An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells.

Abstract

The ability to adapt to diverse micro-environmental challenges encountered within a host is of pivotal importance to the opportunistic fungal pathogen Candida albicans. We have quantified C. albicans and M. musculus gene expression dynamics during phagocytosis by dendritic cells in a genome-wide, time-resolved analysis using simultaneous RNA-seq. A robust network inference map was generated from this dataset using NetGenerator, predicting novel interactions between the host and the pathogen. We experimentally verified predicted interdependent sub-networks comprising Hap3 in C. albicans, and Ptx3 and Mta2 in M. musculus. Remarkably, binding of recombinant Ptx3 to the C. albicans cell wall was found to regulate the expression of fungal Hap3 target genes as predicted by the network inference model. Pre-incubation of C. albicans with recombinant Ptx3 significantly altered the expression of Mta2 target cytokines such as IL-2 and IL-4 in a Hap3-dependent manner, further suggesting a role for Mta2 in host-pathogen interplay as predicted in the network inference model. We propose an integrated model for the functionality of these sub-networks during fungal invasion of immune cells, according to which binding of Ptx3 to the C. albicans cell wall induces remodeling via fungal Hap3 target genes, thereby altering the immune response to the pathogen. We show the applicability of network inference to predict interactions between host-pathogen pairs, demonstrating the usefulness of this systems biology approach to decipher mechanisms of microbial pathogenesis.

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ORIGINAL RESEARCH ARTICLE
published: 12 March 2012
doi: 10.3389/fmicb.2012.00085
An interspecies regulatory network inferred from
simultaneous RNA-seq of Candida albicans invading
innate immune cells
LanayTierney1†, Jörg Linde2†, Sebastian Müller2, Sascha Brunke3,4, Juan Camilo Molina3, Bernhard Hube3,5,
Ulrike Schöck6, Reinhard Guthke2and Karl Kuchler1*
1Christian Doppler Laboratory for Infection Biology, Max F . Perutz Laboratories, Medical University of Vienna, Vienna, Austria
2Research Group Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Jena, Germany
3Department Microbial Pathogenicity Mechanisms, Leibniz-Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Jena, Germany
4Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
5Friedrich Schiller University, Jena, Germany
6GATC Biotech AG, Konstanz, Germany
Edited by:
Franziska Mech, Leibniz-Institute for
Natural Product Research and
Infection Biology – Hans-Knoell-
Institute, Germany
Reviewed by:
Thomas Dandekar, University of
Wuerzburg, Germany
Oliver Kurzai, Friedrich Schiller
University Jena, Germany
*Correspondence:
Karl Kuchler, Christian Doppler
Laboratory for Infection Biology, Max
F . Perutz Laboratories, Medical
University of Vienna, Campus Vienna
Biocenter, Dr. Bohr-Gasse 9/2, A-1030
Vienna, Austria.
e-mail: karl.kuchler@
meduniwien.ac.at
†LanayTierney and Jörg Linde have
contributed equally to this work.
The ability to adapt to diverse micro-environmental challenges encountered within a host
is of pivotal importance to the opportunistic fungal pathogen Candida albicans. We have
quantified C. albicans and M. musculus gene expression dynamics during phagocytosis
by dendritic cells in a genome-wide, time-resolved analysis using simultaneous RNA-seq.
A robust network inference map was generated from this dataset using NetGenerator,
predicting novel interactions between the host and the pathogen.We experimentally veri-
fied predicted interdependent sub-networks comprising Hap3 in C. albicans, and Ptx3 and
Mta2 in M. musculus. Remarkably, binding of recombinant Ptx3 to the C. albicans cell
wall was found to regulate the expression of fungal Hap3 target genes as predicted by the
network inference model. Pre-incubation of C. albicans with recombinant Ptx3 significantly
altered the expression of Mta2 target cytokines such as IL-2 and IL-4 in a Hap3-dependent
manner, further suggesting a role for Mta2 in host–pathogen interplay as predicted in the
network inference model. We propose an integrated model for the functionality of these
sub-networks during fungal invasion of immune cells, according to which binding of Ptx3 to
the C. albicans cell wall induces remodeling via fungal Hap3 target genes, thereby altering
the immune response to the pathogen.We show the applicability of network inference to
predict interactions between host–pathogen pairs, demonstrating the usefulness of this
systems biology approach to decipher mechanisms of microbial pathogenesis.
Keywords: host–pathogen, RNA-seq, network inference, modeling, reverse engineering, Candida, dendritic cells
INTRODUCTION
Bothhostandpathogenicspecieshaveevolvedaplethoraofstrate-
gies to rapidly adapt to the changing environmental dynamics
within the infection milieu. However, the extent of this com-
plexity has only recently been investigated through the use of
system biology approaches (reviewed in Rizzetto and Cavalieri,
2011). On the molecular level, these adaptations are mediated by
complex interaction networks, which sense these environmen-
tal changes and transmit the information throughout the cell,
leading to a cascade of changes in gene and eventually protein
expression. Understanding these underlying interaction networks
is important to elucidate how organisms and defense mechanisms
interact during microbial infection processes. Genome-wide inte-
grativeapproachesformodelinghavebecomeincreasinglypopular
(Rizzetto and Cavalieri, 2011) due to the availability of high-
throughput sequencing technologies, including RNA sequencing
(RNA-seq).Thesetechnologiesnowallowfortheparallelsequenc-
ing of millions of nucleotide sequences simultaneously (Wang
et al., 2009; Zhang et al., 2011). One major advantage to using
sequencingapproachratherthanmicroarraysisthatitisaspecies-
independent platform, allowing for an in-depth investigation of
non-model organism species, as well as multiple organisms from
a single experiment.
Inmanycases,theunderlyinginteractionnetworksbetweenthe
organismsofinterestareunknown.Networkinferenceusesreverse
engineering techniques (Hecker et al., 2009b; Marbach et al.,
2010) to predict unknown interaction networks based on high-
throughput gene expression data. A number of approaches have
been established to predict inference networks including Bayesian
networkmodeling(Friedmanetal.,2000),informationtheoretical
approaches (Butte and Kohane, 2000; Faith et al., 2007), regres-
sion based models (D’Haeseleer et al., 1999; Hecker et al., 2009a),
anddifferentialequationmodels(Holteretal.,2001;Guthkeetal.,
2005,2007).Biologicalnetworksarescalefreenetworkscomposed
of nodes and edges, where nodes represent the objects of interest
and edges show the relations between those objects (Le Novere
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Tierney et al.An interspecies network inference model
et al., 2009). Biological interaction networks often use nodes to
represent genes or proteins, and edges to show either a direct
or indirection interaction, such as protein binding or transcrip-
tional regulation (Barabasi and Oltvai, 2004). Network inference
has been successfully applied to a variety of biological scenarios,
including the modeling of immune diseases (Guthke et al., 2005;
Heckeretal.,2009a),full-genomicmodelsofEscherichiacoli (Faith
et al., 2007), and more recently, small scale networks describing
fungal infections (Linde et al.,2010). So far these model have only
focused on a single species and have not addressed host–pathogen
interactions.
