Essential Proteins and Functional Modules in the Host-Pathogen

Interactions from Innate to Adaptive Immunity

C. albicans-zebraﬁsh Infection Model

Chia-Chou Wu and Bor-Sen Chen

Control and Systems Biology Laboratory, Department of Electrical Engineering,

National Tsing Hua University, Hsinchu, Taiwan

Keywords:

Computational Systems Biology, Network construction, Host-pathogen interaction, Protein-protein interac-

tion network, Infection.

Abstract:

The host and the pathogen are indispensable in the infectious diseases. Besides studying the host defensive and

pathogen invasive mechanisms individually, the cross-species interactions, i.e., the host-pathogen interactions,

become a novel and intense research subjects of the infectious diseases. In this study, two host-pathogen inter-

action networks are constructed for innate and adaptive immunity based on the time course microarray data of

C. albicans-zebraﬁsh infection model. The interaction variations in the host, pathogen, and host-pathogen re-

gions are evaluated by comparing the two constructed networks. Those proteins of larger interaction variations

stand for more pivotal roles in the transition from innate to adaptive immunity. Moreover, in the host-pathogen

region, four signiﬁcantly enriched functional modules are identiﬁed. Meanwhile, the interaction variations of

these four functional groups imply the corresponding strategy shifts of the host and pathogen from innate to

adaptive immunity. In view of these results, this study gives a systematic explanation about the transition from

innate and adaptive immunity from functional modules perspective. Thus, this study provides potential targets

for developing efﬁcient therapies of the infectious diseases.

1 INTRODUCTION

The host and the pathogen are indispensable in the in-

fectious diseases. In particular,the interplays between

the host and pathogen shape the whole infection pro-

cesses from the ﬁrst pathogens exposure to the ﬁnal

outcomes of the infection (Tierney et al., 2012). After

activating the ﬁrst line of host defense mechanisms,

the innate immunity recruits several types of cells

(e.g., macrophages, dendritic cells, NK cells, etc.)

to protect the host from the pathogen invasion and

then striveto eliminate the threats from the pathogens.

In turn, pathogens have evoked multiple strategies

for surviving under the host immune-defense mech-

anisms. After several rounds of attacks and defenses

between the host and pathogen, the host may elimi-

nate pathogens and prepare for the next challenges or

the pathogens may win the battle to cause chronic in-

ﬂammation or death of the host. Despite the tremen-

dous advances in the pathogenic mechanisms and the

following triumph in the drug development (Arnold

et al., 2012), the remaining issues (e.g., drug resis-

tance) of infectious diseases become more trouble-

some. The dynamic and complex interactions be-

tween the host and pathogen may partially explain

why those drugs are often not effective in vivo (Mei-

jer and Spaink, 2011). Until a decade ago, the tradi-

tional viewpoint to treat the host and pathogen sepa-

rately is shifted to a more holistic viewpoint on both

players in the infection processes. This viewpoint

transition results from (i) the realization of the indis-

pensableness of the host-pathogen interactions (HPIs)

in the infectious diseases and (ii) the advent of the

OMICs biotechnology in measuring the genes, tran-

scripts, and proteins at whole cell/organism levels

(Schmidt and Volker, 2011). This permits a compre-

hensive interrogation of the pathogen at the whole-

genome, transcriptome, and proteome levels as well

as the host. From the molecular aspect, the infec-

tion processes can be viewed as the interference of

pathogenic proteins with the hosts’ interaction net-

work (Arnold et al., 2012). Hence, to investigate in-

fection processes from a systematic perspective, in

this study we would construct dynamic host-pathogen

protein-protein interaction (PPI) networks.

Regarding the success of the C. albicans-zebraﬁsh

infection model (Chao et al., 2010) as well as the

Wu C. and Chen B..

Essential Proteins and Functional Modules in the Host-Pathogen Interactions from Innate to Adaptive Immunity - C. albicans-zebraﬁsh Infection Model.

