CoChat
Sign Up
Cited by 210Congenital Ear and Nasal AnomaliesEpigenetics and DNA MethylationGenomics and Rare Diseases

The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.

CHARGE and Kabuki Syndromes: Gene-Specific DNA Methylation Signatures Identify Epigenetic Mechanisms Linking These Clinically Overlapping Conditions

Open access
Butcher, Darci T., Cytrynbaum, Cheryl, Turinsky, Andrei L., Siu, Michelle T., Inbar-Feigenberg, Michal, Mendoza-Londono, Roberto, Chitayat, David, Walker, Susan, Machado, Jerry, Caluseriu, Oana, Dupuis, Lucie, Grafodatskaya, Daria, Reardon, William, Gilbert-Dussardier, Brigitte, Verloes, Alain, Bilan, Frederic, Milunsky, Jeff M., Basran, Raveen, Papsin, Blake, Stockley, Tracy L., Scherer, Stephen W., Choufani, Sanaa, Brudno, Michael, Weksberg, Rosanna·2017·The American Journal of Human Genetics
DOI 10.1016/j.ajhg.2017.04.004PMC PMC5420353
Open this paper in CoChat — free accountOpen PDF

Abstract

Epigenetic dysregulation has emerged as a recurring mechanism in the etiology of neurodevelopmental disorders. Two such disorders, CHARGE and Kabuki syndromes, result from loss of function mutations in chromodomain helicase DNA-binding protein 7 ( CHD7 LOF ) and lysine (K) methyltransferase 2D ( KMT2D LOF ), respectively. Although these two syndromes are clinically distinct, there is significant phenotypic overlap. We therefore expected that epigenetically driven developmental pathways regulated by CHD7 and KMT2D would overlap and that DNA methylation (DNAm) alterations downstream of the mutations in these genes would identify common target genes, elucidating a mechanistic link between these two conditions, as well as specific target genes for each disorder. Genome-wide DNAm profiles in individuals with CHARGE and Kabuki syndromes with CHD7 LOF or KMT2D LOF identified distinct sets of DNAm differences in each of the disorders, which were used to generate two unique, highly specific and sensitive DNAm signatures. These DNAm signatures were able to differentiate pathogenic mutations in these two genes from controls and from each other. Analysis of the DNAm targets in each gene-specific signature identified both common gene targets, including homeobox A5 ( HOXA5 ), which could account for some of the clinical overlap in CHARGE and Kabuki syndromes, as well as distinct gene targets. Our findings demonstrate how characterization of the epigenome can contribute to our understanding of disease pathophysiology for epigenetic disorders, paving the way for explorations of novel therapeutics.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ajhg.2017.04.004

