A systems genomics approach to identify risk loci and pathways to Candida infection
Matzaraki, Vasiliki
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A system genomics approach to identify risk loci
and pathways to Candida infection
Thesis, University of Groningen, with summary in English and Dutch
The research described in this thesis was conducted at the Department
of Genetics, University Medical Center Groningen, University
of Groningen, The Netherlands
Printing of this thesis was financially supported by
the University of Groningen.
Cover Design and Thesis Layout:
Printing: Gilderprint
ISBN: 978-94-6323-509-9
Copyright © 2019 by Vasiliki Matzaraki. All rights reserved. No part of this thesis may be reproduced, stored in a retrieved system, or transmitted in any form or by any means, without prior written permission from the author or from the publisher holding the copyright of the published articles.
identify risk loci and pathways
to Candida infection
PhD thesis
to obtain the degree of PhD at the
University of Groningen
on the authority of the
Rector Magnificus prof. E. Sterken
and in accordance with
the decision by the College of Deans.
This thesis will be defended in public on
Wednesday 6 March 2019 at 11.00 hours
by
Vasiliki Matzaraki
born on 14 April 1984
Co-supervisor
Prof. V.K. Magadi Gopalaiah
Assessment Committee
Prof. G. Molema
Prof. J.N.G. Oude Elberink
Prof. R. van Crevel
Human Genetic Susceptibility to Infectious Diseases
9
Studying the Genetics of Infectious Diseases: What’s New
10
Systems Genomics Approach to Studying Host-Pathogen Interactions
11
Why Do We Study Fungal Diseases?
14
Outline of the Thesis
17
CHAPTER 2
An integrative genomics approach identifies novel pathways that influence
candidaemia susceptibility
27
CHAPTER 3
A functional genomics approach to understanding human antifungal immune
responses
99
CHAPTER 4
Circulatory protein profiles in plasma of candidaemia patients and the
contri-bution of host genetics to their variability
126
CHAPTER 5
The MHC locus and genetic susceptibility to autoimmune and infectious
dis-eases
181
CHAPTER 6
General Discussion
202
What We Learned From the Use of a Systems Genomics Approach in
Candidaemia: From Genes to Potential Drug Targets
202
Limitations, Challenges and Future Perspectives
208
Concluding Remarks
215
10 Take-home messages
217
APPENDICES
Summary
225
Samenvatting
227
Acknowledgements
229
Curriculum vitae
233
List of publications
234
Ta
bl
e o
f C
on
te
nts
CHAPTER
1
Matzaraki V.
INTRODUCTION
AND OUTLINE OF
THE THESIS
CHAPTER 1
INTRODUCTION AND OUTLINE OF THE THESIS
•
Human Genetic Susceptibility to Infectious Diseases
Infectious diseases are a major cause of human morbidity and mortality
world-wide and, despite great advances in human medicine and genomics, they remain
so, particularly in low income countries and in young children
1,2. A main feature
of human infectious diseases is that only a proportion of the individuals exposed
to an infectious agent will actually develop disease, suggesting that genetic and
health factors determine susceptibility to infectious diseases. Heritable factors
were long considered to play a determining role in infectious studies and since
1923 the role of heredity has been studied by Webster and others using mice
3.
Indeed, single-gene (Mendelian) disorders leading to primary
immuno¬deficien-cies, although often rare, provided the first information on the role of genetics
in determining susceptibility to infectious diseases. The best-known, relatively
common, single-gene disorder that influences susceptibility to infection is the
sickle cell heterozygosity against falciparum malaria
4. Further clues came from
early twin studies, which provided significant evidence on the role of human host
genetic factors in diseases such as leprosy, poliomyelitis, hepatitis B, tuberculosis
and Helicopter pylori infection
5–11. In addition, follow-up studies of adopted
chil-dren in the late 1980s showed they had a markedly increased risk to death from
infection if one of the biological parents had died prematurely from an infection
rather than other causes such as cancer or cardiovascular diseases
12. Thus, an
im-portant role for host genetics in determining susceptibility to infectious diseases
has been well established.
Moreover, genome-wide linkage
stud-ies provided further evidence of the
role of genetics in infectious diseases,
including Schistosoma mansoni, H.
py-lori and leprosy
13–17. However, the main
disadvantage of linkage studies is that it
is difficult to recruit sufficient numbers
of sibling pairs affected by the same
infection. Thus, these studies are not
sensitive enough to detect genes with a
relatively small effect size that are
char-acteristic for infectious diseases.
Linkage studies: Studies aimed at
establish-ing linkage between genes. Linkage is the tendency of genes and genetic markers that are close together on a chromosome to be inherited together. These studies usually in-volve large families where the disease affects individuals in several generations.
Sibling pairs: Sibling pairs affected with the
disease of interest are often the design of choice in linkage analysis studies with the aim to identifying genes that increase susceptibil-ity to complex diseases. Such a design has the advantage of making few assumptions about the mode of inheritance of the disease.
Recent advances, such as a map of human genetic variation compiled by the
in-ternational HapMap project
18and the 1000 Genomes project
19, the application of
genome-wide associations studies (GWAS) together with imputation tools (see
chapter 5) and the development of new sequencing platforms, have all
contribut-ed to a better understanding of
genet-ics in various human complex
diseas-es. Such attempts have also focused on
infectious diseases, although GWAS to
identify genetic risk factors for these
have not been as successful as in other
complex diseases, such as
immune-me-diated diseases (Fig. 1A). As shown in
Figure 1, there is a lack of genetic
infor-mation for infectious diseases despite
the fact that genetic variants associated with infectious diseases increase risk by
2-3 fold, an order of magnitude higher than observed for other complex diseases
(Fig. 1B).
One of the major limitations in studying the genetics of infectious diseases is the
lack of power due to small patient cohorts. The collection of such a cohort is
com-plicated by the possible presence of co-morbidities in infected patients. Moreover,
the use of appropriate controls is challenging as it is often unclear whether
indi-viduals have been exposed to the infectious agent due to the presence of
asymp-tomatic infections. In addition to sufficient sample sizes and appropriate controls,
another limitation of GWAS alone is that we cannot functionally interpret the
identified associations and therefore need functional validation of novel findings
from in vitro and/or in vivo experimental models.
Yet another limitation of studying infectious diseases stems from the emergence of
an infectious disease, which is in principle the result of interaction by three main
factors: pathogen, environment and human host genetics. Therefore, to better
un-derstand how individuals become infected, we need to capture this interaction but
classical GWAS studies can detect only the genetic component. We require novel
approaches to address the challenges of studying infectious diseases and highly
integrated research that will offer a better understanding of the complex
inter-play between host, invading pathogen and environment. To this end, the
devel-opment of high-throughput technologies, which allow us to collect multi-omics
data (Table 1), together with advanced computational methods, has opened new
opportunities to successfully implement a systems genomics approach. Instead of
studying individual molecular components that contribute to a disease, we can
Genome-wide association study (GWAS): It
is an approach that involves the genotyping of a genome-wide set of genetic variants in many individuals to find genetic variations associated with a particular disease. Such an approach is particularly useful in finding associations between single-nucleotide poly-morphisms (SNPs) and common, complex diseases.
scriptomics, proteomics) in the context of the disease, which capture the
dynam-ic interactions between pathogen-host-environment. In the next section, we will
discuss the use of a systems genomics approach in the field of infectious diseases.
•
Systems Genomics Approach to Studying Host-Pathogen Interactions
Cancer research was one of the first fields to embrace the use of a systems
genom-ics approach
20. In response to the need for new approaches, the National
Insti-tute of Allergy and Infectious Diseases (NIAID, https://www.niaid.nih.gov/) has
sponsored the Systems Biology Programme for Infectious Disease Research
21.
This programme consists of four centres, each of which focuses on studying
inter-action between the human host and a different bacterial or viral pathogen,
includ-ing Mycobacterium tuberculosis bacillus, H5N1 avian influenza virus, and severe
acute respiratory syndrome-associated coronavirus (SARS-CoV), influenza virus
strains of various pathogenicities, Salmonella enterica serovar Typhimurium and
Yersinia species.
