University of Groningen
Impact of rare and common genetic variation in the interleukin-1 pathway on human cytokine
responses
van Deuren, Rosanne C.; Arts, Peer; Cavalli, Giulio; Jaeger, Martin; Steehouwer, Marloes;
van de Vorst, Maartje; Gilissen, Christian; Joosten, Leo A. B.; Dinarello, Charles A.; Mhlanga,
Musa M.
Published in:
Genome medicine
DOI:
10.1186/s13073-021-00907-w
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Citation for published version (APA):
van Deuren, R. C., Arts, P., Cavalli, G., Jaeger, M., Steehouwer, M., van de Vorst, M., Gilissen, C.,
Joosten, L. A. B., Dinarello, C. A., Mhlanga, M. M., Kumar, V., Netea, M. G., van de Veerdonk, F. L., &
Hoischen, A. (2021). Impact of rare and common genetic variation in the interleukin-1 pathway on human
cytokine responses.
Genome medicine
,
13
(1), 94. [94]. https://doi.org/10.1186/s13073-021-00907-w
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RES E AR C H Open Access
Impact of rare and common genetic
variation in the interleukin-1 pathway on
human cytokine responses
Rosanne C. van Deuren
1,2,3
, Peer Arts
2,4
, Giulio Cavalli
1,5,6
, Martin Jaeger
1,3
, Marloes Steehouwer
2
,
Maartje van de Vorst
2
, Christian Gilissen
2,3
, Leo A. B. Joosten
1,3,7
, Charles A. Dinarello
1,6
, Musa M. Mhlanga
2,3,8
,
Vinod Kumar
1,9,10
, Mihai G. Netea
1,3,11
, Frank L. van de Veerdonk
1,3
and Alexander Hoischen
1,2,3*
Abstract
Background: The interleukin (IL)-1 pathway is primarily associated with innate immunological defense and plays a
major role in the induction and regulation of inflammation. Both common and rare genetic variation in this
pathway underlies various inflammation-mediated diseases, but the role of rare variants relative to common variants
in immun e response variability in healthy individuals remains unclear.
Methods: We performed molecular inversion probe sequencing on 48 IL-1 pathway-related genes in 463 healthy
individuals from the Human Functional Genomics Project. We functionally grouped common and rare variants, over
gene, subpathway, and inflammatory levels and performed the Sequence Kernel Association Test to test for
association with in vitro stimulation-induced cytokine responses; specifically, IL-1β and IL-6 cytokine measurements
upon stimulations that represent an array of microbial infections: lipopolysaccharide (LPS), phytohaemagglutinin
(PHA), Candida albicans (C. albicans), and Staphylococcus aureus (S. aureus).
Results: We identified a burden of NCF4 rare variants with PHA-induced IL-6 cytokine and showed that the
respective carriers are in the 1% lowest IL-6 producers. Collapsing rare variants in IL-1 subpathway genes produces
a bidirectional association with LPS-induced IL-1β cytokine levels, which is reflected by a significant Spearman
correlation. On the inflammatory level, we identified a burden of rare variants in genes encoding for proteins with
an anti-inflammatory function with S. aureus-induced IL-6 cytokine. In contrast to these rare variant findings which
were based on different types of stimuli, common variant associations were exclusively identified with C. albicans-
induced cytokine over various levels of grouping, from the gene, to subpathway, to inflammatory level.
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: Alexander.Hoischen@radboudumc.nl
1
Department of Internal Medicine, Radboud Expertise Center for
Immunodeficiency and Autoinflammation, and Radboud Center for
Infectious Disease (RCI), Radboud University Medical Center, Nijmegen, the
Netherlands
2
Department of Human Genetics, Radboud University Medical Center,
Nijmegen, the Netherlands
Full list of author information is available at the end of the article
van Deuren et al. Genome Medicine (2021) 13:94
https://doi.org/10.1186/s13073-021-00907-w
Conclusions: In conclusion, this study shows that functionally grouping common and rare genetic variants enables
the elucidation IL-1-mediated biological mechanisms, specifically, for IL-1β and IL-6 cytokine responses induced by
various stimuli. The framework used in this study may allow for the analysis of rare and common genetic variants in
a wider variety of (non-immune) complex phenotypes and therefore has the potential to contribute to better
understanding of unresolved, complex traits and diseases.
Keywords: Rare variants, SKAT, Common variants, Region-based analysis, Interleukin-1 pathway, Immunological
mechanisms, Systems biology
Background
The innate immune system is our first line of defense
against invading pathogens and is shaped by a well-
maintained balance in stimulatory and inhibitory mecha-
nisms [1]. The interleukin (IL)-1 family of cytokines and re-
ceptors is primarily associated with innate immunity and
plays a major role in the induction and regulation of host
defense and inflammation [2].TheIL-1familycomprises
pro-inflammatory cytokines (e.g.,IL-1α/β,IL-36α/β/γ),
anti-inflammatory cytokines (e.g., IL-37, IL-38), activating
receptors (e.g., IL1-R1, IL-36R), decoy receptors (e.g., IL-
1R2, IL-18BP), and additional regulators, kinases, and
phosphatases that together are responsible for the IL-1-
mediated response [3]. Next to core IL-1 family effectors,
members of the inflammasome and autophagy pathway are
important contributors to the regulation of IL-1-induced
inflammation. For instance, activation of the inflammasome
allows for cleavage and activation of CASP-1, with subse-
quent activation and release of pro-inflammatory cytokines
IL-1β and IL-18. Conversely, autophagy is able to directly
inhibit the inflammatory response by removing inflamma-
some components and damaged mitochondria [4].
