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Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes

  1. David A Knowles  Is a corresponding author
  2. Courtney K Burrows
  3. John D Blischak
  4. Kristen M Patterson
  5. Daniel J Serie
  6. Nadine Norton
  7. Carole Ober
  8. Jonathan K Pritchard
  9. Yoav Gilad  Is a corresponding author
  1. Stanford University, United States
  2. University of Chicago, United States
  3. Mayo Clinic, United States
Research Article
Cite this article as: eLife 2018;7:e33480 doi: 10.7554/eLife.33480
5 figures, 2 data sets and 17 additional files

Figures

Figure 1 with 8 supplements
The transcriptomic response of cardiomyocytes to doxorubicin is substantial.

(a) Cardiomyocytes were derived from lymphoblastoid cell lines (LCLs) of 45 Hutterite individuals, followed by exposure to differing concentrations of doxorubicin and RNA-sequencing. (b) PCA of gene expression levels across samples reveals that doxorubicin concentration explains more variance than inter-individual differences, and that the response is non-linear with respect to concentration. Lines connect samples from the same individual. (c) A probabilistic mixture model uncovers six distinct patterns of response across genes.

https://doi.org/10.7554/eLife.33480.003
Figure 1—figure supplement 1
qPCR for key pluripotency genes for a subset of iPSCs.

qPCR quality control of iPSC lines. Quantitative PCR (qPCR) of pluripotency genes OCT3/4, NANOG, and SOX2 on randomly selected Hutterite iPSC lines (denoted by *). Relative expression is the RQ value with respect to GAPDH expression, with error bars representing the calculated min and max RQ value. All iPSC lines show endogenous expression of these pluripotency genes. Two original LCL lines are included as control and show little or no expression of pluripotency genes.

https://doi.org/10.7554/eLife.33480.004
Figure 1—figure supplement 2
Genome-wide expression analysis of iPSCs using PluriTest.

PluriTest quality control of iPSCs. PluriTest pluriscore and novelty results shows all iPSC samples fall within the pluripotent and novelty threshold (black lines). All cell lines received a pluripotency score above the pluripotency classification threshold of 20 (average = 27.38) and showed low novelty scores below the threshold of 1.67 (average = 1.34). As a comparison, a subset of the iPSC-cardiomyocytes fall outside of the pluripotency classification for both pluriscore and novelty.

https://doi.org/10.7554/eLife.33480.005
Figure 1—figure supplement 3
EBV integration analysis of iPSCs.

EBV presence/absence quality control of iPSCs.

(a) PCR on DNA for presence or absence of EBV, both integrated and non-integrated (reprogramming vector based). Two iPSC lines had obvious presence of EBV (16, 54) and one was unclear (14). Additional banding in the images was due to RNA in the sample. The three samples, highlighted by a red box, were taken forward for two additional PCRs. First, the three samples were tested for EBV based on the presence of the LMP-2A sequence (b); an EBV gene not found on the reprogramming vector), of which lines 16 and 54 were positive. Lastly, the three samples were tested for the presence of the reprogramming vectors (c), of which no samples were positive. Controls were ordered as followed for all PCRs: mixture of chimp and human fibroblast DNA at 10 ng/μL with all reprogramming plasmids at 0.02 pg/μL), mixture of two LCL lines DNA at 10 ng/μL, mixture of human and chimp fibroblast DNA at 10 ng/μL without any plasmid, and no sample blank.

https://doi.org/10.7554/eLife.33480.006
Figure 1—figure supplement 4
qPCR of EBNA-1 to assess EBV status in iPSCs.

Quantitative PCR of EBNA-1 quality control of iPSC Lines. Quantitative PCR (qPCR) of EBV-required gene EBNA-1. Lines 16 and 54 which both showed the presence of genomic integrations of EBV (as shown in Fig. Figure 1—figure supplement 3) are the only iPSC lines that have EBNA-1 expression. Line 14 is again confirmed as not having EBV.

https://doi.org/10.7554/eLife.33480.007
Figure 1—figure supplement 5
Fluorescence activated cell sorting (FACS) purity estimates for iPSC-derived cardiomyocytes.
https://doi.org/10.7554/eLife.33480.008
Figure 1—figure supplement 6
Our expression data is concordant with an existing small RNA-seqdataset (Burridge et al., 2016).

