Drug-induced liver injury (DILI) is a leading cause of termination in drug development programs and removal of drugs from the market; this is partially due to the inability to identify patients who are at risk1. In this study, we developed a polygenic risk score (PRS) for DILI by aggregating effects of numerous genome-wide loci identified from previous large-scale genome-wide association studies2. The PRS predicted the susceptibility to DILI in patients treated with fasiglifam, amoxicillin–clavulanate or flucloxacillin and in primary hepatocytes and stem cell-derived organoids from multiple donors treated with over ten different drugs. Pathway analysis highlighted processes previously implicated in DILI, including unfolded protein responses and oxidative stress. In silico screening identified compounds that elicit transcriptomic signatures present in hepatocytes from individuals with elevated PRS, supporting mechanistic links and suggesting a novel screen for safety of new drug candidates. This genetic-, cellular-, organoid- and human-scale evidence underscored the polygenic architecture underlying DILI vulnerability at the level of hepatocytes, thus facilitating future mechanistic studies. Moreover, the proposed ‘polygenicity-in-a-dish’ strategy might potentially inform designs of safer, more efficient and robust clinical trials.
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The AmpliSeq data used in this study are deposited in the Gene Expression Omnibus under accession number GSE152447. The genotype data for TAK-875 DILI GWAS, the related phenotype information and the summary statistics are stored at Takeda Pharmaceutical Company, owing to the ethical approval in this study. These data sets are available upon reasonable request to Takeda Pharmaceutical Company via the corresponding author and after being approved by the Ethics Committee of Takeda Pharmaceutical Company. Web links of publicly available data sets are as follows: transcriptome data of PHH under drug treatments (TG-GATEs), https://toxico.nibiohn.go.jp/english/index.html; transcriptome data of multi-donor liver tissues, https://www.synapse.org/#!Synapse:syn4492; transcriptome data of PHH (SRR4000958); GENCODE (v26), https://www.gencodegenes.org; MSigDB (v6), https://www.gsea-msigdb.org/gsea/msigdb/index.jsp; and 1KGP3 imputation reference panel, https://genome.sph.umich.edu/wiki/Minimac3. Source data are provided with this paper.
We used publicly available software and parameters as described in the Methods section for the analysis. The software programs are available from the following URLs: PLINK, https://www.cog-genomics.org/plink2/; Minimac3, https://genome.sph.umich.edu/wiki/Minimac3; SNPTEST, https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html; FaQCs, https://github.com/chienchi/FaQCs; PRSice, http://PRSice.info; STAR, https://github.com/alexdobin/STAR; Cufflinks, http://cole-trapnell-lab.github.io/cufflinks/; GARFIELD, https://www.ebi.ac.uk/birney-srv/GARFIELD/; Pascal, https://www2.unil.ch/cbg/index.php?title=Pascal; GSEA, https://www.gsea-msigdb.org/gsea/; and R, https://cran.r-project.org/.
Chalasani, N. et al. Features and outcomes of 899 patients with drug-induced liver injury: the DILIN prospective study. Gastroenterology 148, 1340–1352 (2015).
Nicoletti, P. et al. Association of liver injury from specific drugs, or groups of drugs, with polymorphisms in HLA and other genes in a genome-wide association study. Gastroenterology 152, 1078–1089 (2017).
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).
Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Burant, C. F. et al. TAK-875 versus placebo or glimepiride in type 2 diabetes mellitus: a phase 2, randomised, double-blind, placebo-controlled trial. Lancet 379, 1403–1411 (2012).
Marcinak, J. F., Munsaka, M. S., Watkins, P. B., Ohira, T. & Smith, N. Liver safety of fasiglifam (TAK-875) in patients with type 2 diabetes: review of the global clinical trial experience. Drug Saf. 41, 625–640 (2018).
Wolenski, F. S. et al. Fasiglifam (TAK-875) alters bile acid homeostasis in rats and dogs: a potential cause of drug induced liver injury. Toxicol. Sci. 157, 50–61 (2017).
McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).
Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).
Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, 1–20 (2016).
Kass, G. E. N. & Price, S. C. Role of mitochondria in drug-induced cholestatic injury. Clin. Liver Dis. 12, 27–51 (2008).
Vatakuti, S., Olinga, P., Pennings, J. L. A. & Groothuis, G. M. M. Validation of precision-cut liver slices to study drug-induced cholestasis: a transcriptomics approach. Arch. Toxicol. 91, 1401–1412 (2017).