In the present work, we have generated the first interspecies
computational model of molecular host–pathogen interactions.
We used RNA-seq expression data from an infection time course
of Candida albicans and bone marrow-derived dendritic cells
(BMDCs) from M. musculus. C. albicans is one of the most preva-
lent opportunistic human fungal pathogens.Although C. albicans
normally colonizes the human host, a variety of factors, most
notably immune suppression,can lead to dissemination of fungal
cells throughout the body. This dissemination can lead to a wide
range of diseases, from thrush to multi-organ failure (Gudlaugs-
son et al., 2003). We focused on dendritic cells as our model host
basedoftheirfunctionasantigen-presentingcells,theirspecializa-
tion in pathogen recognition, and their greater role in activating
and modulating adaptive immune responses (Netea et al., 2008;
Bourgeois et al., 2010). We experimentally verified predicted sub-
networks of the interspecies inferred regulatory network, which
identifies a role of the transcription factor Hap3 in C. albicans
during in vitro infection. We find that fungal Hap3 is regulated
by murine Ptx3, a soluble pattern recognition receptor acting as
an opsonin for pathogens (Diniz et al., 2004). We show that Ptx3
binding to C. albicans regulates fungal Hap3 target genes,altering
the immune response in dendritic cells. Based on the regulation
of downstream cytokines and the regulation of MTA2 mRNA in a
Hap3-dependent manner, we provide indirect evidence for a role
for Mta2, a member of the nucleosome remodeling and histone
deacetylasecomplexNuRD(Manavathietal.,2007).Weproposea
mechanism whereby Ptx3 binding to C. albicans leads to cell wall
remodeling via fungal Hap3 target genes, thereby changing the
ability of the fungi to be recognized by immune cells. The exper-
imental verification of the predicted interspecies interactions is
proof-of-principle that network inference can be used to investi-
gate microbial pathogenesis.We suggest that this could be a useful
method to identify potential antifungal target genes.
MATERIALS AND METHODS
CANDIDA STRAINS AND GROWTH CONDITIONS
All strains were routinely grown on YPD plates (1% yeast extract,
2% peptone, 2% glucose, 2% agar) and in standard rich media
YPD (1% yeast extract, 2% peptone, 2% glucose) for liquid cul-
ture at 30˚C. Fungal cells were collected in the logarithmic growth
phase by a brief centrifugation,washed in sterile PBS,and diluted
for all interaction studies. The following strains were used in this
study: C. albicans clinical isolate SC5314 (Gillum et al., 1984)
and homozygous knock-out of Hap3 (hap3Δ/hap3Δ) and rever-
tant strain (hap3Δ/hap3Δ+CIp10 (HAP3, URA3), abbreviated
in the text as hap3Δ/hap3Δ+HAP3), were generated from the
strain BWP17 (ura3::imm434/ura3::imm434 iro1/iro1::imm434
his1::hisG/his1::hisGarg4::hisG/arg4::hisG)bystepwisedeletionof
both alleles using PCR-amplified HAP3::ARG4 and HAP3::HIS1
cassettes (Gola et al.,2003) and a cIP10 plasmid containing HAP3
anditspromoterandterminatorsequencesintegratedattheRP10
locus (Murad et al., 2000).The homozygous knock-out of cda2
and revertant were kindly provided by Neil Gow (Aberdeen,UK).
CELL CULTURE OF PRIMARY IMMUNE CELLS FROM MOUSE BONE
MARROW
Bone marrow was differentiated to either BMDCs or bone
marrow-derived macrophages (BMDMs) from the femurs of 7-
to 9-week-old wild type C57BL/6 mice and assessed for homo-
geneityaspreviouslydescribedusingapanelof markerantibodies
(Bourgeois et al., 2009).
FUNGAL-MAMMALIAN CELL CO-CULTURE
Fungal-mammalian cell co-cultures were performed as previously
described (Bourgeois et al., 2009). Briefly, immune cells were
plated at a density of 1.0×105cells/cm2in sterile cell culture
dishes and incubated with fungal cells at a multiplicity of infec-
tion (MOI) of five fungal cells per immune cell. Samples were
incubated at 37˚C in 5% CO2,95% humidity for up to 24h.
cDNA PREPARATION FOR RNA-seq
Total RNA was isolated from immune cells and C. albicans using
the SV total RNA isolation system (Promega, Madison, MI, USA)
following manufacturers instructions. To obtain RNA mixtures
from both C. albicans and BMDCs, cells were first scraped in the
provided lysis buffer, followed by homogenization with 200μl
of 0.5mm acid-washed glass beads (Sigma-Aldrich, St. Louis,
MO, USA) in a Fast Prep-24 cooling block at 4˚C (MP Bio-
medicals Europe, Illkirch, France) for 45s at 5m/s. Ribosomal
RNA was depleted from 10μg of pooled total RNA samples using
the RiboMinus eukaryote kit for RNA-seq (Invitrogen, Carlsbad,
CA, USA) and concentrated using the corresponding RiboMi-
nus Concentration Module (Invitrogen) following manufactures
instructions for three independent biological repeats. For each
sample, 1μg of ribosomal-depleted RNA was converted into
cDNAusingtheSMARTerPCRcDNASynthesiskitandtheAdvan-
tage 2 polymerase mix (Clontech, Mountain View, CA, USA).
PCR amplifications were performed on 1/10 of the first strand
synthesis reaction for 18 cycles of 90˚C for 1min, 95˚C for 15s,
65˚C for 30s, and 68˚C for 6min on a GeneAmp PCR system
9700 (Applied Biosystems, Carlsbad, CA, USA), and purified on
ChromaSpin columns (Clontech,MountainView,CA,USA). The
resulting cDNAs were sequenced on the Genome Analyzer IIx at
GATC (Konstanz,Germany) using 36bp,single run,indexed read
mode.
SEQUENCE READ MAPPING, PRE-PROCESSING, AND DATA
NORMALIZATION
All sequencing reads were mapped using TopHat 1.2.0 (Trapnell
et al.,2009) against the SC5314 C. albicans assembly 21 (Skrzypek
et al., 2010) and the M. musculus UCSC version mm9 from the
ENSEMBL database (Flicek et al.,2011). Mapping was carried out
using the default settings in which only unique hits were kept for
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Tierney et al.An interspecies network inference model
further analysis. The gene expression and normalization analysis
was performed as previously described (Mortazavi et al., 2008).
Genes were tested for differential expression using the bioconduc-
tor package baySeq (Hardcastle and Kelly, 2010) relative to the
0-min infection time point. The analysis was carried out for C.
albicans and M. musculus genes individually.