DOXTOX and DOX correspond to samples from patients that did and did not develop ACT after doxorubicin chemotherapy respectively. We additionally see that the transcriptional response at higher concentrations cannot be extrapolated from that at lower concentrations. Higher concentrations are not shown since including these compresses the first PC obscuring the relevant variation.

https://doi.org/10.7554/eLife.33480.009
Figure 1—figure supplement 7
Gene set enrichment analysis of genes in each response cluster confirms expected patterns: metabolic, mitochrondrial and DNA damage processes, as well as existing doxorubicin response genes.
https://doi.org/10.7554/eLife.33480.010
Figure 1—figure supplement 8
Enrichment of transcription factor binding motifs for each response pattern, using HOMER.

** denotes q < 0:05, * denotes q < 0:5.

https://doi.org/10.7554/eLife.33480.011
Figure 2 with 4 supplements
Genetic variation regulates the transcriptomic response to doxorubicin exposure.

(a) Marginal eQTLs show strong replication in GTEx heart data, and lower replication in other tissues (LCL = lymphoblastoid cell line). (b) We detect 100 s of response-eQTLs (reQTLs): variants that modulate response to doxorubicin. The false positive rate (FPR) is estimated using a parametric bootstrap. (c) We developed a statistical method to assign the major and minor allele response to one of the six clusters from Figure 1c. The strongest 46% of detected reQTLs result in a discretely different response, whereas the remainder only modulate the response. (d) For significant reQTLs we calculated relative genotype effect sizes by dividing the fitted effect size at each concentration by the (signed) effect size with the largest absolute value. K-means clustering of these effect size profiles reveals distinct patterns, the most common being a small reduction in absolute effect size from 0 to 0.625μM followed by the largest effects being at the highest concentrations. (e) An example response-eQTL where rs112594884 regulates the response of the mitochondrial complex I chaperone NDUFAF1. Under the major (T) allele we see moderate down-regulation at 0.625μM followed by up-regulation at higher concentrations. Under the minor (G) allele, there is little change at 0.625μM followed by substantial down-regulation. Since the genotype effects are reduced at 0.625μM and largest at high concentrations this reQTL is assigned to cluster 1 of panel d.

https://doi.org/10.7554/eLife.33480.012
Figure 2—figure supplement 1
Using π1 statistics to assess replication is confounded by different sample size/power across GTEx tissues.

Storey’s π1 values to estimate the replication in GTEx tissues of SNP-gene eQTL pairs significant in our data at p<105.This analysis is confounded by variable power across the GTEx tissues.

https://doi.org/10.7554/eLife.33480.013
Figure 2—figure supplement 2
A mutual information approach to control for different sample sizes/power across GTEx tissues.

Estimates of the mutual information between whether SNP-gene pairs are associated in our data compared to all 48 GTEx tissues. Unlike a simple replication analysis our approach controls for differential power across the GTEx tissues (see Materials and methods) and shows that our eQTLs agree most strongly with eQTLs from the two heart tissues.

https://doi.org/10.7554/eLife.33480.014
Figure 2—figure supplement 3
An example response expression QTL that may act through buffering at high expression levels.
https://doi.org/10.7554/eLife.33480.015
Figure 2—figure supplement 4
Trans-eQTLs for rs2229774 in RARG.

Trans-eQTLs (5 response and one marginal) at 5% FDR for rs2229774, a non-synonymous coding variant in RARG which is associated with ACT (Aminkeng et al., 2015). Only two individuals are heterozygous. p-values shown have been adjusted for multiple testing by Benjamini Hochberg.

https://doi.org/10.7554/eLife.33480.016
Figure 3 with 2 supplements
Doxorubicin exposure significantly impacts alternative splicing.

