015b). The study used RNA sequencing of serum pools from 30 epilepsy patients and controls to profile miRNAs and then validated results on a set of miRNAs in a larger cohort (117 patients) using individual samples. This resulted in identification of six candidate miRNA biomarkers of epilepsy. A ROC analysis of the single best performing miRNA (miR-106b) gave an area under the curve (AUC) of 0.88 (equating to a sensitivity and specificity of just over 80%). Levels of miR-146a were among those elevated in patient serum which complements neuropathological and functional evidence for this miRNA in epilepsy (Aronica et al., 2010; Iori et al., 2017). A subsequent study the same year using a similar design reported an additional set of five miRNAs which showed altered levels in serum samples from drug-resistant compared to drug-responsive patients (Wang et al., 2015a). A combination of miRNAs from that study gave an AUC of 0.90 indicating 84.9% sensitivity and 79.8% specificity to identify drug-resistant from drug-responsive patients. The same year also saw reports of changes to individual miRNAs for which there was a priori knowledge of a link to epilepsy (Spain et al., 2015).

Overall, the studies published to date support the hypothesis that there are circulating miRNA biomarkers of epilepsy that could have diagnostic value. Studies have found miRNAs in both serum and plasma and assessed patients with focal as well as generalized epilepsies (Table 37–1). Both broad categories of epilepsy syndromes appear to be associated with circulating miRNAs. Several miRNAs have been identified in more than one study, including miR-106b-5p, miR-134-5p, miR-146a-5p, and miR-194-5p, suggesting some convergence on what might eventually be an epilepsy-specific panel. Indeed, some, such as miR-146a-5p, appear to be elevated in both focal and generalized epilepsies, suggesting they may be broadly diagnostic of epilepsy (or, perhaps, the treatments used since this is a variable rarely controlled for). Among the various miRNAs identified so far, many are CNS-enriched, in broad keeping with their having entered the circulation from the brain. However, several other putative miRNA biomarkers do not show specific CNS enrichment, including miR-106b-5p and miR-194-5p (Ludwig et al., 2016). These perhaps represent miRNA changes brought about by other aspects of the disease such as response to treatment. Many of the ROC results are encouraging, with values above 0.8 (a sensible if somewhat arbitrary cut-off for what might constitute a clinically relevant biomarker) and some as high as 0.9 (Table 37–1). Combining more than one miRNA into a “panel” has also been found to increase sensitivity and specificity (Wang et al., 2015a; Raoof et al., 2018; Martins-Ferreira et al., 2020). Notably, the ROC results are often reported following analysis of the original rather than an independent cohort of samples which inflates the value of the biomarker (Pitkanen et al., 2018). When separate cohorts are tested, the ROC results are often lower and of marginal clinical value (Raoof et al., 2018). Studies have also detected changes to circulating miRNA upon introduction of an effective therapeutic (Wang et al., 2017). However, there have also been some contradictory findings. For example, blood levels of miR-134 have been reported to be higher (Spain et al., 2015; Leontariti et al., 2020) but also lower (Avansini et al., 2017) in patients with epilepsy. Clinical variables and seizure monitoring have been considered in some studies, exploring whether levels of a miRNA correlate to seizure frequency or other phenotypes. Results have found limited relationships among the variables (Table 37–1).

A number of studies have investigated whether levels of circulating miRNAs are associated with clinical variables. Perhaps the most obvious is the frequency of seizures but also epilepsy syndrome, age (and age at onset of epilepsy), sex, and treatment response. As mentioned above, there is evidence that a number of miRNAs show differences between treatment-responsive and treatment-resistant patients. There is also evidence that, upon introduction of an effective treatment, circulating miRNA levels return to baseline (Trelinska et al., 2016). Plasma levels of miR-301a-3p were found to correlate to NHS3 seizure burden scores (National Hospital Seizure Severity Scale 3; formerly Chalfont seizure severity scale) (Wang et al., 2015a) and plasma levels of miR-134 were reported to correlate with seizure burden, at least in patients with severe epilepsy (Wang et al., 2017). However, many other studies have failed to identify associations between levels of blood miRNAs and various important clinical parameters like seizure frequency (Table 37–1).

Presence of miRNAs in Blood in Epilepsy—Preclinical Model Findings

A number of groups have profiled and analyzed circulating miRNAs in preclinical models of seizure and epilepsy, presented in Table 37–2. A key advantage of animal studies is the ability to sample following an epilepsy-inciting event but prior to the occurrence of the first spontaneous seizure, thereby identifying putative miRNA biomarkers of epileptogenesis. Animal models also provide opportunities to test whether treatments, either conventional antiseizure drugs or experimental disease-modifying treatments, change miRNA biomarker profiles.

Table 37–2

Preclinical Rodent Studies to Identify Circulating Blood-Based miRNA Prognostic Biomarkers of Epileptogenesis.

The first report was published in 2010, where Liu and colleagues detected miRNAs in blood samples collected 24 h after systemic kainic acid–induced seizures in rats (Liu et al., 2010). However, only three miRNAs passed the criteria of both meaningful expression change (>1.5 fold) and statistical significance. The study was also notable for comparing these blood profiles to those following stroke and intracerebral hemorrhage, revealing 12 miRNAs that were common to all three brain insults.

There have now been several studies that profiled or individually investigated miRNAs in blood samples from rodents after prolonged seizures as the trigger for epilepsy. Overall, the studies support the notion that there are changes in the blood levels of specific miRNAs, and have identified plausible candidate miRNA biomarkers (Table 37–2). This includes some of the same miRNAs identified as differentially expressed in patient blood such as miR-146a-5p. Notably, few of these studies monitored the development of epilepsy, making it uncertain if the findings reflect response to seizure or true epileptogenesis. Moreover, some of the miRNAs found upregulated in blood after seizures in rodents (Chen et al., 2020) do not have a human homolog or are not CNS expressed (Ludwig et al., 2016).

Preclinical models have also been used to assess the effects of treatments on blood profiles. Having identified two candidate miRNA biomarkers in patient plasma, miR-27a-3p and miR-328-3p, Raoof and colleagues investigated what happened to levels of these upon treatment of epileptic mice with conventional and novel therapeutics (Raoof et al., 2018). Dosing epileptic mice with either diazepam or carbamazepine had no effect on plasma levels of either miRNA. In contrast, injection of an antisense oligonucleotide inhibitor of miR-134 which had been shown to strongly mitigate epilepsy, partly restored plasma levels of both miRNAs toward baseline. A caveat to this study was that spontaneous seizures were not monitored in the mice. A second study by the same group reported similar findings. Plasma levels of a series of five miRNAs that were identified during screening of the epileptogenesis phase in three different models were found not to be altered by dosing mice with conventional antiseizure drugs (Brennan et al., 2020). In contrast, plasma levels of three of the five were corrected to baseline upon treatment with a disease-modifying injection of the miRNA inhibitor (Brennan et al., 2020). Again, spontaneous seizure monitoring was not employed in that study. Nevertheless, these findings suggest that certain circulating miRNAs do reflect underlying pathophysiological states and that their measurement could be used to assess disease-modifying actions of experimental compounds.