One of the main advantages of a systems genomics approach is that multiple
phe-notypes, such as gene and protein expression data, can be collected for a
refer-ence population and subsequently associated with genetic variation. Using this
RNAseq GRO-Seq PRO-seq Nascent-Seq ChIA-PET Hi-C 5-C-Seq DNAse-Seq ATAC-Seq Chip-seq BS-Seq RRBS-Seq ITS1-Seq nanoLC-MS/MS Transcript analysis TranscriptionGenome-wide map of transcriptionally-engaged Pol II Transcription Chromatin conformation Chromatin conformation Chromatin conformation Open chromatin Open chromatin
Mapping DNA regulatory elements Genome methylation
Genome methylation Fungi detection
Host and fungal quantitative proteome analysis without isolation
Nagalakshmi U. et al. 2008 Core L.J. et al. 2008 Hojoong K. et al. 2013 Khodor Y.L. et al. 2011 Fullwood M.J. et al. 2009 Lieberman A.E. et al. 2009; Rao et al. 2014
Dotsie J. et al. 2006 Crawford G.E. et al. 2006 Buenrostro J.D. et al. 2013 Johnson D.S. et al. 2007 Cokus S.J. et al. 2008 Meissner A. et al. 2008 Borman A.M. et al, 2008 Kitahara N. et al. 2015
Method
Purpose
Reference
Abbreviations: RNAseq RNA sequencing, GRO-Seq Global run-on sequencing, PRO-seq Precision nuclear Run-On and sequencing assay, ChIA-PET Chromatin interaction analysis by paired-end tag sequencing, ATAC-Seq Assay for transposase-accessible chromatin using sequencing, Chip-seq Chromatin immunoprecipitation sequencing, BS-Seq Bisulphite sequencing, RRBS-Seq Reduced representa-tion bisulphite sequencing, ITS1-Seq Internal transcribed spacer region 1 sequencing, nanoLC-MS/MS Nanoscale liquid chromatoraphy coupled to tandem mass spectrometry
0 100 200 300 400 500 Crohn's disease Systemic lupus er ythematosus Ulcer ative colitis Rheumatoid ar thritis Psor iasis Multiple sclerosisType 1 diabetes
Primar y biliar y cholangitis Celiac disease Ankylosing spondylitis Grav es' disease
Number of independent associations (P < 1e−06)
0 50 100 Tuberculosis HIV−1 inf ection Influenza (H1N1) inf ection
Hepatitis B Mumps Malar ia
Leprosy Rubella Bacter
ial meningitisHepatitis A Measles
Number of independent associations (P < 1e−06)
Fig. 1.
(A) Number of genetic associations identifi ed in genome-wide association studies (GWAS) with P value< 1x10-6, as recorded in the GWAS catalogue so far (https://www.ebi.ac.uk/gwas/), for a number of common immune-mediated and infectious diseases. (B) Eff ect size (odds ratio, OR) of immune-mediated diseases versus infectious diseases, extracted from the GWAS catalogue (https://www.ebi.ac.uk/gwas/).
0 5 10 15 20 Grav es' diseasePsor
iasis Multiple sclerosisCrohn's disease
Rheumatoid ar thritis
Type 1 diabetesUlcer ative colitis
Systemic lupus er ythematosusCeliac disease
Ankylosing spondylitis Primar y biliar y cholangitis OR (Threshold at P < 1e−06) Immune−mediated diseases B 0 10 20 30
Rubella Measles Mumps Hepatitis A
Bacter ial meningitisHepatitis B
HIV−1 inf ection
Influenza (H1N1) inf ection Leprosy Malar
ia Tuberculosis
OR (Threshold at P < 1e−06)
Box 1. Brief description of fungal pathogens
Candida albicans. Currently, there are 200 species in the genus Candida. These are collectively
respon-sible for as many as 400,000 cases of systemic fungal diseases per year worldwide, with mortality rates of up to 40%34–37. The limited arsenal of anti-fungal drugs, which are classified into three classes, and the emergence of drug resistance through multiple mechanisms, including the formation of biofilms, make Candida the fourth most common cause of nosocomial infections38–44. Of these species, Candida albicans (C. albicans) is one of the best studied and most prevalent of the human fungal pathogens45. It is an opportunistic pathogen and, under physiological conditions, resides in the gut, genito-urinary tract and skin. Infection occurs only if the epithelial barrier function is impaired and/or there are microbiome imbalances and/or the host’s immune system is compromised. It is generally believed that the main reservoir for C. albicans in humans is in the gastrointestinal tract and, once this barrier is disrupted, C. albicans can cause systemic infections46,47.
Under pathogenic conditions, it can lead to superficial infections of the skin up to severe mucosal or life-threatening systemic infections, with the latter being associated with high mortality rates. One of the critical properties that determines the virulence of C. albicans is the phenotype switching be-tween unicellular yeast cells and filamentous forms, called hyphae or pseudohyphae, which is known as morphological dimorphism (Fig. 2)48,49. This fungal dimorphism facilitates invasion of host tissues, escape from macrophages, dissemination in the bloodstream, and differentially affects immune rec-ognition50,51. C. albicans is able to switch morphology in response to various environmental triggers, including a rise in temperature to 37°C, a pH equal to or higher than 7.0, a CO2 concentration of 5.5% or the presence of serum or carbon sources, which stimulate hyphal growth52,53. Hyphae forms cause tissue damage and invasion, whereas yeast cells are thought to be responsible for dissemination to the environment and to new hosts54.
Aspergillus fumigatus. Aspergillus fumigatus (A. fumigatus) is the most prevalent airborne, filamen-tous, saprophytic and ubiquitous fungus responsible for mould. It is found worldwide and its conidia have a small diameter of 2–3 μm; they are abundant in the normal environment55. Everyone can inhale airborne A. fumigatus conidia, because their concentration in air is high (approx. 1–100 conidia per m3)56. Upon inhalation, the fungus penetrates alveolar epithelial and endothelial cells before an ef-fective adaptive immune response is mounted against the fungus57. Depending on the host’s immune status, this fungus can cause a range of pulmonary diseases, including allergic, invasive and non-inva-sive diseases. These diseases include invanon-inva-sive pulmonary aspergillosis (IPA), which is a severe disease characterized by tissue destruction, primarily affecting patients with severe immunodeficiency, but also critically ill patients without neutropenia and patients with chronic obstructive pulmonary disease (COPD)58,59. Chronic pulmonary aspergillosis includes aspergilloma and progressive destructive cavi-tary disease (also known as chronic necrotizing pulmonary aspergillosis)58,59. Aspergilloma is a fungus ball that usually develops in a patient’s body cavity such as a paranasal sinus or an organ such as the lung; it is occasionally complicated by life-threatening haemoptysis. Chronic necrotizing aspergillosis is a locally invasive disease that mainly affects patients with chronic lung disease or mild immunode-ficiency59. Allergic bronchopulmonary aspergillosis (ABPA) is a non-invasive form of lung disease that includes a hypersensitivity reaction to Aspergillus antigens; it is exclusively seen in patients with asthma or cystic fibrosis59.