Defects in IL-1 pathway signaling and its specific
members have been linked to various inflammation-me-
diated diseases [2, 5, 6]. Generally, the clinical presentation
of dysregulated activity of the IL-1 pathway is clearly ex-
plained by the causal genetic defect. For example, patients
with CAPS (cryopyrin associated periodic syndromes)
present with excessive innate inflammation exacerbations
that appear to be caused by an activating mutation in
NLRP3 resulting in an overproduction of IL-1β [5]. In an-
other example, deleterious mutations in IL1RN were
underlying excessive IL-1α/β activity in patients with
DIRA (deficiency of IL-1 receptor antagonist [7]. Con-
trastingly for some diseases, like adult-onset Still’s Disease
(AoSD), Behcet’s disease, and Schnitzler disease, only sub-
sets of patients have presented with mutations in related
genes [7]. Taken together, this underlines the observation
that no causal genetic defect has been identified that ex-
plains all patients, despite clinical similarities with other
inflammation-mediated diseases, like CAPS.
While the IL-1 pathway has been associated with
disease, not much is known about genetic factors that
can explain immune variability in healthy individuals. In
general, immune responses are highly variable between
individuals. Determining the genetic factors that underlie
these variations in immunological response could be in-
strumental in the generation of targeted hypotheses for
genetic studies in inflammatory diseases that are outside
the spectrum of healthy immune variability. For this
reason, in the past few decades, various studies have
assessed the separate and shared contribution of host
and environmental factors to an immunological response
after a specific stimulus [8–12]. However, a considerable
percentage of
“healthy immune response variation”
between individuals remains unexplained, with one im-
portant shortcoming being that most studies to date
have focused on common genetic variants. Unfortu-
nately, this has left the impact of rare or private variants
on healthy immune variability poorly understood. With
recent advancements in sequencing technologies, the
ability to study the role of rare variants has remarkably
improved, and its value has been proven in several
studies. Increasing evidence shows that variability in
phenotypic presentation can be explained by an interplay
between variants of variable frequencies [13, 14], or ag-
gregation of genetic variants in genes underlying dysreg-
ulated biological mechanisms, or even over gen es that
are more distantly involved [15]. The relatively small-to-
moderate effects of common variants can be significantly
modified by the presence or absence of (multiple) rare
variants [16]. We therefore hypothesize that studies on
the genetic basis of inflammatory diseases or healthy im-
mune variability might also benefit from these concepts.
In this study, we aimed to identify and characterize
rare and common genetic variants in 48 genes related to
the IL-1 pathway-mediated immune response and deter-
mine their impact on the inter-individual variability of
cytokine responses in healthy individuals. A complete
overview of the study workflow can be found in Fig. 1.
Methods
Study cohort
Cohort characteristics
The study was conducted using healthy individuals from
the Human Functional Genomics Project (HFGP; 500FG
cohort) [17]. The entire 500FG cohort consists of 534
healthy individuals from the Netherlands (296 fe males
van Deuren et al. Genome Medicine (2021) 13:94 Page 2 of 17
Fig. 1 Flowchart of the study. Figure orientation from top to bottom. a Blood was extracted from 520 healthy individuals on which (b) extensive
immunophenotyping was performed and simultaneously (c) molecular inversion probe sequencing data was produced from the coding regions
of 48 Interleukin-1 pathway-related genes. d The resulting cytokine production after stimulation was measured and log-transformed prior to
analysis. e The identified variants were grouped over three different levels into sets based on gene-encoded protein function: I. Gene level, with
48 genes; II. Subpathway level, grouping genes into 6 subpathways that represent an immunological cascade in the IL-1-mediated inflammatory
response; and III. Inflammatory level, with two groups that distinguish between pro- and anti-inflammatory roles of the respective gene-encoded
proteins. f Variants within each set were appropriately weighed based on minor allele frequency (MAF), and common and rare variants were
classified based on cohort allele frequency (AF) threshold of 5%. g Finally, variant analysis was performed by the Sequence Kernel Association
Test (SKAT) on only common variants (I.SKAToC); common and rare variants combined (II.SKATjoint); and only rare variants using the best
combination of the SKAT and burden test (III.SKATO)
van Deuren et al. Genome Medicine (2021) 13:94 Page 3 of 17
and 237 males) with an age range 18–75, from which we
were able to obtain DNA from 520 individuals for
sequencing. For more details on cohort characteristics,
see previous publications on the 500FG cohort [8, 9, 11].
