Archives of Academic Emergency Medicine. 2020; 8(1): e18 OR I G I N A L RE S E A RC H Proteomic Analysis of patients with Epileptic Seizure and Psychogenic Non-epileptic Seizure; a Cross-Sectional Study Mohsen Parvareshi Hamrah1, Mostafa Rezaei Tavirani2∗, Monireh Movahedi1, Sanaz Ahmadi Karvigh3 1. Department of Biochemistry, Faculty of Biological Science, North Tehran Branch, Islamic Azad University, Tehran, Iran. 2. Proteomics Research Center, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3. Department of Neurology, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran. Received: February 2020; Accepted: February 2020; Published online: 11 March 2020 Abstract: Introduction: There is an increasing interest in the use of different biomarkers to help distinguish psychogenic non-epileptic seizure (PNES) from epileptic seizures (ES). This study aimed to evaluate the patterns of differen- tially expressed serum proteins in ES and PNES cases. Methods: In this cross-sectional study, 4 patients with mesial temporal lobe epilepsy and 4 patients with PNES were selected from patients with history of recurrent seizures. Venous blood samples were obtained within 1 hour after seizure and serum proteomes as well as the extent of protein expression were analyzed. Results: 361 proteins were identified; of these, expression of 197 proteins had altered. 110 (55.9%) proteins were down-regulated and 87 (44.1%) were up-regulated in the PNES samples compared to ES samples. The mean pI for deregulated proteins with 1.5 to 3 fold changes were 6.69 ± 1.68 in proteins with increasing expression in ES group and 5.88 ± 1.39 in proteins with increasing expression in PNES group (p = 0.008). The median and interquartile range (IQR) of molecular weight changes in proteins with 1.5 to 3 fold changes were 64 (22.0-86.0) in proteins whose expression had increased in ES group and 39.5 (26.0-61.5) in proteins whose expression had increased in PNES cases (p = 0.05). Conclusion: Several spots with differential expression were observed by comparing patients with ES against the PNES groups, which could be potential biomarkers of the disease. Damage to the blood-brain barrier is the most important difference be- tween the two groups, thus identifying total protein changes offers a key to the future of differentiating ES and PNES patients. Keywords: Seizures; proteomics; biomarkers; diagnosis, differential Cite this article as: Parvareshi Hamrah M, Rezaei Tavirani M, Movahedi M, Ahmadi Karvigh S. Proteomic Analysis of patients with Epileptic Seizure and Psychogenic Non-epileptic Seizure; a Cross-Sectional Study. Arch Acad Emerg Med. 2020; 8(1): e18. 1. Introduction Psychogenic non-epileptic seizures (PNES) are neither paroxysmal behavioral changes resembling epileptic seizures (ES) without organic cause nor ictal, peri-ictal, and inter- ictal electroencephalography (EEG) changes (1). Approxi- mately 80% of patients with video-EEG (VEEG) confirmed PNES were taking at least one antiepileptic drug (AED) at the time of diagnosis (2). About 5–20% of patients present- ing to an epilepsy unit for VEEG monitoring are diagnosed ∗Corresponding Author: Mostafa Rezaei Tavirani; Proteomics Research Cen- ter, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Tel: 00982122439787, E-mail: tavirany@yahoo.com with PNES, and 20–30% of intractable seizures are finally diagnosed as PNES (3). Misdiagnosis leads to many years of wrong treatment regimens, experiencing side effects of drugs, additional financial burdens, and adverse effects on social life (4-6). The gold standard for diagnosis is the record- ing of a typical event with VEEG to confirm the absence of electrographic changes on the ictal phase. Although VEEG monitoring is the gold standard for diagnosis of PNES, it has the limitations of high cost, low accessibility, and long hos- pitalization. Moreover, the habitual events sometimes may not be captured during monitoring. Considering the men- tioned limitations, other diagnostic tests have been applied to help differentiate ES from PNES. Comparative proteomic analysis is a powerful diagnostic tool to determine the onset, progression, and prognosis of human diseases. It can iden- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Parvareshi Hamrah et al. 2 tify a large number of proteins simultaneously, and protein expression alterations corresponding to certain pathological conditions. This integrated