Circulating Structural Protein Biomarkers of Epilepsy

A variety of brain-enriched and neuronal and glial proteins have been proposed as potential biomarkers of epilepsy and seizure (Table 37–3). The underlying hypothesis is that seizures cause damage to neuronal circuits leading to neuronal loss, gliosis, and reorganization of the extracellular space, accompanied by the release of these proteins into the circulation whereupon they can be detected. Studies have focused on the more abundant proteins in the brain and include neuron-specific enolase (neurons), glial fibrillary acid protein (GFAP; astrocytes), ubiquitin hydroxy terminal hydrolase 1 (UCHL-1, neurons) (Asadollahi and Simani, 2019), and matrix metalloproteinases. Each protein has well-established roles in neuronal and glial structure and function, and some appear to be potential targets for treatment of epilepsy (Ortinski et al., 2010; Reynolds et al., 2017; Pijet et al., 2020).

Table 37–3

Clinical Studies on Circulating, Blood-Based Protein Biomarkers of Epilepsy.

In studies when ROC assessments were made in adequately powered studies, findings indicate good to strong biomarker potential with sensitivity-specificity ranging as high as 90% for some proteins. Two recent studies have reported elevated blood levels of the abundant neuronal protein UCHL1. Asadollahi and colleagues reported serum levels of UCHL1 were elevated in postseizure (6 h) samples from 43 patients diagnosed with epilepsy compared to 20 patients diagnosed with PNES (Asadollahi and Simani, 2019). Serum levels of GFAP, an astrocyte protein, have also been reported to distinguish between epileptic and nonepileptic seizures (Simani et al., 2018). In a well-powered study of 160 patients, plasma levels of UCHL1 were found to be elevated in adults with epilepsy compared to healthy controls (Yasak et al., 2020). The same study reported that levels of UCHL1 did not change in patients who experienced a recent seizure. Two metalloproteinases have been proposed as biomarkers of epilepsy on the basis of their altered levels in serum (Wang et al., 2016a, 2016b). However, in both studies, levels were lower in patients, which is perhaps counterintuitive given the activation of the system in brain tissue from patients. Finally, a recent study reported structure-related protein differences in blood samples, including a set of three proteins and the astrocyte protein S100β, that were predictive of poststroke seizures (Abraira et al., 2020).

The quality and range of evidence for each protein and whether individual or combinations of proteins is required remain uncertain. Some studies are probably underpowered, and important control groups where differential diagnosis is particularly challenging have often been missing. Additional, larger studies with superior study design and a range of control groups are now required.

Current Gaps—What We Know We Don’t Know

Pursuit of circulating blood-based biomarkers among proteins and miRNAs is at an early stage. There remain more questions than answers as to the reproducibility, validity, utility, and practicality of measuring such molecules. The results remain encouraging, however, and enough to continue efforts to find which molecules might best be used. There are a number of clear gaps and opportunities, which were reviewed recently (Enright et al., 2018; Simonato et al., 2021). The most obvious gap is in the overall quantity and quality of evidence. Most published reports have been small, single-center studies. Efforts to expand the knowledge base, replicating findings in separate cohorts and establishing how robust findings are remain essential. A second issue is the heterogeneity of study groups. Many studies have provided limited detail on patient phenotypes, mixing patient groups and syndromes. Standardized protocols for sampling, extraction, and processing are needed, as are common time points, clearly defined groups, objective measurements of seizures, and comparisons between focal and generalized epilepsies. Too often the biomarker value has been assessed using the original cohort of samples. Independent validation cohorts are needed, particularly where studies begin with a discovery arm. Another area for improvement is the inclusion of additional control groups. Many studies have compared findings in patients with chronic, drug-resistant epilepsy to healthy controls. However, it would be of more clinical use to know if the cases could be differentiated against other neurological diseases and clinical presentations such as PNES or syncope. Such groups have been included in some studies (Raoof et al., 2018; Brennan et al., 2020), but this should be more widespread.

Another area that needs additional study is in establishing mechanistic links between the putative molecular biomarker and the disease. For example, several of the miRNAs identified as increased in patient blood are not expressed in the brain when reference atlases are checked (Ludwig et al., 2016). The uptake of miRNAs as biomarkers will be improved if evidence can be provided that the miRNAs come from the brain. This is possible, with experimental models and labeling techniques, such as tracking the presence of brain cell type-specific Ago and their bound miRNAs in the blood during the development of epilepsy in transgenic mice (Brindley et al., 2023). Preclinical models can also be used to measure how introduction of a disease-modifying therapy shifts patterns of circulating miRNAs.

Etiology is likely to be important, in particular for biomarkers of epileptogenesis. Patterns of alterations in circulating molecules are likely to reflect the particular insult that occurred, reflecting a release of these molecules from diverse cell types. The study by Liu et al. showed that three different epileptogenic triggers produce unique as well as some overlapping miRNA signals in the blood (Liu et al., 2010). There is already substantial literature on circulating miRNA biomarkers after traumatic and ischemic brain injuries in humans (Toffolo et al., 2019). Clinical studies that sample blood proteins and miRNAs following epileptogenic injuries (and later follow those cases to record the epilepsy) are needed. Certain genetic syndromes such as tuberous sclerosis complex, often diagnosed before epilepsy emerges, could be a powerful resource for identifying circulating biomarkers of human epileptogenesis (De Ridder et al., 2021).

Summary and Conclusions

There remains an urgent and unmet need for biomarkers for a range of diagnostic applications in epilepsy. Circulating molecules such as proteins and miRNAs are highly attractive in this regard, being simple, cheap, and easy to measure. Recent studies support their potential use, but questions remain and their promise has not yet been realized. Attention to the gaps in our understanding and the design of adequately powered, carefully planned, multicenter studies will be essential to tackle these challenges. A strategic roadmap to facilitate the identification, characterization, and clinical validation of biomarkers has been recently proposed, which may help properly addressing these issues (Simonato et al., 2021). In parallel, experimental models can provide mechanistic support for their use, and technological developments such as rapid, point-of-care diagnostics could help deliver these much-needed supports into neurology clinics, emergency departments, and perhaps even at home.