(a) The entropy of splicing choices increases in response to doxorubicin exposure. We measured splicing entropy at different concentrations within LeafCutter ‘Alternative Splicing Clusters’ (ACSs) and clustered these into patterns of entropy change. (b) We separated introns differentially excised with ΔΨ>0.1 into eight clusters based on their relative excision level at each concentration. Introns in clusters corresponding to increased excision at higher doxorubicin concentrations (e.g. cluster 2) are far more likely to use a cryptic (unannotated) splice site at at least one end. p-values shown are for a hypergeometric test of that cluster against all others. (c) We mapped 42 ASCs with response splicing QTLs, variants that modulate the differential splicing response to doxorubicin.

https://doi.org/10.7554/eLife.33480.017
Figure 3—figure supplement 1
Alternative TSS use in NDUFAF6 on doxorubicin exposure.

Doxorubicin exposure results in the use of a downstream alternative TSS for NDUFAF6 which uncovers an association between rs896853 genotype and inclusion of the exon at chr8:94975318–94975415.

https://doi.org/10.7554/eLife.33480.018
Figure 3—figure supplement 2
The ACT-sensitivity variant rs28714259 weakly modulates TUBGCP5’s response to doxorubicin.

rs28714259, the one replicated variant from (Schneider et al., 2017) is in high LD (R2=0.98) with rs11855704, which is a nominally significant (p=0.036) eQTL for TUBGCP5. TUBGCP5 is strongly down-regulated in the presence of doxorubicin.

https://doi.org/10.7554/eLife.33480.019
Transcriptomic response is predictive of doxorubicin induced damage as measured by cardiac troponin.

(a) We measured media levels of cardiac troponin, a sensitive and specific test for myocardial cell damage, in response to doxorubicin, across all cell lines. (b) We performed differential expression analyses with respect to troponin at each concentration separately, and observed more differentially expressed genes at higher concentrations corresponding to an increased dynamic range of troponin levels. (c) We took differentially expressed genes (5% FDR) at each concentration and checked for ‘replication’ (nominal p<0.05)) at the other concentrations. Note that no differentially expressed genes were discovered in control condition (0μM). (d) We summarized gene expression response by first fitting a ‘principal curve’ following increasing doxorubicin concentration, and then measuring the rate of progression along this curve for each individual. (e) Increased transcriptomic response is associated with reduced cardiac troponin levels, suggesting that the bulk of expression changes we observe are in fact protective against cardiac damage. (f) We trained a model to predict ACT risk from gene expression response using available 3 v. 3 case/control data (Burridge et al., 2016) and applied this model to our data. Predicted ACT risk correlated significantly with the slope of troponin level (Spearman ρ=0.38,p=0.01), supporting the in vivo disease relevance of our IC system.

https://doi.org/10.7554/eLife.33480.020
Figure 5 with 5 supplements
Cardiomyocyte molecular QTLs are enriched in meta-analyzed ACT GWAS (Schneider et al., 2017; Serie et al., 2017).

(a) rs4058287 has a GWAS p-value of 9.68×106 and is a nominally significant eQTL (p=0.0016) for ALPK2, which is down-regulated in response to doxorubicin. (b) SNPs that have a response eQTL with p<105 are enriched in GWAS variants with p<0.05 (hypergeometric test p=2×1037). (c) SNPs with a marginal or response splicing QTL at p<105 show modest enrichment in GWAS p<0.005 (hypergeometric p=0.02).

https://doi.org/10.7554/eLife.33480.021
Figure 5—figure supplement 1
SLC28A3 expression may mediate the association of rs885004 with ACT-sensitivity.

The intronic (to SLC28A3) variant rs885004, and the closely linked (R2=0.98) synonymous variant rs7853758, have been associated with ACT by candidate gene studies (Visscher et al., 2012; Visscher et al., 2013). We find rs885004, but not rs7853758, has a significant marginal effect on SLC28A3 expression.

https://doi.org/10.7554/eLife.33480.022
Figure 5—figure supplement 2
rs885004 falls in an IC ATAC-seq peak. rs885004 (shown in red) falls in an ATAC-seq footprint in (unrelated) iPSC-derived cardiomyocytes (unpublished data).
https://doi.org/10.7554/eLife.33480.023
Figure 5—figure supplement 3
Enrichment of different classes of expression QTLs in ACT GWAS.