DOI: 10.5220/0005201700170025

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 17-25

ISBN: 978-989-758-070-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

amenability to genetic manipulations (Gratacap and

Wheeler, 2014), the zebraﬁsh is a novel and poten-

tial model organism to study the immunity. Fur-

thermore, the zebraﬁsh and human immune systems

are remarkably similar and more than 75% of hu-

man genes implicated in diseases have counterparts

in zebraﬁsh (Schier, 2013). This provides a strong

connection between the zebraﬁsh and human on the

pathogenic mechanisms as well as immune responses,

which are important for biomedical applications. The

immune system of zebraﬁsh as well as other verte-

brates can further be divided into two subsystems,

i.e., innate (unspeciﬁc) and adaptive (speciﬁc) im-

munity (Trede et al., 2004). Hence, the ﬁrst dataset

we used to construct the dynamic host-pathogen PPI

network (HP-PPIN) measured the gene expression

proﬁles during the ﬁrst 18 hours after zebraﬁsh is

ﬁrstly exposed to a lethal dose of C. albicans (Chen

et al., 2013). This dataset sampled the gene ex-

pression proﬁles at 9 time points (i.e., 0.5, 1, 2, 4,

8, 12, 16, 18-hour post-injection) with three repli-

cates for C. albicans and zebraﬁsh, respectively (Fig-

ure 1A). The outcomes of the interplay between the

host and pathogen after their ﬁrst contact are cap-

tured by time course microarray experiments. Im-

mediately after the injection of C. albicans into ze-

braﬁsh, the immune surveillance system of zebraﬁsh

senses the existence of the invaders. The recogni-

tion of the pathogen-associated molecular patterns

(PAMPs) and/or damage-associated molecular pat-

terns (DAMPs) by the pattern recognition receptors

(PRRs) (e.g., toll-like receptors, C-type lectin recep-

tors, etc.) (Trede et al., 2004; Romani, 2011) may be

viewed as a starting point of a series of complex HPIs.

Those PRRs would initiate downstream pathways that

promote the activation of other parts of innate im-

mune system and the clearance of pathogens (e.g.,

production and secretion of cytokines, chemokines,

and chemotactic cues to recruit more immunocytes).

Thus, the morphological transitions (yeast-to-hyphal

form) (Kuo et al., 2013), required ions and small

molecules transportation (Wang et al., 2014), and

structures and components of molecules on the cell

wall changes (Romani, 2011) are the strategies uti-

lized by C. albicans to acquire nutrients and evade the

clearance from the host innate defensive mechanisms.

Those primary responses of the host and pathogen are

recorded in the ﬁrst dataset which is a result from a

constant innate immune response and a delayed adap-

tive immune response (see Figure 1).

In addition to the activation of innate defensive

mechanisms, the PRRs and antigen presenting cells

would further activate the speciﬁc cells to clear the

pathogens much more efﬁciently (Romani, 2011). To

investigate the host-pathogen interactions in the spe-

ciﬁc defensive mechanisms (i.e., the adaptive immu-

nity), we adopted the second dataset. The experimen-

tal design of the second dataset comprises two C. al-

bicans injections into zebraﬁsh. In the ﬁrst injection,

a nonlethal dose is used to cause the host primary re-

sponses and immunological memory in the host. And

in 14 days after the ﬁrst injection, a lethal dose is

applied to zebraﬁsh. Then, the gene expression pro-

ﬁles are recorded through microarray after the second

injection and comprise 8 time points (2, 6, 12, 18,

24, 30, 36, 42-hour post-reinjection) with two repli-

cates for C. albicans and zebraﬁsh, respectively (Fig-

ure 1A). Due to the previous exposure to C. albicans,

the immunological memory in zebraﬁsh would be ac-

tivated and force the activation of the adaptive im-

munity. Thus, the main portion of the information

recorded in the second dataset reﬂects the outcomes

of the HPIs in the adaptive immunity. In contrast

to the innate immunity, the adaptive immunity of ze-

braﬁsh and the responses of C. albicans to the adap-

tive immunity are less well-known. Here, in this study

the combination of these two datasets provides an op-

portunity to investigate the HPIs and their roles in the

innate and adaptive immunity.