Acknowledgments

Disclosure Statement

References

1. Abraira, L., Santamarina, E., Cazorla, S., Bustamante, A., Quintana, M., Toledo, M., Fonseca, E., Grau- Lopez, L., Jimenez, M., Ciurans, J., Luis Becerra, J., Millan, M., Hernandez- Perez, M., Cardona, P., Terceno, M., Zaragoza, J., Canovas, D., Gasull, T., Ustrell, X., Rubiera, M., Castellanos, M., Montaner, J. & Alvarezsabin, J.  2020. Blood biomarkers predictive of epilepsy after an acute stroke event.  Epilepsia, 61, 2244–2253. [PubMed: 32857458]
2. Alves De Lima, K., Rustenhoven, J. & Kipnis, J.  2020. Meningeal immunity and its function in maintenance of the central nervous system in health and disease.  Annu Rev Immunol, 38, 597–620. [PubMed: 32340575]
3. Andersen, P. & Moseklide, L.  1977. Immunoglobulin levels and autoantibodies in epileptics on long-term anticonvulsant therapy.  Acta Med Scand, 201, 69–74. [PubMed: 835374]
4. Aronica, E., Fluiter, K., Iyer, A., Zurolo, E., Vreijling, J., Van Vliet, E. A., Baayen, J. C. & Gorter, J. A.  2010. Expression pattern of miR-146a, an inflammation-associated microRNA, in experimental and human temporal lobe epilepsy.  Eur J Neurosci, 31, 1100–7. [PubMed: 20214679]
5. Arroyo, J. D., Chevillet, J. R., Kroh, E. M., Ruf, I. K., Pritchard, C. C., Gibson, D. F., Mitchell, P. S., Bennett, C. F., Pogosova-Agadjanyan, E. L., Stirewalt, D. L., Tait, J. F. & Tewari, M.  2011. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma.  Proc Natl Acad Sci U S A, 108, 5003–8. [PMC free article: PMC3064324] [PubMed: 21383194]
6. Asadollahi, M. & Simani, L.  2019. The diagnostic value of serum UCHL-1 and S100-B levels in differentiate epileptic seizures from psychogenic attacks.  Brain Res, 1704, 11–15. [PubMed: 30253122]
7. Avansini, S. H., De Sousa Lima, B. P., Secolin, R., Santos, M. L., Coan, A. C., Vieira, A. S., Torres, F. R., Carvalho, B. S., Alvim, M. K., Morita, M. E., Yasuda, C. L., Pimentel-Silva, L. R., Dogini, D. B., Rogerio, F., Cendes, F. & Lopes-Cendes, I.  2017. MicroRNA hsa-miR-134 is a circulating biomarker for mesial temporal lobe epilepsy.  PLoS One, 12, e0173060. [PMC free article: PMC5383023] [PubMed: 28384161]
8. Bartel, D. P.  2018. Metazoan MicroRNAs.  Cell, 173, 20–51. [PMC free article: PMC6091663] [PubMed: 29570994]
9. Bartolini, L., Piras, E., Sullivan, K., Gillen, S., Bumbut, A., Lin, C. M., Leibovitch, E. C., Graves, J. S., Waubant, E. L., Chamberlain, J. M., Gaillard, W. D. & Jacobson, S.  2018. Detection of HHV-6 and EBV and cytokine levels in saliva from children with seizures: results of a multi-center cross-sectional study. Front Neurol, 9, 834. [PMC free article: PMC6182262] [PubMed: 30344507]
10. Beamer, E., Lacey, A., Alves, M., Conte, G., Tian, F., De Diego-Garcia, L., Khalil, M., Rosenow, F., Delanty, N., Dale, N., El-Naggar, H., Henshall, D. C. & Engel, T.  2021. Elevated blood purine levels as a biomarker of seizures and epilepsy.  Epilepsia, 62, 817–828. [PubMed: 33599287]
11. Brennan, G. P., Bauer, S., Engel, T., Jimenez-Mateos, E. M., Del Gallo, F., Hill, T. D. M., Connolly, N. M. C., Costard, L. S., Neubert, V., Salvetti, B., Sanz-Rodriguez, A., Heiland, M., Mamad, O., Brindley, E., Norwood, B., Batool, A., Raoof, R., El-Naggar, H., Reschke, C. R., Delanty, N., Prehn, J. H. M., Fabene, P., Mooney, C., Rosenow, F. & Henshall, D. C.  2020. Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy.  Neurobiol Dis, 144, 105048. [PubMed: 32800995]
12. Brennan, G. P. & Henshall, D. C.  2020. MicroRNAs as regulators of brain function and targets for treatment of epilepsy.  Nature Reviews Neurology, 16, 506–519. [PubMed: 32546757]
13. Brindley, E., Heiland, M., Mooney, C., Diviney, M., Mamad, O., Hill, T.d.m., Yan, Y., Veno, M.t., Reschke, C.r., Batool, A., Langa, E., Sanz-Rodriguez, A., Heller, J.p., Morris, G., Conboy, K., Kjems, J., Brennan, G.P., Henshall, D.C.  2023. Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy.  Front Mol Neurosci, 16, 1230942. [PMC free article: PMC10556253] [PubMed: 37808470]
14. Caldonazzo, C., Senini, G. & Bignami, M.  1964. Electrophoretic research on serum and cerebrospinal fluid proteins in epileptic subjects in inter- and post-seizure phases.  Acta Neurol (Napoli), 19, 600–15. [PubMed: 14208403]
15. Caramaschi, D., Hatcher, C., Mulder, R. H., Felix, J. F., Cecil, C. A. M., Relton, C. L. & Walton, E.  2020. Epigenome-wide association study of seizures in childhood and adolescence.  Clin Epigenetics, 12, 8. [PMC free article: PMC6950851] [PubMed: 31915053]
16. Chandradoss, S. D., Schirle, N. T., Szczepaniak, M., Macrae, I. J. & Joo, C.  2015. A dynamic search process underlies microRNA targeting.  Cell, 162, 96–107. [PMC free article: PMC4768356] [PubMed: 26140593]
17. Chen, D. K., So, Y. T., Fisher, R. S., Therapeutics & Technology Assessment Subcommittee Of The American Academy Of, N. 2005. Use of serum prolactin in diagnosing epileptic seizures: report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology.  Neurology, 65, 668–75. [PubMed: 16157897]
18. Chen, M., Zhao, Q. Y., Edson, J., Zhang, Z. H., Li, X., Wei, W., Bredy, T. & Reutens, D. C.  2020. Genome-wide microRNA profiling in brain and blood samples in a mouse model of epileptogenesis.  Epilepsy Res, 166, 106400. [PubMed: 32590288]