Cryptococcus neoformans. Cryptococcus neoformans (C. neoformans) is a saprophytic, basidiomycet-ous, dimorphic organism found worldwide; its natural habitats are pigeon droppings and contaminated soil. Small-sized basidiospores (1.8 to 3.0 μm in diameter)60,61 can form yeast cells, which are favoured at 37°C, or dikaryotic hyphae, favoured at 24°C53. When humans inhale basidiospores or yeast cells, the pathogen can disseminate throughout the respiratory tract leading to pulmonary infections. In some rare cases, C. neoformans can also infect the central nervous system leading to life-threatening meningoencephalitis, in both immuno-compromised and immuno-competent patients. If meningoen-cephalitis is left untreated, it is always fatal, while even after treatment the mortality rate lies between 10–25%60,62.
regulate a given phenotype can be identified. In this way, we can connect phenotypes
with genes and gene networks that better reflect the complex nature of host-pathogen
interactions. For instance, genetic variants that influence gene or protein expression in
close proximity to the gene of interest are known as cis-expression quantitative trait loci
(eQTLs) or cis-protein quantitative trait loci (pQTLs). When genetic variants affect gene
expression or protein levels in a distal manner, they are known as eQTLs or
trans-pQTLs. Importantly, it has been found that some diseases showed marked cell-type
specificity, which means that genetic variants associated with a certain disease are
en-riched for cell-type-specific cis-eQTLs
22,23. Furthermore, a systems genomics approach
is a hypothesis-free approach, which can reveal novel, previously unknown associations.
In general, studies of infectious diseases in humans are compromised because of
pa-tients’ uncertain history of lifetime infections and the pathogens involved; moreover,
experimental infection in volunteers is performed to a very limited extent for obvious
ethical reasons. Therefore, another advantage of using a systems approach in infectious
diseases is that we can generate and integrate molecular data from in vitro (cell lines,
stem cells, etc.) and in vivo experimental models (animal models) and translate the
re-sults into a clinical situation by predicting disease outcome in human patients. For
in-stance, in the case of influenza in which the responses in mice are very similar to those in
human and non-human primates, the experimental animal models represent excellent
systems for using systems biology to gain a comprehensive understanding of
influen-za-host interactions
24.
Thus, in the last few years, we have witnessed how the study of host-pathogen
interac-tions during infection has moved towards implementing a systems approach. In this
thesis, my aim is to identify risk genes and molecular pathways underlying the immune
defence to infectious diseases by implementing a systems genomics approach. My main
focus has been on fungal diseases, particularly those caused by the opportunistic fungal
pathogen Candida albicans (C. albicans). Next, I describe what is currently known about
genetics and immune defence mechanisms during C. albicans infections and provide an
outline of the thesis chapters.
•
Why Do We Study Fungal Diseases?
Fungal diseases kill more than 1.5 million people worldwide every year and affect over a
billion people
25. In recent decades, the problem of severe nosocomial fungal diseases has
become more serious, especially in immune-compromised patients. The risk factors for
developing fungal diseases have been well-described
26, although not all at-risk patients
will develop disease, suggesting that host genetics also determines susceptibility to
fun-gal infections. Although the epidemiology of funfun-gal diseases has changed greatly over
the past few decades, the most frequently diagnosed fungal infections are caused by
pathogens from the genera Candida, Cryptococcus and Aspergillus
27(Box 1). These
fungi are ubiquitous and, in particular, Cryptococcus neoformans (C. neoformans)
and Aspergillus fumigatus (A. fumigatus) can be found in the host environment,
whereas Candida albicans (C. albicans) is part of the normal microbiota of an
individual’s skin, mucocutaneous surfaces and gastro-intestinal tract
28. C.
albi-cans is the main cause of mucosal and systemic infections, A. fumigatus of most
allergic fungal diseases, and C. neoformans of lung diseases that may spread to the
brain and cause meningoencephalitis. Diagnosing a fungal infection is often
dif-ficult due to the presence of non-specific symptoms, while challenges in isolating
and identifying fungi make it ineffective to prevent and treat these infections
29,30.
There are no vaccines available and, despite the availability of potent antifungal
drugs, fungal diseases have a high mortality, estimated at 1.5 million annually
25. In
addition, identifying novel drug targets and developing effective antifungal drugs
is complicated by the similarity between eukaryotic fungi and humans.
C. albicans is considered to be the major fungal pathogen of humans in the
het-erogeneous group of Candida species (Box 1, Fig. 2)
31. It is an opportunistic
fun-gus that is potentially pathogenic when the immune system is weakened causing
superficial or systemic infections (candidaemia). Several risk factors are known
to increase the risk of developing Candida infections, including a prolonged stay
in intensive care (ICU), use of immunosuppressive agents/antibiotics,
transplan-tation, surgical intervention, neutropenia, solid tumour and haematological
ma-lignancies, and parenteral nutrition
32. Although these factors are important, they
do not explain all Candida infections and only a minority of patients at risk will
actually develop disease, suggesting the critical role of genetics in determining
Pseudohyphae Hyphae
Yeast cells
Fig. 2.
Forms of Candida albicans. C. albicans is referred to as dimorphic fungus since it grows as round yeastcells and as filamentous cells known as hyphae of pseudo hyphae, depending on the micro-environment. Picture adapted from: http://www.usask.ca/biology/kaminskyj/images/BachewichCandida.jpg, University of Saskatchewan, Canada
primary immunodeficiencies, and mutations and common polymorphisms in
immune genes, have been associated with an increased susceptibility to mucosal
and/or invasive Candida infections (Box 2).
Despite significant progress over the last few years in identifying susceptibility
genes for Candida infections, there is still much genetic information unexplored
and particularly the molecular mechanisms underlying susceptibility are not fully
understood. Both innate and adaptive immune responses are important for host
immune defence against fungal pathogens (Box 3).
However, it is difficult to identify genetic factors using a traditional GWAS
be-cause of the limited size of patient cohorts and use of inappropriate controls (such
as individuals with asymptomatic infections). In the case of invasive Candida
infections, it is difficult to gather large cohorts of patients, as they are
relative-ly scarce, especialrelative-ly in individual medical centres. In addition, a GWAS alone
cannot explain how genetic variation affects disease or which organ or tissue, or
even which cell type, will be affected. To overcome these challenges, we use a
systems genomics approach to identify risk loci and molecular pathways
under-lying host immune defence and disease pathogenesis. In particular, to investigate
the effect of Candida infection on host immune response, we explore changes at
the transcriptomic or proteomic level in a relevant mixture of lymphocytes (T
cells, B cells, and NK cells) and monocytes, i.e. in peripheral blood mononuclear
cells (PBMCs), which are easily available. By integrating different molecular data
(genomics, transcriptomics, proteomics and immunological data), we aim to gain
a better understanding of the host immune defence mechanisms against C.
albi-cans (Fig. 3). In the next section, I present an outline of this thesis.
Box 2. Genetic susceptibility to Candida infection. Several monogenetic disorders have been linked to increased susceptibility to both mucosal and systemic Candida infections. These disorders have mutations in immune genes, such as CARD9, STAT1, IL-23 and IL-22 as heterodimer with STAT3 or STAT4, STAT3, IL-17A, IL-17F, dedicator of cytokinesis (DOCK)8 and TYK2; they have been reviewed in detail elsewhere63. Patients carrying mutations in genes coding for cytokines and their receptors, such as IL-12RB1 and CD25, also have an increased susceptibility to Candida infections64. Moreover, an increased susceptibility to Candida (both mucosal and systemic infections) can also be explained by the presence of common polymorphisms mainly in genes that encode cytokines and receptors that recognize fungal antigens, such as Dectin-1, TLR-1, TLR-2, TLR3, TLR-4, MBL-2, IL-4, IL-10, IL-12b,
DEFB1, (coding for beta-defensin 1), PTPN22 and NLPR363. Lastly, a genome-wide screen of
approx-imately 200,000 SNPs in 186 loci recently identified three more genes (CD58, LCE4A-C1orf68 and TAGAP) associated with candidaemia susceptibility 65.