way is a useful tool to investi- gate molecular mechanism of diseases (7). Two dimensional polyacrylamide gel electrophoresis (2D-PAGE) enables sep- aration of proteins with different molecular weight and iso- electric point (pI) (8). This method is widely used to perform functional proteomics (i.e., the large-scale analysis of alter- ations in protein expression levels) (9). Using 2D-PAGE to investigate various diseases has indicated that this method provides vital information regarding discovery of biomarkers and understanding the molecular mechanism of disease on- set and development (10-12). Many studies have shown that 2D-PAGE can determine the differences between normal and ES proteomes of cells (13). Based on the above-mentioned points, this study aimed to evaluate the patterns of differen- tially expressed serum proteins in ES and PNES cases. 2. Methods 2.1. Study design and setting In this cross-sectional study, 4 patients with mesial tempo- ral lobe epilepsy and 4 patients with PNES were selected from patients with history of recurrent seizures, who were ad- mitted to epilepsy monitoring unit of Sina Hospital, Tehran, Iran, for proteomic analysis. The informed consent form was signed by all the patients before being recruited in the study. This study was approved by Ethics committee of Shahid Beheshti University of Medical Sciences (code: IR.SBMU.RETECH.REC.1397.289). 2.2. Participants Patients with other medical, neurologic or psychiatric dis- eases (rather than seizure) or history of recent head trauma were excluded from the study. Moreover, using medications except anti-epileptic drugs (AEDs) or psychoactive drugs was considered an exclusion criterion. 2.3. Data gathering Age, gender, epilepsy duration and frequency, and the his- tory of medications were recorded for all using a predesigned checklist. All the patients underwent V-EEG to capture enough habitual events. ES and PNES cases were differen- tiated based on V-EEG findings. The epilepsy type was deter- mined by an expert neurologist (epileptologist) based on ic- tal and inter-ictal EEG findings and the seizures’ semiology. Venous blood samples were obtained from all the patients within 1 hour after habitual seizures. Serum was separated by centrifugation at 4000 rpm for 10 minutes and aliquots were stored at –80 ◦C. All the proteomics materials were obtained from GE Health Care Life Sciences and SERVA Company. We used 2-DE Clean-Up Kit (GE Healthcare) for proteome ex- traction from the two groups. Following the extraction, deter- mination of protein concentration was done by 2-DE Quant Kit (GE Healthcare). The first dimension, isoelectric Focus- ing (14) separates proteins based on their pI. Before this step, immobilized pH gradient (IPG) strips were rehydrated for 8 hours. After that, IPG strips were equilibrated for 30 minutes at room temperature in equilibration solution (Serva Kit). In the next step, separation based on MW was performed by ap- plying HPE Flattop Tower (horizontal electrophoresis) using 2D HPET M Double-Gel 12.5% Kit (Serva Company) for about 3.5 hours. After electrophoresis, the gels were stained us- ing SERVA HPET M Coomassie@ Staining Kit according to the protocol and then scanned using a calibrated GS-800 densit- ometer (Bio-Rad) scanner (20). Gel analysis was done using Progenesis Same Spots Software and 1.5-fold increase or de- crease was used as the cut-off value. Analysis of image spots (proteins) that appeared on the gel was done based on the following steps: scanning the gel image, identifying protein spots and quantifying (evaluating the color intensity of spot), matching gels, data analysis, data interpretation and finally creation of two-dimensional electrophoresis databases (9). An epileptologist was responsible for data gathering. 2.4. Statistical Analysis We used X2 test, analysis of variance (ANOVA), and least sig- nificant difference (LSD) Post Hoc test for statistical analysis. The data were analyzed by using SPSS-16 software. The find- ings were reported as mean ± standard deviation (SD), me- dian (inter-quartile range; IQR); or frequency (%). The signif- icance level was considered p< 0.05. 