19. Chowdhury, F. A., Nashef, L. & Elwes, R. D.  2008. Misdiagnosis in epilepsy: a review and recognition of diagnostic uncertainty.  Eur J Neurol, 15, 1034–42. [PubMed: 18717721]
20. Daneman, R. & Prat, A.  2015. The blood-brain barrier.  Cold Spring Harb Perspect Biol, 7, a020412. [PMC free article: PMC4292164] [PubMed: 25561720]
21. Davies, V. A., Rothberg, A. D., Argent, A. C., Atkinson, P. M., Staub, H. & Pienaar, N. L.  1985. Precursor prothrombin status in patients receiving anticonvulsant drugs.  Lancet, 1, 126–8. [PubMed: 2857211]
22. De Ridder, J., Verhelle, B., Vervisch, J., Lemmens, K., Kotulska, K., Moavero, R., Curatolo, P., Weschke, B., Riney, K., Feucht, M., Krsek, P., Nabbout, R., Jansen, A. C., Wojdan, K., Domanska-Pakiela, D., Kaczorowska-Frontczak, M., Hertzberg, C., Ferrier, C. H., Samueli, S., Benova, B., Aronica, E., Kwiatkowski, D. J., Jansen, F. E., Jozwiak, S., Lagae, L. & Consortium, E.  2021. Early epileptiform EEG activity in infants with tuberous sclerosis complex predicts epilepsy and neurodevelopmental outcomes.  Epilepsia, 62, 1208–1219. [PubMed: 33778971]
23. Ebert, M. S. & Sharp, P. A.  2012. Roles for microRNAs in conferring robustness to biological processes.  Cell, 149, 515–24. [PMC free article: PMC3351105] [PubMed: 22541426]
24. Enright, N., Simonato, M. & Henshall, D. C.  2018. Discovery and validation of blood microRNAs as molecular biomarkers of epilepsy—ways to close current knowledge gaps.  Epilepsia Open, 3, 427–436. [PMC free article: PMC6276772] [PubMed: 30525113]
25. French, J. A., Cole, A. J., Faught, E., Theodore, W. H., Vezzani, A., Liow, K., Halford, J. J., Armstrong, R., Szaflarski, J. P., Hubbard, S., Patel, J., Chen, K., Feng, W., Rizzo, M., Elkins, J., Knafler, G., Parkerson, K. A. & Group, O. S.  2021. Safety and Efficacy of Nataluzimab as Adjunctive Therapy for People With Drug-Resistant Epilepsy: A Phase 2 Study.  Neurology, 97, e1757-e1767. [PMC free article: PMC8610627] [PubMed: 34521687]
26. Fujita, A., Ota, M. & Kato, K.  2019. Urinary volatile metabolites of amygdala-kindled mice reveal novel biomarkers associated with temporal lobe epilepsy.  Sci Rep, 9, 10586. [PMC free article: PMC6646363] [PubMed: 31332211]
27. Gallentine, W. B., Shinnar, S., Hesdorffer, D. C., Epstein, L., Nordli, D. R., JR., Lewis, D. V., Frank, L. M., Seinfeld, S., Shinnar, R. C., Cornett, K., Liu, B., Moshe, S. L., Sun, S. & Team, F. I.  2017. Plasma cytokines associated with febrile status epilepticus in children: A potential biomarker for acute hippocampal injury.  Epilepsia, 58, 1102–1111. [PMC free article: PMC5482499] [PubMed: 28448686]
28. Gledhill, J. M., Brand, E. J., Pollard, J. R., St Clair, R. D., Wallach, T. M. & Crino, P. B.  2021. Association of epileptic and nonepileptic seizures and changes in circulating plasma proteins linked to neuroinflammation.  Neurology, 96, e1443-e1452. [PMC free article: PMC8055314] [PubMed: 33495377]
29. Gomes-Duarte, A., Bauer, S., Veno, M. T., Norwood, B. A., Henshall, D. C., Kjems, J., Rosenow, F., Vangoor, V. R. & Pasterkamp, R. J.  2021. Enrichment of circular RNA expression deregulation at the transition to recurrent spontaneous seizures in experimental temporal lobe epilepsy.  Front Genet, 12, 627907. [PMC free article: PMC7876452] [PubMed: 33584828]
30. Gong, G. H., An, F. M., Wang, Y., Bian, M., Wang, D. & Wei, C. X.  2018. Comprehensive circular RNA profiling reveals the regulatory role of the CircRNA-0067835/miR-155 pathway in temporal lobe epilepsy.  Cell Physiol Biochem, 51, 1399–1409. [PubMed: 30485839]
31. Gorter, J. A., Iyer, A., White, I., Colzi, A., Van Vliet, E. A., Sisodiya, S. & Aronica, E.  2014. Hippocampal subregion-specific microRNA expression during epileptogenesis in experimental temporal lobe epilepsy.  Neurobiol Dis, 62, 508–20. [PubMed: 24184920]
32. Gorter, J. A., Van Vliet, E. A. & Aronica, E.  2015. Status epilepticus, blood-brain barrier disruption, inflammation, and epileptogenesis.  Epilepsy Behav, 49, 13–6. [PubMed: 25958228]
33. Gray, L. G., Mills, J. D., Curry-Hyde, A., Devore, S., Friedman, D., Thom, M., Scott, C., Thijs, R. D., Aronica, E., Devinsky, O. & Janitz, M.  2020. Identification of specific circular RNA expression patterns and microRNA interaction networks in mesial temporal lobe epilepsy.  Front Genet, 11, 564301. [PMC free article: PMC7546880] [PubMed: 33101384]
34. Hansen, T. B., Jensen, T. I., Clausen, B. H., Bramsen, J. B., Finsen, B., Damgaard, C. K. & Kjems, J.  2013. Natural RNA circles function as efficient microRNA sponges. Nature, 495, 384–388. [PubMed: 23446346]
35. Hawkins, N. A., Jurado, M., Thaxton, T. T., Duarte, S. E., Barse, L., Tatsukawa, T., Yamakawa, K., Nishi, T., Kondo, S., Miyamoto, M., Abrahams, B. S., During, M. J. & Kearney, J. A.  2021. Soticlestat, a novel cholesterol 24-hydroxylase inhibitor, reduces seizures and premature death in Dravet syndrome mice.  Epilepsia, in press. [PMC free article: PMC9291096] [PubMed: 34510432]