The infection processes are often described as bat-

tles between the host and pathogen (Leroy and Raoult,

2010). The simultaneous considerations on the roles

of the host and pathogen in the infection processes

and the available genome-wide measurements make

the possibility of the systematic viewpoints on the ef-

fects of the HPIs in the innate and adaptive immu-

nity (Wang et al., 2013). In this study, the usage of

the C. albicans-zebraﬁsh infection model (Chao et al.,

2010) shed light on the infectious diseases of the hu-

man host. As for the pathogen, the C. albicans is

the most virulent member of the CUG clade of yeasts

and a common cause of both superﬁcial and invasive

infections (Lohberger et al., 2014) which may cause

life-threatening infections in immune-comprised host

(Odds, 1979), such as HIV positive patients. Investi-

gating the infection processes of C. albicans in detail

can improve the knowledge of the pathogenic mecha-

nisms and promote the control of infectious diseases.

Hence, the two datasets (GSE32119 and GSE51603)

are used for further analyses on the HPIs. To extract

the interaction information from the time course mi-

croarray data, two dynamic HP-PPINs are built up

for innate and adaptive immunity (Wang et al., 2013).

The HP-PPINs consist of the PPIs between zebraﬁsh

and C. albicans, zebraﬁsh and zebraﬁsh, and C. albi-

cans and C. albicans. Through examining the interac-

tion variation between the innate and adaptive PPINs,

the interactions of the largest difference in the net-

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

work indicates the occurrences of the most dramatic

change. By evaluating the average interaction vari-

ation per edge, the critical proteins of the high in-

teraction variations in the interface of the host and

pathogen can be identiﬁed. Moreover, taking the ad-

vantage of advances in the ontology analysis, the sig-

niﬁcantly enriched functional modules in the interface

of the host and pathogen can also be identiﬁed. Those

functional modules may imply the strategies taken by

the host and pathogen in the battles, the infection pro-

cesses. Thus, those function modules and the pro-

teins are potential drug targets of infectious diseases

(Schmidt and Volker, 2011).

2 MATERIALS AND METHODS

2.1 Overview of Microarray Data

In this study, we adapted two microarray datasets:

one is the temporal gene expression proﬁles of the

host (zebraﬁsh) and pathogen (C. albicans) in the pe-

riod that they are ﬁrstly exposed to each other; the

other is the temporal expression proﬁles of the host

and pathogen in the period that they are secondly ex-

posed to each other. In the ﬁrst set of microarray data,

the microarray experiments were performed to simul-

taneously proﬁle genome-wide gene expressions in

both C. albicans and zebraﬁsh during the infection

processes. Adult AB strain zebraﬁsh were intraperi-

toneally injected with 1 × 10

C. albicans (SC5314

strain) cells (a lethal dosage). Whereas the second

microarray data measured the genome-wide gene ex-

pression level of the host and pathogen since their

second contact (with 1 × 10

C. albicans injection,

also a lethal dosage), that is, fourteen days after their

ﬁrst contact (with 1 × 10

C. albicans, a non-lethal

dosage). Then, a two-step homogenization/mRNA

extraction procedure was performed using the whole

zebraﬁsh infected with C. albicans. This approach

could provide separate pools of gene transcripts from

both the host and the pathogen, enabling individual

estimation of speciﬁc gene expression proﬁles in ei-

ther the host or the pathogen using sequence-targeted

probes derived from the individual genome. Agi-

lent in situ oligonucleotide microarrays, which cover

6,202 and 26,206 genes for C. albicans and zebraﬁsh

respectively, were used to proﬁle time-course gene

expression at 9 time-points (0.5, 1, 2, 4, 6, 8, 12, 16,

18 hours post-infection) with three replicates for both

organisms in the ﬁrst microarray dataset (Chen et al.,

2013) and 8 time points (2, 6, 12, 18, 24, 30, 36, and

42 hours post-second infection) with two replicates

for both organisms in the second microarray dataset.

