Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Polygenic architecture informs potential vulnerability to drug-induced liver injury

Nature Medicine volume 26, pages 1541–1548 (2020)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Polygenic architecture of DILI.
Fig. 2: Polygenic risk score based human stratification by in vitro multi-drug-induced CM-DILI assays.
Fig. 3: Mechanistic association studies for CM-DILI vulnerability.
Fig. 4: PRS associated transcriptomic signatures associate with known CM-DILI responses.

Similar content being viewed by others

Data availability

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.

Code availability

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/.

References

  1. Chalasani, N. et al. Features and outcomes of 899 patients with drug-induced liver injury: the DILIN prospective study. Gastroenterology 148, 1340–1352 (2015).

    Article  Google Scholar 

  2. 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).

    Article  CAS  Google Scholar 

  3. 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).

    Article  CAS  Google Scholar 

  4. Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. 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).

    Article  CAS  Google Scholar 

  7. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).

    Article  Google Scholar 

  9. Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).

    Article  CAS  Google Scholar 

  10. 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).

    Article  Google Scholar 

  11. Kass, G. E. N. & Price, S. C. Role of mitochondria in drug-induced cholestatic injury. Clin. Liver Dis. 12, 27–51 (2008).

    Article  Google Scholar 

  12. 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).

    Article  CAS  Google Scholar 

  13. Cirulli, E. T. et al. A missense variant in PTPN22 is a risk factor for drug-induced liver injury. Gastroenterology 156, 1707–1716 (2019).

    Article  CAS  Google Scholar 

  14. Takebe, T. et al. Massive and reproducible production of liver buds entirely from human pluripotent stem cells. Cell Rep. 21, 2661–2670 (2017).

    Article  CAS  Google Scholar 

  15. 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).

    Article  CAS  Google Scholar 

  16. Delaneau, O. et al. Chromatin three-dimensional interactions mediate genetic effects on gene expression. Science 364, eaat8266 (2019).

    Article  CAS  Google Scholar 

  17. Schadt, E. E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, 1020–1032 (2008).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  Google Scholar 

  19. Igarashi, Y. et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 43, D921–D927 (2015).

    Article  CAS  Google Scholar 

  20. Kaliyaperumal, K. et al. Pharmacogenomics of drug-induced liver injury (DILI): molecular biology to clinical applications. J. Hepatol. 69, 948–957 (2018).

    Article  CAS  Google Scholar 

  21. Chen, M. et al. Drug-induced liver injury: interactions between drug properties and host factors. J. Hepatol. 63, 503–514 (2015).

    Article  CAS  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. 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).

    Article  CAS  Google Scholar 

  24. European Association for the Study of the Liver. EASL Clinical Practice Guidelines: drug-induced liver injury. J. Hepatol. 70, 1222–1261 (2019).

    Article  Google Scholar 

  25. Fredriksson, L. et al. Drug-induced endoplasmic reticulum and oxidative stress responses independently sensitize toward TNFα-mediated hepatotoxicity. Toxicol. Sci. 140, 144–159 (2014).

    Article  CAS  Google Scholar 

  26. 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).

    Article  CAS  Google Scholar 

  27. Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    Article  CAS  Google Scholar 

  28. Gibbs, R. A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  29. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  Google Scholar 

  30. Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    Article  CAS  Google Scholar 

  31. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  Google Scholar 

  32. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).

    Article  CAS  Google Scholar 

  33. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    Article  CAS  Google Scholar 

  34. Asai, A. et al. Paracrine signals regulate human liver organoid maturation from induced pluripotent stem cells. Development 144, 1056–1064 (2017).

    Article  CAS  Google Scholar 

  35. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  36. Lo, C.-C. & Chain, P. S. G. Rapid evaluation and quality control of next generation sequencing data with FaQCs. BMC Bioinf. 15, 366 (2014).

    Article  Google Scholar 

  37. 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).

    Article  CAS  Google Scholar 

  38. 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).