36. Hegde, M. & Lowenstein, D. H.  2014. The search for circulating epilepsy biomarkers.  Biomark Med, 8, 413–27. [PubMed: 24712433]
37. Heischmann, S., Quinn, K., Cruickshank-Quinn, C., Liang, L. P., Reisdorph, R., Reisdorph, N. & Patel, M.  2016. Exploratory Metabolomics Profiling in the Kainic Acid Rat Model Reveals Depletion of 25-Hydroxyvitamin D3 during Epileptogenesis.  Sci Rep, 6, 31424. [PMC free article: PMC4985632] [PubMed: 27526857]
38. Hogg, M.C., Raoof, R., El Naggar, H., Monsefi, N., Delanty, N., O'brien, D.f., Bauer, S., Rosenow, F., Henshall, D.c., Prehn, J.h. Elevation in plasma tRNA fragments precede seizures in human epilepsy. 2019. J Clin Invest. 129, 2946–2951. [PMC free article: PMC6597227] [PubMed: 31039137]
39. Hu, K., Zhang, C., Long, L., Long, X., Feng, L., Li, Y. & Xiao, B.  2011. Expression profile of microRNAs in rat hippocampus following lithium-pilocarpine-induced status epilepticus.  Neurosci Lett, 488, 252–7. [PubMed: 21094214]
40. Iori, V., Iyer, A. M., Ravizza, T., Beltrame, L., Paracchini, L., Marchini, S., Cerovic, M., Hill, C., Ferrari, M., Zucchetti, M., Molteni, M., Rossetti, C., Brambilla, R., Steve White, H., D’incalci, M., Aronica, E. & Vezzani, A.  2017. Blockade of the IL-1R1/TLR4 pathway mediates disease-modification therapeutic effects in a model of acquired epilepsy.  Neurobiol Dis, 99, 12–23. [PubMed: 27939857]
41. Jeppesen, D. K., Fenix, A. M., Franklin, J. L., Higginbotham, J. N., Zhang, Q., Zimmerman, L. J., Liebler, D. C., Ping, J., Liu, Q., Evans, R., Fissell, W. H., Patton, J. G., Rome, L. H., Burnette, D. T. & Coffey, R. J.  2019. Reassessment of Exosome Composition.  Cell, 177, 428–445 e18. [PMC free article: PMC6664447] [PubMed: 30951670]
42. Jimenez-Mateos, E. M., Arribas-Blazquez, M., Sanz-Rodriguez, A., Concannon, C., Olivos-Ore, L. A., Reschke, C. R., Mooney, C. M., Mooney, C., Lugara, E., Morgan, J., Langa, E., Jimenez-Pacheco, A., Silva, L. F., Mesuret, G., Boison, D., Miras-Portugal, M. T., Letavic, M., Artalejo, A. R., Bhattacharya, A., Diaz-Hernandez, M., Henshall, D. C. & Engel, T.  2015. microRNA targeting of the P2X7 purinoceptor opposes a contralateral epileptogenic focus in the hippocampus.  Sci Rep, 5, 17486. [PMC free article: PMC4668358] [PubMed: 26631939]
43. Jimenez-Mateos, E. M., Bray, I., Sanz-Rodriguez, A., Engel, T., Mckiernan, R. C., Mouri, G., Tanaka, K., Sano, T., Saugstad, J. A., Simon, R. P., Stallings, R. L. & Henshall, D. C.  2011. miRNA Expression profile after status epilepticus and hippocampal neuroprotection by targeting miR-132.  Am J Pathol, 179, 2519–32. [PMC free article: PMC3204080] [PubMed: 21945804]
44. Kamasak, T., Dilber, B., Yaman, S. O., Durgut, B. D., Kurt, T., Coban, E., Arslan, E. A., Sahin, S., Karahan, S. C. & Cansu, A.  2020. HMGB-1, TLR4, IL-1R1, TNF-alpha, and IL-1beta: novel epilepsy markers?  Epileptic Disord, 22, 183–193. [PubMed: 32301731]
45. Kan, A. A., Van Erp, S., Derijck, A. A., De Wit, M., Hessel, E. V., O’duibhir, E., De Jager, W., Van Rijen, P. C., Gosselaar, P. H., De Graan, P. N. & Pasterkamp, R. J.  2012. Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response.  Cell Mol Life Sci, 69, 3127–3145. [PMC free article: PMC3428527] [PubMed: 22535415]
46. Kan, M., Song, L., Zhang, X., Zhang, J. & Fang, P.  2019. Circulating high mobility group box-1 and toll-like receptor 4 expressions increase the risk and severity of epilepsy.  Braz J Med Biol Res, 52, e7374. [PMC free article: PMC6596364] [PubMed: 31241711]
47. Kaplan, L., Chow, B. W. & Gu, C.  2020. Neuronal regulation of the blood-brain barrier and neurovascular coupling.  Nat Rev Neurosci, 21, 416–432. [PMC free article: PMC8934575] [PubMed: 32636528]
48. Kearney, H., Byrne, S., Cavalleri, G. L. & Delanty, N.  2019. Tackling epilepsy with high-definition precision medicine: A review.  JAMA Neurol, 76, 1109–1116. [PubMed: 31380988]
49. Kenny, A., Jimenez-Mateos, E. M., Zea-Sevilla, M. A., Rabano, A., Gili-Manzanaro, P., Prehn, J. H. M., Henshall, D. C., Avila, J., Engel, T. & Hernandez, F.  2019. Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer’s disease.  Sci Rep, 9, 15437. [PMC free article: PMC6817868] [PubMed: 31659197]
50. Kur, I. M., Prouvot, P. H., Fu, T., Fan, W., Muller-Braun, F., Das, A., Das, S., Deller, T., Roeper, J., Stroh, A. & Momma, S.  2020. Neuronal activity triggers uptake of hematopoietic extracellular vesicles in vivo.  PLoS Biol, 18, e3000643. [PMC free article: PMC7075544] [PubMed: 32176686]
51. Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W. & Tuschl, T.  2002. Identification of tissue-specific microRNAs from mouse.  Curr Biol, 12, 735–9. [PubMed: 12007417]
52. Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., Lin, C., Socci, N. D., Hermida, L., Fulci, V., Chiaretti, S., Foa, R., Schliwka, J., Fuchs, U., Novosel, A., Muller, R. U., Schermer, B., Bissels, U., Inman, J., Phan, Q., Chien, M., Weir, D. B., Choksi, R., De Vita, G., Frezzetti, D., Trompeter, H. I., Hornung, V., Teng, G., Hartmann, G., Palkovits, M., Di Lauro, R., Wernet, P., Macino, G., Rogler, C. E., Nagle, J. W., Ju, J., Papavasiliou, F. N., Benzing, T., Lichter, P., Tam, W., Brownstein, M. J., Bosio, A., Borkhardt, A., Russo, J. J., Sander, C., Zavolan, M. & Tuschl, T.  2007. A mammalian microRNA expression atlas based on small RNA library sequencing.  Cell, 129, 1401–14. [PMC free article: PMC2681231] [PubMed: 17604727]
