Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response

Autoři: Dylan H. de Vries aff001;  Vasiliki Matzaraki aff001;  Olivier B. Bakker aff001;  Harm Brugge aff001;  Harm-Jan Westra aff001;  Mihai G. Netea aff002;  Lude Franke aff001;  Vinod Kumar aff001;  Monique G. P. van der Wijst aff001
Působiště autorů: Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands aff001;  Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands aff002;  Human Genomics Laboratory, Craiova University of Medicine and Pharmacy, Craiova, Romania aff003
Vyšlo v časopise: Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response. PLoS Pathog 16(4): e32767. doi:10.1371/journal.ppat.1008408
Kategorie: Research Article
doi: 10.1371/journal.ppat.1008408


Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection.

Klíčová slova:

Candida – Candida albicans – Gene expression – Genome-wide association studies – Immune response – Monocytes – Quantitative trait loci – Small interfering RNAs


1. Mavor AL, Thewes S, Hube B. Systemic fungal infections caused by Candida species: epidemiology, infection process and virulence attributes. Curr Drug Targets 2005 Dec;6(8):863–874. doi: 10.2174/138945005774912735 16375670

2. Quindos G. Epidemiology of candidaemia and invasive candidiasis. A changing face. Rev Iberoam Micol 2014 Jan-Mar;31(1):42–48. doi: 10.1016/j.riam.2013.10.001 24270071

3. Leroy O, Gangneux JP, Montravers P, Mira JP, Gouin F, Sollet JP, et al. Epidemiology, management, and risk factors for death of invasive Candida infections in critical care: a multicenter, prospective, observational study in France (2005–2006). Crit Care Med 2009 May;37(5):1612–1618.

4. Moran C, Grussemeyer CA, Spalding JR, Benjamin DK Jr, Reed SD. Comparison of costs, length of stay, and mortality associated with Candida glabrata and Candida albicans bloodstream infections. Am J Infect Control 2010 Feb;38(1):78–80. doi: 10.1016/j.ajic.2009.06.014 19836856

5. Johnson MD, Plantinga TS, van de Vosse E, Velez Edwards DR, Smith PB, Alexander BD, et al. Cytokine gene polymorphisms and the outcome of invasive candidiasis: a prospective cohort study. Clin Infect Dis 2012 Feb 15;54(4):502–510. doi: 10.1093/cid/cir827 22144535

6. Delsing CE, Gresnigt MS, Leentjens J, Preijers F, Frager FA, Kox M, et al. Interferon-gamma as adjunctive immunotherapy for invasive fungal infections: a case series. BMC Infect Dis 2014 Mar 26;14:166-2334-14-166.

7. Li Y, Oosting M, Deelen P, Ricano-Ponce I, Smeekens S, Jaeger M, et al. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat Med 2016 Aug;22(8):952–960. doi: 10.1038/nm.4139 27376574

8. Jaeger M, Matzaraki V, Aguirre-Gamboa R, Gresnigt MS, Chu X, Johnson MD, et al. A genome-wide functional genomics approach identifies susceptibility pathways to fungal bloodstream infection in humans. jid 2019.

9. Smeekens SP, Ng A, Kumar V, Johnson MD, Plantinga TS, van Diemen C, et al. Functional genomics identifies type I interferon pathway as central for host defense against Candida albicans. Nat Commun 2013;4:1342. doi: 10.1038/ncomms2343 23299892

10. Matzaraki V, Gresnigt MS, Jaeger M, Ricano-Ponce I, Johnson MD, Oosting M, et al. An integrative genomics approach identifies novel pathways that influence candidaemia susceptibility. PLoS One 2017 Jul 20;12(7):e0180824. doi: 10.1371/journal.pone.0180824 28727728

11. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015 Feb 19;518(7539):317–330. doi: 10.1038/nature14248 25693563

12. Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc 2018 Apr;13(4):599–604. doi: 10.1038/nprot.2017.149 29494575

13. Blecher-Gonen R, Bost P, Hilligan KL, David E, Salame TM, Roussel E, et al. Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity. Cell Syst 2019 Feb 27;8(2):109–121.e6. doi: 10.1016/j.cels.2019.01.001 30772378

14. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 2015 Dec 10;16:278-015-0844-5.

15. Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 2019 Jan 18;20(1):40-019-2599-6.

16. Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 2018 Nov;563(7731):347–353. doi: 10.1038/s41586-018-0698-6 30429548

17. Smeekens SP, van de Veerdonk FL, Joosten LA, Jacobs L, Jansen T, Williams DL, et al. The classical CD14(+)(+) CD16(-) monocytes, but not the patrolling CD14(+) CD16(+) monocytes, promote Th17 responses to Candida albicans. Eur J Immunol 2011 Oct;41(10):2915–2924. doi: 10.1002/eji.201141418 21695694

18. Ngo LY, Kasahara S, Kumasaka DK, Knoblaugh SE, Jhingran A, Hohl TM. Inflammatory monocytes mediate early and organ-specific innate defense during systemic candidiasis. J Infect Dis 2014 Jan 1;209(1):109–119. doi: 10.1093/infdis/jit413 23922372

19. Romani L, Mencacci A, Cenci E, Spaccapelo R, Schiaffella E, Tonnetti L, et al. Natural killer cells do not play a dominant role in CD4+ subset differentiation in Candida albicans-infected mice. Infect Immun 1993 Sep;61(9):3769–3774. 8359898

20. Whitney PG, Bar E, Osorio F, Rogers NC, Schraml BU, Deddouche S, et al. Syk signaling in dendritic cells orchestrates innate resistance to systemic fungal infection. PLoS Pathog 2014 Jul 17;10(7):e1004276. doi: 10.1371/journal.ppat.1004276 25033445

21. Quintin J, Voigt J, van der Voort R, Jacobsen ID, Verschueren I, Hube B, et al. Differential role of NK cells against Candida albicans infection in immunocompetent or immunocompromised mice. Eur J Immunol 2014 Aug;44(8):2405–2414. doi: 10.1002/eji.201343828 24802993

22. GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)-Analysis Working Group, Statistical Methods groups-Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, et al. Genetic effects on gene expression across human tissues. Nature 2017 Oct 11;550(7675):204–213. doi: 10.1038/nature24277 29022597

23. Võsa U, Claringbould A, Westra H, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv 2018 Cold Spring Harbor Laboratory:447367.

24. Tada Y, Koarada S, Morito F, Mitamura M, Inoue H, Suematsu R, et al. Toll-like receptor homolog RP105 modulates the antigen-presenting cell function and regulates the development of collagen-induced arthritis. Arthritis Res Ther 2008;10(5):R121. doi: 10.1186/ar2529 18847495

25. Wezel A, van der Velden D, Maassen JM, Lagraauw HM, de Vries MR, Karper JC, et al. RP105 deficiency attenuates early atherosclerosis via decreased monocyte influx in a CCR2 dependent manner. Atherosclerosis 2015 Jan;238(1):132–139. doi: 10.1016/j.atherosclerosis.2014.11.020 25484103

26. Chen X, Pan H, Li J, Zhang G, Cheng S, Zuo N, et al. Inhibition of myeloid differentiation 1 specifically in colon with antisense oligonucleotide exacerbates dextran sodium sulfate-induced colitis. J Cell Biochem 2019 May 19.

27. Divanovic S, Trompette A, Atabani SF, Madan R, Golenbock DT, Visintin A, et al. Negative regulation of Toll-like receptor 4 signaling by the Toll-like receptor homolog RP105. Nat Immunol 2005 Jun;6(6):571–578. doi: 10.1038/ni1198 15852007

28. Schultz TE, Blumenthal A. The RP105/MD-1 complex: molecular signaling mechanisms and pathophysiological implications. J Leukoc Biol 2017 Jan;101(1):183–192. doi: 10.1189/jlb.2VMR1215-582R 27067450

29. Ogata H, Su I, Miyake K, Nagai Y, Akashi S, Mecklenbrauker I, et al. The toll-like receptor protein RP105 regulates lipopolysaccharide signaling in B cells. J Exp Med 2000 Jul 3;192(1):23–29. doi: 10.1084/jem.192.1.23 10880523