Immunophenotyping
Here, we made use of the publicly available extensive
immunophenotyping data that was generated as part of
the Human Functional Genom ics Project [18]. Specific-
ally, interleukin-1β (IL-1β) and interleukin-6 (IL-6)
production by whole blood (consisting mainly of poly-
morphonuclear cells (PMNs)) from 471 individuals,
stimulated with either lipopolysaccharide (LPS, 100 ng/
mL), phytohaemagglutinin (PHA, 10 μg/mL), heat-killed
Candida albicans (C. albicans 10
6
CFU/mL), or
Staphylococcus aureus (S. aureus 1×10
6
/mL). A detailed
description of these experiments can be found elsewhere
[9]. In brief, blood was drawn from participants and
100 μL of heparin blood was stimulated with 400 μLof
stimulus, subsequently incubated for 48 h at 37 °C and
5% CO
2
and supernatants were collected and stored in −
20 °C until cytokine measurements were performe d by
ELISA. Cytokine production by whole blood (consisting
of a mix of immune cell subtypes) is most comparable
to the in vivo situation, as the cross-regulation between
different cell types is very important in determination of
the final immune response. The investigated stimuli
were chosen as representatives for an array of microbia l
infections, spe cifically, LPS is expressed on the bacterial
cell wall of Gram-negative bacteria, PHA is synthesized
by Bacillus Rhodococcus and Pseudomonas species, and
C. albicans and S. aureus are major invading pathogens
representative of fungi and Gram-positive bacteria,
respectively.
Sequencing
MIP panel design
We sequenced all coding exons of 48 genes of the IL-1
pathway in 520 healthy individuals by Molecular Inver-
sion Probe (MIP) sequencing, a targeted resequencing
technology that allows for the identification of both
common and rare genetic variation in regions of interest.
A detailed description of MIP probe design and sequen-
cing methods can be found elsewhere [19–21]. In short,
1285 MIP probes were designed to cover all coding
exons of 48 genes related to the IL-1 pathway and
sequencing was performed using the Illumina NextSeq500
system. These 48 IL-1 pathway-related genes were chosen
for their effector (e.g., IL1A/B, IL36A/B/G, IL38), regulatory
(e.g., IL1RN, IL18BP), and modulatory (e.g., NLRP3, NCF4,
ATG16L1) roles in the innate immune response. They can
be further functionally subclassified into six subpathways
that represent a specific modulatory mechanism or im-
munological cascade in the IL-1-mediated inflammatory
response: IL-1 subpathway, IL-18 subpathway, IL-30s
subpathway, inflammasome, reactive oxygen species (ROS)
production, and autophagy. In addition, distinguishing be-
tween pro- and anti-inflammatory roles of the respective
gene-encoded proteins resulted in a third sub-classification
of two inflammatory groups. A full ex plana tion on the sub-
classifications can be found in Additional file 1:TableS1.
Data processing
A carefully developed filtering pipeline, validated by
Sanger sequencing, was applied to ensure high sensitivity
and specificity in our final variant set. First, the reads
were aligned using BWA-MEM [ 22 ] and subsequently
filtered on Mapping Quality ≥ 60, no soft-clipping,
properly paired and less than five mismatches from the
reference per read, with the exception of multi-basepair
insertions and deletions. Variants were then called using
the Genome Analysis Toolkit (GATK) unified genotyper
[23], which uses a Bayesian genotype likelihood model
to estimate the most likely genotypes. Rare variants (here
defined as absent in dbSnp build 150 common [24], or
defined as rare by our custom annotator as explained
below), were further filtered on the QUAL param eter ≥
1000 in the vcf. Additionally, the percentage of alterna-
tive alleles for each variant position was determ ined
using samtools mpileup [25], with maximum read depth
10,000, no BAQ, a minimal mapping quality of 20, and a
minimal base quality of 30. Homozygous rare variants
required an alternative allele percentage of ≥ 90%,
heterozygous an alternative allele percentage of ≥ 25%
and < 90%, and an alternative allele percentage of < 25%
was considered false positive. The final variant set was
annotated using our custom annotator, which makes use
of several annotation sources, among others the Variant
Effect Predictor from Ensembl [26], Combined Annota-
tion Dependent Depletion score [27], SpliceAI [28], and
several population-based variant databases (e.g., dbSnp,
ExAc and gnomAD [29]) and an “inHouse” database
consisting of > 25,000 clinical exomes run at the diag-
nostic division of the Department of Human Genetics of
the Radboud University Medical Center (Radboudumc).
We used within-cohort allele frequencies (AFs) to separ-
ate rare and common variants, based on a common vari-
ant cut-off of ≥ 5%. Samples with an average coverage
depth of all MIPs ≥ 100× were included for analysis.
Variant analysis
Continuous trait analysis
A rare variant burden analysis (RVBA) was performed
on the log-transformed cytokine levels by using the
Sequence Kernel Association Test (SKAT) [14, 30]inR
version 3.5.2. The SKAT is a kernel-based test method
that aggregates weighted individual variant-score test
statistics while allowing variant-variant interactions and
van Deuren et al. Genome Medicine (2021) 13:94 Page 4 of 17