Pathology >> ALS | Alzheimer's | SCI


amyotrophic lateral sclerosis (ALS)

Amyotrophic Lateral Sclerosis (ALS) is a debilitating, paralyzing, and ultimately fatal disease that typically results in death within 3-5 years after diagnosis.  Most cases are sporadic with no known genetic history, but a small percentage are familial.  There currently is only one treatment, Riluzole, which extends life by a few months.   ALS has been shown to involve multiple cellular factors.  However, no single factor has been shown to be the root cause of the disease.  It is likely that ALS results from a system instability that could be initiated by multiple factors and/or their interactions.  Thus, many different perturbations could initiate the regulatory cascade failure that results in the same general phenotype pathology of ALS.

G93A SOD1 transgenic mouse model of ALS

G93A Database:  Most of the science of ALS has been performed in transgenic mouse models, namely the G93A Superoxide Dismutase-1 mouse model. To examine the pathology dynamics of ALS, the underlying regulatory factors and loops must first be identified and quantified. To do this we continue to build a comprehensive database of every quantifiable data point that has been published in peer-reviewed journals using the G93A SOD1 transgenic mouse. We have over 30,000 searchable figure captions and descriptions from over 3,000 different articles.

Articles that are a direct result of statistical analysis or meta-analysis of the database includes:

Pfohl SR, Halicek MT, Mitchell CS. (2015).  Characterization of the Contribution of Genetic Background and Gender to Disease Progression in the SOD1 G93A Mouse Model of Amyotrophic Lateral Sclerosis: A Meta-Analysis. J Neuromuscular Dis.

Kim RB, Irvin CW, Mitchell CS. (2015).  State of the Field:  An informatics-based systematic review of the SOD1 G93A Amyotrophic Lateral Sclerosis mouse model.  Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. (In Press).

SOD1 G93A Article Database Search

ALS SystemG93A dynamic meta-analysis:  Dynamic meta-analysis is similar to traditional meta-analysis except that it explicitly accounts for time and implicitly includes interactions between factors.   We have successfully applied DMA to a preliminary G93A dataset.  Interestingly, an examination of pathology dynamics revealed an oscillating instability among 10 categorical processes known to be either involved or affected by the disease (axonal transport, cellular chemistry, excitotoxicity, free radicals, genetics, inflammation, necro-apoptosis, proteomics, and systemic).  The oscillations would appear to explain a system that is trying to re-stabilize but continually over-shoots homeostasis by overcompensation. Thus, it oscillates above and below homeostasis. Using pathology dynamics, novel strategies were identified that partially re-stabilize the pathology by applying combination treatments targeting 3 or more categories.  We continue to add data to test the accuracy of these predictions as well as to make more specific predictions.

Mitchell, C.S. and Lee, R.H. (2012). Dynamic Meta-Analysis as a Therapeutic Prediction Tool for Amyotrophic Lateral Sclerosis. Amyotrophic Lateral Sclerosis. M. H. Maurer. Intech ISBN 979-953-307-199-1

Click this link to be taken to a free copy of the published chapter, available from Intech.

clinical predictors of ALS

clinical database: We are currently gathering patient data, including demographics, onset data, medical history, and measures of progression and prescribed treatments and intervention on a per clinic visit basis, in hopes of utilizing pathology dynamics to identify key clinical predictors-- key measures, correlations, and/or relationships that could assist clinicians in forecasting disease prognosis and provide objective criteria for making timely treatment or intervention decisions. 

Mitchell CS, Hollinger SK, Goswami SD, Polak MA, Lee RH, Glass JD.  (2015).  Antecedent Disease Is Less Prevalent in Amyotrophic Lateral Sclerosis.  Neurodegenerative Diseases.