CLUSTERING AND OVER-REPRESENTED GENE ONTOLOGY TERMS
Fuzzy c-means clustering (Bezdek, 1992) was applied to the
two expression matrices of differentially expressed genes from
C. albicans and M. musculus. The optimal number of clus-
ters was estimated as previously described (Guthke et al., 2005;
Linde et al., 2010). Functional categorization and significantly
over-represented categories were identified using the tool Fungi-
Fun (Priebe et al., 2011). All four hierarchical levels of Funcat
(Ruepp et al., 2004) and Gene Ontology (Ashburner et al., 2000)
categorization were used in this study.
NETWORK INFERENCE PREDICTION AND MEASURING INTERACTION
ROBUSTNESS
Network inference was performed as previously described using
the NetGenerator tool (Guthke et al., 2005; Linde et al., 2010).
Briefly, NetGenerator is based on a set of linear differential equa-
tions and models the temporal change of the expression intensity
xi(t) of gene i (i =1...n) at time t as the weighted sum of the
expression intensities of all other genes and an external stimulus
u(t) at time t. The external stimulus u(t) is modeled as a stepwise
constantfunctionrepresentingthechangefromnohost–pathogen
interactiontotheonsetoftheinteraction.Thetoolaimstoidentify
anetworkstructure,whichbestfitstothemeasuredRNA-seqdata,
while it minimizes the number of predicted interactions (Guthke
et al.,2005). Thus,a sparse network is inferred.
NetGenerator offers the possibility to integrate prior knowl-
edge(i.e.,putativeregulatoryinteractionsbasedonadditionaldata
besides the initial time series expression data). Based on the con-
fidence of the prior knowledge source, it is possible to score each
proposed interaction. The confidence of the prior knowledge is
basedonthelevelofexperimentationusedtoverifyaspecificinter-
action and the number of independent experiments showing the
same interaction. Since different data sources might be contradic-
tory, prior knowledge is softly integrated, i.e., if a proposed inter-
action contradicts the measured data too much it can be removed
by NetGenerator. Furthermore, the tool may add new interac-
tions not covered by the prior knowledge in order to fit to the
measured data. In this study, prior knowledge from public data-
bases was softly integrated (Guthke et al., 2005). Each proposed
interaction was scored in an additive manner based on the con-
fidence of the prior knowledge source as follows: direct evidence
that a gene is involved in a host–pathogen interaction (confidence
score=0.5),co-expressionoftwogenes(confidencescore=0.25),
and the occurrence of the respective transcription factor bind-
ing motif in the upstream intergenic regions of genes (confidence
score=0.125). Prior knowledge was obtained from GeneMania1,
IntAct(Arandaetal.,2010),BioGrid(Starketal.,2011),theC.albi-
cans database (Skrzypek et al.,2010),the mouse genome database
1http://genemania.org/data/
(Blake et al., 2011), and a number of peer-reviewed publications
(Lane et al., 2001; Doedt et al., 2004; Martchenko et al., 2004;
Zhao et al.,2004;Fradin et al.,2005;Oberholzer et al.,2006;Wang
et al.,2006; Spira et al.,2007; Thewes et al.,2007; Zakikhany et al.,
2007; Almeida et al., 2008; Baek et al., 2008; Nobile et al., 2008;
Frohner et al.,2009;Griffin et al.,2009;Raman et al.,2009;Sellam
et al., 2009; Hinze et al., 2010; Hou et al., 2010; Smith et al., 2010;
Wachtler et al., 2011) summarized in Figure 1C. Putative regula-
tory interactions were tested for robustness using two methods.
First,Gaussiannoisewasintroducedwithameanof 0andSD0.05
to the estimated mRNA concentrations for 1000 iterations. Sec-
ondly,predicted interactions were screened for robustness against
changes in prior knowledge by iterating the modeling approach
1000 times while randomly skipping 10% of all interactions in the
set of prior knowledge for each run. Only edges that were con-
firmed by more than 50% of the iterations were considered to be
robust and used in the resultant model.
REAL-TIME qPCR ANALYSIS
RNA sample preparation, reverse transcription, and real-
timePCRwereperformed
geois et al., 2009) using the following primers: mouse β-
Actin, forward 5?-GCGTGACATCAAAGAGAAG-3?reverse 5?-
AGGAGCCAGAGCAGTAATC-3?(RTPrimerDB)2mouse MTA2,
forward 5?-CACTGCTATAGCCTCACGCC-3?, reverse 5?-GCTAG
GAGCTGGAACCTCAC-3?,mousePTX3,forward5?-CCTGCTTT
GTGCTCTCTGGT-3?,reverse5?-TCTCCAGCATGATGAACAGC-
3?
(Diniz et al., 2004), C. albicans TUP1, forward 5?-
GACTACGCCTCAAACGAAGC-3?reverse 5?-TGGTGCCACAAT
CTGTTGTT-3?,C.albicans FRE6 forward5?-CCGGTAAACATCC
ATTCCAC-3?, reverse 5?-TTGATCCAAATGCCATT-CAA-3?, C.
albicans SEF1, forward 5?-GTGGAGGACTCGTTCATGGT-3?,
reverse 5?-TGAACCAGCACGATTCAGAG-3?, C. albicans RIP1,
forward5?-TGCTGACAGAGTCAAGA-AACC-3?
GAACCAACCACCGAAATCAC-3?
sequence analysis software Vector NTI (Invitrogen, Carlsbad, CA,
USA). Results were calculated using the ΔΔct method and are
expressed as the fold of the gene expression of interest versus the
expression of a housekeeping gene in M. musculus (β-Actin) or C.
albicans (RIP1) in treated versus untreated conditions.
previouslydescribed(Bour-
reverse5?-
as determined using the
CYTOKINE QUANTIFICATION FROM CO-CULTURE SUPERNATANTS
Theamountof cytokinesreleasedintocellculturesupernatantsby
immunecellsduringinvitro interactionstudieswithheatkilledC.
albicans wereassayedafter24hofco-cultureusingthemouseIL-2,
IL-4,or TNFα Ready-set-go ELISA kit (R&D Systems,Minneapo-
lis, MN, USA) or the Mouse Cytokine Array Panel A kit (R&D
Systems) according to the manufacturers instructions.
BINDING AND LABELING Ptx3 IN VITRO
Recombinant mouse Ptx3 (rmPtx3) protein (R&D Systems) was
reconstituted in sterile PBS and diluted for all experiments.
Some 0.5×106fungal cells were incubated for 1h at 37˚C
with 5μg reconstituted rmPtx3. Ptx3 was labeled with the pri-
mary antibody against Ptx3 (Abnova, Taiwan) and secondarily
2http://medgen.ugent.be/rtprimerdb/index.php
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Tierney et al.An interspecies network inference model
30 6090120

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Oberholzer, 2006
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Doedt,2004
Thewes,2007
Sellam,2009
Zakikhany, 2007
Nobile,2008
Lane, 2001
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Almeida,2008
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Zhao,2004
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FIGURE 1 | RNA-seq predicts regulatory host-fungus interactions. (A) M.
musculus clustering of differentially expressed genes (scaled and centered
log2values) over the time course of the infection. Black lines represent the
expression of individual genes.The mean (dotted line, red) and the variance
(solid, red) are shown. (B) C. albicans clustering of differentially expressed
genes (scaled and centered log2values) over the time course of the infection.