The ﬁrst set of microarray was downloaded from the

GEO database (GSE32119) and the second set of mi-

croarray (GSE51603) was prepared under the similar

condition as the ﬁrst set. Manipulation of the animal

model was approved by the Institutional Animal Care

and Use Committee of National Tsing Hua University

(IRB Approval No. 09808).

2.2 Protein Pool Selection and Database

Integration

There are two things to be completed before con-

structing the dynamic protein-protein interaction

(PPI) network. The ﬁrst is to have a protein pool from

which the nodes in the resultant networks are cho-

sen. And the second step is to have all possible PPIs

among the proteins in the protein pool through in-

tegrating the interaction information from databases.

Here, our protein pool is consisted of the union of

the differentially expressed genes in the ﬁrst and sec-

ond set of microarray data and the differentially ex-

pressed genes between the ﬁrst and second microar-

ray datasets. The criterion to select the differentially

expressed genes in the ﬁrst and second microarray

datasets is to compute the p-value of ANOVA test

whether the average expression levels are different

along the time (i.e., for the ﬁrst dataset, the null hy-

pothesis is µ

= · ·· = µ

and for the second dataset,

the null hypothesis is µ

= · ·· = µ

) and then to se-

lect those proteins with the corrected p-value<0.05

into the protein pool. Also the genes in the top 5% of

the expression difference between the ﬁrst and second

datasets were chosen into the protein pool. Next, for

the all possible interactions among the proteins in the

protein pool, the interaction information of zebraﬁsh-

zebraﬁsh, C. albicans-C. albicans, and zebraﬁsh-C.

albicans are needed. However, the lack of the infor-

mation about these three kinds of interaction infor-

mation makes it difﬁcult to collect all possible inter-

actions. Also it is impossible to consider full inter-

actions among the proteins in the protein pool. To

overcome the issue, the interaction information from

the human and yeast are used due to their similarity

to our studying subjects (zebraﬁsh and C. albicans)

and data availability. To infer the possible interac-

tions of the studying subjects (zebraﬁsh and C. albi-

cans), the orthologs information in the Inparanoid is

used to convert the interactions of human and yeast

into the interactions of zebraﬁsh and C. albicans. It

should be noticed that the interactions inferred from

the ortholog-based method were derived under many

different experimental conditions, which cannot accu-

rately reﬂect the actual condition of host-pathogen in-

teractions during C. albicans infection processes; that

EssentialProteinsandFunctionalModulesintheHost-PathogenInteractionsfromInnatetoAdaptiveImmunity-C.

albicans-zebrafishInfectionModel

is, there exist false positives interactions in the all in-

ferred possible interactions of zebraﬁsh and C. albi-

cans.

2.3 Host-Pathogen Protein-Protein

Interaction Network (HP-PPIN)

Construction

To construct the interspecies network from the protein

pool and inferred interactions, the dynamic model of

the protein-protein interaction is used to determine the

realistic interaction network with one protein by one

protein fashion. For a target protein i of the host, the

dynamic interaction model is as follows (Wang et al.,

2013):

(h)

[k+ 1] =σ

(h)

[k] +

∑

n=1

(h)

[k]

∑

m=1

(p)

[k] + β

+ ε

[k+ 1]

(1)

and of the pathogen

(p)

[k+ 1] =σ

(p)

[k] +

∑

m=1

(p)

[k]

∑

n=1

(h)

[k] + β

+ ε

[k+ 1]

