Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder involving both upper motor neurons (UMN) and lower motor neurons (LMN). Enormous research has been done in the past few decades in unveiling the genetics of ALS, successfully identifying at least fifteen candidate genes associated with familial and sporadic ALS. Numerous studies attempting to define the pathogenesis of ALS have identified several plausible determinants and molecular pathways leading to motor neuron degeneration, which include oxidative stress, glutamate excitotoxicity, apoptosis, abnormal neurofilament function, protein misfolding and subsequent aggregation, impairment of RNA processing, defects in axonal transport, changes in endosomal trafficking, increased inflammation, and mitochondrial dysfunction. This review is to update the recent discoveries in genetics of ALS, which may provide insight information to help us better understanding of the disease neuropathogenesis.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder caused by selective motor neuron degeneration. Abnormal protein aggregation and impaired protein degradation pathways may contribute to the disease pathogenesis. Although it has been reported that autophagy is altered in patients and animal model of ALS, little is known about the role of autophagy in motor neuron degeneration in this disease. Our previous study shows that rapamycin, an MTOR-dependent autophagic activator, accelerates disease progression in the SOD1G93A mouse model of ALS. In the present report, we have assessed the role of the MTOR-independent autophagic pathway in ALS by determining the effect of the MTOR-independent autophagic inducer trehalose on disease onset and progression, and on motor neuron degeneration in SOD1G93A mice. We have found that trehalose significantly delays disease onset prolongs life span, and reduces motor neuron loss in the spinal cord of SOD1G93A mice. Most importantly, we have docume...

Background: Parkinson’s disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.Methods: We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.Results: A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.Conclusions: This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.