Black lines represent the expression of individual genes.The mean (dotted
line, red) and the variance (solid, red) are shown. (C) Prior knowledge
incorporated into the inference network for all genes, where the input
corresponds to the external perturbation (co-culture) and regulation by target
corresponds to induction by the co-culture environment but not associated
with a specific gene set. (D) Measured (dots), interpolated (dotted line),
simulated (solid line) gene expression for all genes used in the inference
model from NetGenerator over the time course of the infection. (E) Inferred
network model between C. albicans and M. musculus, where all C. albicans
(blue) and M. musculus genes (green) in the study are included.The following
interactions are represented on the model: predicted interactions based on
the RNA-seq data set from individual species where no prior knowledge
exists (gray), predicted interactions between a C. albicans and M. musculus
gene where no prior knowledge exists (red), or where prior knowledge exists
and corresponds to expression data set (orange). Here, activation is shown as
an arrow and a repression with a bar.The “C. albicans/M. musculus”
rectangle represents the influence from the external perturbation (co-culture
during in vitro infection) on the gene expression level.
labeled with goat-anti-rabbit 649 Dylight (Thermo Scientific,
Rockford, Illinois). Fungal cell wall chitin was labeled using
10μMofCalcofluorWhite(Sigma-Aldrich).Intracellularlabeling
of Ptx3 was performed using the BD Cytofix/Cytoperm Fixa-
tion/Permeabilization kit with BD GolgiPlug protein transport
inhibitor (BD Biosciences, Heidelberg) after 6h of C. albicans
infection following manufactures instructions. Preparations were
assessed by flow cytometry or visualized on an Olympus Cell-R
live imaging unit (Olympus,Essex,UK) for all experiments.
STATISTICAL ANALYSIS FOR INFERENCE MODEL VERIFICATION
Statistical analysis of data was performed using the GraphPad
Prism graphing and analysis software (GraphPad Software, San
Diego, USA) for all in vitro experiments excluding the RNA-seq
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Tierney et al.An interspecies network inference model
analysisdescribedabove.Statisticalsignificancewasassessedusing
with the Student t-test and a p-value <0.05 was considered
significant.
RESULTS
INFERRED REGULATORY NETWORK IDENTIFIES NOVEL INTERSPECIES
HOST–PATHOGEN INTERACTIONS
We used massively parallel RNA sequencing of cDNA (RNA-seq)
obtainedfromco-culturesof C.albicans andM.musculus BMDCs
over 2h to model an infection time course from fungal adhesion
to early host cell lysis. In total, we obtained approximately 120
million reads, which were mapped to the C. albicans 21 assembly
or M. musculus mm9 genome and analyzed each for differential
gene expression relative to the pre-infection state. We identified
545 differentially expressed genes for C. albicans and 240 for M.
musculus over the complete time course.
The small number of measured data points for each gene over
thetimecourserestrictsthemodelingapproachtoalimitednum-
ber of genes. If there was no pre-selection of the genes, or a large
number of genes were to be used,it would result in an over-fitting
of the measured data that would not produce a robust inference
model. For this reason, it is necessary to select a set of relevant
genestoberepresentedbynodesinthenetworkmodel.Toidentify
candidate genes in C. albicans and M. musculus, all differentially
expressedgeneswerefirstclustered(Bezdek,1992)bytheirkinetics
during the time course (Figures 1A,B). From each cluster, one or
more representative genes were chosen for use within the model.
Several considerations were taken into account for the selection
of candidate genes. In C. albicans, we preferentially chose genes
that have been either annotated as virulence genes (i.e., adhe-
sion, hyphal formation, or response to host) or strongly respond
to infection or infection-like conditions (i.e., temperature stress,
nutrient limitation, or iron regulation). For M. musculus, we pri-
oritized genes with phenotypes relating to the immune defense or
response,orsusceptibilitytopathogensinasystemicmousemodel
of infection.
A number of recent studies have shown the reverse engineer-
ing approach is greatly improved by the integration of different
data sources (Werhli and Husmeier, 2007; Gustafsson et al., 2008;
Hecker et al., 2009a,b). We therefore collected putative regulatory
interactions based on additional data obtained from literature,
referred to as prior knowledge, for each gene. Based on the con-
fidence of the prior knowledge source, a score is attributed to
each interaction (see Materials and Methods). Since different data
sources might be contradictory, prior knowledge was softly inte-
grated so that if a proposed interaction contradicts the measured
datatoagreatextent,itcanberemovedfromtheresultingnetwork
(see Materials and Methods). Genes with no known or predicted
functionwerethereforeexcludedfromtheanalysis.Basedonthese
criteria,wenarrowedourgeneliststofivefrom C.albicans andsix
fromM.musculus.Priorknowledgescores(Figure1C)andexpres-
sionkinetics(Figure1D)forthecandidategeneswerecombinedin
NetGenerator to generate the final network inference (Figure1E).
To verify the fit of the model to the actual expression kinetics
of the candidate genes, we first used NetGenerator to interpolate
andsimulategeneexpressionforthemeasureddatapointsof each
gene(Figure1D).Thecloserexpressionprofilesfortheindividual
genes fit to the measured data points,the better the inference pre-
diction is. We found a close relationship between the simulated
and measured data points, showing that the NetGenerator model
is representative of the measured data. The final interspecies net-
work was based on these predictions (Figure1E). Only edges that
were robust against the addition of Gaussian noise and partial
skipping of prior knowledge were used in the construction of the
model. The final network predicts 21 putative edges, including 4
interspecies edges.
To specifically test the robustness of the interspecies edges
experimentally, we focused on a sub-network composed of a sin-
gle C. albicans transcription factor Hap3 that was predicted in
the inference model to contain two interactions with M. musculus
genes. These interactions include a predicted blunt or repressing
edge between fungal Hap3 by murine Ptx3, and a predicted blunt
edge of murine Mta2 by fungal Hap3 itself.
THE BINDING OF MURINE Ptx3 REGULATES Hap3 TARGET GENES
Pentraxin 3 (Ptx3) is a soluble pattern recognition receptor that
has been previously shown to function as an opsonin to facili-
tate pathogen uptake by phagocytic cells in a dectin-1 dependent
manner (Diniz et al., 2004). To determine if M. musculus Ptx3
blockedfungalHap3functionortheexpressionofHap3-regulated
genes in C. albicans as suggested by the inference model, we
first asked whether Ptx3 was induced upon infection with Can-
dida cells. Using intracellular staining for Ptx3, we detected a
strong fluorescence signal in BMDMs infected with C. albicans,
whereas no signal was detected in BMDMs alone or ptx3−/−
macrophages (Figures 2A,B). Furthermore, we found an increase
in PTX3 mRNA levels in BMDCs (Figure 2C) and BMDMs (data
not shown) after 1h of C. albicans infection, verifying that Ptx3
is indeed induced in our experimental setup. Interestingly, the
amount of PTX3 induced significantly decreased in the absence
of Hap3 (Figure 2C). We detected a similarly significant decrease
in PTX3 induction in the gene containing the Hap3 binding box,
Cda2,a predicted chitin deacetylase in C. albicans.