53. Lee, R. C., Feinbaum, R. L. & Ambros, V.  1993. The C.  elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.  Cell, 75, 843–54. [PubMed: 8252621]
54. Lee, W. J., Moon, J., Jeon, D., Kim, T. J., Yoo, J. S., Park, D. K., Lee, S. T., Jung, K. H., Park, K. I., Jung, K. Y., Kim, M., Lee, S. K. & Chu, K.  2018. Possible epigenetic regulatory effect of dysregulated circular RNAs in epilepsy.  PLoS One, 13, e0209829. [PMC free article: PMC6310357] [PubMed: 30592747]
55. Leontariti, M., Avgeris, M., Katsarou, M. S., Drakoulis, N., Siatouni, A., Verentzioti, A., Alexoudi, A., Fytraki, A., Patrikelis, P., Vassilacopoulou, D., Gatzonis, S. & Sideris, D. C.  2020. Circulating miR-146a and miR-134 in predicting drug-resistant epilepsy in patients with focal impaired awareness seizures.  Epilepsia, 61, 959–970. [PubMed: 32314378]
56. Levy, E.  2017. Exosomes in the diseased brain: First insights from in vivo studies.  Front Neurosci, 11, 142. [PMC free article: PMC5362612] [PubMed: 28386213]
57. Li, J., Lin, H., Sun, Z., Kong, G., Yan, X., Wang, Y., Wang, X., Wen, Y., Liu, X., Zheng, H., Jia, M., Shi, Z., Xu, R., Yang, S. & Yuan, F.  2018. High-throughput data of circular RNA profiles in human temporal cortex tissue reveals novel insights into temporal lobe epilepsy. Cell Physiol Biochem, 45, 677–691. [PubMed: 29428937]
58. Liu, D. Z., Tian, Y., Ander, B. P., Xu, H., Stamova, B. S., Zhan, X., Turner, R. J., Jickling, G. & Sharp, F. R.  2010. Brain and blood microRNA expression profiling of ischemic stroke, intracerebral hemorrhage, and kainate seizures.  J Cereb Blood Flow Metab, 30, 92–101. [PMC free article: PMC2949089] [PubMed: 19724284]
59. Long, H. Y., Feng, L., Kang, J., Luo, Z. H., Xiao, W. B., Long, L. L., Yan, X. X., Zhou, L. & Xiao, B.  2017. Blood DNA methylation pattern is altered in mesial temporal lobe epilepsy.  Sci Rep, 7, 43810. [PMC free article: PMC5343463] [PubMed: 28276448]
60. Loscher, W., Klitgaard, H., Twyman, R. E. & Schmidt, D.  2013. New avenues for anti-epileptic drug discovery and development.  Nat Rev Drug Discov, 12, 757–76. [PubMed: 24052047]
61. Lovisari, F. & Simonato, M.  2019. Gene networks and microRNAs: Promises and challenges for treating epilepsies and their comorbidities.  Epilepsy Behav, 106488. [PubMed: 31494060]
62. Ludwig, N., Leidinger, P., Becker, K., Backes, C., Fehlmann, T., Pallasch, C., Rheinheimer, S., Meder, B., Stahler, C., Meese, E. & Keller, A.  2016. Distribution of miRNA expression across human tissues.  Nucleic Acids Res, 44, 3865–77. [PMC free article: PMC4856985] [PubMed: 26921406]
63. Marchi, N., Granata, T., Ghosh, C. & Janigro, D.  2012. Blood-brain barrier dysfunction and epilepsy: pathophysiologic role and therapeutic approaches.  Epilepsia, 53, 1877–86. [PMC free article: PMC4842020] [PubMed: 22905812]
64. Martins-Ferreira, R., Chaves, J., Carvalho, C., Bettencourt, A., Chorao, R., Freitas, J., Samoes, R., Boleixa, D., Lopes, J., Ramalheira, J., Da Silva, B. M., Martins Da Silva, A., Costa, P. P. & Leal, B.  2020. Circulating microRNAs as potential biomarkers for genetic generalized epilepsies: a three microRNA panel.  Eur J Neurol, 27, 660–666. [PubMed: 31746515]
65. Mcardle, H., Jimenez-Mateos, E. M., Raoof, R., Carthy, E., Boyle, D., Elnaggar, H., Delanty, N., Hamer, H., Dogan, M., Huchtemann, T., Krtvelyessy, P., Rosenow, F., Forster, R. J., Henshall, D. C. & Spain, E.  2017. “TORNADO”—Theranostic One-Step RNA Detector; microfluidic disc for the direct detection of microRNA-134 in plasma and cerebrospinal fluid.  Sci Rep, 7, 1750. [PMC free article: PMC5431952] [PubMed: 28496112]
66. Mcdonald, J. S., Milosevic, D., Reddi, H. V., Grebe, S. K. & Algeciras-Schimnich, A.  2011. Analysis of circulating microRNA: preanalytical and analytical challenges.  Clin Chem, 57, 833–40. [PubMed: 21487102]
67. Mckiernan, R. C., Jimenez-Mateos, E. M., Bray, I., Engel, T., Brennan, G. P., Sano, T., Michalak, Z., Moran, C., Delanty, N., Farrell, M., O’brien, D., Meller, R., Simon, R. P., Stallings, R. L. & Henshall, D. C.  2012. Reduced mature microRNA levels in association with Dicer loss in human temporal lobe epilepsy with hippocampal sclerosis.  PLoS One, 7, e35921. [PMC free article: PMC3352899] [PubMed: 22615744]
68. Nakanishi, K.  2016. Anatomy of RISC: how do small RNAs and chaperones activate Argonaute proteins?  Wiley Interdiscip Rev RNA, 7, 637–60. [PMC free article: PMC5084781] [PubMed: 27184117]
69. Nowakowski, T. J., Rani, N., Golkaram, M., Zhou, H. R., Alvarado, B., Huch, K., West, J. A., Leyrat, A., Pollen, A. A., Kriegstein, A. R., Petzold, L. R. & Kosik, K. S.  2018. Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development.  Nat Neurosci, 21, 1784–1792. [PMC free article: PMC6312854] [PubMed: 30455455]
70. Nudelman, A. S., Dirocco, D. P., Lambert, T. J., Garelick, M. G., Le, J., Nathanson, N. M. & Storm, D. R.  2010. Neuronal activity rapidly induces transcription of the CREB-regulated microRNA-132, in vivo.  Hippocampus, 20, 492–8. [PMC free article: PMC2847008] [PubMed: 19557767]