Enrichment of low (<0.05) ACT GWAS p-values in different classes of expression QTLs (p<105). First three are GTEx tissues (steady-state). IC baseline: iPSC-derived cardiomyocytes at baseline (no doxorubicin). IC main effect: marginal effect of genotype across concentrations. IC combined: jointly testing for a main effect or interaction effect with concentration. IC either: either a marginal or response eQTL. IC response: testing for an interaction effect between genotype and concentration. IC response (AS): as for IC response but using both total and allele-specific expression to map reQTLs.

https://doi.org/10.7554/eLife.33480.024
Figure 5—figure supplement 4
Enrichment of different classes of splicing QTLs in ACT GWAS.

Enrichment of low (<0.05) ACT GWAS p-values in different classes of splicing QTLs (p<105). LCL: LeafCutter splicing QTLs mapped in the Yoruban LCLs (Li et al., 2016). IC main effect: marginal effect of genotype across concentrations. IC combined: jointly testing for a main effect or interaction effect with concentration. IC either: either a marginal or response eQTL. IC response: testing for an interaction effect between genotype and concentration.

https://doi.org/10.7554/eLife.33480.025
Figure 5—figure supplement 5
Colocalization analysis.

Colocalization analysis for genes where the posterior probability of no association estimated by coloc is <0.75.

https://doi.org/10.7554/eLife.33480.026

Data availability

All the custom analysis scripts used for this project are available at https://github.com/davidaknowles/dox (Knowles and Blischak, 2017). The suez response eQTL mapping R package is available at https://github.com/davidaknowles/suez (Knowles, 2017). The following data are available as Supplementary Data: 1) differential expression cluster assignments, 2) significant (5% FDR) eQTLs and sQTLs, 3) differential splicing results, 4) levels of cardiac troponin and the predicted transcriptomic response. In addition to the Supplementary Data included with this paper, further results are hosted at Dryad (doi:10.5061/dryad.r5t8d04) including 1) gene-by-sample matrix of RNA-seq quantification (log counts per million), 2) LeafCutter intron excision quantification 3) p-values for all tested eQTLs, reQTLs, sQTLs, and rsQTLs, 4) RARG variant response and marginal trans-eQTLs, 5) RIN, RNA concentration and other technical covariates, 6) embryoid body imaging for all iPSC lines. The RNA-seq FASTQ files will be added to the dbGaP database (Tryka et al., 2014) under dbGaP accession phs000185 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000185). The genotype data files cannot be shared because releasing genotype data from a subset of individuals in the pedigree would enable the reconstruction of genotypes of other members of the pedigree, which would violate the original protocol approved by the research ethics board (Livne et al., 2015). The summary statistics for the ACT GWAS were given to us by the authors of the study (Schneider et al., 2016; Serie et al. 2017).

The following data sets were generated
  1. 1
  2. 2
    Genetic Studies in the Hutterites
    1. Courtney K Burrows
    2. Knowles DA
    3. John D Blischak
    4. Kristen M Patterson
    5. Carole Ober
    6. Jonathan K Pritchard
    7. Yoav Gilad
    (2018)
    Available at dbGaP via dbGaP Authorized Access (accession no. phs000185.v5.p1).

Additional files

Supplementary file 1

Assignment of each gene’s response profile to differing doxorubicin concentrations into one of 6 clusters.

https://doi.org/10.7554/eLife.33480.027
Supplementary file 2

HOMER2 pathway enrichment for each cluster from Supplementary file 1.

Cluster: One of six clusters. TermID: Gene set ID. Term: gene set name. Odds_ratio: enrichment of this gene set in this cluster compared to background. p: p-value from hypergeometric test. p_adjust: Benjamini-Hochberg adjusted p-value. Overlap: number of genes shared by the gene set and the cluster. cluster_size: number of genes not in the cluster. Geneset_size: number of genes in the cluster. Total genes: total number of genes considered.

https://doi.org/10.7554/eLife.33480.028
Supplementary file 3

HOMER2 promoter motif enrichment for each cluster from Supplementary file 1.

cluster: One of six clusters. motif_info: full motif name including GEO source if applicable. motif: name of transcription factor. mphConsensus: consensus motif using IUPAC notation. odds_ratio: enrichment of motif in promoters of genes in this cluster compared to background. log_p: log p-value from hypergeometric test. p_adjust: Benjamini-Hochberg adjusted p-value. Overlap: number of genes in the cluster with this motif in their promoter. Background_overlap: number of genes not in the cluster with this motif in their promoter. cluster_size: number of genes not in the cluster. hbackground_size: number of genes not in the cluster.