Ptx3 has been previously shown to bind to numerous fungi,
including Aspergillus fumigatus (Jaillon et al., 2007) as well as
zymosan-coated particles (Diniz et al.,2004). Therefore,we asked
whether recombinant mouse Ptx3 (rmPtx3) could also bind to
the C. albicans cell wall. We assessed rmPtx3 binding using
fluorescence microscopy and flow cytometry. Fungal cells pre-
incubatedwithrmPtx3for1hat37˚Cshowedsurfacelocalization
of Ptx3 to the cell wall. No signal was visible on cells treated
with the PE-conjugated secondary alone or the untreated con-
trol (Figure 2E). Notably, the labeling pattern of Ptx3 coincided
with areas of expected chitin exposure. Hence, we also stained C.
albicans cells with Calcofluor White, a fluorescent dye that binds
to exposed chitin (Bulawa et al., 1995). Interestingly, the local-
ization of rmPtx3 on the C. albicans overlapped with the signal
detected for Calcofluor White alone (Figure 2E), suggesting that
Ptx3 might bind to accessible cell surface chitin. We confirmed
and quantified the amount of Ptx3 binding to the cell wall using
flowcytometry.Basedonthebindingobservedinthefluorescence
microscopy,weanalyzedourdatausingboththecompletepoolof
C. albicans cells as well as discriminating yeast and hyphal forms
(Figure 2D; Figure A1 in Appendix). We detected a high level of
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Tierney et al.An interspecies network inference model
-20
-15
-10
-5
0
5
10
*
*
*
DIC
DAPI
PE
WT BMDM
20μM
0 102
103
PE-A
104
105
0
20
40
60
80
100
0 102
103
PE-A
PE
104
105
0
20
40
60
80
100
WT BMDM + SC5314
ptx3 + SC5314
DIC
DAPI
PE
20μM
WT BMDM + SC5314
Yeast
Hyphal
% of max
A
B
10μM
DIC
calcofluor white
PE
C.albicans + rmPtx3
C.albicans + PE
C.albicans
E
D
C
F
TUP1
FRE6
SEF1
Fold change
0
5
10
15
* *
*
Fold change
SC5314
BWP17

hap3Δ/hap3 Δ
hap3Δ/hap3Δ + HAP3
CAI-4
cda2Δ/cda2Δ
cda2Δ/cda2Δ + CDA2
-/-
FIGURE 2 | Binding of rmPtx3 to C. albicans mediates the expression of
Hap3 target genes. (A) Intracellular labeling of endogenous Ptx3 induction
after 6h of C. albicans stimulation of macrophages derived from wild type or
ptx3−/−bone marrow. (B) Intracellular labeling of endogenous Ptx3 after 6h
of C. albicans stimulation of macrophages derived from wild type bone
marrow. Macrophages directly associated with fungal cells and show a
strong signal for endogenous Ptx3, while those not associated have only
background signal levels. (C) qPCR of PTX3 in BMDCs after 1h of infection
with different C. albicans strains. Results represent the mean of 3 pooled
experiments±SD. (D) FACS analysis of wild type strain SC5314 after 1-h
treatment with rmPtx3, where untreated cells stained with PE only (red) and
rmPtx3 and SC5314 (blue) are shown. Cells were gated by size to
differentiate yeast and hyphal morphologies. (E) Fluorescence microscopy
of SC5314 after 1h pre-treatment with 5μg rmPtx3 (red) or 10μM
Calcofluor White (blue). (F) qPCR of predicted targets genes of Hap3.
SC5314 (white), BWP17 (gray), hap3Δ/hap3Δ (blue), and
hap3Δ/hap3Δ+HAP3 (black) after 1h pre-incubation with 5μg rmPtx3 are
shown. Results represent the mean of 3 pooled experiments±SD.
binding in fungal hyphae compared to yeast form cells. This is
consistent with our fluorescence microscopy analysis, where we
detected much stronger signals on the hyphal cell walls compared
to the bud scars on yeast form cells.
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Tierney et al.An interspecies network inference model
Given that rmPtx3 binds to the fungal cell surface, we assessed
if rmPtx3 binding influenced the expression of predicted fungal
Hap3targetgenesaspredictedbytheinferrednetworkusingqPCR
(Figure2F).Thereare10predictedtargetgenesof Hap3thatwere
recently identified in a network inference study using microarray
data from C. albicans during in vitro epithelial infection, where
iron is assumed to be limited (Linde et al., 2010). Out of the 10
putativeHap3targetgenes,wefoundthree,TUP1,FRE6,andSEF1,
whose expressions were significantly decreased in C. albicans after
rmPtx3binding,verifyingtheirfunctionalityasHap3targetgenes.
Interestingly,thelevelsofthesegenesincreasedintheHap3knock-
out.Thesedatastronglysuggestthattheirdown-modulationupon
binding of rmPtx3 is Hap3-dependent.
CANDIDA ALBICANS BOUND BY Ptx3 ATTENUATES THE IMMUNE
RESPONSE IN BMDCs
Recently it was shown that the binding of recombinant human
Ptx3 increases A. fumigatus conidia phagocytosis and influences
cytokine production. Those mice lacking Ptx3 were additionally
found to be more susceptible to A. fumigatus infection (Moalli
et al., 2011). To determine if the binding of Ptx3 to C. albicans
changedthecytokineproductionof murineimmunecells invitro,
we first investigated the gross immune response using a cytokine
array after 24h of co-culture with BMDCs (Figure A2 in Appen-
dix).RmPtx3-boundC.albicans inducedmultiplecytokinescom-
paredtountreatedC.albicans includingIL-2,acytokineregulated
by Mta2, as well as the inflammatory cytokines KC, JE, and TNFα
(FiguresA2 in Appendix). Interestingly, when we then compared
Hap3 knock-out cells pre-incubated with rmPtx3, we detected a
general increase in these cytokines in addition to IL-23, IL-17,
IL-16, and IL-10 that were not detected in using wild type C.
albicans (FigureA2 in Appendix).
Since the cytokine array is a qualitative assessment of cytokine
production with a relatively high detection threshold, we verified
the changes in cytokine levels for the cytokines most relevant to
the inference model, namely, IL-2 and IL-4, both target cytokines
of Mta2, and TNFα, a pleiotropic inflammatory cytokine, by
ELISA. Mta2 is a member of the NuRD (nucleosome remodel-
ing and histone deacetylase) complex in M. musculus (Manavathi
et al., 2007) and predicted in our network model as repressed
by Hap3. The cytokines IL-2 and IL-4 produced during the host
immune response were both recently identified as targets of the
Mta2/NuRD complex (Lu et al.,2008). We found that in BMDCs,
MTA2 increased in the absence of Hap3, suggesting that Hap3
might indirectly regulate expression of Mta2 (Figure 3A). We
quantified cellular cytokine release using wild type BMDCs with
C.albicans,wildtypeBMDCswithrmPtx3-boundC.albicans,and
ptx3−/−BMDCs. We found that the production of IL-2 and IL-
4 significantly increased in the absence of Hap3 (Figures 3B,C).