3. Results 3.1. Baseline characteristics of studied cases Based on V-EEG findings, 4 patients had mesial temporal lobe epilepsy (ES) and 4 patients had PNES. Table 1 shows the baseline characteristics of the studied cases. The mean dura- tion of disease was 37.5 ± 7.76 years in ES group and 33.25 ± 18.7 years in PNES group. The mean frequency of seizure was in the range of 16.75 ± 15.30 attacks per month in the PNES group, and 3 ± 0.95 episodes per month in the ES group. MRI findings were normal in PNES group. All ES cases and 2 PNES cases were on poly-therapy. Only one patient in PNES group and none of those in ES group were on psychiatric medica- tions (anti-depressants or neuroleptics). 3.2. Proteomic analysis As shown in the figure 1, 361 proteins were identified; of these, expression of 197 proteins had altered. 110 (55.9%) proteins were down-regulated and 87 (44.1%) were up- regulated in the PNES samples compared to ES samples. Dis- tributions of isoelectric point (pI) and molecular weight fold This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2020; 8(1): e18 Table 1: Baseline characteristics of patients with epileptic seizure (ES) and psychogenic non-epileptic seizure (PNES) Variables ES* Group (n = 4) PNES Group (n = 4) Gender Male 2 (50) 2 (50) Female 2 (50) 2 (50) Age (years) Mean ± SD 37.5 ± 7.76 33.25 ± 18.7 Disease duration (years) Mean ± SD 7.5 ± 4.20 17.2 ± 9.06 Seizure frequency (per month) Mean ± SD 3 ± 0.95 16.75 ± 15.30 MRI finding Left Hippocampus sclerosis 2 (50) - Right Hippocampus sclerosis 2 (50) - Normal NA 4 Medications AED Mono-therapy NA 1 (25 ) AED Poly-therapy 4 (100 ) 2 (50 ) Psychoactive drugs NA 1 (25 ) Data are presented as mean ± standard deviation (SD) or frequency (%). *: Patients with temporal lobe epilepsy. MRI: Magnetic resonance imaging; AED: Antiepileptic drug. changes for deregulated proteins with 1.5 to 3 fold changes in expression are presented in figures 2 and 3. The mean pI for deregulated proteins with 1.5 to 3 fold changes were 6.69 ± 1.68 in proteins with increasing expression in ES group and 5.88 ± 1.39 in proteins with increasing expression in PNES group (p = 0.008). The median and interquartile range (IQR) of molecular weight changes in proteins with 1.5 to 3 fold changes were 64 (IQR: 22.0-86.0) in proteins whose expres- sion had increased in ES group and 39.5 (IQR: 26.0-61.5) in proteins whose expression had increased in PNES cases (p = 0.05). 4. Discussion New diagnostic methods have been found over the past years to help differentiate ES from PNES. To date, many biomark- ers have been assessed as potential candidates to differenti- ate ES from PNES, such as prolactin (PRL) (15, 16), cortisol (17, 18), neuron-specific enolase (NSE) (19), brain-derived neurotrophic factor (BDNF) (20), and Ghrelin and Nesfatin-1 (21) but until now no single biomarker has successfully dif- ferentiated PNES from ES; in fact, PNES is only diagnosed via the negation of ES (22) In this study, different expres- sion of proteins between the two groups allows us to con- sider the idea of potential protein biomarkers for differen- tial diagnosis of ES and PNES. Our findings, as depicted in figure 2, showed that up-regulated proteins with higher molecular weights were observed in temporal lobe epilepsy (TLE) group relative to the PNES samples. A similar pat- tern was obtained for pI values and up-regulated proteins in TLE had a higher pI value (figure 3). In various neurologi- cal disorders, including epilepsy, the BBB (Blood Brain Bar- rier) is disrupted. Disruption of the blood brain barrier also contributes to epileptogenesis by facilitating the exposure of neurons to pro-inflammatory cytokines. It is also thought to play a role in drug resistance by becoming permeable to var- ious transporters and enzymes (16). As a result of increased permeability in the BBB, the molecules that are normally ex- pected to be found only in the central nervous system (CNS) may find a chance to diffuse into peripheral blood or on the contrary, serum proteins may reach the brain tissue. Some of these molecules are S100 calcium-binding protein β (S100β), neuron-specific enolase, glial fibrillary acidic protein (GFAP), and albumin (5). Other studies reported that nano bodies with a high pI 9/5 (23, 24) spontaneously cross the BBB. There are two main hypotheses for explaining this observa- tion. First it may be a failure of the BBB. In this regard, small serum proteins with low pI value can penetrate the brain tis- sue, which leads to decrease in the serum level of these pro- teins. On the other hand, protein composition of serum is prominently made up of heavy proteins. The second hypoth- esis