71. O’carroll, D. & Schaefer, A.  2013. General principals of miRNA biogenesis and regulation in the brain.  Neuropsychopharmacology, 38, 39–54. [PMC free article: PMC3521995] [PubMed: 22669168]
72. Obermeier, B., Daneman, R. & Ransohoff, R. M.  2013. Development, maintenance and disruption of the blood-brain barrier.  Nat Med, 19, 1584–96. [PMC free article: PMC4080800] [PubMed: 24309662]
73. Ortinski, P. I., Dong, J., Mungenast, A., Yue, C., Takano, H., Watson, D. J., Haydon, P. G. & Coulter, D. A.  2010. Selective induction of astrocytic gliosis generates deficits in neuronal inhibition.  Nat Neurosci, 13, 584–91. [PMC free article: PMC3225960] [PubMed: 20418874]
74. Oto, M. M.  2017. The misdiagnosis of epilepsy: Appraising risks and managing uncertainty.  Seizure, 44, 143–146. [PubMed: 28017581]
75. Ozata, D. M., Gainetdinov, I., Zoch, A., O’carroll, D. & Zamore, P. D.  2019. PIWI-interacting RNAs: small RNAs with big functions.  Nat Rev Genet, 20, 89–108. [PubMed: 30446728]
76. Perez-Gonzalez, R., Gauthier, S. A., Kumar, A., Saito, M., Saito, M. & Levy, E.  2017. A Method for Isolation of extracellular vesicles and characterization of exosomes from brain extracellular space.  Methods Mol Biol, 1545, 139–151. [PubMed: 27943212]
77. Permin, H. & Sestoft, L.  1977. Deposits of plasma proteins in the skin during treatment with carbamazepine and diphenylhydantoin.  Acta Med Scand, 202, 113–7. [PubMed: 331881]
78. Pijet, B., Konopka, A., Rejmak, E., Stefaniuk, M., Khomiak, D., Bulska, E., Pikul, S. & Kaczmarek, L.  2020. The matrix metalloproteinase inhibitor marimastat inhibits seizures in a model of kainic acid-induced status epilepticus.  Sci Rep, 10, 21314. [PMC free article: PMC7718901] [PubMed: 33277582]
79. Pitkanen, A., Ekolle Ndode-Ekane, X., Lapinlampi, N. & Puhakka, N.  2018. Epilepsy biomarkers—Toward etiology and pathology specificity.  Neurobiol Dis, 123, 42–58. [PMC free article: PMC6240498] [PubMed: 29782966]
80. Pitkanen, A., Loscher, W., Vezzani, A., Becker, A. J., Simonato, M., Lukasiuk, K., Grohn, O., Bankstahl, J. P., Friedman, A., Aronica, E., Gorter, J. A., Ravizza, T., Sisodiya, S. M., Kokaia, M. & Beck, H.  2016. Advances in the development of biomarkers for epilepsy.  Lancet Neurol, 15, 843–56. [PubMed: 27302363]
81. Pollard, J. R., Eidelman, O., Mueller, G. P., Dalgard, C. L., Crino, P. B., Anderson, C. T., Brand, E. J., Burakgazi, E., Ivaturi, S. K. & Pollard, H. B.  2012. The TARC/sICAM5 ratio in patient plasma is a candidate biomarker for drug resistant epilepsy.  Front Neurol, 3, 181. [PMC free article: PMC3535822] [PubMed: 23293627]
82. Porter, R. J. & Layzer, R. B.  1975. Plasma albumin concentration and diphenylhydantoin binding in man.  Arch Neurol, 32, 298–303. [PubMed: 237499]
83. Raoof, R., Bauer, S., El Naggar, H., Connolly, N. M. C., Brennan, G. P., Brindley, E., Hill, T., Mcardle, H., Spain, E., Forster, R. J., Prehn, J. H. M., Hamer, H., Delanty, N., Rosenow, F., Mooney, C. & Henshall, D. C.  2018. Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy.  EBioMedicine, 38, 127–141. [PMC free article: PMC6306312] [PubMed: 30396857]
84. Raoof, R., Jimenez-Mateos, E. M., Bauer, S., Tackenberg, B., Rosenow, F., Lang, J., Onugoren, M. D., Hamer, H., Huchtemann, T., Kortvelyessy, P., Connolly, N. M. C., Pfeiffer, S., Prehn, J. H. M., Farrell, M. A., O’brien, D. F., Henshall, D. C. & Mooney, C.  2017. Cerebrospinal fluid microRNAs are potential biomarkers of temporal lobe epilepsy and status epilepticus.  Sci Rep, 7, 3328. [PMC free article: PMC5468228] [PubMed: 28607431]
85. Rasmussen, M. K., Mestre, H. & Nedergaard, M.  2022. Fluid Transport in the Brain.  Physiol Rev, in press, 102, 1025–1151. [PMC free article: PMC8897154] [PubMed: 33949874]
86. Reynolds, J. P., Jimenez-Mateos, E. M., Cao, L., Bian, F., Alves, M., Miller-Delaney, S. F., Zhou, A. & Henshall, D. C.  2017. Proteomic analysis after status epilepticus identifies UCHL1 as protective against hippocampal injury.  Neurochem Res, 42, 2033–2054. [PMC free article: PMC5505805] [PubMed: 28397067]
87. Roncon, P., Soukupova, M., Binaschi, A., Falcicchia, C., Zucchini, S., Ferracin, M., Langley, S. R., Petretto, E., Johnson, M. R., Marucci, G., Michelucci, R., Rubboli, G. & Simonato, M.  2015. MicroRNA profiles in hippocampal granule cells and plasma of rats with pilocarpine-induced epilepsy—comparison with human epileptic samples.  Sci Rep, 5, 14143. [PMC free article: PMC4585664] [PubMed: 26382856]
88. Roy, R., Pattnaik, S., Sivagurunathan, S. & Chidambaram, S.  2020. Small ncRNA binding protein, PIWI: A potential molecular bridge between blood brain barrier and neuropathological conditions.  Med Hypotheses, 138, 109609. [PubMed: 32070788]
89. Ruber, T., David, B., Luchters, G., Nass, R. D., Friedman, A., Surges, R., Stocker, T., Weber, B., Deichmann, R., Schlaug, G., Hattingen, E. & Elger, C. E.  2018. Evidence for peri-ictal blood-brain barrier dysfunction in patients with epilepsy.  Brain, 141, 2952–2965. [PubMed: 30239618]