This increase was augmented by C. albicans cells pre-incubated
with rmPtx3,confirming our observation from the cytokine array
thatthereisageneralincreaseintheproductionof thesecytokines
when there is an increase in Ptx3. Interestingly, compared to wild
type BMDCs, the basal level of cytokine production of IL-2 and
IL-4 increased in ptx3−/−BMDCs, corresponding to our inferred
network prediction that the loss of its predicted negative regu-
lators, Ptx3 and fungal Hap3, would increase the expression of
Mta2 and thereby increase the expression of its target cytokines.
InHap3knock-outcells,wefoundbothonthecytokinearraysand
by ELISA a significant decrease in TNFα (Figure 3D). These data
show that the binding of Ptx3 to fungal cells alters the cytokine
production by immune cells in a Hap3-dependent manner, and
the regulation of Mta2 target cytokines indirectly suggests an
involvementof Mta2aspredictedbythenetworkinferencemodel.
IDENTIFYING CELL SURFACE Hap3 TARGET GENES
To identify how immune cells could detect the regulation of the
transcription factor Hap3 in C. albicans, we searched for puta-
tive Hap3 target genes that could have more direct contact with
immune cells,including: cell wall,plasma membrane or secretory
proteins. We focused on C. albicans genes of cluster 2, since their
expression strongly increased expression over the time course of
invasion (Figure 1B). Within this cluster, we scanned for genes
harboring the binding site of the Hap-complex in their upstream
regulatory regions (Baek et al., 2008). We further narrowed down
the candidate list by removing genes that did not have a pre-
dicted cellular localization or function in the C. albicans database
(Skrzypeketal.,2010).Followingtheseselectioncriteria,ninecan-
didategeneswereleftthatweusedtoinferanadditionalnetworkin
combination with Ptx3, Hap3, and Mta2 to determine if an inter-
action could be inferred with a protein that could come in direct
contact with immune cells (Figure A3 in Appendix). To increase
the reliability of the putative Hap3 interactions within the new
interaction network, we included the validated interactions from
our experiments within this study (repression of HAP3 by Ptx3
and MTA2 by Hap3), as additional prior knowledge. Of all of the
candidate genes, only the activation of CDA2 (a putative chitin
deacetylase in C. albicans) by Hap3, was robust against Gaussian
noise and partial skipping of prior knowledge.
DISCUSSION
Inthisstudy,weaimedtoinferanetworkthatpredictsinteractions
between host and pathogenic species under infection settings. To
our knowledge, this is the first network inference approach pre-
dicting host–pathogen interactions. This approach allowed for
the prediction, identification, and experimental verification of
interdependent sub-networks composed of a single C. albicans
transcription factor Hap3, and the M. musculus genes Ptx3 and
Mta2. The experimental validation suggests a putative mecha-
nism to explain how these interactions could be regulated during
infection of immune cells by fungal pathogens.
Our modeling approach was fundamentally based on differen-
tialequations,whichhavebeenpreviouslyusedtoinferregulatory
network models (Toepfer et al., 2007; Linde et al., 2010). This
approach is generally suitable for time series data. Nevertheless,
this approach is inappropriate for large-scale modeling, because
a large number of genes incorporated into a differential equa-
tion based model leaves open a number of parameters to be
identified.Thismayresultinanover-fittingof thedata.Themod-
eling approach uses four attempts to prevent over-fitting. First,
we restrict the number of genes within the model such that a
smaller number of parameters need to be identified. Second, it
aimsatinferringasparsenetworkwheremanyparametersarezero.
Thirdly,itmakesuseof re-samplingtechniqueswherethedataare
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Tierney et al.An interspecies network inference model
0
200
400
600
TNF alpha ng/mL
WT only
WT only
PTX3 -/- only
ptx3 only
WT + rmPTX3
WT + rmPtx3
*
*
ns
A
SC5314
BWP17
Hap D
Hap R
0
2
4
6
8
10
*
fold change MTA2
Media
SC5314
BWP17
Hap3 D Hap3 R
Media
SC5314
BWP17
Hap3 D Hap3 R
Media
SC5314
BWP17
Hap3 DHap3 R
0
2000
4000
6000
IL2 pg/mL

-/-


*
*
ns
0
100
200
300
400

WT only
WT only
PTX3 -/- only
ptx3 only
WT + rmPTX3
WT + rmPtx3
* *
ns
*
D
C
Media
SC5314
BWP17
hap3Δ/hap3Δ
hap3Δ/hap3Δ + HAP3
Fold change
SC5314
BWP17
hap3Δ/hap3Δ
B
IL-4 pg/mL
TNFα ng/mL
IL-2 pg/mL
WT only
WT + rmPtx3
ptx3 only
hap3Δ/hap3Δ + HAP3

-/-
-/-
FIGURE 3 | Mouse MTA2, IL-2 and IL-4 increase upon stimulation with C.
albicans in a Hap3-dependent manner. (A) qPCR of M. musculus MTA2
expression in BMDCs after 1h incubation with fungal cells at an MOI of five
fungal cells to immune cells are shown. Results represent the mean of 3
pooled experiments±SD. (B–D) ELISA measurement of IL-2 (B), IL-4 (C), and
TNFα (D) from the supernatants of BMDCs after 24h incubation with fungal
cells in WT BMDCs, C. albicans cells pre-incubated with rmPtx3 on wild type
BMDCs, or fungal cells on ptx3−/−BMDCs. Incubations were performed at an
MOI of five fungal cells to immune cells. Results represent the mean of 2
pooled experiments±SD.
perturbed in a random manner. Finally, we make use of prior
knowledge guiding the inferred structure to a knowledge-based
solution. Thus skipping incorrect network structures.
Gene expression levels, as well as available prior biological
knowledge, were used to aid in the narrowing of genes that we
chose to incorporate into the model. For this reason, genes where
no biological knowledge was available were excluded from fur-
ther analysis. However, we cannot exclude the possibility that
additional genes of unknown function might also play a role in
our inference model. This remains a limitation of the modeling
approach,insofaraspredictionscanonlybemadeforgeneswhere
a reasonable amount or prior knowledge is available. The genes
incorporated into the model represent only one possible scenario
of interactions and we do not exclude the possibility that other
genes may play a role under other conditions. We have already
started to take first step for a full-genomic network modeling for
C.albicans utilizingacompendiumof allavailableexpressiondata
(Altwasser et al., 2012). Moreover, we primarily focused on genes
actingasputativenetwork“hubs”intheirorganisms(Bulawaetal.,
1995). Hubs are genes such as transcription factors that regulate
many other downstream genes within a network either directly or
indirectly. Hubs were chosen because they are less likely to have
redundant roles. Therefore, we would expect a stronger pheno-
typethaninvestigatinggenesthataresparselyconnected.Thisalso
means that the interactions we are investigating are more likely to
be indirect and should be interpreted with caution.
