Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes
Preprint posted on 27 August 2018 https://www.biorxiv.org/content/early/2018/08/27/400291
Article now published in Nature Genetics at http://dx.doi.org/10.1038/s41588-019-0457-0
Introduction and context
One of the main two types of diabetes, diabetes mellitus (type 2, T2D) is a polygenic disease that occurs usually in obese subjects and results in systemic peripheral tissue insulin resistance (DeFronzo et al. 2015). A popular approach to study common polygenic diseases is to look at genetic variants that occur in the population and correlate them with the risk for the disease (Torkamani et al. 2018). In the case of T2D, several recent efforts have implicated common Single Nucleotide Polymorphisms (SNPs) in the T2D phenotype and identified a number of susceptibility loci e.g. (K. Sanghera and R. Blackett 2012; Mishra and Hawkins 2017; Mahajan et al. 2018). However, deriving functional and causal information from these variants and their associated genes often proves difficult, especially when they occur in the non-coding portion of the genome, and when information is lacking on what the target genes are and how the variants affect them.
So far, previously identified T2D SNPs and their function in the T2D phenotype have only been studied in the context of a linear DNA map (eg. Which cell-specific transcription factors bind in the enhancer clusters in which the SNP occurs? Or, how do the different variants contribute to gene expression within the Topologically Associated Domain (TAD)?). However, how these SNPs specifically contribute to the disease phenotype on the molecular level in the context of the 3D genome remains to be elucidated.
In a recent preprint, members of the group of Prof. Jorge Ferrer and colleagues set out to construct a chromatin interactome map of human pancreatic islets, with the goal to determine beta-cell specific enhancer-promoter interactions and to link variation in the non-coding portion of the genome to the relevant target genes. First, they combined ATAC-sequencing and ChIP-sequencing for Mediator, Cohesin and H3K27Ac with promoter-capture hi-C (pcHi-C). This latter technique increases sensitivity of Hi-C by using fragment baits that cover all known promoter regions (Schoenfelder et al. 2018) to detect lower-frequency, cell type-specific interactions between promoters and their potential regulatory sequences. These experiments demonstrated that islet-specific interactions are strongly enriched for islet-specific enhancers showing ample binding of Mediator, a protein complex known to regulate gene expression by bridging the signals from transcription factors in regulatory regions to RNA Polymerase II. Because these enhancers also showed the most prominent H3K27 acetylation, the authors named these enhancers Class I enhancers.
To identify the target genes of (Class I) pancreatic islet enhancers from Hi-C, the authors first needed to address an important limitation of the Hi-C technique: its data analysis is highly stringent and shows a strong proximity bias and excessive tissue/physiological condition specificity. Therefore, cell type-specific DNA proximity outcomes can easily be underestimated. To overcome this problem, the authors imputed additional potential interactions based on promoter-associated 3D space (PATs). Here, a PAT is defined as the ‘space’ that is constructed from taking together all pcHi-C interactions of a specific bait. Like this, they found that the PATs that show high confidence interactions with islet specific enhancers have a higher chance of also interacting with other enhancers. Next, the PATs were used to identify the target genes of the T2D-relevant enhancers (i.e. those that showed disease-relevant regulatory variation in their sequences). These functional interactions were then validated with several approaches, a.o. using CRISPR-Cas9 modulation of the PAT enhancers, some computational work, and a dynamic-perturbation strategy in which the activity of the enhancers could be modulated by challenging the cells with high or low glucose media.
Next, the authors assessed the T2D-associated SNPs locating to these 3D regions and asked if they impact on the structure and function of enhancer ‘hubs’ defined by the PATs and their secondary interactors (i.e. their target genes, and other interacting enhancers). Suggestive of a strong tissue-specific role, they observed that the SNPs in the 109 T2D – Fasting Glucose (FG) risk loci were enriched in high-confidence pcHi-C regions with increased Class I (high H3K27Ac – Mediator occupancy) enhancers. Furthermore, they showed also that these SNPs showed higher genetic heritability scores, and thus that they play a prominent role in the heritability of islet function and T2D.
Finally, the authors asked if they could use the islet enhancer hub SNPs to better predict the risk for T2D. They found that, although quantitatively the hub-SNVs were not better at predicting T2D risk than the collection of all common SNPs genome-wide, hub-SNPs could help stratify patients qualitatively. For instance, the Polygenic Risk Scores (PRS) calculated from hub-SNPs in non-obese, younger patients showed higher Odds Ratios than the PRS in obese, older patients, indicating that in these younger individuals the variation occurring in the genomic regions that affect islet transcription impacts more strongly on T2D. Like this, the authors postulated that hub-only SNVs may be useful to further study how islet-specific regulatory variation affects T2D pathogenesis.
What I think about this preprint & open questions
I chose this preprint because it addresses a critical question in cell / systems biology: How does the three-dimensionality of chromatin contribute to cellular identity and disease?
First of all, I like that the 3D interactome map constructed by the authors is browsable with a user-friendly interface; and thus that any individual could go to his/her favorite gene and look at its 3D environment. Furthermore, the authors push the limits of their data by several means, and, although many of these imputed interactions A) may be dynamic/transient and B) may need to be subjected to experimental validation, I love the idea of using ‘hubs’ to represent cell type specific, 3D spatial interactions. Indeed, with this approach, the authors overcome some of the limitations inherent to genome-wide chromatin-capture techniques and identify potential novel, cell type specific or even disease-specific domains, laying the groundwork for further studies.
The fact that many of the ‘hub’ interactions are imputed may also confer a strong limitation to the work: there is no direct evidence that these hubs or all the interactions within them are actually formed (simultaneously), and interactions may be mutually exclusive, or temporally distinct. Therefore, it would be interesting to study if these interactions occur and what their dynamics are using for instance imaging-based methods (e.g. DNA-FISH, or CRISPR/Cas-guided locus identification in live cells, see eg. (Chen et al. 2013)).
With this work, another question emerges: are these 3D interactions functionally relevant to pancreatic islet biology? For instance, would it be interesting to study a time course and see if the enhancer becomes inactive (loses active chromatin marks) before its target gene expression does? Are the 3D connections lost in these and other (experimental/physiological) circumstances?
I am further intrigued by the Class I enhancers defined in this study by high Mediator occupancy and H3K27Ac. Several recent studies (eg. Cho et al. 2018; Sabari et al. 2018) demonstrated that Mediator and RNA Pol II are involved in a phase-separated complex involving super-enhancers. Thinking of how 3D chromatin architecture helps to establish cell identity mechanistically, the question emerges whether the observations in these recent papers can be extended to the islet-specific Class I enhancers observed in this study. More generally, does the liquid demixing through Mediator and RNA Pol II binding provide a chemically favorable environment to establish relevant DNA-DNA contacts in a cell-type specific manner? Thus, it will be interesting to study further what these Class I enhancers are, how they emerge and are maintained, and if they contribute to phase-separation mediated transcriptional output. Furthermore, it remains to be determined what the driving forces are to establish the liquid-demixed environment in this cell type specific manner.
In conclusion, I like this study because it advances the understanding of 1. islet-specific enhancer-promoter interactions and their potential influence on cell-type specific gene expression in the context of human pancreatic beta cell islets; and 2. the functional and mechanistic role of disease-associated variants in establishing relevant interactions in the 3D genome leading to disease through disruption of the normophysiological 3D architecture. Finally, this study also demonstrates the importance for continuous validation studies of predicted interactions in a cell type or physiological condition specific manner, and ultimately may be an important resource to further our understanding of cell identity and how it is influenced by local chromatin architecture.
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Posted on: 20 September 2018 , updated on: 6 October 2018Read preprint