30. Kimoto M, Nagasawa K, Miyake K. Role of TLR4/MD-2 and RP105/MD-1 in innate recognition of lipopolysaccharide. Scand J Infect Dis 2003;35(9):568–572. doi: 10.1080/00365540310015700 14620136

31. Parker LC, Whyte MK, Vogel SN, Dower SK, Sabroe I. Toll-like receptor (TLR)2 and TLR4 agonists regulate CCR expression in human monocytic cells. J Immunol 2004 Apr 15;172(8):4977–4986. doi: 10.4049/jimmunol.172.8.4977 15067079

32. Loures FV, Pina A, Felonato M, Araujo EF, Leite KR, Calich VL. Toll-like receptor 4 signaling leads to severe fungal infection associated with enhanced proinflammatory immunity and impaired expansion of regulatory T cells. Infect Immun 2010 Mar;78(3):1078–1088. doi: 10.1128/IAI.01198-09 20008536

33. Meier A, Kirschning CJ, Nikolaus T, Wagner H, Heesemann J, Ebel F. Toll-like receptor (TLR) 2 and TLR4 are essential for Aspergillus-induced activation of murine macrophages. Cell Microbiol 2003 Aug;5(8):561–570. doi: 10.1046/j.1462-5822.2003.00301.x 12864815

34. Netea MG, Gow NA, Joosten LA, Verschueren I, van der Meer JW, Kullberg BJ. Variable recognition of Candida albicans strains by TLR4 and lectin recognition receptors. Med Mycol 2010 Nov;48(7):897–903. doi: 10.3109/13693781003621575 20166865

35. Mukherjee S, Karmakar S, Babu SP. TLR2 and TLR4 mediated host immune responses in major infectious diseases: a review. Braz J Infect Dis 2016 Mar-Apr;20(2):193–204. doi: 10.1016/j.bjid.2015.10.011 26775799

36. van der Wijst MGP, de Vries DH, Groot HE, Trynka G, Hon CC, Nawijn MC, et al. Single-cell eQTLGen consortium: a personalized understanding of disease. arXiv 2019;1909.12550:1–26 doi: 10.7554/eLife.52155

37. van der Wijst MGP, de Vries DH, Brugge H, Westra HJ, Franke L. An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Med 2018 Dec 19;10(1):96-018-0608-4.

38. Tigchelaar EF, Zhernakova A, Dekens JA, Hermes G, Baranska A, Mujagic Z, et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 2015 Aug 28;5(8):e006772-2014-006772.

39. van der Wijst MGP, Brugge H, de Vries DH, Deelen P, Swertz MA, Franke L. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat Genet 2018 04/02;50(4):493–497. doi: 10.1038/s41588-018-0089-9 29610479

40. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018 Apr 2;36(5):411–420. doi: 10.1038/nbt.4096 29608179

41. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009 Jul;37(Web Server issue):W305–11. doi: 10.1093/nar/gkp427 19465376

42. Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet 2014 Aug;46(8):818–825. doi: 10.1038/ng.3021 24974849

43. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15(12):550-014-0550-8.

44. Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 2017 print;49(1):139–145. doi: 10.1038/ng.3737 27918533

45. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 2013 Oct;45(10):1238–1243. doi: 10.1038/ng.2756 24013639

46. Kumar V, Cheng SC, Johnson MD, Smeekens SP, Wojtowicz A, Giamarellos-Bourboulis E, et al. Immunochip SNP array identifies novel genetic variants conferring susceptibility to candidaemia. Nat Commun 2014 Sep 8;5:4675. doi: 10.1038/ncomms5675 25197941

47. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 2016 Oct;48(10):1279–1283. doi: 10.1038/ng.3643 27548312

48. Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet 2016 Oct;48(10):1284–1287. doi: 10.1038/ng.3656 27571263

49. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007 Sep;81(3):559–575. doi: 10.1086/519795 17701901

50. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001 Dec;25(4):402–408. doi: 10.1006/meth.2001.1262 11846609

51. Boyden S. The chemotactic effect of mixtures of antibody and antigen on polymorphonuclear leucocytes. J Exp Med 1962 Mar 1;115:453–466. doi: 10.1084/jem.115.3.453 13872176

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