(2)

where p

[k] denotes the protein activity level at time

k, the superscript of p

indicates the species of pro-

teins (h: host, zebraﬁsh; p: pathogen, C.albicans),

[k] denotes the environment noise at time k, σ

de-

notes the self-regulation ability, α

denotes the regu-

lation ability from the regulator n of the same species

as the target protein to the target protein i, γ

denotes

the regulation ability from the regulator m of the other

species to the target protein i, and β

denotes the basal

activity level of target protein i. The biological mean-

ing of this formulation is the protein activity level of

target protein i in the future (at time k + 1) is deter-

mined by the current protein activity level (at time k)

of itself (σ

), the regulations from other proteins of

the same species (α

) and the other species (γ

), the

basal activity, and the environmental noise. Due to

the unavailability of the proteomic data, the expres-

sion levels measured by the microarray experiments

are used to stands for the activity levels in the formu-

lation. The dynamic model for the host target protein

i can be further rewritten into a concise form as fol-

lows:

(h)

= Φ

+ ε (3)

where

(h)

[1] ··· p

(h)

[K + 1]



··· α

··· γ





[1] · ·· ε

[K + 1]



and







(h)

[0] ··· p

(h)

[0] p

(p)

[0] ··· p

(p)

[0] p

[0] 1

(h)

[K] · ·· p

(h)

[K] p

(p)

[K] · ·· p

(p)

[K] p

[K] 1







Similarly, the dynamic model for the pathogen can

also be rewritten into a similar form. The only un-

known parameter θ

can then be estimated by param-

eter estimation methods, such as least square estima-

tion. However, due to the lack of large scale measure-

ment of host and pathogen proteins, we alternatively

used gene expression proﬁles as a substitute of pro-

tein activities to identify the parameters in the model.

Furthermore, to make sure the model is unnecessar-

ily complex, the Akaike information criterion (AIC)

is introduced for model selection to balance the com-

peting objectives of conformity to the data and par-

simony, i.e., a trade-off between the model error and

model complexity. Hence, the ﬁnal network encom-

pass the dynamic models of each protein with the

minimum AIC values.

2.4 Relevance Score Calculation

To target the essential proteins in the host-pathogen

protein-protein interaction network, the relevance

scores are calculated for proteins or functional mod-

ules to correlate proteins with the evolution of the

host-pathogen interactions from innate to adaptive

immunity. The relevance score is basically a measure-

ment of the variation of the regulation activity under a

condition transition. According to the dynamic mod-

els, the constructed PPI network under a speciﬁc mi-

croarray experiment condition can be written as fol-

lows:







(h)

[k+ 1]

(h)

[k+ 1]

(p)

[k+ 1]

(p)

[k+ 1]













(h)

··· α

(h)

··· γ

(h)

··· σ

(h)

··· γ

(p)

··· α

(p)

··· γ

(p)

··· σ

(p)







BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms







(h)

[k]

(h)

[k]

(p)

[k]

(p)

[k]













(h)

(p)













(h)

[k+ 1]

(h)

[k+ 1]

(p)

[k+ 1]

(p)

[k+ 1]







(4)

or in a more concise form:

p[k+ 1] = Ap[k] + β + ε[k+ 1] (5)

where A is a matrix representation of the network con-

structed under a speciﬁc microarray experiment con-

dition. The regulation ability difference of two PPI

networks between innate and adaptive immunity can

be expressed as the following interaction difference

matrix form (Wang et al., 2014):

cond2−cond1

= A

cond2

− A

cond1

(6)

In the condition transition, if the variation of the reg-

ulation abilities of a protein is larger, it may implies

the protein plays a more important role in the condi-

tion transitions. So the relevance score of a protein

can be deﬁned as follows:

∑

q=1

Degree of protein p

(7)

where d

is the pq-entry of D

cond2−cond1

, that is, the

average regulation ability variation of the protein p.

The degree of protein p is the number of non-zero ele-

ment in pth row of the difference matrix D

cond2−cond1

The relevance score is proposed to evaluate the inter-

action variations of proteins in the network.

3 RESULTS

3.1 Overview of the Host-Pathogen

Protein-Protein Interaction

Networks (HP-PPINs) for Innate

and Adaptive Immunity

In this study, we aimed to understand the roles of

host-pathogen interactions in the innate and adaptive

immunity and the transition from innate to adaptive

immunity with a systems biology approach. The out-

comes of host-pathogen interactions are represented

in the simultaneous measurements of the temporal

gene expression proﬁles during the periods when in-

nate and adaptive immunity are activated (Figure 1A).