is that naturally, the heavy proteins with higher pI value are significantly deregulated in patients. The second hypoth- esis cannot be true because it is impossible for heavy pro- teins to be targeted naturally in the patients. As mentioned earlier, BBB damage is reported in ES patients. The gross al- teration in permeability of BBB after ES promotion has been confirmed by several researches. Few proteomic studies have been done on epilepsy. Some authors have employed pro- teomic analysis to identify proteins that are differentially ex- pressed in the hippocampus of patients with mesial tempo- ral lobe epilepsy (MTLE) compared to control tissue obtained This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Parvareshi Hamrah et al. 4 Figure 1: Position of matched spots on 2DE gel by comparing serum samples of epileptic seizure and psychogenic non-epileptic seizure (PNES) cases. The detected proteins were identified using SameSpots software. via autopsy. The researchers found that several proteins with different roles in the CNS were deregulated. The cytosolic enzyme acyl-CoA thioester hydrolase, known for its role in energy production via Κ-oxidation in mitochondria and per- oxisomes, signal transduction, ion fluxes, and activation of protein kinase C, was down-regulated in hippocampus of pa- tients with MTLE (25). In subsequent studies, these authors verified a decrease in expression of collapsing response- mediated protein- 2 (CMRP-2, 55 kDa protein), which is of- ten involved in axonal outgrowth, path finding, and neuronal polarity processes (26). They also observed a decrease in ex- pression of 18 proteins playing different roles in brain (25, 27). Danis et al. (28) performed a 2D-PAGE study, compar- ing GAERS (Genetic Absence Epilepsy Rat from Strasbourg) to non-epileptic control (NEC) rats. This study showed five differentially expressed proteins, two in the parietal cortex (ATP synthase sub- unit delta and the 14-3-3 zeta isoform), two in the thalamus (myelin basic protein and macrophage migration inhibitory factor (MIF)), and the other in the hip- pocampus (MIF and 0-beta 2 globulin). Almost all proteins were up-regulated in GAERS compared to NEC with the ex- ception of 0-beta globulin. In line with this study, MIF was also found to be up-regulated in the frontal cortex and in the hippocampus of rats subjected to kainic acid-induced epilepsy (29). MIF is a pro-inflammatory cytokine released in response to inflammatory stimuli and is highly expressed in immune and non-immune cells, including those in the brain. A recent study by Conboy et al. (30) showed that MIF is im- portant to the process of hippocampal neurogenesis, affect- ing cell proliferation in the dentate gyrus. In a recent study, by using proteomics (2D-PAGE), Persike et al. (13) showed that the total number of spots were noticeably smaller in the hippocampus of patients with pharmacoresistant TLE than in the control tissue. A total of 16 proteins were differentially This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2020; 8(1): e18 Figure 2: Isoelectric point (pI) distribution of proteins with 1.5-3.0 fold change in patients with temporal lobe epilepsy (TLE) and psychogenic non-epileptic seizure (PNES) (p = 0.008). Figure 3: Molecular weight distribution of proteins with 1.5-3.0 fold change in patients with temporal lobe epilepsy (TLE) and psychogenic non-epileptic seizure (PNES) (p = 0.05). expressed in the hippocampus of these patients compared to the control. However, only nine proteins were identified in this study. Among the nine deregulated proteins, six were up- regulated while one of them was down-regulated in the TLE group compared to controls. The other two proteins were only identified in the 2D-PAGE of epilepsy patients. In recent decades, new proteomics technologies have been developed to help us find appropriate protein biomarkers in serum. Si- multaneous changes in many proteins in this disease can indicate biochemical mechanisms involved in its incidence, which can be effective for medicinal purposes in the treat- ment process. Few studies have measured and compared PI/MW of proteins whose expression significantly changed in temporal lobe epileptic patients and PI/MW in data base (31, 32). By measuring pI and molecular weight, Behboodi et al. reported that a more malignant cancer is associated with a more acidic pI and lower molecular weight for proteins de- tected in tissue (33). Further studies are needed to identify This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Parvareshi Hamrah et al. 6 differentially expressed proteins between the two groups. 