From our original candidate gene list, we inferred HAP3 as a
putative network hub targeted by innate immune cells. Interest-
ingly,several putative target genes of Hap3 identified in this study
are predicted to localize to the plasma membrane, cell wall, or are
involved with cell wall reorganization in C. albicans. The fungal
cellwallisadynamicstructure,whichundergoessignificantstruc-
tural and molecular composition remodeling throughout its life
cycle,aswellasinresponsetoavarietyofexternalstimuli(Chaffin,
2008).AsHap3inC.albicans isatranscriptionfactorup-regulated
under iron-limiting conditions (Linde et al., 2010), it is likely
that its function during fungal recognition or phagocytosis by
immune cells is indirect. Of all of the candidate cell surface Hap3
targets, only Cda2, a putative chitin deacetylase forms a robust
interaction with Hap3 within the second network (Figure A3 in
Appendix). Chitin deacetylase enzymes exists in both intracellu-
lar and secreted forms in different fungi, where they hydrolyzes
the acetamido group in the N-acetylglucosamine units of chitin
and chitosan, leaving glucosamine units and acetic acid form as
byproducts (Zhao et al., 2010). Chitin deacetylases exist in both
Saccharomyces cerevisiae (Martinou et al.,2002) and in the oppor-
tunisticfungalpathogenCryptococcusneoformans,wheretheyhave
been suggested as an antifungal target due to their severe effect on
cell wall integrity (Baker et al., 2007). Notably, chitin deacety-
lases are secreted during different developmental stages of some
other fungi (Zhao et al., 2010). For example, in Colletotrichum
lindemuthianum, a plant fungal pathogen, chitin deacetylases are
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Tierney et al.An interspecies network inference model
exclusively secreted during hyphal penetration into plant tissue
(Tokuyasu et al., 1996). We find that Ptx3 induction is decreased
in the CDA2 knock-out (Figure2C),further suggesting a possible
connection to the inferred network model. These data are consis-
tentwiththeoverlapof Ptx3stainingandthatof CalcofluorWhite
(Figure2E),which binds to exposed chitin. Therefore,it is tempt-
ing to speculate that the recognition of C. albicans by immune
cells triggers the production of this enzyme to induce cell wall
remodeling as an evasion strategy. However,further work beyond
the scope of this study is needed to decipher the specific function
of Cda2 in C. albicans and its connection to Hap3.
We observed that upon binding of rmPtx3 to fungal cells, the
C. albicans virulence genes TUP1, FRE6, and SEF1 mRNA levels
significantly decreased in a Hap3-dependent manner (Figure2F).
Tup1 has a well-characterized role as a key regulator in C. albicans
morphogenesis (Braun and Johnson, 1997). We cannot exclude
the possibility that Hap3 and Tup1 may have similar or even
complementary functions during interaction with immune cells.
Interestingly, both Tup1 and Fre6 are either directly or indirectly
involved in the C. albicans cell wall homeostasis. Tup1 is a multi-
functional transcriptional co-repressor of filamentous growth in
C. albicans whose lack leads to constitutive filamentous growth
(Braun and Johnson,1997;Park and Morschhäuser,2005). Fre6 is
an uncharacterized protein,for which in silico predictions suggest
it to reside in the plasma membrane with a putative functional
similarity to the ferric reductase Fre10, an important protein in
iron acquisition (Knight et al., 2005). Therefore, their regulation
upon binding or phagocytosis might play an additional role in
cell wall remodeling during infection. Since fungal cells experi-
ence severe iron-limiting condition within phagosomes of host
cells, Hap3 and Fre6 appear as logical candidates involved in this
reciprocal interaction. Likewise, Sef1 regulates iron uptake, and
has recently been shown to promote virulence in a mouse model
of bloodstream infections (Chen et al., 2011). Interestingly, it was
shown that knock-out mice lacking Ptx3 are hyper-susceptible to
A. fumigatus (Moalli et al., 2010). However, no in vivo work has
been performed to date using ptx3−/−mice and C. albicans. A
recent study has shown that the activation of the complement
system via the lectin pathway can be triggered via a complex of
Ptx3 and mannose binding lectin (MBL) on C. albicans mannan
in vitro (Ma et al., 2011). They showed the MBL–Ptx3 complex
could enhance the deposition of the complement components
C3 and C4 and thereby increase phagocytosis of C. albicans by
polymorphonuclear leukocytes. It has previously been shown that
C3 knock-out mice are additionally more susceptible to C. albi-
cans infections (Han et al.,2001). Therefore,it is possible that the
absence of Ptx3 could result in reduced activation of the comple-
ment pathway and reduced fungal killing. In vivo studies using
ptx3−/−mice would be needed to investigate this hypothesis.
We found that the expression of MTA2 and the regulation
of its downstream targets such as cytokines IL-2 and IL-4, are
increased during immune cell invasion by C. albicans in a Hap3-
dependent manner (Figures 3B,C). Moreover, we found that an
altered-immune response is one consequence of rmPtx3 binding.
Mta2 knock-out mice display partial embryonic lethality, while
the surviving mice develop lupus-like autoimmune symptoms,
includingseveredevelopmentalphenotypes(Luetal.,2008).Mta2
is uniquely associated with the NuRD chromatin complex, which
has both nucleosome remodeling and histone deacetylase activ-
ity (Feng and Zhang, 2003). Although there has been no data
Immune response
Altered-immune response
WT
.hap3Δ
Δ
/hap3
6-24 h
1-5 h
0 h
Time
FIGURE 4 |A proposed model for the mode of action of Ptx3 on C.
albicans. After mutual recognition between C. albicans (yellow) and host
immune cells (blue), Ptx3 (blue, stars) is released into the surrounding
milieu where it can bind to the invading fungal cell wall.The binding of
Ptx3 induces a change in the C. albicans cell wall (purple) after the
activation of Hap3 target genes, influencing its recognition by immune
cells and the subsequent immune response. Arrows represent
progression of time during an infection.
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Tierney et al.An interspecies network inference model
to date in fungi indicating a role for host chromatin in patho-
genesis, recent work in bacteria and viruses (Hamon and Cossart,
2008;Rohde,2011)showsthatchromatinremodelingisinducedin
hostcellsduringinvasion.Consistentwiththeseobservations,our
data suggests that the regulation of MTA2 may affect chromatin
remodeling in immune cells in the response to fungal pathogens.
The resultant altered-immune response may be disadvantageous
to the pathogen because it would promote fungal clearance.
We propose that Hap3 constitutes a target hub of C. albicans,
which actively regulates immune responses through the reorga-
nization of the C. albicans cell wall during invasion of innate
immunecells(Figure4).Specifically,weproposeamodelinwhich
the binding of Ptx3 released from immune cells to C. albicans
cell wall triggering the reorganization of the C. albicans cell wall
and plasma membrane via the activation of Hap3 target genes.
