Periodic Reporting for period 4 - ONCOmetENHANCERS (Elucidating the Role of Enhancer Methylation Variation in Cancer and Developing Enhancer-based Markers and Targets for Precision Medicine)
Reporting period: 2021-10-01 to 2023-03-31
Common human diseases including metabolic, autoimmune, psychiatric and cancer illnesses affect hundreds of millions of people around the globe. However, the underlying mechanisms are poorly understood, the molecular basis for the genetic-environment interaction is not clear, and effective markers and treatments for personalized medicine are lacking. An important disadvantage of the current mutation databases is that they strongly biased to coding-sequence mutations, whereas other genomic regions were less studied. Specifically, the effect of mutation in cis-acting gene enhancers, which built a substantial portion of our genome and may affect all genes, were not well explained.
We and others have previously showed that the level of DNA methylation characterizes enhancer activity and may both affect and report their activity. Therefore, we hypothesized that through the mapping and analysis of enhancer methylation sites, the association between enhancers and their controlled genes may be revealed, leading to the discovery of key regulatory circuits governing disease predisposition and development.
In this research project, we aimed to: 1. To elucidate the enhancer circuits of key cancer genes and understand their alternation in cancer. 2. To develop a new class of enhancer-based gene expression biomarkers. 3. To explore the function of enhancer methylation in cancer. By achieving these specific aims, we wished to provide important insight into the mechanism a severe model disease, as well as to shed light on the general problem that described above.
We and others have previously showed that the level of DNA methylation characterizes enhancer activity and may both affect and report their activity. Therefore, we hypothesized that through the mapping and analysis of enhancer methylation sites, the association between enhancers and their controlled genes may be revealed, leading to the discovery of key regulatory circuits governing disease predisposition and development.
In this research project, we aimed to: 1. To elucidate the enhancer circuits of key cancer genes and understand their alternation in cancer. 2. To develop a new class of enhancer-based gene expression biomarkers. 3. To explore the function of enhancer methylation in cancer. By achieving these specific aims, we wished to provide important insight into the mechanism a severe model disease, as well as to shed light on the general problem that described above.
Via developing novel methodologies, we were able achieve the above aims, as follow:
Work Package 1: Methylation-based analysis of gene-enhancer coupling. We developed a methylation-orientated strategy to explored gene-associated, cis-regulatory elements. Focusing on 125 pan-cancer or glioblastoma (GBM) driver genes, and 52 reference genes, we performed analyses of two million base-pairs (bp) centered at the promoters of the genes. Utilizing deep methylation-sequencing data of 24 capturing libraries, we analyzed the association between methylation levels of the captured sites and expression levels of the targeted genes, across GBM tumors. The analysis revealed cis-regulatory circuits (n=1,154; q<0.05; R2 > 0.3) between certain methylation sites and controlled genes. Most (78%) of the genes had multiple (2-68) circuits, averaging 8.3 circuits per gene, of them 3.5 circuits in average were positive (expression raised with methylation), and 4.8 negative. This extensive mapping data pave the way for the following stages of the study.
Work Package 2: Massive parallel function analysis of genetic and epigenetic enhancer alleles. We developed a method for systematic functional analysis of enhancer sequences before or after DNA methylation. For this experiment, an entire library of captured DNA segments was cloned into gene-reporter vectors, downstream to minimal promoters. The obtained expression vectors were inserted into GBM cells, and allowed to produce RNAs. The transcriptional effect of each segment was then examined, in windows of 500 bp. We next compared the functionalities of the captured segments under whole-methylation versus de-methylation conditions. Of the 26,152 annotated segments, 10,998 displayed activity differences of at least one and a half fold between methylated and un-methylated states. Of the methylation-sensitive elements, the majority (83.7%) reduced their original activity, or shifted to the opposite functionality (i.e. enhancers became weaker or turned silencers, and vice versa), upon methylation.
Work Package 3: Development of DNA-based expression markers. We utilized the enhancer-gene coupling and the functional data that have been produced in WPs 1-2, to develop gene-expression prediction models. Overall, significant models of inter-patient’s expression variation were developed for 81 of the genes (58 drivers and 23 reference genes). Out of these, the expression of 58 genes (39 drivers and 19 reference genes) were best described by synergic combinations of sites, that together provide better description than each of the sites alone. Of these synergic models, 23 used sites from both negative and positive sites.
Work Package 4: Targeted modification of regulatory elements. We applied genome editing to verify the uncovered gene regulatory circuits. Take together, the results reveal the internal organizations of gene regulatory domains, as well as on the regulatory role of DNA methylation. We found that cis-regulatory domains are composed of spatially-overlapped, gene-specific regulatory networks. These networks comprised multiple regulatory units, each of them provides a define, positive or negative effect on the expression of the targeted genes. Under control conditions, DNA methylation dictates the mode and the level of these effects. The sum effects of methylation variation in a small number of key methylation sites, located in positive and negative units, effectively describe the variation in the expression of cancer genes among glioblastoma patients.
Work Package 1: Methylation-based analysis of gene-enhancer coupling. We developed a methylation-orientated strategy to explored gene-associated, cis-regulatory elements. Focusing on 125 pan-cancer or glioblastoma (GBM) driver genes, and 52 reference genes, we performed analyses of two million base-pairs (bp) centered at the promoters of the genes. Utilizing deep methylation-sequencing data of 24 capturing libraries, we analyzed the association between methylation levels of the captured sites and expression levels of the targeted genes, across GBM tumors. The analysis revealed cis-regulatory circuits (n=1,154; q<0.05; R2 > 0.3) between certain methylation sites and controlled genes. Most (78%) of the genes had multiple (2-68) circuits, averaging 8.3 circuits per gene, of them 3.5 circuits in average were positive (expression raised with methylation), and 4.8 negative. This extensive mapping data pave the way for the following stages of the study.