https://doi.org/10.7554/eLife.33480.029
Supplementary file 4

All significant (5% FDR) marginal effect eQTL.

gene: Ensembl gene ID. chr: Chromosome. pos: eSNP position. RSID: Rapid Stain Identification Series of the eSNP. p: p from suez Bonferroni corrected for the number of SNPs tested for the gene. q: Benjamini-Hochberg adjusted p-value.

https://doi.org/10.7554/eLife.33480.030
Supplementary file 5

All significant (5% FDR) response eQTL using total expression only.

Columns are as for Supplementary file 4

https://doi.org/10.7554/eLife.33480.031
Supplementary file 6

All significant (5% FDR) response eQTL mapped using total and allele specific expression.

Columns are as for Supplementary file 4

https://doi.org/10.7554/eLife.33480.032
Supplementary file 7

LeafCutter splicing clusters and their association with doxorubicin concentration. cluster: Cluster ID.

p: p-value for Dirichlet-Multinomial GLM likelihood ratio test for differential splicing across doxorubicin concentrations. q: Benjamini-Hochberg adjusted p-value.

https://doi.org/10.7554/eLife.33480.033
Supplementary file 8

LeafCutter estimates of Percent Spliced In (PSI) for each splice junction (intron) at each doxorubicin concentration.

https://doi.org/10.7554/eLife.33480.034
Supplementary file 9

Splicing QTL mapped using LeafCutter and suez.

Columns are as for Supplementary file 4 except that column one is the splice junction (intron) rather than the gene.

https://doi.org/10.7554/eLife.33480.035
Supplementary file 10

Splicing response QTL mapped using LeafCutter and suez.

Columns are as for Supplementary file 9.

https://doi.org/10.7554/eLife.33480.036
Supplementary file 11

Cardiac troponin levels at different doxorubicin dosages.

cell_line: internal cell line ID and replicate number. dbgap: individual ID in dbGaP. dosage: doxorubicin concentration. troponin: cardiac troponin level.

https://doi.org/10.7554/eLife.33480.037
Supplementary file 12

Optimal lasso model predicting normalized troponin level from gene expression.

Troponin level was normalized to be zero-mean and unit-variance across individuals at each concentration. gene: Ensembl gene id. hugo: HUGO approved gene symbol. beta: regression coefficient from lasso.

https://doi.org/10.7554/eLife.33480.038
Supplementary file 13

Optimal lasso model predicting normalized troponin level from the component of gene expression predictable from genotype.

Troponin level was normalized to be zero-mean and unit-variance across individuals at each concentration. gene: Ensembl gene id. hugo: HUGO approved gene symbol. beta: regression coefficient from lasso.

https://doi.org/10.7554/eLife.33480.039
Supplementary file 14

Genes significantly differentially expressed with respect to troponin level at each doxorubicin concentration.

conc: doxorubicin concentration. gene: Ensembl gene id. rho: Spearman correlation between gene expression and troponin level. p: Spearman correlation p-value. p_adjust: Benjamini-Hochberg adjusted p-value.

https://doi.org/10.7554/eLife.33480.040
Supplementary file 15

Optimal lasso model predicting ACT risk from gene expression using previously published data (Burridge et al., 2016).

gene: Ensembl gene id. beta: regression coefficient from lasso.

https://doi.org/10.7554/eLife.33480.041
Supplementary file 16

Colocalization analysis of response-eQTL and ACT GWAS.

gene: Ensembl gene id. snps: number of SNPs tested. PP.H0.abf: posterior probability of no association. PP.H1.abf: posterior probability of GWAS association only. PP.H2.abf posterior probability of reQTL only. PP.H3.abf: posterior probability of GWAS variant and reQTL being independent. PP.H4.abf: posterior probability of GWAS and reQTL colocalizing to the same variant.fa

https://doi.org/10.7554/eLife.33480.042
Transparent reporting form
https://doi.org/10.7554/eLife.33480.043

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