    Article  CAS  Google Scholar 

  39. Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  Google Scholar 

  40. Frazer, K. A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Masaru Koido, Eri Kawakami.

Authors and Affiliations

  1. Department of Regenerative Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan

    Masaru Koido, Junko Fukumura, Yui Noguchi & Takanori Takebe

  2. Organoid Medicine Project, T-CiRA Joint Program, Fujisawa, Kanagawa, Japan

    Masaru Koido, Eri Kawakami, Junko Fukumura, Yui Noguchi, Momoko Ohori, Yasunori Nio, Tadahiro Shinozawa & Takanori Takebe

  3. T-CiRA Discovery, Takeda Pharmaceutical Company, Fujisawa, Kanagawa, Japan

    Eri Kawakami, Momoko Ohori, Yasunori Nio, Tadahiro Shinozawa & Takanori Takebe

  4. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Paola Nicoletti

  5. NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK

    Guruprasad P. Aithal

  6. Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

    Ann K. Daly

  7. Institute for Drug Safety Sciences, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    Paul B. Watkins

  8. Drug Safety Research and Evaluation, Takeda Pharmaceutical Company, Fujisawa, Kanagawa, Japan

    Hisashi Anayama & Tadahiro Shinozawa

  9. Global Discovery and Investigative Toxicology, Takeda Pharmaceutical Company, Cambridge, MA, USA

    Yvonne Dragan

  10. Institute of Research, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan

    Takanori Takebe

  11. Division of Gastroenterology, Hepatology and Nutrition & Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA

    Takanori Takebe

  12. The Center for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Takanori Takebe

  13. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Takanori Takebe

Authors
  1. Masaru Koido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eri Kawakami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junko Fukumura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yui Noguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Momoko Ohori
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasunori Nio
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paola Nicoletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guruprasad P. Aithal
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ann K. Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paul B. Watkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hisashi Anayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yvonne Dragan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tadahiro Shinozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Takanori Takebe
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.K., E.K. and T.T. conceived and designed the study, analyzed the data and wrote the manuscript. P.N., A.K.D., G.P.A. and P.B.W. conducted iDILIC/DILIN DILI GWAS and critically revised the manuscript. M.K., M.O., E.K. and T.T. conducted the other data analysis. E.K. J.F., Yu.N. performed cell culture experiments. Ya.N., T.S, H.A., Y.D. and T.T. provided critical discussion.

Corresponding author

Correspondence to Takanori Takebe.

Ethics declarations

Competing interests

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.

Additional information

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.

Extended data

Extended Data Fig. 1 Overview of our polygenicity analysis.

Workflow of shared genetic aetiology analysis was shown. Left: Processing strategy of iDILIC/DILIN GWAS summary statistics (summary data); Right: TAK-875 GWAS procedures.

Extended Data Fig. 2 TAK-875 DILI severity and GWAS analysis.

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. 3 Distribution and performance of each PRS.

a, Distribution of CM-DILI PRSlimited in TAK-875 DILI patients and the tolerances (see Fig. 1 and Supplementary Text). b, AUROC values and their 95% confidence interval for the indicated PRS. c, Histogram of the indicated PRS.

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.

Extended Data Fig. 6 GARFIELD plot of CM-DILI GWAS summary statistics in Cirulli et al., 2019.

a, Chromatin state, b, FAIR-seq, c, ENCODE DNase1 footprints, d, Genic annotation.

Extended Data Fig. 7 Multi-donor iPSC-HLO cholestatic DILI assays.

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).

Extended Data Fig. 8 Transcriptomic expression profiling of PHH cells at basal state.

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.

Extended Data Fig. 9 Reproducibility of drug toxicity assay of PHH cells under LCA pretreatment.

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.

Extended Data Fig. 10 Decreased mitochondria activity by cholestatic DILI drug treatment.

Live staining for TMRM and mitochondria levels (mitotracker) in 1383 iPSC-HLO model under CsA treatment for 72hr.

Supplementary information

Supplementary Information

Supplementary Discussion, Methods and References.

Reporting Summary

Supplementary Tables 1-10.

Source data

Source Data Fig. 3

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koido, M., Kawakami, E., Fukumura, J. et al. Polygenic architecture informs potential vulnerability to drug-induced liver injury. Nat Med 26, 1541–1548 (2020). https://doi.org/10.1038/s41591-020-1023-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-020-1023-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research