The HPIs in these two periods are then extracted by

Primary response

Day

Magnitude of response

Data 1

Data 2

Secondary response

(A)

Innate Adaptive

PathogenHost

0.5 1 2 4 6 8 12 16 18 2 6 12 18 24 30 36 42

hours post-reinjectionhours post-injection

(B)

(C)

CFU C. albicans

CFU C. albicans 10

CFU C. albicans

Temporal gene expression

proﬁles of innate immunity

(Data 1)

Inferred PPIs from databases

and ortholog information

Innate dynamic PPI

network

Adaptive dynamic PPI

network

Temporal gene expression

proﬁles of adaptive immunity

(Data 2)

Protein pool

Feature selection

Parameter identiﬁcation

Figure 1: The overview of microarray experiments. (A) The

schematic of two microarray datasets. In the ﬁrst dataset,

gene expression proﬁles of the C. albicans and zebraﬁsh

are recorded at 9 time points (+). It mainly represent the

outcomes of HPIs in the innate immunity (green dot line).

In the second dataset, gene expression proﬁles of the C. al-

bicans and zebraﬁsh are recorded at 8 time points (×). It

mainly represents the outcomes of HPIs in the adaptive im-

munity (yellow dash line). (B) The overview of microarray

data. (C) Flowchart for network construction. Features se-

lected from the two microarray datasets consist the protein

pool. The parameters in the dynamic models are identiﬁed

based on the expression proﬁles and the interaction infor-

mation of the selected features. In the end, two dynamic

HP-PPINs are constructed for innate and adaptive immu-

nity.

the dynamic models of the microarray data and fur-

ther visualized as two HP-PPINs (Figure 1B). The

protein pool encompassed 1620 proteins of interest

including differentially expressed features and the top

5% of the expression level difference between innate

and adaptive immunity (see Figure 1C) and there are

EssentialProteinsandFunctionalModulesintheHost-PathogenInteractionsfromInnatetoAdaptiveImmunity-C.

albicans-zebrafishInfectionModel

26060 PPI candidates. Then using dynamic network

construction (see Figure 1C and methods for details),

the resultant networks are consisted of 1512 proteins

(1431 for the C. albicans; 81 for zebraﬁsh) and 5722

PPIs (5510 for the intracellular region of C. albicans;

145 for the interspecies interaction; 66 for intracellu-

lar region of zebraﬁsh) for innate immunity and 1578

proteins (1480 for the C. albicans; 98 for zebraﬁsh)

and 3755 PPIs (3577 for the intracellular region of C.

albicans; 96 for the interspecies interaction; 82 for

intracellular region of zebraﬁsh) for adaptive immu-

nity. The details of the amount of nodes and edges

are summarized in the Figure 2B. In the amount vari-

ation of the nodes and edges of the pathogen, al-

though there exists plenty of nodes shared by both in-

nate and adaptive immunity in the host-pathogen and

pathogen-pathogen regions (Figure 2B), the number

of edges has changed from 5511 to 3577, that is, only

1203 edges are shared (Figure 2B). This implicates

that the pathogen may use the almost the same set of

protein (∼85%) but the different links to interact with

the host and regulate functions within the pathogen it-

self under different challenges at innate and adaptive

immunity. In contrast, the host might use a slightly

different strategy for self-regulation. To efﬁciently

identify and evaluate the importance of proteins in the

innate and adaptive immunity, we aggregated the two

networks (the innate and the adaptive immunity net-

work) into an interaction difference network (IDN),

i.e., the matrix D in the method (Figure 2A).