Cirulli, E. T. et al. A missense variant in PTPN22 is a risk factor for drug-induced liver injury. Gastroenterology 156, 1707–1716 (2019).
Takebe, T. et al. Massive and reproducible production of liver buds entirely from human pluripotent stem cells. Cell Rep. 21, 2661–2670 (2017).
Ogimura, E., Sekine, S. & Horie, T. Bile salt export pump inhibitors are associated with bile acid-dependent drug-induced toxicity in sandwich-cultured hepatocytes. Biochem. Biophys. Res. Commun. 416, 313–317 (2011).
Delaneau, O. et al. Chromatin three-dimensional interactions mediate genetic effects on gene expression. Science 364, eaat8266 (2019).
Schadt, E. E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, 1020–1032 (2008).
Hybertson, B. M., Gao, B., Bose, S. K. & McCord, J. M. Oxidative stress in health and disease: the therapeutic potential of Nrf2 activation. Mol. Asp. Med. 32, 234–246 (2011).
Igarashi, Y. et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 43, D921–D927 (2015).
Kaliyaperumal, K. et al. Pharmacogenomics of drug-induced liver injury (DILI): molecular biology to clinical applications. J. Hepatol. 69, 948–957 (2018).
Chen, M. et al. Drug-induced liver injury: interactions between drug properties and host factors. J. Hepatol. 63, 503–514 (2015).
Daly, A. K. et al. HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat. Genet. 41, 816–819 (2009).
Lucena, M. I. et al. Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology 141, 338–347 (2011).
European Association for the Study of the Liver. EASL Clinical Practice Guidelines: drug-induced liver injury. J. Hepatol. 70, 1222–1261 (2019).
Fredriksson, L. et al. Drug-induced endoplasmic reticulum and oxidative stress responses independently sensitize toward TNFα-mediated hepatotoxicity. Toxicol. Sci. 140, 144–159 (2014).
Burban, A., Sharanek, A., Guguen-Guillouzo, C. & Guillouzo, A. Endoplasmic reticulum stress precedes oxidative stress in antibiotic-induced cholestasis and cytotoxicity in human hepatocytes. Free Radic. Biol. Med. 115, 166–178 (2018).
Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).
Gibbs, R. A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).
Asai, A. et al. Paracrine signals regulate human liver organoid maturation from induced pluripotent stem cells. Development 144, 1056–1064 (2017).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Lo, C.-C. & Chain, P. S. G. Rapid evaluation and quality control of next generation sequencing data with FaQCs. BMC Bioinf. 15, 366 (2014).
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Frazer, K. A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).
We thank S. Yamanaka, S. Izumo and Y. Kajii for their critical comments. We thank T. Kono, K. Araki, K. Enya and H. Kawaguchi for technical and analytical supports and W. L. Thompson for critical reading of the manuscript. The authors thank the Drug Induced Liver Injury Network (DILIN) and the international Drug-Induced Liver Injury Consortium (iDILIC) for providing data included in this paper. The DILIN and iDILIC were not involved in data analyses, manuscript preparation or manuscript review. This study was supported by the T-CiRA Joint Program from Takeda Pharmaceutical Company to T.T. T.T. is a New York Stem Cell Foundation Robertson Investigator and also the recipient of a Cincinnati Children’s Research Foundation grant, National Institutes of Health grant UG3 DK119982, the Dr. Ralph and Marian Falk Medical Research Trust Awards Program, a Takeda Science Foundation award, a Mitsubishi Foundation award and AMED JP19fk0210037, JP19bm0704025, JP19fk0210060, JP19bm0404045 and JSPS JP18H02800, 19K22416. G.P.A. was supported by Medical Research Council: Confidence in Concept, reference number MC_PC_17173.
T.T. received research funding related to this project from Takeda Pharmaceutical Company. M.O., E.K., Y. Nio, T.S., Y.D. and H.A. are employees of Takeda Pharmaceutical Company. P.N. is an employee of Sema4. The remaining authors declare no competing interests.
