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. 2022 Oct;9(10):1551-1564.
doi: 10.1002/acn3.51648. Epub 2022 Sep 9.

Effects of PB-TURSO on the transcriptional and metabolic landscape of sporadic ALS fibroblasts

Jasmine A Fels  1   2 , Jalia Dash  1 , Kent Leslie  3 , Giovanni Manfredi  1 , Hibiki Kawamata  1
Affiliations

Effects of PB-TURSO on the transcriptional and metabolic landscape of sporadic ALS fibroblasts

Jasmine A Fels et al. Ann Clin Transl Neurol. 2022 Oct.

Abstract

Objective: ALS is a rapidly progressive, fatal disorder caused by motor neuron degeneration, for which there is a great unmet therapeutic need. AMX0035, a combination of sodium phenylbutyrate (PB) and taurursodiol (TUDCA, TURSO), has shown promising results in early ALS clinical trials, but its mechanisms of action remain to be elucidated. Therefore, our goal was to obtain an unbiased landscape of the molecular effects of AMX0035 in ALS patient-derived cells.

Methods: We investigated the transcriptomic and metabolomic profiles of primary skin fibroblasts from sporadic ALS patients and healthy controls (n = 12/group) treated with PB, TUDCA, or PB-TUDCA combination (Combo). Data were evaluated with multiple approaches including differential gene expression and metabolite abundance, Gene Ontology and metabolic pathway analysis, weighted gene co-expression correlation analysis (WGCNA), and combined multiomics integrated analysis.

Results: Combo changed many more genes and metabolites than either PB or TUDCA individually. Most changes were unique to Combo and affected the expression of genes involved in nucleocytoplasmic transport, unfolded protein response, mitochondrial function, RNA metabolism, and innate immunity. WGCNA showed significant correlations between ALS gene expression modules and clinical parameters that were abolished by Combo treatment.

Interpretation: This study is the first to explore the molecular effects of Combo in ALS patient-derived cells. It shows that Combo has a greater and distinct impact compared with the individual compounds and provides clues to drug targets and mechanisms of action, which may underlie the benefits of this investigational drug combination.

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Conflict of interest statement

This work was supported in part by a sponsored research agreement with Amylyx Pharmaceuticals. At the time when the work was performed, KL was employed by Amylyx.

Figures

Figure 1
Figure 1
Combo changes more metabolites than either phenylbutyrate (PB) or taurursodiol (TUDCA) alone. (A) Partial least squares‐discriminant analysis of normalized abundance data for all metabolites. (B) Bar graph showing the number of significantly different metabolites (p value <0.05) for each treatment compared with vehicle. (C) Venn diagram of metabolites significantly changed by each treatment (p value <0.05). (D) Heatmap of Z scores of all metabolites significantly changed by Combo. *Indicates targeted metabolites. (E and F) Small Molecule Pathway Database category and pathway analysis of Combo‐upregulated (E) and Combo‐downregulated (F) metabolites.
Figure 2
Figure 2
Combo changes more genes than either phenylbutyrate (PB) or taurursodiol (TUDCA) alone. (A) Principal component analysis of the top 500 genes with the highest variability. (B) Principal least squares‐discriminant analysis of the top 500 genes with the highest variability. (C–E) Volcano plots of differentially expressed genes (DEGs) of TUDCA (C), PB (D), and Combo (E) relative to the vehicle. (F) Venn diagram of DEGs in each treatment. (G) Category analysis of all significant gene ontology (GO) terms enriched in Combo upregulated DEGs. (H) Top 20 most significant GO:BP terms enriched in Combo‐upregulated DEGs, simplified with the clusterProfiler package. (I) Category analysis of all significant GO terms enriched in Combo downregulated DEGs. (J) Top 20 most significant GO:BP terms enriched in Combo downregulated DEGs.
Figure 3
Figure 3
A set of 20 genes and 5 metabolites can discriminate Combo samples from the vehicle. (A) Principal least squares‐discriminant analysis of multiomics comparison normalized RNA and metabolites for all treatments, based on the top 20 most discriminatory genes and top 5 most discriminatory metabolites. (B) Receiver‐operating characteristic curves for the discrimination of each treatment from all other treatments based on the same 20 genes and 5 metabolites as in (A). (C) Principal least squares‐discriminant analysis of multiomics comparison normalized RNA and metabolites for Combo versus PBS based on the top 20 most discriminatory genes or top 5 most discriminatory metabolites. (D) Bar graphs of the top 20 genes and top 5 metabolites for Combo versus PBS. (E) Hierarchical clustering of samples based on the values of the 20 genes and 5 metabolites as in (C and D).
Figure 4
Figure 4
Combo has different transcriptional effects in sALS and CTL cells. (A and B) Volcano plots of differentially expressed genes (DEGs) in the Combo versus PBS comparison in CTL (A) and sALS (B) lines. (C and D) Venn diagram comparing DEGs (C) and gene ontology (GO) terms (D) changed by Combo in sALS and CTL lines. (E) Top 4 most significant GO:BP terms enriched in Combo‐upregulated DEGs in CTL. (F) Top 20 most significant GO:BP terms enriched in Combo‐upregulated DEGs in ALS. (G) Category analysis of all significant GO terms enriched in Combo‐upregulated DEGs in ALS. (H) Top 20 most significant GO:BP terms enriched in Combo downregulated DEGs in CTL. (I) Category analysis of all significant GO terms enriched in Combo downregulated DEGs in CTL. (J) Top 20 most significant GO:BP terms enriched in Combo downregulated DEGs in ALS. (K) Category analysis of all significant GO terms enriched in Combo downregulated DEGs in ALS.
Figure 5
Figure 5
WGCNA identifies modules of genes associated with ALS clinical traits that are altered by Combo. (A and B) Dendrograms of hierarchical clustering of gene co‐expression modules for vehicle‐treated samples (A) and Combo samples (B). (C and D) Heatmaps showing correlations between module eigengene expression values and clinical traits (individual is a cell line identifier, disease reduced is a disease state, age is the age at biopsy, disease duration is the time between symptom onset and biopsy, ALSFRS.R is the score at the time of biopsy, rate_decline is the decrease in ALSFRS‐R/disease duration, FVC is %FVC at time of biopsy) for vehicle‐treated samples (C) and Combo samples (D). The individual cell line identifier was included as a negative control. The numbers in each box are p values, and box colors correspond to the correlation coefficient. (E) Top 20 most significant GO (gene ontology):BP terms enriched in the vehicle Black module. (F) Category analysis of all significant GO terms enriched in the vehicle Black module. (G) Venn diagram of overlap between significantly enriched GO pathways found in the vehicle Black module and the Combo modules (Darkred and Royalblue) paired with Black. (H) Correlations between the expression of the 758 Black module genes in vehicle and Combo samples and disease traits. Size correlates inversely to p value, and the color corresponds to Pearson's correlation coefficient.
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