•
Outline of the Thesis
In chapter 2, we demonstrate the potential of using a systems genomics approach
to identify genes and pathways underlying susceptibility to Candida infection. In
this study, we integrated genetic data from our candidaemia patient cohort with
gene-expression profiles recorded upon Candida stimulation in PBMCs isolated
from eight healthy volunteers, Candida-induced cytokine production capacity in
PBMCs (TNFα, IL-6 and IFNγ), and circulating concentrations of three
pro-in-flammatory cytokines (IL-6, IFNγ and IL-8) in patient serum. Our candidaemia
cohort consisted of 217 patients of European ancestry and genotyped with the
Im-munochip SNP array that covers 186 loci
33. For association testing, we followed a
two-stage analysis and used two different control groups: 11,920 population-based
healthy controls in the first stage and 146 case-matched but candidiasis-free
con-trols in the second stage (Table 2). By integrating all these different datasets, we
could prioritize genes from the Immunochip study and investigate the pathways
in which these genes are enriched. Lastly, we followed up a gene from one of the
Box 3. Immune defence against Candida fungal infections. The innate immune system forms the first line of defence against all invading organisms. Its first response involves recognizing invading fungi from immune cells, such as phagocytes, via specialized receptors that recognize conserved pathogen-associated molecular patterns (PAMPs), which are known as pattern recognition receptors (PRRs)66–68. This recognition induces signalling-mediated gene transcription that leads to the secretion of inflammatory proteins, such as chemokines and cytokines66–68. In turn, the inflammatory proteins recruit neutrophils and other immune cells to the infection site, killing the fungal cells and activating the adaptive immune responses66–68.
Neutrophils play a pivotal role in controlling fungal infections, with neutropenia being a major risk fac-tor for invasive fungal infections69,70. These cells kill Candida cells through phagocytosis, oxidative and non-oxidative effector mechanisms, and the release of neutrophil extracellular traps (NETs)71–73. The PRRs include the well-known Toll-like receptors (TLRs; TLR2 and 4), C-type lectin receptors (CLRs, such as dectin-1, dectin-2 and mannose receptor (MR)), NOD-like receptors (NLRs), and RIG-I-like receptors (RLRs)74.
The fungal PAMPs mainly consist of polysaccharides that are present on the fungal cell wall. Of note, the polysaccharide structures differ between yeasts and hyphae of C. albicans, which can explain the differential immune responses reported between the two forms of C. albicans50,75–81. As a crucial part of the innate immune response, early studies showed that C. albicans activates the complement system via all three pathways (the classical, the alternative and the lectin pathway)82–85. The interaction of C.
albi-cans with the complement system has been reviewed in detail 86. Activation of the complement system
by C. albicans contributes to the enhanced induction of pro-inflammatory cytokines in PBMCs, such as IL-6 and IL-1β, by the activation of the anaphylatoxin C5a87. In an attempt to escape the immune surveillance, C. albicans has been shown to bind two central complement regulator factors, H and FHL-1, from human serum and it thus escapes the control of the complement system88. Platelets are less appreciated players of the host immune defence, despite the fact that the interaction of C. albicans with platelets has been reported in early in vitro and in vivo studies89–91. Platelet-rich plasma presents anti-microbial peptides with anti-candida activity, such as the chemokine RANTES, platelet-factor-4 (PF-4) and thrombocidin-1 (TC-1), and it also inhibits candida growth92–94.
candidaemia-associated loci (MAP3K8) with in vitro experiments to validate its
role in cytokine regulation in response to Candida infection.
In chapter 3, we first describe exploring the genetic architecture of cytokine
re-sponses induced by the two forms of C. albicans (yeast and hyphae). Then we
extended our studies to the two other common fungal pathogens acquired from
the environment (A. fumigatus and C. neoformans) and investigated the
hypoth-esis that susceptibility to candidaemia is explained by modulating the levels of
pro-inflammatory cytokines. To identify genetic loci that regulate cytokine
re-sponses in response to stimulation with these three opportunistic fungi, we made
use of genetic data and measurements of six cytokines (IL-6, TNFα, IL-1β, IL-17,
Genetic data Genetic data Fungus Candida albicans Functional experiments Functional experiments Functional experiments Fungus Fungus Candida albicans PBMCs RNA A RNA B RNA C Expression data PBMCs PBMCs RNA A RNA B RNA C Expression data Expression data Expression data Expression data Inflammatory profile PBMCs TNFα IL-6 IL-1β IFNγ and more… Inflammatory profile PBMCs PBMCs PBMCs TNFα TNFα IL-6 IL-6 IL IL-1β IFNγ and more… Fungus Fungus Candida albicans Candida albicans Candida albicans Candida albicans SNP trans-pQTL SNP cis-eQTL gene gene pQTL cis-eQTL
Quantitative trait loci SNP SNP gene cis-is pQTL trans-pQTL SNP genegene pQTL SNP genegene
SNP genegenegenegene
trans -eQTL
Fig. 3.
A systems genomics approach towards understanding the interactions of C. albicans with its humanhost. To identify risk loci and pathways to Candida infection, we integrate different molecular data (from left to right): (1) Genome-wide genetic data generated from our candidaemia cohort that was used for the GWAS. (2) To study how the gene immune responses are modulated by Candida infection, we used RNA sequencing to generate expression data from PBMCs stimulated by C. albicans. (3) To further investigate response modulation by Candida infection, we measured the inflammatory responses from PBMCs stimulated by C. albicans. By using genetic data and expression data or measurements of inflammatory proteins, we were able to map the genetic variation that influences gene expression or inflammatory proteins, respectively, i.e. expression-quantitative trait loci (eQTLs) or protein-QTLs (pQTLs). Our ultimate goal is to prioritize good candidates genes for follow-up in vitro and in vivo functional experiments, leading to a better understanding of their role in human immune defence against C. albicans.
500 Human Functional Genomics (500FG) cohort (Human Functional
Genom-ics Project, HFGP, see www.humanfunctionalgenomGenom-ics.org). Using these data, we
mapped genetic variation that influences cytokine levels, i.e.
cytokine-quantita-tive trait loci (cQTLs). cQTL studies can offer mechanistic insight into how
genet-ic variation can affect disease and, thus, help in prioritizing genes that potentially
cause disease. The measured cytokines were derived from three different cell
sys-tems (PBMCs, whole blood and monocyte-derived macrophages) stimulated by
the three fungi (Table 2). Since the Immunochip SNP array covers only 3% of the
genome, we went on to perform the first GWAS to date to identify novel
suscep-tibility loci in our candidaemia patient cohort (Table 2) and to explore whether
genetic variants that influence cytokine levels are also associated with
susceptibil-ity to candidaemia.
With the aim of systematically exploring the inflammatory proteins released into
the blood circulation upon Candida stimulation, we extended our studies in
chap-ter 4 by profiling 92 inflammatory proteins in plasma from our candidaemia patient
cohort (n = 42) and from healthy individuals in the Lifelines cohort (https://www.
lifelines.nl/researcher/biobank-lifelines/) (n = 89), and also in
Candida-stimulat-ed PBMCs from two independent population-basCandida-stimulat-ed cohorts, the 500FG (n = 360)
and Lifelines DEEP cohort (https://www.lifelines.nl/researcher/biobank-lifelines/
additional-studies/lifelines-deep) (n = 76), using the high-throughput technology
OLINK (https://www.olink.com/). Using genetic data and protein measurements
from the 500FG and Lifelines DEEP cohorts, we investigated the genetic factors
that determine inflammatory responses in the context of Candida infection by
performing genome-wide protein-quantitative-trait loci (pQTL) mapping. We
also checked whether genetic variation influencing inflammatory responses could
explain susceptibility to candidaemia and survival outcome among candidaemia
patients. Table 2 describes the cohorts, technologies and molecular data that we
generated in the context of Candida infection for this PhD research.
In chapter 5, we focus on the major histocompatibility complex (MHC) locus, also
known as the human leukocyte antigen (HLA), as it plays an important role in
recognition of pathogenic antigens during host immune defence and it has been
reported as a major risk factor for complex diseases. We review recent advances
that have contributed to a better understanding of the role of MHC in two
ma-jor classes of complex diseases: autoimmune and infectious diseases. Of note, we
compare the associations reported so far for autoimmune and infectious diseases
and discuss the role of pathogens in autoimmune disease development.
