Comparing therapeutic modulators of the SOD1 G93A Amyotrophic Lateral Sclerosis mouse pathophysiology

Abstract

Introduction: Amyotrophic Lateral Sclerosis (ALS) is a paralyzing, multifactorial neurodegenerative disease with limited therapeutics and no known cure. The study goal was to determine which pathophysiological treatment targets appear most beneficial.

Methods: A big data approach was used to analyze high copy SOD1 G93A experimental data. The secondary data set comprised 227 published studies and 4,296 data points. Treatments were classified by pathophysiological target: apoptosis, axonal transport, cellular chemistry, energetics, neuron excitability, inflammation, oxidative stress, proteomics, or systemic function. Outcome assessment modalities included onset delay, health status (rotarod performance, body weight, grip strength), and survival duration. Pairwise statistical analysis (two-tailed t-test with Bonferroni correction) of normalized fold change (treatment/control) assessed significant differences in treatment efficacy. Cohen's d quantified pathophysiological treatment category effect size compared to "all" (e.g., all pathophysiological treatment categories combined).

Results: Inflammation treatments were best at delaying onset (d = 0.42, p > 0.05). Oxidative stress treatments were significantly better for prolonging survival duration (d = 0.18, p < 0.05). Excitability treatments were significantly better for prolonging overall health status (d = 0.22, p < 0.05). However, the absolute best pathophysiological treatment category for prolonging health status varied with disease progression: oxidative stress was best for pre-onset health (d = 0.18, p > 0.05); excitability was best for prolonging function near onset (d = 0.34, p < 0.05); inflammation was best for prolonging post-onset function (d = 0.24, p > 0.05); and apoptosis was best for prolonging end-stage function (d = 0.49, p > 0.05). Finally, combination treatments simultaneously targeting multiple pathophysiological categories (e.g., polytherapy) performed significantly (p < 0.05) better than monotherapies at end-stage.

Discussion: In summary, the most effective pathophysiological treatments change as function of assessment modality and disease progression. Shifting pathophysiological treatment category efficacy with disease progression supports the homeostatic instability theory of ALS disease progression.

Keywords: Amyotrophic Lateral Sclerosis (ALS); SOD1; SOD1 G93A mouse; motoneuron disease; mouse model; neuromuscular; pharmacology.

FIGURE 1

Analysis of pathophysiological treatment category performance aggregated across all mouse age groups (0–131 + days). (A) Normalized effect size for each pathophysiological treatment category. Error bars represent standard deviation. Each pathophysiological treatment category was compared to the “all” treatments category (black bar) with * denoting a significant difference (p < 0.05). (B) Calculated Cohen’s effect size (also known as Cohen’s d) for each pathophysiological treatment category. Cohen’s effect is a statistical measure of each pathophysiological treatment category’s effect size relative to the “all” treatments category (e.g., all pathophysiological treatment categories combined).

FIGURE 2

Visualization of how the Cohen’s effect size, d, changes over time for each pathophysiological treatment category. No one pathophysiological category consistently performed better than the “all” treatments category (e.g., all pathophysiological treatment categories combined). Note that axonal transport is excluded in a few time bins due to inadequate sample size. Cohen’s effect is a statistical measure of each pathophysiological treatment category’s effect size for the specified time bin relative to the “all” treatments category of the corresponding time bin.

FIGURE 3

Assessment of pathophysiological treatment category on delaying onset. (A) Normalized effect size for onset metrics for each pathophysiological treatment category. Error bars represent standard deviation. Axonal transport was omitted due to inadequate sample size. Each pathophysiological treatment category was compared to the “all” treatments category (black bar) with * denoting a significant difference (p < 0.05). (B) Calculated Cohen’s effect size for symptom onset for each pathophysiological treatment category. Cohen’s effect is a statistical measure of each pathophysiological treatment category’s effect size on onset relative to “all” treatments.

FIGURE 4

Health status metrics assessed for each pathophysiological treatment category over time (in post-natal days). Normalized effect size is illustrated on the left. Error bars represent standard deviation. Axonal transport is excluded in a few time bins due to inadequate sample size. For each respective time bin, the pathophysiological treatment category was compared to the overall treated average or “all” (black bar) with * denoting a significant difference (p < 0.05). Calculated Cohen’s effect size for each pathophysiological treatment category and time bin is illustrated on the right. (A,B) 0- 70 days. (C,D) 71–85 days. (E,F) 86–100 days. (G,H) 101–110 days. (I,J) 111–120 days. (K,L) 121–130 days. (M,N) 131 + days.

FIGURE 5

Assessment of pathophysiological treatment category on survival. (A) Normalized effect size for survival for each pathophysiological treatment category. Error bars represent standard deviation. Each pathophysiological treatment category was compared to the “all” treatments category (black bar) with * denoting a significant difference (p < 0.05). (B) Calculated Cohen’s effect size of survival for each pathophysiological treatment category. Cohen’s effect is a statistical measure of each pathophysiological treatment category’s effect size on survival relative to “all” treatments (e.g., all pathophysiological treatment categories combined).

FIGURE 6

Percent composition of beneficial (solid bars) versus harmful (striped bars) treatments by pathophysiological treatment category. While some treatment categories reported negative results more frequently than others, there was no significant differences (p > 0.05).

FIGURE 7

Comparison of the treatment effects of monotherapy and polytherapy. Polytherapy significantly (p < 0.05) outperformed monotherapy monotherapy at end stage (131 + days). *Represents p < 0.05.

FIGURE 8

Summary of top SOD1 G93A pathophysiological treatment category performers. (A) The “best” pathophysiological category for maintaining health status (rotarod, grip strength, body weight, etc.) changes with disease progression. (B) Triad illustrating the best overall treatment performers based on three different assessment modalities. Oxidative stress treatments best delay onset. Excitability treatments were best overall for maintaining health status and/or delaying functional decline. Oxidative stress treatments were best for prolonging survival duration.