Amyotrophic lateral sclerosis microgenomics

How to make spinal motor neurons

All muscle movements, including breathing, walking, and fine motor skills rely on the function of the spinal motor neuron to transmit signals from the brain to individual muscle groups. Loss of spinal motor neuron function underlies several neurological disorders for which treatment has been hampered by the inability to obtain sufficient quantities of primary motor neurons to perform mechanistic studies or drug screens. Progress towards overcoming this challenge has been achieved through the synthesis of developmental biology paradigms and advances in stem cell and reprogramming technology, which allow the production of motor neurons in vitro. In this Primer, we discuss how the logic of spinal motor neuron development has been applied to allow generation of motor neurons either from pluripotent stem cells by directed differentiation and transcriptional programming, or from somatic cells by direct lineage conversion. Finally, we discuss methods to evaluate the molecular and functional properties of motor neurons generated through each of these techniques.

Current and future directions in genomics of amyotrophic lateral sclerosis

New knowledge of the structure and function of the human genome and novel genomic technologies are being applied to the study of sporadic amyotrophic lateral sclerosis (ALS). These studies can examine tens to hundreds of thousands of items at once, and depend on sophisticated computer processing. Current studies are focused on genetic susceptibility and gene expression and future studies will likely focus on structural variation, gene regulation and non-protein coding regions. The hope is that they will lead to deeper understanding of molecular aspects of the disease and to rational therapeutic targets.

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

BACKGROUND

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis

RESULTS

Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.

CONCLUSION

This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.