DNA Methylation Description of Hippocampus, Cortex, Amygdala, and Blood of Drug-Resistant Temporal Lobe Epilepsy.

Epigenetic changes such as DNA methylation were observed in drug-resistant temporal lobe epilepsy (DR-TLE), a disease that affects 25-30% of epilepsy patients. The main objective is to simultaneously describe DNA methylation patterns associated with DR-TLE in hippocampus, amygdala, surrounding cortex to the epileptogenic zone (SCEZ), and peripheral blood. An Illumina Infinium MethylationEPIC BeadChip array was performed in 19 DR-TLE patients and 10 postmortem non-epileptic controls. Overall, 32, 59, and 3210 differentially methylated probes (DMPs) were associated with DR-TLE in the hippocampus, amygdala, and SCEZ, respectively. These DMP-affected genes were involved in neurotrophic and calcium signaling in the hippocampus and voltage-gated channels in SCEZ, among others. One of the hippocampus DMPs (cg26834418 (CHORDC1)) showed a strong blood-brain correlation with BECon and IMAGE-CpG, suggesting that it could be a potential surrogate peripheral biomarker of DR-TLE. Moreover, in three of the top SCEZ's DMPs (SHANK3, SBF1, and MCF2L), methylation status was verified with methylation-specific qPCR. The differentially methylated CpGs were classified in DMRs: 2 in the hippocampus, 12 in the amygdala, and 531 in the SCEZ. We identified genes that had not been associated to DR-TLE so far such as TBX5, EXOC7, and WRHN. The area with more DMPs associated with DR-TLE was the SCEZ, some of them related to voltage-gated channels. The DMPs found in the amygdala were involved in inflammatory processes. We also found a potential surrogate peripheral biomarker of DR-TLE. Thus, these results provide new insights into epigenetic modifications involved in DR-TLE.

Neuronal and astrocytic tetraploidy is increased in drug-resistant epilepsy.

AIMS: Epilepsy is one of the most prevalent neurological diseases. A third of patients with epilepsy remain drug-resistant. The exact aetiology of drug-resistant epilepsy (DRE) is still unknown. Neuronal tetraploidy has been associated with neuropathology. The aim of this study was to assess the presence of tetraploid neurons and astrocytes in DRE. METHODS: For that purpose, cortex, hippocampus and amygdala samples were obtained from patients subjected to surgical resection of the epileptogenic zone. Post-mortem brain tissue of subjects without previous records of neurological, neurodegenerative or psychiatric diseases was used as control. RESULTS: The percentage of tetraploid cells was measured by immunostaining of neurons (NeuN) or astrocytes (S100ß) followed by flow cytometry analysis. The results were confirmed by image cytometry (ImageStream X Amnis System Cytometer) and with an alternative astrocyte biomarker (NDRG2). Statistical comparison was performed using univariate tests. A total of 22 patients and 10 controls were included. Tetraploid neurons and astrocytes were found both in healthy individuals and DRE patients in the three brain areas analysed: cortex, hippocampus and amygdala. DRE patients presented a higher number of tetraploid neurons (p = 0.020) and astrocytes (p = 0.002) in the hippocampus than controls. These results were validated by image cytometry. CONCLUSIONS: We demonstrated the presence of both tetraploid neurons and astrocytes in healthy subjects as well as increased levels of both cell populations in DRE patients. Herein, we describe for the first time the presence of tetraploid astrocytes in healthy subjects. Furthermore, these results provide new insights into epilepsy, opening new avenues for future treatment.

