Редактирование: NHLRC1 (раздел)

==Publications==

{{medline-entry
|title=DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28254385
|abstract=The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R =0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R =0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes [[NHLRC1]], [[SCGN]] and [[CSNK1D]]. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R =0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq  platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.
|mesh-terms=* Adult
* Aged
* Aging
* CpG Islands
* DNA
* DNA Methylation
* Epigenomics
* Forensic Genetics
* High-Throughput Nucleotide Sequencing
* Humans
* Machine Learning
* Middle Aged
* Neural Networks, Computer
* Saliva
* Twins, Monozygotic
|keywords=* Artificial neural networks
* Chronological age prediction
* DNA methylation
* Forensic epigenetics
* Next generation sequencing
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392537
}}
{{medline-entry
|title=Loss of GABAergic cortical neurons underlies the neuropathology of Lafora disease.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/24472629
|abstract=Lafora disease is an autosomal recessive form of progressive myoclonic epilepsy caused by defects in the [[[[EPM2A]]]] and EPM2B genes. Primary symptoms of the pathology include seizures, ataxia, myoclonus, and progressive development of severe dementia. Lafora disease can be caused by defects in the [[[[EPM2A]]]] gene, which encodes the laforin protein phosphatase, or in the [[NHLRC1]] gene (also called EPM2B) codifying the malin E3 ubiquitin ligase. Studies on cellular models showed that laforin and malin interact and operate as a functional complex apparently regulating cellular functions such as glycogen metabolism, cellular stress response, and the proteolytic processes. However, the pathogenesis and the molecular mechanism of the disease, which imply either laforin or malin are poorly understood. Thus, the aim of our study is to elucidate the molecular mechanism of the pathology by characterizing cerebral cortex neurodegeneration in the well accepted murine model of Lafora disease [[[[EPM2A]]]]-/- mouse. In this article, we want to asses the primary cause of the neurodegeneration in Lafora disease by studying GABAergic neurons in the cerebral cortex. We showed that the majority of Lafora bodies are specifically located in GABAergic neurons of the cerebral cortex of 3 months-old [[[[EPM2A]]]]-/- mice. Moreover, GABAergic neurons in the cerebral cortex of younger mice (1 month-old) are decreased in number and present altered neurotrophins and p75NTR signalling. Here, we concluded that there is impairment in GABAergic neurons neurodevelopment in the cerebral cortex, which occurs prior to the formation of Lafora bodies in the cytoplasm. The dysregulation of cerebral cortex development may contribute to Lafora disease pathogenesis.
|mesh-terms=* Actins
* Aging
* Animals
* Caspase 3
* Cell Count
* Cell Nucleus
* Cerebral Cortex
* Dendrites
* Dual-Specificity Phosphatases
* GABAergic Neurons
* Inclusion Bodies
* Lafora Disease
* Lysosomes
* Mice
* Nerve Growth Factors
* Protein Transport
* Protein Tyrosine Phosphatases, Non-Receptor
* Proteolysis
* Subcellular Fractions
* Synapses
* Tumor Suppressor Protein p53

|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3917365
}}