Cites

92 of 92 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Hierarchical Clustering of the Discovery Cohorts Using the CHD7LOF and KMT2DLOF DNAm Signatures The heatmap shows the unsupervised hierarchical clustering of (A) 19 CHD7LOF individuals and 29 matching controls samples, using only 163 differentially methylated CpG sites specific to CHD7LOF. The color gradient of the heatmap indicates the methylation level, from low (blue) to high (yellow). DNAm profiles fall into two separate clusters corresponding to CHD7LOF mutations (red) and controls (green). Euclidean distance metric is used in the clustering. (B) 11 KMT2DLOF individuals and 11 matching controls samples, using only 221 differentially methylated CpG sites specific to KMT2DLOF. DNAm profiles fall into two separate clusters corresponding to KMT2DLOF mutations (blue) and controls (green).
Figure 1 Hierarchical Clustering of the Discovery Cohorts Using the CHD7LOF and KMT2DLOF DNAm Signatures The heatmap shows the unsupervised hierarchical clustering of (A) 19 CHD7LOF individuals and 29 matching controls samples, using only 163 differentially methylated CpG sites specific to CHD7LOF. The color gradient of the heatmap indicates the methylation level, from low (blue) to high (yellow). DNAm profiles fall into two separate clusters corresponding to CHD7LOF mutations (red) and controls (green). Euclidean distance metric is used in the clustering. (B) 11 KMT2DLOF individuals and 11 matching controls samples, using only 221 differentially methylated CpG sites specific to KMT2DLOF. DNAm profiles fall into two separate clusters corresponding to KMT2DLOF mutations (blue) and controls (green).CC BY-NC-ND · PubMed Central
Figure 2 Specificity of the CHD7LOF and KMT2DLOF DNAm Classification Signatures The plot shows the predictions for all samples from the original Discovery Cohorts, as well as for 162 normal blood samples extracted from the GEO repository. The x axis shows the predictive scores generated from the CHD7LOF-specific predictive model derived using the CHD7LOF individuals and matching controls. The y axis shows the predictive score of the KMT2DLOF-specific predictive model derived using the KMT2DLOF individuals and matching controls. Importantly, using the KMT2DLOF-specific model all 19 CHD7LOF (red C) received low scores, along with all CHD7LOF matching controls (red circles) and all GEO samples (green crosses). Similarly, using the CHD7LOF-specific model all 11 KMT2DLOF (blue K) received low scores, along with all KMT2DLOF matching controls (blue diamonds) and all GEO samples (green crosses).
Figure 2 Specificity of the CHD7LOF and KMT2DLOF DNAm Classification Signatures The plot shows the predictions for all samples from the original Discovery Cohorts, as well as for 162 normal blood samples extracted from the GEO repository. The x axis shows the predictive scores generated from the CHD7LOF-specific predictive model derived using the CHD7LOF individuals and matching controls. The y axis shows the predictive score of the KMT2DLOF-specific predictive model derived using the KMT2DLOF individuals and matching controls. Importantly, using the KMT2DLOF-specific model all 19 CHD7LOF (red C) received low scores, along with all CHD7LOF matching controls (red circles) and all GEO samples (green crosses). Similarly, using the CHD7LOF-specific model all 11 KMT2DLOF (blue K) received low scores, along with all KMT2DLOF matching controls (blue diamonds) and all GEO samples (green crosses).CC BY-NC-ND · PubMed Central
Figure 3 Validation of CHD7LOF and KMT2DLOF DNAm Classification Signatures on a Blinded Cohort We derived the scores for each sample using the two predictive models built for the CHD7LOF and KMT2DLOF DNAm classification signatures (x axis and y axis, respectively; see Figure 2), for a validation set of DNAm samples. This set included both pathogenic mutations and VUS in CHD7 (red squares), KMT2D (blue triangles) and KDM6A (turquoise diamond). The mutation and their pathogenicity were initially blinded and were revealed only after the prediction scores were determined. Importantly, all CHD7 mutations received low scores by the KMT2D-specific predictive model, and vice versa. Pathogenic mutations in CHD7 (filled red squares) received high scores from the CHD7LOF model, and pathogenic mutations in KMT2D (filled blue triangles) received very high scores from the KMT2DLOF model. Interestingly, a pathogenic mutation in the Kabuki-associated gene KDM6A also received a very high score from the KMT2DLOF model, indicating a potential methylation-signature overlap between these two genes.
Figure 3 Validation of CHD7LOF and KMT2DLOF DNAm Classification Signatures on a Blinded Cohort We derived the scores for each sample using the two predictive models built for the CHD7LOF and KMT2DLOF DNAm classification signatures (x axis and y axis, respectively; see Figure 2), for a validation set of DNAm samples. This set included both pathogenic mutations and VUS in CHD7 (red squares), KMT2D (blue triangles) and KDM6A (turquoise diamond). The mutation and their pathogenicity were initially blinded and were revealed only after the prediction scores were determined. Importantly, all CHD7 mutations received low scores by the KMT2D-specific predictive model, and vice versa. Pathogenic mutations in CHD7 (filled red squares) received high scores from the CHD7LOF model, and pathogenic mutations in KMT2D (filled blue triangles) received very high scores from the KMT2DLOF model. Interestingly, a pathogenic mutation in the Kabuki-associated gene KDM6A also received a very high score from the KMT2DLOF model, indicating a potential methylation-signature overlap between these two genes.CC BY-NC-ND · PubMed Central
Figure 4 Sequence Variants in CHD7 and KMT2D Sorted Using the CHD7LOF and KMT2DLOF DNAm Classification Signatures We derived the scores for each individual using the two models generated for CHD7LOF and KMT2DLOF DNAm classification signatures (x axis and y axis, respectively; see Figure 2), for a set of 13 mutation variants in CHD7 (red crossed circles) and 10 mutation variants in KMT2D (blue crossed squares). The details of the sample classification are shown in Tables 1 and 2.
Figure 4 Sequence Variants in CHD7 and KMT2D Sorted Using the CHD7LOF and KMT2DLOF DNAm Classification Signatures We derived the scores for each individual using the two models generated for CHD7LOF and KMT2DLOF DNAm classification signatures (x axis and y axis, respectively; see Figure 2), for a set of 13 mutation variants in CHD7 (red crossed circles) and 10 mutation variants in KMT2D (blue crossed squares). The details of the sample classification are shown in Tables 1 and 2.CC BY-NC-ND · PubMed Central
Figure 5 Targeted Sodium Bisulfite Pyrosequencing Validation of DNAm Alterations in CHD7 and KMT2D Discovery Cohorts (A–C) DNAm was assessed for three CpG sites in the promoter of HOXA5 (cg01370449, cg04863892, and cg19759481). The gain of DNAm for the three sites in CHD7LOF: 18%, 20%, and 20%. For KMT2DLOF there was also a gain of DNAm: 18%, 18%, and 19%, respectively. Both the CHD7LOF and KMT2DLOFgroup are statistically different from the controls for all three probes, but not from each other. (D–F) DNAm was assessed for three CpG sites in the gene body of SLITRK5 (cg16787483, cg24626752, and cg09823859). A loss of DNAm of 20%, 14%, and 12% in the CHD7LOF samples and a gain of DNAm of 21%, 24%, and 24% in KMT2DLOF samples are shown. Both the CHD7LOF and KMT2DLOFgroup are statistically different from the controls for all three probes, and from each other. (G and H) DNAm was analyzed for FOXP2 (cg18546840 and cg18871253) in CHD7LOF, which had a 15% loss of DNAm compared to controls. (I) DNAm was analyzed for MYO1F (cg15254671) in KMT2DLOF, which had a loss of DNAm of 33% compared to controls. Testing for a statistical difference between all groups was performed using a Kruskal-Wallis test; ∗p < 0.0001.
Figure 5 Targeted Sodium Bisulfite Pyrosequencing Validation of DNAm Alterations in CHD7 and KMT2D Discovery Cohorts (A–C) DNAm was assessed for three CpG sites in the promoter of HOXA5 (cg01370449, cg04863892, and cg19759481). The gain of DNAm for the three sites in CHD7LOF: 18%, 20%, and 20%. For KMT2DLOF there was also a gain of DNAm: 18%, 18%, and 19%, respectively. Both the CHD7LOF and KMT2DLOFgroup are statistically different from the controls for all three probes, but not from each other. (D–F) DNAm was assessed for three CpG sites in the gene body of SLITRK5 (cg16787483, cg24626752, and cg09823859). A loss of DNAm of 20%, 14%, and 12% in the CHD7LOF samples and a gain of DNAm of 21%, 24%, and 24% in KMT2DLOF samples are shown. Both the CHD7LOF and KMT2DLOFgroup are statistically different from the controls for all three probes, and from each other. (G and H) DNAm was analyzed for FOXP2 (cg18546840 and cg18871253) in CHD7LOF, which had a 15% loss of DNAm compared to controls. (I) DNAm was analyzed for MYO1F (cg15254671) in KMT2DLOF, which had a loss of DNAm of 33% compared to controls. Testing for a statistical difference between all groups was performed using a Kruskal-Wallis test; ∗p < 0.0001.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