Potenciales biomarcadores predictores de mortalidad en pacientes COVID-19 en el Servicio de Urgencias / Potential biomarkers predictors of mortality in COVID-19 patients in the Emergency Department

OBJETIVO: Identificar qué biomarcadores realizados en la primera analítica de urgencias ayudan a estratificar según riesgo de mortalidad a pacientes COVID 19. MÉTODO: Estudio observacional descriptivo y transversal realizado con datos recogidos de los pacientes con sospecha de COVID-19 en el Servicio de Urgencias del 24 de febrero al 16 de marzo del 2020. Se realizó el estudio univariante y multivariante para encontrar los marcadores independientes de mortalidad y calcular el riesgo mediante la construcción de una escala de gravedad. RESULTADOS: Se incluyeron 163 pacientes de los que fallecieron 33 y 29 de ellos resultaron positivos para la prueba PCR COVID-19. Obtuvimos como posibles factores para conformar el score de riesgo de mortalidad edad>75 años ((OR ajustada=12,347, IC95%: 4,138-36,845 p = 0.001), leucocitos totales> 11.000 cel/mm3 (OR ajustada=2,649, IC95%: 0,879-7,981 p = 0,083), glucosa> 126 mg/dL (OR ajustada=3,716, IC95%: 1,247-11,074 p = 0,018) y creatinina>1,1 mg/dL (OR ajustada= 2,566, IC95%: 0,889-7,403, p = 0,081). Este score se denominó COVEB (COVID, Edad, perfil Básico analítico) con un AUC 0,874 (IC95%: 0,816-0,933, p < 0.001; punto de corte= 1 (sensibilidad= 89,66% (IC95%: 72,6%-97,8%), especificidad= 75,59% (IC95%: 67,2%-82,8%). Un score < 1 posee un valor predictivo negativo = 100% (IC95%: 93,51%-100%) y un valor predictivo positivo = 18,59% (IC95%: 12,82%-25,59%). CONCLUSIONES: Las escalas clínicas de gravedad, los biomarcadores de función renal, los parámetros del recuento leucocitario, el ratio neutrófilos totales/linfocitos y procalcitonina son factores de riesgo tempranos de mortalidad. Destacan las variables edad, glucosa, creatinina y leucocitos totales como mejores predictores de mortalidad. Un score COVEB< 1 indica con un 100% de probabilidad, que el paciente con sospecha de COVID-19 no va a fallecer en los próximos 30 días

OBJECTIVE: Identify which biomarkers performed in the first emergency analysis help to stratify COVID-19 patients according to mortality risk. METHOD: Observational, descriptive and cross-sectional study performed with data collected from patients with suspected COVID-19 in the Emergency Department from February 24 to March 16, 2020. The univariate and multivariate study was performed to find independent mortality markers and calculate risk by building a severity score. RESULTS: A total of 163 patients were included, of whom 33 died and 29 of them were positive for the COVID-19 PCR test. We obtained as possible factors to conform the Mortality Risk Score age> 75 years ((adjusted OR = 12,347, 95% CI: 4,138-36,845 p = 0.001), total leukocytes> 11,000 cells / mm3 (adjusted OR = 2,649, 95% CI: 0.879-7.981 p = 0.083), glucose> 126 mg / dL (adjusted OR = 3.716, 95% CI: 1.247-11.074 p = 0.018) and creatinine> 1.1 mg / dL (adjusted OR = 2.566, 95% CI: 0.889- 7.403, p = 0.081) This score was called COVEB (COVID, Age, Basic analytical profile) with an AUC 0.874 (95% CI: 0.816-0.933, p <0.001; Cut-off point = 1 (sensitivity = 89.66 % (95% CI: 72.6% -97.8%), specificity = 75.59% (95% CI: 67.2% -82.8%). A score <1 has a negative predictive value = 100% (95% CI: 93.51% -100%) and a positive predictive value = 18.59% (95% CI: 12.82% -25.59%). CONCLUSIONS: Clinical severity scales, kidney function biomarkers, white blood cell count parameters, the total neutrophils / total lymphocytes ratio and procalcitonin are early risk factors for mortality. The variables age, glucose, creatinine and total leukocytes stand out as the best predictors of mortality. A COVEB score <1 indicates with a 100% probability that the patient with suspected COVID-19 will not die in the next 30 days