Lastly, in chapter 6, we discuss the challenges of studying the genetics of infectious
diseases, what we have learned from using a systems genomics approach in
Can-broader perspective and future directions for studying the genetics of infectious
diseases. In summary, we aimed to implement a systems genomics approach to
reveal the genetic architecture and molecular pathways that underlie
susceptibil-ity to candidaemia. By integrating different layers of molecular data, our ultimate
goal was to pinpoint and prioritize genes that could serve as potential
immuno-therapeutic targets to improve patient outcome (Fig. 3). Importantly, we aimed
to provide a framework to identify genes and molecular pathways not only for
candidaemia, but also for other infectious diseases.
Genetic data Transcriptomic data Cytokine data Candidaemia cohort Cohort Cohort GWAS Population-based cohorts Cases Controls (population-based) Controls (case-matched) 8 healthy volunteers 200FG 500FG Lifelines Deep Healthy volunteers Healthy volunteers Candidaemia patients 500FG 500FG 500FG 500FG 500FG 500FG 500FG 500FG 500FG 500FG 500FG (n = 360) Lifelines Deep (n= 76) Lifelines (n = 89) Cases (n = 42)
*HRC Human Reference Consortium, used as reference panel for imputation, **non-surv. = non-survivors, ***GoNL Genome of the Netherlands, used as a reference panel for imputation, , PBMCs peripheral blood mononuclear cells
Cases Controls (case-matched) 217 (European ancestry) 11,920 (European ancestry) 146 (European ancestry) PBMCs PBMCs PBMCs serum macrophages PBMCs blood PBMCs PBMCs PBMCs PBMCs PBMCs PBMCs PBMCs PBMCs PBMCs plasma plasma 178 (Europeans) of which 31 non-surv.** ( Days < 14) 18 non-surv.** ( 14 < Days < 30 ) 16 non-surv.** ( 30 < Days < 90 ) 83 survivors 175 (European ancestry) ~200 healthy individuals (Europeans) 534 healthy individuals (Europeans) 1539 healthy individuals (Europeans) Immunochip (Illumina) Immunochip (Illumina) Immunochip (Illumina) 4 and 24 hours TNFα, IL-6 IFNγ IL-6, IL-8, IFNγ IL-6, TNFα IL-6, TNFα, IL-1β IL-6, TNFα, IL-1 β,IFNγ IL-17, IL-22, IFNγ IL-6, TNFα, IL-1 β IL-17, IL-22, IFNγ IL-6, TNFα IL-17, IL-22, IFNγ IL-6, TNFα, IL-1 β IL-17, IL-22, IFNγ 92 inflammatory proteins 92 inflammatory proteins 92 inflammatory proteins 92 inflammatory proteins HumanCoreExome-24 v1.0 and HumanCoreExome-12 v1.0 (Illumina) HumanCoreExome-24 v1.0 and HumanCoreExome-12 v1.0(Illumina) ImmunoChip (Illumina) Illumina Human OmniExpress Exome-8 v1.0
CytoSNP, ImmunoChip (Illumina)
no no no C. albicans yeast 1 day 2 days 1 day 1 day 2 days 7 days 1 day 7 days 1 day 7 days 1 day 7 days 1 day 1 day yes (HRC*) yes (HRC*) yes (HRC*) yes (GoNL***) yes (GoNL***) 2 2 2 RNA sequencing C. albicans yeast C. albicans yeast C. albicans yeast C. albicans yeast C. albicans yeast C. albicans yeast C. albicans hyphae C. albicans hyphae A. fumigatus A. fumigatus C. neoformans C. neoformans C. albicans yeast C. albicans yeast 2 ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA ELISA OLINK OLINK OLINK OLINK 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 3 4 4 4 4 3 3 3, 4 4
Immunochip-wide association analysis Number of individuals
Cell system
Cell system Cytokines
Genotyping array Stimulation time Stimulation time Imputation Stimulant Stimulant Chapter Technique Technique Chapter Chapter
References
1. Black, R. E. et al. Global, regional, and national causes of child mortality in 2008: a systematic analysis. Lancet 375, 1969–1987 (2010).
2. Dye, C. After 2015: infectious diseases in a new era of health and development. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 369, 20130426 (2014).
3. Fantus, B. The therapy of the Cook County Hospital. J. Am. Med. Assoc. 111, 317–321 (1938).
4. Allison, A. C. Protection afforded by sick-le-cell trait against subtertian malarial infec-tion. Br. Med. J. 1, 290–294 (1954).
5. Misch, E. A., Berrington, W. R., Vary, J. C. & Hawn, T. R. Leprosy and the human genome. Microbiol. Mol. Biol. Rev. 74, 589–620 (2010). 6. Herndon, C.N., Jennings, R. G. A twin-fami-ly study of susceptibility to poliomyelitis. Am. J. Hum. Genet. 3, 17–46 (1951).
7. Comstock, G. W. Tuberculosis in twins: a re-analysis of the Prophit survey. Am Rev Respir Dis 117, 621–624 (1978).
8. Kallmann, F.J., Reisner, D. Twin Studies on the Significance of Genetic Factors in Tubercu-losis. Am. Rev. Tuberc. Pulm. Dis. 47, 549–71 (1943).
9. Van Der Eijk, E. A., Van De Vosse, E., Van-denbroucke, J. P. & Van Dissel, J. T. Heredity versus environment in tuberculosis in twins: The 1950s United Kingdom Prophit Survey - Simonds and Comstock revisited. Am. J. Respir. Crit. Care Med. 176, 1281–1288 (2007). 10. Malaty, H.M., Engstrand, L., Pedersen, N.L., Graham, D. Y. Helicobacter pylori infection: genetic and environmental influences. A study of twins. Ann. Intern. Med. 120, 982–986 (1994).
11. Lin, T. M. et al. Hepatitis B virus markers in Chinese twins. Anticancer Res 9, 737–741 (1989).
12. Sørensen, T. I. A., Nielsen, G. G., Andersen, P. K. & Teasdale, T. W. Genetic and environ-mental influences on premature death in adult adoptees. N. Engl. J. Med. 318, 727–732 (1988). 13. Marquet, S. et al. Genetic localization of a locus controlling the intensity of infection
5q31-q33. Nat. Genet. 14, 181–184 (1996). 14. Thye, T., Burchard, G. D., Nilius, M., Müller-Myhsok, B. & Horstmann, R. D. Ge-nomewide linkage analysis identifies poly-morphism in the human interferon-gamma receptor affecting Helicobacter pylori infection. Am. J. Hum. Genet. 72, 448–453 (2003). 15. Mira, M. T. et al. Chromosome 6q25 is linked to susceptibility to leprosy in a Vietnam-ese population. Nat. Genet. 33, 412–415 (2003). 16. Siddiqui, M. R. et al. A major susceptibility locus for leprosy in India maps to chromosome 10p13. Nat. Genet. 27, 439–441 (2001). 17. Mira, M. T. et al. Susceptibility to leprosy is associated with PARK2 and PACRG. Nature 427, 636–640 (2004).
18. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–61 (2007).
19. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
20. Laubenbacher, R. et al. A systems biology view of cancer. Biochimica et Biophysica Acta - Reviews on Cancer 1796, 129–139 (2009). 21. Aderem, A. et al. A systems biology ap-proach to infectious disease research: Inno-vating the pathogen-host research paradigm. MBio 2:e00325-10, (2011).
22. Gregersen, P. K. Cell type-specific eQTLs in the human immune system. Nature Genetics 44, 478–480 (2012).
23. Bahcall, O. Immune cell–specific eQTLs. Nat. Genet. 46, 532 (2014).
24. Kollmus, H., Wilk, E. & Schughart, K. Systems biology and systems genetics-novel innovative approaches to study host-pathogen interactions during influenza infection. Curr. Opin. Virol. 6C, 47–54 (2014).
25. Bongomin, F., Gago, S., Oladele, R. & Den-ning, D. Global and Multi-National Prevalence of Fungal Diseases—Estimate Precision. J. Fungi 3, 57 (2017).
