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    • The observed correction of the phenotypes

    2018-11-09

    The observed correction of the phenotypes was not due to altered simvastin of SOD1 transcripts between the diseased and isogenic MNs (Figure S2C). Aggregation of mutant SOD1 into insoluble inclusions is commonly observed in SOD1 ALS (Taylor et al., 2016). Treatment of MN simvastin cultures with the proteasome inhibitor MG132 revealed significant levels of insoluble SOD1 in the diseased MNs, although similar levels of soluble SOD1 protein were observed between the diseased and isogenic controls (Figure 2J). Interestingly, an extra band that migrated slightly faster was detected for SOD1 specifically in the mutant MN soluble fraction, probably representing misfolded protein (Chen et al., 2014). Taken together, the phenotypes observed in our in vitro model closely recapitulate findings from ALS postmortem tissue and rodent models. In addition, amelioration of the phenotypes upon genetic correction indicated that the observed phenotypes resulted from the underlying mutation and were not due to genotypic variations between the iPSC lines. To understand the molecular pathways driving MN loss, we performed genome-wide transcriptome profiling of the diseased and corrected MNs using RNA-seq. We chose to use young MNs (day 30) to avoid transcriptional changes associated with cell death. Unsupervised hierarchical clustering of the RNA-seq data showed that the diseased MNs segregated distinctly from the corrected ones, indicating that correction of the SOD1 mutant allele had induced significant changes in the transcriptome (Figure 3A). We identified 480 genes in the SOD1 dataset that were differentially regulated (p < 0.01) with a majority of the genes being activated in SOD1 mutant MNs (Figure 3A). To enable sensitive detection of altered regulatory pathways from the differential gene expression data, we performed gene set enrichment analysis (GSEA), which relies on ranking genes in order of significance and avoids setting hard thresholds to identify differentially expressed genes (Mootha et al., 2003; Subramanian et al., 2005). GSEA identified several pathways as differentially regulated in ALS MNs. For SOD1, significantly upregulated pathways were associated with p53 activation, cell-cycle regulation, WNT signaling, AP1 activation, and the unfolded protein response (UPR) (Figure 3B). Strikingly, the downregulated gene sets were associated with mitochondrial function, including electron transport, ATP synthesis, and oxidative phosphorylation (Figure 3B). Mitochondrial defects and an activated UPR have previously been shown to be associated with the SOD1 A4V mutation in MNs (Kiskinis et al., 2014). Here, we find that the SOD1 E100G mutation results in similar molecular defects in MNs. In addition, genes involved in ion channel transport, especially γ-aminobutyric acid receptors, were also downregulated, which could partially explain the previously reported excitotoxicity in SOD1 MNs (Wainger et al., 2014) (Figure 3B). For further investigation, we decided to focus on the pathways that were activated in ALS MNs. qRT-PCR confirmed differential expression of several genes identified from RNA-seq analysis and representative of pathways found to be activated by GSEA (Figure 3C). We hypothesized that the activation of seemingly disparate pathways in mutant SOD1 MNs could result from the differential activation of a few upstream effectors. When placed in the context of a functional gene network, we observed that several of these pathways shared functional interactions with MAPK signaling and displayed extensive cross-talk between the identified pathways (Figure 3D). For example, the AP1 complex protein JUN can be directly activated by the ERK (MAPK1,3), JNK (MAPK8,9), and p38 (MAPK14) kinases, while the WNT mediator CTNNB1 (β-catenin) can be activated by the p38 (MAPK14) kinase. In addition, JUN as well as the cell-cycle mediators CDK1 and CDK2 have functional interactions with TP53 (Figure 3D). The network analysis raised the possibility that activation of a select few pathways may be sufficient to initiate the cascade of signaling perturbations observed from our RNA-seq data.