Epigenetic insights into GABAergic development in Dravet Syndrome iPSC and therapeutic implications

  1. Jens Schuster
  2. Xi Lu
  3. Yonglong Dang
  4. Joakim Klar
  5. Amelie Wenz
  6. Niklas Dahl
  7. Xingqi Chen

  • Curated by eLife

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    eLife assessment

    This is a useful study that shows changes in the chromatin landscape of GABAergic neurons in induced pluripotent stem cells (iPSCs) derived from both Dravet Syndrome (DS) patients and healthy donors. The strength of the evidence is currently incomplete because the authors compared iPSCs from different individuals, rather than isogenic controls, and they did not examine the expression of the gene and encoded protein (SCN1A or Nav1.1) that are thought to be responsible for the majority of DS cases in these iPSCs. The work would be of interest to scientists who study development, developmental disorders, and epigenetic contributions to disease.

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Abstract

Dravet syndrome (DS) is a devastating early onset refractory epilepsy syndrome caused by variants in the SCN1A gene. A disturbed GABAergic interneuron function is implicated in the progression to DS but the underlying developmental and pathophysiological mechanisms remain elusive, in particularly at the chromatin level. In this study, we utilized induced pluripotent stem cells (iPSCs) derived from DS cases and healthy donors to model disease- associated epigenetic abnormalities of GABAergic development. Employing the ATAC-Seq technique, we assessed chromatin accessibility at multiple time points (Day 0, Day 19, Day 35, and Day 65) of GABAergic differentiation. Additionally, we elucidated the effects of the commonly used anti-seizure drug valproic acid (VPA) on chromatin accessibility in GABAergic cells. The distinct dynamics in chromatin profile of DS iPSC predicted accelerated early GABAergic development, evident at D19, and diverged further from the pattern in control iPSC with continued differentiation, indicating a disrupted GABAergic maturation. Exposure to VPA at D65 reshaped the chromatin landscape at a variable extent in different iPSC-lines and rescued the observed dysfunctional development in some DS iPSC-GABA. This study provides the first comprehensive investigation on the chromatin landscape of GABAergic differentiation in DS-patient iPSC, offering valuable insights into the epigenetic dysregulations associated with interneuronal dysfunction in DS. Moreover, our detailed analysis of the chromatin changes induced by VPA in iPSC-GABA holds the potential to improve development of personalized and targeted anti-epileptic therapies.

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  1. Version published to 10.1101/2023.10.10.561691v2 on bioRxiv
  2. Version published to 10.7554/elife.92599.1 on eLife
  3. Version published to 10.7554/elife.92599 on eLife
  4. eLife

    eLife assessment

    This is a useful study that shows changes in the chromatin landscape of GABAergic neurons in induced pluripotent stem cells (iPSCs) derived from both Dravet Syndrome (DS) patients and healthy donors. The strength of the evidence is currently incomplete because the authors compared iPSCs from different individuals, rather than isogenic controls, and they did not examine the expression of the gene and encoded protein (SCN1A or Nav1.1) that are thought to be responsible for the majority of DS cases in these iPSCs. The work would be of interest to scientists who study development, developmental disorders, and epigenetic contributions to disease.

  5. eLife

    Joint Public Review:

    This study used ATAC-Seq to characterize chromatin accessibility during stages of GABAergic neuron development in induced pluripotent stem cells (iPSCs) derived from both Dravet Syndrome (DS) patients and healthy donors. The authors report accelerated GABAergic maturation to a point, followed by further differentiation into a perturbed chromatin profile, in the cells from patients. In a preliminary analysis, valproic acid, an anti-seizure medication commonly used in patients with DS, increased open chromatin in both patient and control iPSCs in a nonspecific manner, and to different degrees in cultures derived from different patients. These findings provide new information about DS-associated changes in chromatin, and provide further evidence for developmental abnormalities in interneurons with DS.

    Strengths:

    This is a novel study that aims to investigate the epigenetic changes that occur in a sodium channel model of epilepsy; these changes are often ignored but may be an interesting area for future therapeutics. In general, the flow of the paper is good, and the figures are well-designed.