26. Perfect, J. & Casadevall, A. in Molecular Principles of Fungal Pathogenesis. (ed. Heit-man, J., Filler, S., Edwards, Jr. J, M. A.) 3–11 (ASM Press, Washington, DC) 3-11 (2006). doi:10.1128/9781555815776.ch1
microbial Chemotherapy 56, Suppl 1:i5-i11 (2005).
28. Underhill, D. M. & Iliev, I. D. The mycobio-ta: interactions between commensal fungi and the host immune system. Nat. Rev. Immunol. 14, 405–16 (2014).
29. Hinrikson, H. P., Hurst, S. F., De Aguirre, L. & Morrison, C. J. Molecular methods for the identification of Aspergillus species. Med. Mycol. 43 Suppl 1, S129–S137 (2005). 30. Caliendo, A. M. et al. Better Tests, better care: improved diagnostics for infectious dis-eases. Clin. Infect. Dis. 57, S139–S170 (2013). 31. Turner, S. A. & Butler, G. The Candida pathogenic species complex. Cold Spring Harb. Perspect. Med. 4(9):a019778 (2014).
32. Yapar, N. Epidemiology and risk factors for invasive candidiasis. Therapeutics and Clinical Risk Management 10, 95–105 (2014).
33. Cortes, A. & Brown, M. A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).
34. Gudlaugsson, O. et al. Attributable mortal-ity of nosocomial candidemia, revisited. Clin. Infect. Dis. 37, 1172–1177 (2003).
35. Pappas, P. G. et al. A prospective observa-tional study of candidemia: epidemiology, ther-apy, and influences on mortality in hospitalized adult and pediatric patients. Clin. Infect. Dis. 37, 634–643 (2003).
36. Campion, E. W., Kullberg, B. J. & Arendrup, M. C. Invasive Candidiasis. N. Engl. J. Med. 373, 1445–1456 (2015).
37. Barnett, J. A. A history of research on yeasts 8: Taxonomy. Yeast 21, 1141–1193 (2004). 38. Prasad, R., Shah, A. H. & Rawal, M. K. Antifungals: Mechanism of action and drug resistance. Adv. Exp. Med. Biol. 892, 327–349 (2016).
39. Wisplinghoff, H. et al. Nosocomial blood-stream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. Clin. Infect. Dis. 39, 309–17 (2004).
40. Wisplinghoff, H. et al. Nosocomial bloodstream infections in pediatric patients in United States hospitals: epidemiology, clinical features and susceptibilities. Pediatr. Infect. Dis. J. 22, 686–691 (2003).
41. Wisplinghoff, H., Seifert, H., Wenzel, R. P.
miology of nosocomial bloodstream infections in patients with hematological malignancies and solid neoplasms in hospitals in the United States. Clin. Infect. Dis. 36, 1103–1110 (2003). 42. Douglas, L. J. Candida biofilms and their role in infection. Trends in Microbiology 11, 30–36 (2003).
43. Tournu, H. & Van Dijck, P. Candida bio-films and the host: Models and new concepts for eradication. International J Microbiology (2012). doi:10.1155/2012/845352
44. Taff, H. T. et al. A Candida Biofilm-Induced Pathway for Matrix Glucan Delivery: Impli-cations for Drug Resistance. PLoS Pathog. 8(8):e1002848 (2012).
45. Bassetti, M. et al. Epidemiology, species dis-tribution, antifungal susceptibility and outcome of nosocomial candidemia in a tertiary care hospital in Italy. PLoS One 6(9):e24198 (2011). 46. Schulze, J. & Sonnenborn, U. Yeasts in the gut: from commensals to infectious agents. Dtsch. Arztebl. Int. 106, 837–42 (2009). 47. Nucci, M. & Anaissie, E. Revisiting the source of candidemia: skin or gut? Clin. Infect. Dis. 33, 1959–1967 (2001).
48. Lo, H. J. et al. Nonfilamentous C. albicans mutants are avirulent. Cell 90, 939–949 (1997). 49. Sudbery, P., Gow, N. & Berman, J. The dis-tinct morphogenic states of Candida albicans. Trends in Microbiology 12, 317–324 (2004). 50. Mukaremera, L., Lee, K. K., Mora-Montes, H. M. & Gow, N. A. R. Candida albicans yeast, pseudohyphal, and hyphal morphogenesis differentially affects immune recognition. Front. Immunol. 8, 629 (2017). doi: 10.3389/ fimmu.2017.00629
51. McKenzie, C. G. J. et al. Contribution of Candida albicans cell wall components to recognition by and escape from murine macro-phages. Infect. Immun. 78, 1650–1658 (2010). 52. Hall, R. A. et al. CO2 acts as a signalling molecule in populations of the fungal pathogen Candida albicans. PLoS Pathog. 6(11):e1001193 (2010).
53. Karkowska-Kuleta, J., Rapala-Kozik, M. & Kozik, A. Fungi pathogenic to humans: Mo-lecular bases of virulence of Candida albicans, Cryptococcus neoformans and Aspergillus fu-migatus. Acta Biochimica Polonica 56, 211–224 (2009).
hyphae. Nat Rev Microbiol 9, 737–748 (2011). 55. Latgé, J. P. Aspergillus fumigatus and Aspergillosis. Clin. Microbiol. Rev. 12, 310–350 (1999).
56. Latgé, J. P. The pathobiology of Aspergillus fumigatus. Trends in Microbiology 9, 382–389 (2001).
57. Hope, W. W. et al. Pathogenesis of Asper-gillus fumigatus and the kinetics of galacto-mannan in an in vitro model of early invasive pulmonary aspergillosis: implications for antifungal therapy. J. Infect. Dis. 195, 455–466 (2007).
58. Kousha, M., Tadi, R. & Soubani, A. O. Pul-monary aspergillosis: A clinical review. Europe-an Respiratory Review 20, 156–174 (2011). 59. Patterson, K. C. & Strek, M. E. Diagnosis and treatment of pulmonary aspergillosis syn-dromes. Chest 146, 1358–1368 (2014). 60. Buchanan, K. L. & Murphy, J. W. What makes Cryptococcus neoformans a pathogen? Emerging Infectious Diseases 4, 71–83 (1998). 61. Lin, X. & Heitman, J. Chlamydospore formation during hyphal growth in Cryptococ-cus neoformans. Eukaryot. Cell 4, 1746–1754 (2005).
62. Perfect, J. R. & Casadevall, A. Crypto coccosis. Infectious Disease Clinics of North America 16, 837–874 (2002).
63. Smeekens, S. P., van de Veerdonk, F. L., Kullberg, B. J. & Netea, M. G. Genetic suscepti-bility to Candida infections. EMBO Mol. Med. 5, 805–813 (2013).
64. Smeekens, S. P. et al. Autophagy is redun-dant for the host defense against systemic Can-dida albicans infections. Eur. J. Clin. Microbiol. Infect. Dis. 33, 711–722 (2014).
65. Kumar, V. et al. Immunochip SNP array identifies novel genetic variants conferring susceptibility to candidaemia. Nat. Commun. 5, 4675 (2014). doi: 10.1038/ncomms5675 66. Netea, M. G., Brown, G. D., Kullberg, B. J. & Gow, N. a R. An integrated model of the recognition of Candida albicans by the innate immune system. Nat. Rev. Microbiol. 6, 67–78 (2008).
67. Hoebe, K., Janssen, E. & Beutler, B. The in-terface between innate and adaptive immunity. Nature Immunology 5, 971–974 (2004). 68. Medzhitov, R. Recognition of
microorgan-Nature 449, 819–826 (2007).
69. Horn, D. L. et al. Epidemiology and Out-comes of Candidemia in 2019 Patients: data from the prospective antifungal therapy alli-ance registry. Clin. Infect. Dis. 48, 1695–1703 (2009).
