Transcriptomic Effects of Cannabis-Derived Therapies in a Drosophila model of ALS

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron demise, leading to paralysis and death within approximately 3-5 years of symptom onset.Current FDA-approved therapies offer limited disease modification, highlighting the need for treatments that address neuroinflammation and excitotoxicity.Cannabis-derived compounds, particularly flavonoids like Cannflavin A (CFA), have emerged as potential neurotherapeutics due to their anti-inflammatory properties but remain unresearched.In this study, an in-vivo C9orf72 Drosophila melanogaster model of ALS, expressing toxic poly-GR dipeptide repeats, was used to evaluate the transcriptomic impact of CFA and CNR-401 (a proprietary mixture of flavonoids and terpenes).RNA-sequencing was performed on fly brains followed by differential depression analysis and functional enrichment to identify modulated molecular pathways.Both compounds were found to elicit significant transcriptional remodeling relevant to neurodegenerative pathophysiology.CNR-401 uniquely upregulated ABC transporter genes (NES = 3.17) and fatty acid metabolism (median NES = 2.7515), suggesting a shift toward lipid homeostasis, metabolic resistance, and management of neuroinflammation.CFA treatment primarily enriched gene sets associated with synaptic activity, both excitatory (Glutamatergic Synaptic Transmission NES = 3.165) and inhibitory (GABAergic Synapse NES = 2.175), suggesting a homeostatic stabilization of neurons against excitotoxicity.Both treatments suppressed glucuronidation and xenobiotic metabolism, which may enhance drug bioavailability and support endogenous neuroprotective steroids and coordinated modulation of the circadian clock.In summary, CNR-401 and CFA were shown to be strong therapeutic candidates for ALS patients.Future studies should utilize better controls and mammalian models of ALS to facilitate more accurate ortholog mapping and support gene-level claims.Deol 2 2016).To do this, raw counts for each sample were first normalized using the median-of-ratios method (Love et al., 2014) to estimate sample-specific size factors.This effectively accounts for differences in sequencing depth and allows for fair comparisons between samples.Dispersion was then estimated for each gene using Maximum Likelihood.To improve reliability, these estimates were then moderated by shrinking them towards a global trend line capturing the mean-variance relationship across all genes.Lastly, the size factors and shrunken dispersion estimates were plugged into a GLM for each gene to solve for the coefficients representing log 2 fold changes (LFCs) for each contrast. Deol 11Following the fitting of the GLM, statistical significance for each LFC was assessed using Wald tests.This test evaluated the null hypothesis that the LFC for a given contrast is equal to zero, and produced p-values accordingly.After p-value calculation, LFCs were then shrunk using the bayesian technique apeglm to account for the naturally larger LFC in lowly expressed genes (Zhu et al., 2019), improving downstream visualization and ranking. Significance FilteringTo account for the multiple testing problem resulting from performing thousands of individual Wald tests, p-values were adjusted using the Benjamini-Hochberg procedure (Benjamini & Hochberg, 1995).A False Discovery Rate (FDR) threshold of 0.05 was applied, ensuring that proportion of false positives among the rejected null hypotheses was controlled below 5%.Genes were then called significantly differentially expressed if they achieved an adjusted p-value (FDR) ≤ 0.05.It was decided that an LFC threshold would not be applied in order to capture the maximum number of DEGs in this exploratory experiment.Results were visualized through volcano and MA plots (Rosati et al., 2024), LFC distributions (Supplementary Figure S6) and cross-comparison plots including a Jaccard overlap heatmap, DEG summary barplot, and venn diagram of DEG overlap. Functional AnalysisDownstream functional analyses were conducted on the differential expression tables to characterize biological processes, find connections to relevant diseases, and identify potential biomarkers and therapeutic targets.Deol 12