5. Conclusion It can be concluded that blood brain barrier damage in epileptic seizures is the main event that can discriminate PNES patients from ES individuals. Therefore, identification of the deregulated proteins will provide a clear perspective of ES. 6. Declarations 6.1. Acknowledgements The authors would like to thank the proteomics research cen- ter, Shahid Beheshti University of Medical Sciences, Tehran, Iran for their support, cooperation and assistance through- out the study. 6.2. Authors’ contribution All authors had equal role in design, work, statistical analysis, and manuscript writing. Authors ORCIDs Mohsen parvareshi hamrah: 0000-0002-9209-9348 Mostafa Rezaei Tavirani: 0000-0003-1767-7475 6.3. Funding/Support This study was funded by proteomics research center, Shahid Beheshti University of Medical Sciences. 6.4. Conflict of interest The authors declare that they have no conflict of interest. 6.5. Human and Animal Rights All procedures performed on human subjects this study were in accordance with the ethical standards of the insti- tutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or com- parable ethical standards. Additionally, ethics committee of Shahid Beheshti University of Medical Sciences approved this study by IR.SBMU.RETECH.REC.1397.289. 6.6. Informed Consent Informed consent was obtained from all individual partici- pants included in the study. References 1. Bodde NM, Brooks JL, Baker GA, Boon PA, Hendriksen JG, Aldenkamp AP. Psychogenic non-epileptic seizures– diagnostic issues: a critical review. Clinical neurology and neurosurgery. 2009;111(1):1-9. 2. Gedzelman ER, LaRoche SM. Long-term video EEG monitoring for diagnosis of psychogenic nonepilep- tic seizures. Neuropsychiatric disease and treatment. 2014;10:1979. 3. Gordon PC, Valiengo LdCL, Proenca IC, Kurcgant D, Jorge CL, Castro LH, et al. Comorbid epilepsy and psy- chogenic non-epileptic seizures: how well do patients and caregivers distinguish between the two. Seizure. 2014;23(7):537-41. 4. Bodde N, Brooks J, Baker G, Boon P, Hendriksen J, Mulder O, et al. Psychogenic non-epileptic seizures–definition, etiology, treatment and prognostic issues: a critical re- view. Seizure. 2009;18(8):543-53. 5. LaFrance Jr WC, Reuber M, Goldstein LH. Manage- ment of psychogenic nonepileptic seizures. Epilepsia. 2013;54:53-67. 6. Chmielewska N, Szyndler J, Makowska K, Wojtyna D, Ma- ciejak P, Plaznik A. Looking for novel, brain-derived, pe- ripheral biomarkers of neurological disorders. Neurolo- gia i neurochirurgia polska. 2018;52(3):318-25. 7. Shahrokh S, Razzaghi Z, Mansouri V, Ahmadi N. Impact of Proteomics in Laser Therapy of Skin: A Review Article. Journal of Lasers in Medical Sciences. 2019;10(4):90-5. 8. Wilkins MR, Sanchez J-C, Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnology and genetic engineering reviews. 1996;13(1):19-50. 9. Wilkens S, Zhang Z, Zheng Y. A structural model of the vacuolar ATPase from transmission electron microscopy. Micron. 2005;36(2):109-26. 10. Ashrafmansouri M, Sadjjadi FS, Seyyedtabaei S, Haghighi A, Rezaei-Tavirani M, Ahmadi N. Comparative Two- dimensional Gel Electrophoresis Maps for Amastigote- like Proteomes of Iranian Leishmania Tropica and Leishmania major Isolates. Galen Medical Journal. 2019;8:1520. 11. Fayazfar S, Zali H, Oskouie AA, Aghdaei HA, Tavirani MR, Mojarad EN. Early diagnosis of colorectal cancer via plasma proteomic analysis of CRC and advanced ade- nomatous polyp. Gastroenterology and hepatology from bed to bench. 2019;12(4):328. 12. Riquelme A, Valdes-Tovar M, Ugalde O, Maya-Ampudia V, FernÃąndez M, Mendoza-DurÃąn L, et al. Potential Use of Exfoliated and Cultured Olfactory Neuronal Precursors for In Vivo Alzheimer’s Disease Diagnosis: A Pilot Study. Cellular and molecular neurobiology. 2020;40(1):87-98. 13. da Silva Fernandes MJ, Amorim RP, Carneiro JEM, Araujo MGL, Persike DS. Proteomics-Based Strategy to Iden- tify Biomarkers and Pharmacological Targets in Tem- poral Lobe Epilepsy. Pharmacoresistance in Epilepsy: This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2020; 8(1): e18 Springer; 2013. p. 115-26. 