Genetic basis of sleep phenotypes and rare neurodevelopmental syndromes reveal shared molecular pathways.

Sleep-related phenotypes have been frequently reported in early on-set epileptic encephalopathies and in developmental delay syndromes, in particular in syndromes related to autism spectrum disorder. Yet the convergent pathogenetic mechanisms between these comorbidities are largely unknown. We first performed a gene enrichment study that identified shared risk genes among rare epileptic encephalopathies/neurodevelopmental disorders, rare developmental delay genetic syndromes and sleep disturbances. We then determined cellular and molecular pathways enriched among genes shared between sleep phenotypes and those two early onset mental illnesses, aiming to identify genetic disparities and commonalities among these phenotypic groups. The sleep gene set was observed as significantly overlapped with the two gene lists associated to rare genetic syndromes (i.e., epileptic encephalopathies/neurodevelopmental disorders and developmental delay gene sets), suggesting shared genetic contribution. Similarities across significantly enriched pathways between the two intersect lists comprehended mostly synapse-related pathways, such as retrograde endocannabinoid signaling, serotonergic, and GABAergic synapse. Network analysis indicates epileptic encephalopathies/neurodevelopmental disorders versus sleep-specific clusters and developmental delay versus sleep-specific clusters related to synaptic and transcriptional regulation, respectively. Longstanding functional patterns previously described in epileptic encephalopathies and neurodevelopmental disorders genetic architecture were recaptured after dissecting the overlap between the genes associated to those developmental phenotypes and sleep disturbances, suggesting that during neurodevelopment different molecular and functional mechanisms are related to alterations on circadian rhythm. The overlapping gene set and biological pathways highlighted by this study may serve as a primer for new functional investigations of shared molecular mechanisms between sleep disturbances and rare developmental syndromes.

Posttraumatic epilepsy: Integrating clinical, inflammatory, and genetic profiles in traumatic brain injury patients.

OBJECTIVE: This study aims to assess the clinical, inflammatory, and genetic profiles of traumatic brain injury (TBI) patients over a 2-year follow-up period, focusing on the development of posttraumatic epilepsy (PTE). METHODS: Fifty-nine patients with acute TBI were recruited in the emergency unit of a hospital in Brazil. Clinical data and blood samples were collected after 10 days of hospitalization for posterior genetic profile (Apolipoprotein E- ApoE and Glutamic Acid Descarboxylase-GAD sequencing) analyses. A subset of 19 patients were assessed for cytokine markers (mRNA expression). The development of PTE was investigated for two years following TBI. Statistical analyses including univariate analysis, multiple correspondence analysis, and Mann-Whitney test were performed. RESULTS: Analysis revealed an association between severe TBI and requirement for neurosurgery and polytrauma (p<0.05), as well as the development of PTE over a two-year follow-up period (p<0.05). Multiple correspondence analysis identified two distinct profiles associated with PTE and Non-PTE outcomes. The PTE profile showed a higher prevalence of the ApoE genotype E3/E3 and GAD1 SNP (rs769391) genotype AA in our study, while the Non-PTE profile showed a higher presence of E3/E4. mRNA expression analysis demonstrated acute elevated levels of TNF-α in the PTE group as compared to Non-PTE patients (6.70±1.53 vs 5.31 ±0.33, p<0.01). SIGNIFICANCE: Our findings underscore the multifactorial nature of aspects potentially contributing to PTE. It is unlikely that any single factor might in isolation have a strong causative influence over the development of epilepsy after TBI. Our results provide a suggestion of potential clustering that might be relevant as prognostic factors for PTE.

Identification and Evaluation of Reference Genes for Quantitative Analysis of Brazilian Pine (Araucaria angustifolia Bertol. Kuntze) Gene Expression.

Quantitative analysis of gene expression is a fundamental experimental approach in many fields of plant biology, but it requires the use of internal controls representing constitutively expressed genes for reliable transcript quantification. In this study, we identified fifteen putative reference genes from an A. angustifolia transcriptome database. Variation in transcript levels was first evaluated in silico by comparing read counts and then by quantitative real-time PCR (qRT-PCR), resulting in the identification of six candidate genes. The consistency of transcript abundance was also calculated applying geNorm and NormFinder software packages followed by a validation approach using four target genes. The results presented here indicate that a diverse set of samples should ideally be used in order to identify constitutively expressed genes, and that the use of any two reference genes in combination, of the six tested genes, is sufficient for effective expression normalization. Finally, in agreement with the in silico prediction, a comprehensive analysis of the qRT-PCR data combined with validation analysis revealed that AaEIF4B-L and AaPP2A are the most suitable reference genes for comparative studies of A. angustifolia gene expression.