Work Package 2: Massive parallel function analysis of genetic and epigenetic enhancer alleles. We developed a method for systematic functional analysis of enhancer sequences before or after DNA methylation. For this experiment, an entire library of captured DNA segments was cloned into gene-reporter vectors, downstream to minimal promoters. The obtained expression vectors were inserted into GBM cells, and allowed to produce RNAs. The transcriptional effect of each segment was then examined, in windows of 500 bp. We next compared the functionalities of the captured segments under whole-methylation versus de-methylation conditions. Of the 26,152 annotated segments, 10,998 displayed activity differences of at least one and a half fold between methylated and un-methylated states. Of the methylation-sensitive elements, the majority (83.7%) reduced their original activity, or shifted to the opposite functionality (i.e. enhancers became weaker or turned silencers, and vice versa), upon methylation.
Work Package 3: Development of DNA-based expression markers. We utilized the enhancer-gene coupling and the functional data that have been produced in WPs 1-2, to develop gene-expression prediction models. Overall, significant models of inter-patient’s expression variation were developed for 81 of the genes (58 drivers and 23 reference genes). Out of these, the expression of 58 genes (39 drivers and 19 reference genes) were best described by synergic combinations of sites, that together provide better description than each of the sites alone. Of these synergic models, 23 used sites from both negative and positive sites.
Work Package 4: Targeted modification of regulatory elements. We applied genome editing to verify the uncovered gene regulatory circuits. Take together, the results reveal the internal organizations of gene regulatory domains, as well as on the regulatory role of DNA methylation. We found that cis-regulatory domains are composed of spatially-overlapped, gene-specific regulatory networks. These networks comprised multiple regulatory units, each of them provides a define, positive or negative effect on the expression of the targeted genes. Under control conditions, DNA methylation dictates the mode and the level of these effects. The sum effects of methylation variation in a small number of key methylation sites, located in positive and negative units, effectively describe the variation in the expression of cancer genes among glioblastoma patients.
The revealed mechanism shed light on a long-standing enigma regarding the regulatory role of DNA methylation, explains inter-individual gene-expression variation, and flags particular methylation sites as candidates for RNA-free monitoring of inter-patient expression variation. Whether genetic or epigenetic editing of these sites will improve GBM phenotypes, and the relevance for other illnesses, remains to be determined.
Whereas the general structure of gene regulatory domains was comprehensively described, their internal organization are less understood. Particularly, the operational sites and units within these domains, the interaction between them, and the genetic and epigenetic mechanisms that organize their effects on genes, were not well mapped and explained. Due to this lack of knowledge, the origin of variable expression levels of disease genes remained unclear.
Some critical technology barriers were hindered the achieving of our goals: First, it was necessary to develop a method for deep sequencing of enhancer methylation sites, as available methylation mapping technologies as meDIP, RRBS, and Illumina’s BeadChip arrays provide a very limited coverage of enhancer methylation sites, whereas whole-genome bisulfite sequencing projects generally result in too low a per-site read number (typically 10-20 reads-per-site) compared to the several hundred sequencing reads per site that require for the precise measurements needed for our analyses. Second, it was necessary to develop a method for high-throughput functional assessment of methylated versus unmethylated enhancer sites. Third, due to the high level of redundancy typical to enhancer sites, it was necessary to map enhancer networks and identify the key network sites in order to verify enhancer functioning by sequence modifications of endogenous loci.
Via developing novel methodologies, we were able to overcome the above hindrances. Our research presented a practical way to decipher the internal structure of large regulatory domains, by mapping and annotation of cis-regulatory methylation schemes. Utilizing this approach, we revealed a main source of gene variation among glioblastoma patients. Furthermore, our study shed light on the mechanism of gene regulation and explains some long-standing wonderings regarding the effect of epigenetic mutations.
Whereas the general structure of gene regulatory domains was comprehensively described, their internal organization are less understood. Particularly, the operational sites and units within these domains, the interaction between them, and the genetic and epigenetic mechanisms that organize their effects on genes, were not well mapped and explained. Due to this lack of knowledge, the origin of variable expression levels of disease genes remained unclear.
Some critical technology barriers were hindered the achieving of our goals: First, it was necessary to develop a method for deep sequencing of enhancer methylation sites, as available methylation mapping technologies as meDIP, RRBS, and Illumina’s BeadChip arrays provide a very limited coverage of enhancer methylation sites, whereas whole-genome bisulfite sequencing projects generally result in too low a per-site read number (typically 10-20 reads-per-site) compared to the several hundred sequencing reads per site that require for the precise measurements needed for our analyses. Second, it was necessary to develop a method for high-throughput functional assessment of methylated versus unmethylated enhancer sites. Third, due to the high level of redundancy typical to enhancer sites, it was necessary to map enhancer networks and identify the key network sites in order to verify enhancer functioning by sequence modifications of endogenous loci.
Via developing novel methodologies, we were able to overcome the above hindrances. Our research presented a practical way to decipher the internal structure of large regulatory domains, by mapping and annotation of cis-regulatory methylation schemes. Utilizing this approach, we revealed a main source of gene variation among glioblastoma patients. Furthermore, our study shed light on the mechanism of gene regulation and explains some long-standing wonderings regarding the effect of epigenetic mutations.