14. Azadegan-Dehkordi F, Bagheri N, Shirzad M, Sanei MH, Hashemzadeh-Chaleshtori M, Rafieian-Kopaei M, et al. Correlation between mucosal IL-6 mRNA expression level and virulence factors of Helicobacter pylori in Ira- nian adult patients with chronic gastritis. Jundishapur journal of microbiology. 2015;8(8). 15. Ehsan T, Fisher RS, Johns D, Lukas RJ, Blum D, Es- kola J. Sensitivity and specificity of paired capillary pro- lactin measurement in diagnosis of seizures. Journal of Epilepsy. 1996;9(2):101-5. 16. Anzola GP. Predictivity of plasma prolactin levels in dif- ferentiating epilepsy from pseudoseizures: a prospective study. Epilepsia. 1993;34(6):1044-8. 17. Tunca Z, Ergene U, Fidaner H, Cimilli C, Ozerdem A, Alkin T, et al. Reevaluation of serum cortisol in con- version disorder with seizure (pseudoseizure). Psychoso- matics. 2000;41(2):152. 18. Bakvis P, Spinhoven P, Giltay EJ, Kuyk J, Edelbroek PM, Zitman FG, et al. Basal hypercortisolism and trauma in patients with psychogenic nonepileptic seizures. Epilep- sia. 2010;51(5):752-9. 19. Willert C, Spitzer C, Kusserow S, Runge U. Serum neuron- specific enolase, prolactin, and creatine kinase after epileptic and psychogenic non-epileptic seizures. Acta neurologica scandinavica. 2004;109(5):318-23. 20. LaFrance W, Leaver K, Stopa E, Papandonatos G, Blum A. Decreased serum BDNF levels in patients with epilep- tic and psychogenic nonepileptic seizures. Neurology. 2010;75(14):1285-91. 21. Aydin S, Dag E, Ozkan Y, Arslan O, Koc G, Bek S, et al. Time-dependent changes in the serum levels of pro- lactin, nesfatin-1 and ghrelin as a marker of epileptic at- tacks young male patients. Peptides. 2011;32(6):1276-80. 22. Sundararajan T, Tesar G, Jimenez X. Biomarkers in the di- agnosis and study of psychogenic nonepileptic seizures: a systematic review. Seizure. 2016;35:11-22. 23. Li T, Bourgeois J-P, Celli S, Glacial F, Le Sourd A- M, Mecheri S, et al. Cell-penetrating anti-GFAP VHH and corresponding fluorescent fusion protein VHH-GFP spontaneously cross the blood-brain barrier and specifi- cally recognize astrocytes: application to brain imaging. The FASEB Journal. 2012;26(10):3969-79. 24. Muruganandam A, Tanha J, Narang S, Stanimirovic D. Se- lection of phage-displayed llama single-domain antibod- ies that transmigrate across human blood–brain barrier endothelium. The FASEB Journal. 2002;16(2):240-2. 25. Yang J, Czech T, Yamada J, Csaszar E, Baumgartner C, Slavc I, et al. Aberrant cytosolic acyl-CoA thioester hy- drolase in hippocampus of patients with mesial temporal lobe epilepsy. Amino acids. 2004;27(3-4):269-75. 26. Bandtlow CE, Dlaska M, Pirker S, Czech T, Baumgartner C, Sperk G. Increased expression of Nogo-A in hippocam- pal neurons of patients with temporal lobe epilepsy. Eu- ropean Journal of Neuroscience. 2004;20(1):195-206. 27. Yang J, Czech T, Felizardo M, Baumgartner C, Lubec G. Aberrant expression of cytoskeleton proteins in hip- pocampus from patients with mesial temporal lobe epilepsy. Amino acids. 2006;30(4):477-93. 28. Danis O, Demir S, Gunel A, Aker RG, Gulcebi M, Onat F, et al. Changes in intracellular protein expression in cortex, thalamus and hippocampus in a genetic rat model of ab- sence epilepsy. Brain research bulletin. 2011;84(6):381-8. 29. Lo W-Y, Tsai F-J, Liu C-H, Tang N-Y, Su S-Y, Lin S-Z, et al. Uncaria rhynchophylla upregulates the expression of MIF and cyclophilin A in kainic acid-induced epilepsy rats: a proteomic analysis. The American journal of Chi- nese medicine. 2010;38(04):745-59. 30. Conboy L, Varea E, Castro JE, Sakouhi-Ouertatani H, Calandra T, Lashuel HA, et al. Macrophage migra- tion inhibitory factor is critically involved in basal and fluoxetine-stimulated adult hippocampal cell prolifera- tion and in anxiety, depression, and memory-related be- haviors. Molecular psychiatry. 2011;16(5):533. 31. Eun J-P, Choi H-Y, Kwak Y-G. Proteomic analysis of hu- man cerebral cortex in epileptic patients. Experimental & molecular medicine. 2004;36(2):185. 32. Lee CW, Yu ST, Choi HY, Koh BJ, Kwak YG. Pro- teomic analysis of human serum from patients with temporal lobe epilepsy. Korean Journal of Pediatrics. 2009;52(5):567-75. 33. Behboodi F, Tavirani MR, Yousefzadeh S, Bashizadeh- Fakhar H, Ashoorizadeh B, Faraji R, et al. Studying the proteomic pattern of cancerous tissue in patients with breast cancer and its’ comparing with healthy breast. Zahedan Journal of Research in Medical Sciences. 2015;17(11). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Conclusion Declarations References