This reorganization in turn changes the recognition of the fun-
gus by immune cells and attenuates the host immune response.
This work demonstrates the possibility to experimentally verify
predicted host–pathogen relationships based on an interspecies
model of network inference,showing that inference modeling can
beusedintheinvestigationof microbialpathogenesis.Wepropose
thatthismethodcouldbeusefulfortheidentificationofantifungal
target genes.
CONTRIBUTION
Lanay Tierney designed research, performed experiments, ana-
lyzed data and co-wrote the manuscript. Jörg Linde generated
network inference maps, analyzed data, and co-wrote the manu-
script. Sebastian Müller performed the sequence alignment and
normalization. Sascha Brunke, Juan Camilo Molina, and Bern-
hard Hube provided C. albicans Hap3 deletion strains. Reinhard
Guthke and Karl Kuchler designed the research and co-wrote the
manuscript.
ACKNOWLEDGMENTS
We would like to thank Alberto Mantovani and Cecilia Garlanda
(Milan, Italy) for kindly providing ptx3−/−bone marrow. We are
alsoindebtedtoNeilGow(Aberdeen,UK)forkindlyprovidingthe
Cda2 knock-out C. albicans strains. This work was supported by a
grant from the Christian Doppler Research Society to Karl Kuch-
ler, and in part by the FWF-DACH grant of the Austrian Science
Foundation(FWF-Proj.:I-746-B11)toKarlKuchlerandBernhard
Hube.JörgLindewassupportedbytheexcellencegraduateschool
“Jena School for Microbial Communication (JSMC).” We would
like to thank laboratory members for critical reading and helpful
comments on the manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at http://www.frontiersin.org/Microbial_Immunology/10.3389/
fmicb.2012.00085/abstract
Table S1 | RPKM values for C. albicans genes over the infection time course.
Table S2 | RPKM values for M. musculus genes over the infection time course.
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential con-
flict of interest.
Received: 08 December 2011; accepted:
20 February 2012; published online: 12
March 2012.
Citation: Tierney L, Linde J, Müller
S, Brunke S, Molina JC, Hube B,
Schöck U, Guthke R and Kuchler K
(2012) An interspecies regulatory net-
work inferred from simultaneous RNA-
seq of Candida albicans invading innate
immune cells. Front. Microbio. 3:85. doi:
10.3389/fmicb.2012.00085
This article was submitted to Frontiers
in Microbial Immunology, a specialty of
Frontiers in Microbiology.
Copyright © 2012 Tierney, Linde,
Müller, Brunke, Molina, Hube, Schöck,
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Tierney et al.An interspecies network inference model
APPENDIX
Hyphal cells
All cells
100
101
102
103
104
0
20
40
60
80
100
100
101
102
103
104
0
20
40
60
80
100
89.4
100
101
102
103
104
0
20
40
60
80
100
0200 400600 800 1000
0
200
400
600
800
1000
Yeast cells
% of max
% of max
% of max
FSC-H
SSC-1
97.5%
61.9%
33.6%
89.4%
FIGUREA1 | FACS analysis of rmPtx3 binding to C. albicans yeast and
hyphal cells. SC5314 after 1-h treatment with rmPtx3. Cells were gated
according to morphology based on size, all Candida cells analyzed (green
gate), yeast form only (black gate) and hyphal form only (the brown gate).
Histograms for untreated cells (red), and treated rmPtx3 and SC5314 (blue)
are shown.
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
ctrl (-) ctrl (+)
TARCTIMP-1 TNF
TREM-1
MIG MIP-1
MIP-1 MIP-2 RANTESSDF-1 IP-10I-TAC KC M-CSF JEMCP-5
IL-13IL-12p70IL-16IL-17IL-23 IL-27IL-3 IL-4 IL-5IL-6 IL-7IL-10
sICAM-1IFN
IL-1IL-1IL-1raIL-2BLCG-CSFG-CSFGM-CSFI-309Eotaxin
ctrl (+)ctrl (+)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
ctrl (-)ctrl (+)
TARCTIMP-1TNF
TREM-1
MIGMIP-1
MIP-1MIP-2RANTES SDF-1 IP-10I-TACKCM-CSFJEMCP-5
IL-13IL-12p70IL-16IL-17IL-23IL-27IL-3IL-4IL-5IL-6 IL-7IL-10
sICAM-1IFN
IL-1IL-1IL-1raIL-2BLCG-CSFG-CSFGM-CSFI-309Eotaxin
ctrl (+)ctrl (+)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
ctrl (-)ctrl (+)
TARCTIMP-1TNF
TREM-1
MIGMIP-1
MIP-1MIP-2RANTESSDF-1IP-10I-TAC KCM-CSF JEMCP-5
IL-13 IL-12p70 IL-16IL-17 IL-23IL-27 IL-3IL-4 IL-5IL-6 IL-7 IL-10
sICAM-1IFN
IL-1IL-1 IL-1ra IL-2BLCG-CSF G-CSF GM-CSFI-309 Eotaxin
ctrl (+)ctrl (+)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
ctrl (-) ctrl (+)
TARCTIMP-1TNF
TREM-1
MIGMIP-1
MIP-1MIP-2 RANTESSDF-1 IP-10I-TAC KCM-CSF JEMCP-5
IL-13IL-12p70 IL-16 IL-17IL-23IL-27 IL-3IL-4 IL-5IL-6IL-7 IL-10
sICAM-1IFN
IL-1IL-1IL-1raIL-2 BLC G-CSFG-CSFGM-CSF I-309 Eotaxin
ctrl (+)ctrl (+)
Cytokine array
BMDC + S5314
BMDC + S5314 + rmPtx3
BMDC + hap3Δ/hap3Δ
FIGUREA2 | rmPtx3 binds to the C. albicans surface and alters the immune response.The cytokine array panel for M. musculus (R&D systems) where
each spot represents an individual cytokine (in duplicate) for supernatants from BMDCs treated fungal cells pre-treated with rmPtx3 after 24h.
www.frontiersin.org
March 2012 | Volume 3 | Article 85 | 13

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Tierney et al.An interspecies network inference model
input
CIP1
OPT4
orf19.1887
PIR1
CSP37
GAL10
IFC1
PTX3
MTA2
HAP3
PST2
CDA2
in vitro infection
C.albicans M.musculus
FIGUREA3 | Inferred network model between C. albicans and M.
musculus using of candidate Hap3 effector genes. C. albicans (blue)
and M. musculus genes (green) included in the model are shown.The
following interactions are represented on the model: predicted
interactions based on the RNA-seq data set from individual species where
no prior knowledge exists (black) or predicted interactions between a C.
albicans and M. musculus gene where no prior knowledge exists (red), or
where prior knowledge exists but does not corresponds to expression
data set (gray, dotted). Here, activation is shown as a pointed arrow and a
repression a blunted arrow.
Frontiers in Microbiology | Microbial Immunology
March 2012 | Volume 3 | Article 85 | 14