    Weaknesses:

    The most substantial weakness relates to the observation that DS is often viewed as a monogenic form of epilepsy. It is directly linked to SCN1A gene haploinsufficiency (Yu et al, 2006; Ogiwara et al, 2007). The gene product is Nav1.1, the alpha subunit of voltage-gated sodium channel type I that regulates neuronal excitability. Yet, analysis was conducted at time points of GABAergic interneuron differentiation in which SCN1A is likely not expressed. The paper would be strengthened if SCN1A expression and Nav1.1 protein were examined across the experimental time course. If SCN1A is not yet expressed, this would complicate any explanation of how the observed epigenetic changes might arise. It also seems counterintuitive that the absence of a sodium channel can accelerate differentiation, when, a priori, one might expect the opposite (a 'less neuronal' signal).

    Related to this, another important limitation of the study is that the controls are cells derived from healthy individuals and not from isogenic lines. The usage of isogenic lines is extremely relevant for every study in which iPSC-derived somatic cells are used to model a disease, but specifically in diseases like DS, in which the genetic background has an ascertained impact on disease phenotype (Cetica et al, 2017 and others). This serious limitation should be considered. In addition, the authors should provide data on variability across cell lines and differentiations to help convince the reader that the results can be attributed to genetic defects, rather than variability across individuals.

    Additionally, the authors acknowledge the variability of the differentiations and cell lines, which is commendable, and they attribute this to "possibly reflecting cell line specific and endogenous differences reported previously", but could also have to do with cell death. This is a large confounding factor for ATAC-seq. Certainly, Sup Fig 1C shows lower FrIP scores, consistent with cell death, and there seems to be a lot of death in the representative images. Moreover, the iGABA neurons are very difficult to keep alive, especially to 65 days, without co-culturing with glia and/or glutamatergic neurons. The authors should comment on how much these factors may have influenced their results.

    Finally, changes in gene expression are only inferred, as no RNA levels were measured. If RNA-seq was not possible it would have been good to see at least some of the key genes/findings corroborated with RNA/protein levels vs chromatin accessibility alone, particularly given that these molecular readouts do not always correlate.

    Additional Points:

    1. Representative images for cell-identity markers for only D65 are shown, and not D0, D19, and D35 though it is stated in the text that this was performed. At a minimum, these representative images should be shown for all lines.
    2. What QC was performed on iPSC lines, i.e. karyotype/CNV analysis and confirmation of genotypes?
    3. Were all experiments performed on a single differentiation? Or multiples? Were the differentiations performed with the same type? If not, was batch considered in the analysis? I also assume that technical replicates were merged, and then all three biological replicates were kept for each analysis and outliers were not removed, e.g. Control_D19_8F seems like an example of an outlier.
    4. In Figure 1C, it is intriguing that the ATACseq signal gets stronger in imN. One might expect it to be strongest in the iPSCs which are undifferentiated and have the highest levels of open chromatin. Is this a function of sequencing depth, or are all the Y-axes normalized across all time points?
    5. In Figure 1F, are these all enriched terms, or were they prioritized somehow?
    6. In Figure 1G (also the same plots in Fig 2/3), are all these images normalized i.e. there is no scale bar for each track, and do they represent and aggregate BAM/bigwig? It would be good to show in supplement the variability across cell lines/diffs - particularly given the variability in the heatmap/PCA - and demonstrate the rigor/reproducibility of these results. This comment applies to all these plots across the 3 figures, particularly as in some instances the samples appear to cluster by individual first and then time point (Sup Fig 3B). How confident are the authors that these effects are driven by genotype and not a single cell line? In the Fig 3D representation of NANOG, it is very difficult to see any difference between patient and control.
    7. For the changes in occupancy annotation (UTR/exon/intron etc), are these differences still significant after correcting for variability from cell line to cell line at each time point? I.e. rather than average across all three samples, what is the range?
    8. The VPA timepoint is not well-justified. Given that VPA would be administered in patients with fully mature inhibitory neurons, it is difficult to determine the biological relevance. I appreciate that this is a limitation of the model, but this should at least be addressed in the manuscript.

  6. Version published to 10.1101/2023.10.10.561691v1 on bioRxiv