70. Uzun, O., Ascioglu, S., Anaissie, E. J. & Rex, J. H. Risk factors and predictors of outcome in patients with cancer and breakthrough candi-demia. Clin. Infect. Dis. 32, 1713–7 (2001). 71. Vonk, A. G., Wieland, C. W., Netea, M. G. & Kullberg, B. J. Phagocytosis and intracellular killing of Candida albicans blastoconidia by neutrophils and macrophages: A comparison of different microbiological test systems. J. Micro-biol. Methods 49, 55–62 (2002).
72. Aratani, Y. et al. Critical role of myeloper-oxidase and nicotinamide adenine dinucleotide phosphate-oxidase in high-burden systemic infection of mice with Candida albicans. J. Infect. Dis. 185, 1833–1837 (2002).
73. Urban, C. F., Reichard, U., Brinkmann, V. & Zychlinsky, A. Neutrophil extracellular traps capture and kill Candida albicans and hyphal forms. Cell. Microbiol. 8, 668–676 (2006). 74. Netea, M. G., Joosten, L. A., van der Meer, J. W., Kullberg, B. J. & van de Veerdonk, F. L. Immune defence against Candida fungal infec-tions. Nat Rev Immunol 15, 630–642 (2015). 75. Shibata, N., Suzuki, A., Kobayashi, H. & Okawa, Y. Chemical structure of the cell-wall mannan of Candida albicans serotype A and its difference in yeast and hyphal forms. Biochem. J. 404, 365–372 (2007).
76. Lowman, D. W. et al. Novel structural features in Candida albicans hyphal glucan provide a basis for differential innate immune recognition of hyphae versus yeast. J. Biol. Chem. 289, 3432–3443 (2014).
77. Munro, C. A., Schofield, D. A., Gooday, G. W. & Gow, N. A. R. Regulation of chitin synthesis during dimorphic growth of Candida albicans. Microbiology 144, 391–401 (1998). 78. Gow, N. A., van de Veerdonk, F. L., Brown, A. J. & Netea, M. G. Candida albicans mor-phogenesis and host defence: discriminating invasion from colonization. Nat. Rev. Microbi-ol. 10, 112–122 (2011).
79. Gantner, B. N., Simmons, R. M. & Under-hill, D. M. Dectin-1 mediates macrophage
filaments. EMBO J. 24, 1277–1286 (2005). 80. van der Graaf, C. A., Netea, M. G., Ver-schueren, I., van der Meer, J. W. & Kullberg, B. J. Differential cytokine production and toll-like receptor signaling pathways by Candida albi-cans blastoconidia and hyphae. Infect. Immun. 73, 7458–7464 (2005).
81. Moyes, D. L. et al. Candida albicans yeast and hyphae are discriminated by MAPK signaling in vaginal epithelial cells. PLoS One 6(11):e26580 (2011). doi: 10.1371/journal. pone.0026580
82. Zhang, M. X., Lupan, D. M. & Kozel, T. R. Mannan-specific immunoglobulin G antibod-ies in normal human serum mediate classical pathway initiation of C3 binding to Candida albicans. Infect. Immun. 65, 3822–3827 (1997). 83. Zhang, M. X. & Kozel, T. R. Mannan-spe-cific immunoglobulin G antibodies in normal human serum accelerate binding of C3 to Can-dida albicans via the alternative complement pathway. Infect. Immun. 66, 4845–4850 (1998). 84. Sealy, P. I., Garner, B., Swiatlo, E., Chap-man, S. W. & Cleary, J. D. The interaction of mannose binding lectin (MBL) with mannose containing glycopeptides and the resultant potential impact on invasive fungal infection. Med. Mycol. 46, 531–539 (2008).
85. Kozel, T. R. Activation of the complement system by pathogenic fungi. Clin. Microbiol. Rev. 9, 34–46 (1996).
86. Luo, S., Skerka, C., Kurzai, O. & Zipfel, P. F. Complement and innate immune evasion strategies of the human pathogenic fungus Candida albicans. Molecular Immunology 56, 161–169 (2013).
87. Cheng, S. C. et al. Complement plays a cen-tral role in Candida albicans-induced cytokine production by human PBMCs. Eur. J. Immu-nol. 42, 993–1004 (2012).
88. Meri, T. et al. The yeast Candida albicans binds complement regulators factor H and FHL-1. Infect. Immun. 70, 5185–5192 (2002). 89. Skerl, K. G., Calderone, R. A. & Sreevalsan, T. Platelet interactions with Candida albicans. Infect. Immun. 34, 938–943 (1981).
90. Robert, R. et al. Adherence of platelets to Candida species in vivo. Infect. Immun. 68, 570–576 (2000).
91. Willcox, M. D. P., Webb, B. C., Thakur, A. &
species and platelets. J. Med. Microbiol. 47, 103–110 (1998).
92. Drago, L., Bortolin, M., Vassena, C., Taschieri, S. & Del Fabbro, M. Antimicrobial activity of pure platelet-rich plasma against microorganisms isolated from oral cavity. BMC Microbiol. 13, (2013).
93. Kwakman, P. H. S. et al. Native thrombo-cidin-1 and unfolded thrombothrombo-cidin-1 exert antimicrobial activity via distinct structural ele-ments. J. Biol. Chem. 286, 43506–43514 (2011). 94. Tang, Y. Q., Yeaman, M. R. & Selsted, M. E. Antimicrobial peptides from human platelets. Infect. Immun. 70, 6524–33 (2002).
Page 26 | Chapter 2
CHAPTER
2
Vasiliki Matzaraki, Mark S. Gresnigt,
Martin Jaeger, Isis Ricaño-Ponce,
Melissa D. Johnson, Marije Oosting,
Lude Franke, Sebo Withoff, John R. Perfect,
Leo A. B. Joosten, Bart Jan Kullberg,
Frank L. van de Veerdonk, Iris Jonkers,
Yang Li, Cisca Wijmenga, Mihai G. Netea,
Vinod Kumar
An integrative
genomics approach
identifies
novel pathways that
influence candidaemia
susceptibility
RESEARCH ARTICLE
An integrative genomics approach identifies
novel pathways that influence candidaemia
susceptibility
Vasiliki Matzaraki1, Mark S. Gresnigt2, Martin Jaeger2, Isis Ricaño-Ponce1, Melissa
D. Johnson3, Marije Oosting2, Lude Franke1, Sebo Withoff1, John R. Perfect3, Leo A.
B. Joosten2, Bart Jan Kullberg2, Frank L. van de Veerdonk2, Iris Jonkers1, Yang Li1,
Cisca Wijmenga1,4, Mihai G. Netea2,5☯, Vinod Kumar1☯*
1 Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands, 2 Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands, 3 Duke University Medical Center, Durham, North Carolina, United States of America, 4 K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway, 5 Human Genomics Laboratory, Craiova University of Medicine and Pharmacy, Craiova, Romania
☯ These authors contributed equally to this work. *v.kumar@umcg.nl
Abstract
Candidaemia is a bloodstream infection caused by Candida species that primarily affects specific groups of at-risk patients. Because only small candidaemia patient cohorts are avail-able, classical genome wide association cannot be used to identify Candida susceptibility genes. Therefore, we have applied an integrative genomics approach to identify novel sus-ceptibility genes and pathways for candidaemia. Candida-induced transcriptome changes in human primary leukocytes were assessed by RNA sequencing. Genetic susceptibility to can-didaemia was assessed using the Illumina immunochip platform for genotyping of a cohort of 217 patients. We then integrated genetics data with gene-expression profiles, Candida-induced cytokine production capacity, and circulating concentrations of cytokines. Based on the intersection of transcriptome pathways and genomic data, we prioritized 31 candidate genes for candidaemia susceptibility. This group of genes was enriched with genes involved in inflammation, innate immunity, complement, and hemostasis. We then validated the role of
MAP3K8 in cytokine regulation in response to Candida stimulation. Here, we present a new
framework for the identification of susceptibility genes for infectious diseases that uses an unbiased, hypothesis-free, systems genetics approach. By applying this approach to candi-daemia, we identified novel susceptibility genes and pathways for candicandi-daemia, and future studies should assess their potential as therapeutic targets.