1 retracted

Show all 92 references

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Butcher, D. T., Cytrynbaum, C., Turinsky, A. L., Siu, M. T., Inbar-Feigenberg, M., Mendoza-Londono, R., Chitayat, D., Walker, S., Machado, J., Caluseriu, O., Dupuis, L., Grafodatskaya, D., Reardon, W., Gilbert-Dussardier, B., Verloes, A., Bilan, F., Milunsky, J. M., Basran, R., Papsin, B., Stockley, T. L., Scherer, S. W., Choufani, S., Brudno, M., & Weksberg, R.
Affiliations
  • Hospital for Sick Children (CA)
  • University of Toronto (CA)
  • Mount Sinai Hospital (CA)
  • Manitoba Harm Reduction Network (CA)
  • University of Alberta (CA)
  • McMaster University (CA)
  • Children's Health Ireland at Crumlin (IE)
  • Center for Human Genetics (US)
  • University Health Network (CA)
Corresponding author
Weksberg, Rosanna
ORCID
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2017-05
Volume
100
Issue
5
Pages
773-788
ISSN
0002-9297 (ISSN-L)
DOI
10.1016/j.ajhg.2017.04.004
PMC
PMC5420353
OpenAlex
W2610666651
Cited by
200
References
92
Citations by year
2026: 12 · 2025: 12 · 2024: 17 · 2023: 27 · 2022: 26 · 2021: 24 · 2020: 29 · 2019: 29 · 2018: 15 · 2017: 7
FWCI
13.35
Type
journal article
Language
en
Open access
Licence
Locations
  • The American Journal of Human Genetics (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Genetics (clinical) › Congenital Ear and Nasal Anomalies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
CHARGE syndrome, Kabuki syndrome, CHD7, KMT2D, DNA methylation signature, epigenetic dysregulation, neurodevelopmental disorders, loss-of-function mutations, HOXA5, phenotypic overlap, epigenetic disorders, disease pathophysiology
MeSH terms
Abnormalities, Multiple, Cell Line, DNA Helicases, DNA-Binding Proteins, Face, Gene Expression Regulation, Hematologic Diseases, Humans, Leukocytes, Mononuclear, Mutation, Neoplasm Proteins, Sensitivity and Specificity, Reproducibility of Results, Vestibular Diseases, Genome, Human, Homeodomain Proteins, DNA Methylation, Epigenesis, Genetic, CHARGE Syndrome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review