Peer review information Kate Gao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Workflow of shared genetic aetiology analysis was shown. Left: Processing strategy of iDILIC/DILIN GWAS summary statistics (summary data); Right: TAK-875 GWAS procedures.
a, Ratio of ALT, AST, and BILT peak values to their basal values. b, Distribution of time from start of drug to DILI onset (days). c, Exclusion criteria of TAK-875 samples based on genetic ancestors. d, The outlier of TAK-875 samples was not observed in Northern Europeans from Utah (CEU), Tuscans from Italy (TSI) and Mexican (MEX) ancestry. e, Quantile-Quantile plot for the TAK-875 GWAS. f, g, Polygenic test using hepatocellular DILI-GWAS summary statistics in (a) and All DILI-GWAS ones in (b). X-axis, the total number of SNPs, ordered by iDILIC/DILIN GWAS association; y-axis, explained the variance of TAK-875 white GWAS; color scale, p-value for the shared genetic aetiology analysis.
Extended Data Fig. 4 Correlation between CM-DILI PRSgw and biomarkers for DILI in TAK-875 treated subjects.
Scatter plot and the linear regression line of clinical laboratory test value in basal state and CM-DILI PRSgw in 172 TAK-875 treated patients (cases and controls). *, p < 0.05; **, p < 0.01, ***, p < 0.001 in coefficients of risk score. ALTB, Basal ALT; ASTB, Basal AST; BILTB, Basal total bilirubin.
Extended Data Fig. 5 Predictive accuracy of CM-DILI PRSgw for Flucloxacillin or Amoxicillin-clavulanate DILI.
Distribution of CM-DILI PRSgw and HC-DILI PRSgw in Flucloxacillin or Amoxicillin-clavulanate DILI patients with the indicated DILI type (cholestasis/mixed or hepatocellular). AUROC (95% CI) and P-value of two-tailed Wilcoxon–Mann–Whitney U test were shown.
a, Chromatin state, b, FAIR-seq, c, ENCODE DNase1 footprints, d, Genic annotation.
a, Bright field images of multi-donor iPSC and iPSC-HLO. b, Immunofluorescence staining of Albumin (ALB), BSEP, CD31 and HNF4a in multi-donor iPSC-HLO. c, ALB production during 24 hours in multi-donor iPSC-HLO. Data represent means ± SD (n = 3). d, CYP3A4 activity. Data represent means ± SD (n = 3). e, CLF accumulation and cell death signal in 1383D2 iPSCs derived iPSC-HLOs under CsA treatment for 24hr and 72hr. f, Viability after 24hr (dotted line) and 72hr (black line) CsA treatment in iPSC-HLO model. The ATP levels were normalized by the area of iPSC-HLO (see Materials and Methods).
a-g, Basal expression levels of mRNA were shown by the indicated signature of gene sets. Color scale and sample annotation colors were shown in the bottom right. (a) Liver signature genes1. (b) Signature of gene set characterizing liver sinusoidal endothelial cell (LSEC)2. (c) Signature of gene set characterizing hepatic stellate cell (HSC)2. (d) all drug transport proteins and proteins involved in bile transport and cholestasis (TCP, OATPs, OCT1, OAT2, CNT1, CNT2, ENT1, ENT2, MDR1, MDR3, MRP2, BSEP, BCRP, MATE1, MRP3, MRP4), mRNA coding proteins of N; (e-g) mRNA coding phase 1, 2, and 3 enzymes3. The Z-scores of log2(RPM+1) values were calculated within the indicated samples. Gene with RPM = 0 in all of the samples were excluded. PHH_1day_1/_2, PHH cells after 1 day of culturing (suffix means experimental batch), which was used for assessments of drug-induced transcriptomic change; PHH_2d, PHH cells after 2days of culturing; FLT, fetal liver tissue; ALT, adult liver tissue.
a–c, Drug sensitivity comparison between different days’ experiments under LCA pretreatments. (a) HUM4133. (b) HEP187269. (c) HEP187277. d–f, Drug sensitivity comparison between different days’ experiments under none pretreatments. (d) HUM4133. (e) HEP187269. (f) HEP187277. (g, h) Viability comparison of multi-donor iPSC-HLO models under the indicated CsA treatment without LCA. Pearson’s r for correlation with PRSgw (g) or PRSgw+ h), and its P-value is described. *, p<0.05. We regress mean viability for each donor by PRSgw or PRSgw+ and calculated the P-values.
Live staining for TMRM and mitochondria levels (mitotracker) in 1383 iPSC-HLO model under CsA treatment for 72hr.
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Koido, M., Kawakami, E., Fukumura, J. et al. Polygenic architecture informs potential vulnerability to drug-induced liver injury. Nat Med (2020). https://doi.org/10.1038/s41591-020-1023-0