Introduction
Genome-wide association studies (GWAS) have greatly contributed to the identification of susceptibility genes for human complex diseases. However, the need for large cohorts
PLOS ONE |https://doi.org/10.1371/journal.pone.0180824 July 20, 2017 1 / 18
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Citation: Matzaraki V, Gresnigt MS, Jaeger M, Ricaño-Ponce I, Johnson MD, Oosting M, et al. (2017) An integrative genomics approach identifies novel pathways that influence candidaemia
susceptibility. PLoS ONE 12(7): e0180824.https://
doi.org/10.1371/journal.pone.0180824 Editor: Joy Sturtevant, Louisiana State University, UNITED STATES
Received: April 1, 2017 Accepted: June 21, 2017 Published: July 20, 2017
Copyright:© 2017 Matzaraki et al. This is an open
access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement: All relevant data used within the paper are provided in the supplementary files.
Funding: This work was supported by an ERC Consolidator Grant [FP/2007-2013/ERC grant 2012-310372 to M.G.N.], an ERC Advanced grant [FP/2007-2013/ERC grant 2012-322698 to C.W.], a Spinoza prize grant [NWO SPI 92-266 to C.W.], a European Union Seventh Framework Programme grant (EU FP7) TANDEM project [HEALTH-F3-2012-305279 to C.W. and V.K.] and an NWO VENI
precludes the use of GWAS to identify susceptibility genes for infectious diseases for which only relatively small patient cohorts can be recruited. So far, relatively few GWAS studies have been performed in patients with infections, including studies of viral and bacterial infections [1]. Compared to the hundreds of genetic loci that have been associated to human complex diseases, the number of susceptibility genes identified as associated to infectious diseases remains low. Given that the risk of death due to infectious diseases has a large genetic heritabil-ity [2], methods other than GWAS that can be applied to smaller cohorts are crucial to make progress in understanding and treating these diseases.
Candidaemia is the fourth most common systemic bloodstream infection in the United States (US) and is associated with mortality rates of up to 40% [3,4]. It is caused by opportunis-tic fungal pathogens belonging to theCandida species, particularly Candida albicans (C. albi-cans), and primarily affects patients with a compromised immune system [5]. However, not all at-risk patients develop candidaemia, indicating that individual differences—including genetic background—influence their susceptibility to the infection. Despite the availability of potent antifungal drugs, the mortality rate of systemicCandida infections remains unacceptably high
[6]. In addition to current treatment strategies, only adjuvant immunotherapy such as the administration of recombinant cytokines is believed to improve outcome [7]. Therefore, an understanding of the molecular pathways involved in the human host defence and identifica-tion of susceptibility genes is crucial for the design of appropriate prophylactic and immuno-therapeutic strategies.
To identify genes underlying susceptibility toCandida infection, we have applied a systems
genomics approach that integrates genetic data from candidaemia patients genotyped with the Immunochip platform [8] withCandida-induced gene-expression profiles in human leukocytes,
cytokine production fromCandida-stimulated peripheral blood mononuclear cells (PBMCs),
and circulating cytokine concentrations in candidaemia patients. Using this approach, we dem-onstrate that genetic susceptibility loci with suggestive associations (P < 9.99 x 10−5) could play a major role in candidaemia susceptibility and we identify genes involved in inflammation, innate immunity, complement and hemostasis as having an important role in determining sus-ceptibility to candidaemia.
Materials and methods
Study populations
To identify genetic variants associated with candidaemia, we performed a two-stage Immu-nochip-wide analysis of a candidaemia cohort using two control groups in 2014: a popula-tion-based healthy cohort and disease-matched cohort (European ancestry), as previously described [9]. Briefly, for Immunochip-wide association analysis, we first used a cohort consisting of 217 candidaemia patients of European ancestry and 11,920 population-based healthy controls (Discovery stage). The demographic and clinical characteristics of the can-didaemia cohort have been previously described [9]. Re-analysis of the data and prioritiza-tion of genes from susceptibility loci with suggestive associaprioritiza-tions (P < 9.99 x 10−5) was performed in 2017.
After the discovery of single nucleotide polymorphisms (SNPs) for susceptibility to can-didaemia using population-based healthy controls, we then validated our findings using a validation control consisting of 146 disease-matched but candidiasis-free controls. These candidiasis-free control patients were recruited from the same hospital wards as the candi-daemia patients so that co-morbidities and clinical risk factors for infection were as similar as possible between patients and controls.
Integrative genomics approach and candidaemia susceptibility pathways
PLOS ONE |https://doi.org/10.1371/journal.pone.0180824 July 20, 2017 2 / 18
grant [863.13.011 to Y.L.]. V.M. is supported by a PhD scholarship from Graduate School of Medical Sciences, University of Groningen, the Netherlands. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Ethics statement
The study was approved by the institutional review boards at each study centre, and enroll-ment occurred between January 2003 and January 2009. The study centers are the Duke Uni-versity Hospital (DUMC, Durham, North Carolina, USA) and Radboud UniUni-versity Nijmegen Medical Centre (Nijmegen, The Netherlands). All adult subjects provided informed written consent.
Genotyping and case-control analysis of candidaemia cohort
DNA was isolated using the Gentra Pure Gene Blood kit (Qiagen, Venlo, the Netherlands) according to the protocol of the manufacturer. Genotyping of candidaemia patients and both control groups was performed using Immunochip according to Illumina’s protocol [10]. Genotype data analysis and quality control of this cohort has been previously reported [9]. Briefly, in the discovery stage, the associations of Immunochip SNPs and susceptibility with candidaemia were tested by logistic regression using the first four components from the multi-dimensional scaling analysis as covariates. We considered a P value < 9.99 x 10−5as the thresh-old for suggestive association to select 268 SNPs in 77 independent loci for validation. For validation analysis using candidaemia case-matched controls, the association between SNPs and candidaemia was tested by logistic regression using the first four multidimensional scaling analysis components as covariates. A validation P value < 0.05 was considered significant.
PBMC isolation and stimulation with Candida albicans
Isolation of PBMCs from eight healthy volunteers and stimulation of PBMCs was performed as described previously [11]. After cell counting with a hemocytometer, the cell number was adjusted to 5 x 106/mL. To identify the transcriptome uponCandida stimulation, 5 x 105
iso-lated PBMCs were incubated with 1 x 106/mL heat-killedC. albicans (UC 820) (C. albicans:
PBMC ratio of 1:2.5) and RPMI culture medium as a control for 4 and 24 hours.C. albicans
UC 820 is a well-described strain regarding its immune responses in PBMCs [12].
Analysis of RNA sequencing reads
The RNA sequencing analysis of this dataset was described previously [13]. Briefly, sequencing reads were mapped to the human genome using STAR (version 2.3.0). The aligner was provided with a file containing junctions from Ensembl GRCh37.71. The Htseq-count of the Python pack-age HTSeq (version 0.5.4p3) was used (http://www-huber.embl.de/users/anders/HTSeq/doc/ overview.html) to quantify the read counts per gene based on annotation version GRCh37.71, using the default union-counting mode. Differentially expressed genes were identified by statisti-cal analysis using the DESeq2 package. The statististatisti-cally significant threshold (False Discovery Rate P < 0.05 and Fold Change 2) was applied.
Pathway enrichment analysis
We performed gene enrichment analysis using ConsensusPathDB-human database (CPDB; http://cpdb.molgen.mpg.de/) [14]. The over-representation analysis is done using the default setting in which the database compares the predefined lists of functionally associated genes (pathways and Gene Ontology categories) to the list of differentially expressed genes and gen-erates P values based on the hypergeometric test. The hypergeometric test P values are further corrected for multiple testing using the false discovery rate method.
Integrative genomics approach and candidaemia susceptibility pathways