90. Simani, L., Elmi, M. & Asadollahi, M.  2018. Serum GFAP level: A novel adjunctive diagnostic test in differentiate epileptic seizures from psychogenic attacks.  Seizure, 61, 41–44. [PubMed: 30077862]
91. Simonato, M., Agoston, D., Brooks-Kayal, A., Dulla, C., Fureman, B., Henshall, D. C., Pitkänen, A., Theodore, W., Twyman, R., Kobeissy, F., Wang, K., Whittemore, V. & K., W.  2021. Identification of clinically relevant biomarkers of epileptogenesis — a strategic roadmap.  Nat Rev Neurol, 17, 231–242. [PubMed: 33594276]
92. Solimena, M., Folli, F., Denis-Donini, S., Comi, G. C., Pozza, G., De Camilli, P. & Vicari, A. M.  1988. Autoantibodies to glutamic acid decarboxylase in a patient with stiff-man syndrome, epilepsy, and type I diabetes mellitus.  N Engl J Med, 318, 1012–20. [PubMed: 3281011]
93. Spain, E., Jimenez-Mateos, E. M., Raoof, R., El Naggar, H., Delanty, N., Forster, R. J. & Henshall, D. C.  2015. Direct, non-amplified detection of microRNA-134 in plasma from epilepsy patients.  RSC Advances, 5, 90071–90078.
94. Statello, L., Guo, C. J., Chen, L. L. & Huarte, M.  2021. Gene regulation by long non-coding RNAs and its biological functions.  Nat Rev Mol Cell Biol, 22, 96–118. [PMC free article: PMC7754182] [PubMed: 33353982]
95. Toffolo, K., Osei, J., Kelly, W., Poulsen, A., Donahue, K., Wang, J., Hunter, M., Bard, J., Wang, J. & Poulsen, D.  2019. Circulating microRNAs as biomarkers in traumatic brain injury.  Neuropharmacology, 145, 199–208. [PubMed: 30195586]
96. Trelinska, J., Fendler, W., Dachowska, I., Kotulska, K., Jozwiak, S., Antosik, K., Gnys, P., Borowiec, M. & Mlynarski, W.  2016. Abnormal serum microRNA profiles in tuberous sclerosis are normalized during treatment with everolimus: possible clinical implications.  Orphanet J Rare Dis, 11, 129. [PMC free article: PMC5041396] [PubMed: 27680012]
97. Trimble, M. R.  1978. Serum prolactin in epilepsy and hysteria.  Br Med J, 2, 1682. [PMC free article: PMC1608938] [PubMed: 737437]
98. Valadi, H., Ekstrom, K., Bossios, A., Sjostrand, M., Lee, J. J. & Lotvall, J. O.  2007. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells.  Nat Cell Biol, 9, 654–9. [PubMed: 17486113]
99. Vezzani, A., Balosso, S. & Ravizza, T.  2019. Neuroinflammatory pathways as treatment targets and biomarkers in epilepsy.  Nat Rev Neurol, 15, 459–472. [PubMed: 31263255]
100. Vezzani, A., French, J., Bartfai, T. & Baram, T. Z.  2011. The role of inflammation in epilepsy.  Nat Rev Neurol, 7, 31–40. [PMC free article: PMC3378051] [PubMed: 21135885]
101. Wang, J., Tan, L., Tan, L., Tian, Y., Ma, J., Tan, C. C., Wang, H. F., Liu, Y., Tan, M. S., Jiang, T. & Yu, J. T.  2015a. Circulating microRNAs are promising novel biomarkers for drug-resistant epilepsy.  Sci Rep, 5, 10201. [PMC free article: PMC4435024] [PubMed: 25984652]
102. Wang, J., Yu, J. T., Tan, L., Tian, Y., Ma, J., Tan, C. C., Wang, H. F., Liu, Y., Tan, M. S., Jiang, T. & Tan, L.  2015b. Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy.  Sci Rep, 5, 9522. [PMC free article: PMC4379481] [PubMed: 25825351]
103. Wang, R., Zeng, G. Q., Liu, X., Tong, R. Z., Zhou, D. & Hong, Z.  2016a. Evaluation of serum matrix metalloproteinase-3 as a biomarker for diagnosis of epilepsy.  J Neurol Sci, 367, 291–7. [PubMed: 27423606]
104. Wang, R., Zeng, G. Q., Tong, R. Z., Zhou, D. & Hong, Z.  2016b. Serum matrix metalloproteinase-2: A potential biomarker for diagnosis of epilepsy.  Epilepsy Res, 122, 114–9. [PubMed: 27016865]
105. Wang, X., Luo, Y., Liu, S., Tan, L., Wang, S. & Man, R.  2017. MicroRNA-134 plasma levels before and after treatment with valproic acid for epilepsy patients.  Oncotarget, 8, 72748–72754. [PMC free article: PMC5641166] [PubMed: 29069823]
106. Weber, Y. G., Biskup, S., Helbig, K. L., Von Spiczak, S. & Lerche, H.  2017. The role of genetic testing in epilepsy diagnosis and management.  Expert Rev Mol Diagn, 17, 739–750. [PubMed: 28548558]
107. Wightman, B., Ha, I. & Ruvkun, G.  1993. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans.  Cell, 75, 855–62. [PubMed: 8252622]
108. Yasak, I. H., Yilmaz, M., M, G. O., Atescelik, M., Gurger, M., Ilhan, N. & Goktekin, M. C.  2020. Evaluation of ubiquitin C-terminal hydrolase-L1 enzyme levels in patients with epilepsy.  Arq Neuropsiquiatr, 78, 424–429. [PubMed: 32756860]
109. Zheng, D., Li, M., Li, G., Hu, J., Jiang, X., Wang, Y. & Sun, Y.  2021. Circular RNA circ_DROSHA alleviates the neural damage in a cell model of temporal lobe epilepsy through regulating miR-106b-5p/MEF2C axis.  Cell Signal, 80, 109901. [PubMed: 33370579]
110. Zhu, M., Chen, J., Guo, H., Ding, L., Zhang, Y. & Xu, Y.  2018. High Mobility Group Protein B1 (HMGB1) and Interleukin-1beta as Prognostic Biomarkers of Epilepsy in Children.  J Child Neurol, 33, 909–917. [PubMed: 30303442]
111. Zucchini, S., Marucci, G., Paradiso, B., Lanza, G., Roncon, P., Cifelli, P., Ferracin, M., Giulioni, M., Michelucci, R., Rubboli, G. & Simonato, M.  2014. Identification of miRNAs differentially expressed in human epilepsy with or without granule cell pathology.  PLoS One, 9, e105521. [PMC free article: PMC4141756] [PubMed: 25148080]