3.2 The Essential Proteins and

Functional Modules based on the

Relevance Scores

The relevance score stated in the previous section is

a quantity to represent the average interaction varia-

tions per links of a protein, that is, the ratio of the to-

tal interaction variation of a protein to the number of

links possessed by the protein. Hence, the relevance

score is considered to evaluate the extent of the inter-

action variations which may be critical in this study

for the observation of the amount of the nodes and

edges in the IDN (Figure 2A), the difference between

innate HP-PPIN and adaptive HP-PPIN. Comparing

to the other similar calculation (Wang et al., 2014),

our proposed relevance score is more proper to evalu-

ate the interaction variation of proteins in the network

since the consideration of degree of protein excludes

the proteins have many links of little variations. In

the following we would focus on the proteins of the

top ten in relevance scores at three regions, that is,

the host-host, host-pathogen, and pathogen-pathogen

region, respectively.

Figure 2: Result summary of dynamic HP-PPI network con-

struction. (A) The difference of the constructed innate and

adaptive networks (Node color: blue and red stand for ex-

clusive existence in the innate and adaptive immune re-

sponses respectively and purple stands for coexistence in

both immune responses. Edge color: blue and red stands

for the attenuated and enhanced interaction, respectively.).

(B) The number of the nodes and edges in the two dynamic

HP-PPINs.

3.2.1 The Host-host Region

In the region of host-host interaction (Figure 3), the

top ten proteins in the relevance scores showed their

close relationships with innate and adaptive immune

responses. Extracting the ten proteins and their ﬁrst

neighbors from the IDN, there are ﬁve components

in the host-host region. The biggest one is consisted

of f2, LOC798231, LOC793315, ace2, gnai1, and

their ﬁrst neighbors. Starting from gnat2, a host G-

protein, also one end of HPIs has connections with

chemokines-related proteins (cck-c5a and si:dkey-

269d20.3) and chemotaxis-related proteins (ENS-

DARP00000105159 and ENSDARP0000111107).

Then angiogenesis- and coagulation-related proteins

(agt, ace2, f2, and ENSDARP00000098661) are con-

nected to the chemokines-related proteins. In the ﬁ-

nal part of the components, there are three more pro-

teins, serine proteinase inhibitor (serpinc1), proki-

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

Angiogenesis;

Coagulation

Chemokine

Complements

system

Figure 3: The ten proteins highest in relevance scores in the

host-host region and their ﬁrst neighbors. The nodes with

shadow are the proteins of higher relevance scores (The

meaning of node color, edge color, and line style are the

same as Figure 2.).

neticin (ENSDARP00000109666), and suppressor of

IKBKE 1 (sike1). In this component, the roles of

angiogenesis and coagulation are manifest in the in-

nate and adaptive immunity. The second compo-

nent mainly consists of complements (c7b, c8g, c8a,

c8b, c9) and vitronectins (vtna and vtnb). Given

the well-known roles of complement system in im-

munity, the vitronectins got researchers’ attention in

the ﬁeld of immunity recently (Gerold et al., 2008).

The cd36 and apolipoproteins form the third compo-

nent. CD36 plays a pivotal role in macrophage foam-

cell formation and atherogenesis, which is reduced by

apolipoproteins. Although the last two components

are less documented, the versican (vcanb) and tank

are reported their roles on the inﬂammation (Wight

et al., 2014).

3.2.2 The Pathogen-Pathogen Region

In the pathogen-pathogen region (Figure 4), the ten

proteins with the highest relevance scores and their

ﬁrst neighbors form a single component. In this com-

ponent, the importance of redox status in the innate

and adaptive immune responses is emphasized again

(Wang et al., 2014). ERG1, CAL0005908, MET10,

and GCV3 are all related to the redox status of C. al-

bicans. Also CAL0005225,ERG1, and SDS24 are re-

sponsible to the expansion of C. albicans due to their

functions on the budding, ﬁlament growth, and cell

cycle, respectively. Especially, MET10 is also respon-

sible to the responses to the stress from the host and

environment. Another major function in this compo-

nent is the transferase activity. MET2 is a homoser-

ine acetyltransferase which can transform homoser-

ine, a toxin for C. albicans, to another compound.

ARG3 would facilitate the production of citrulline,

which can induce the pseudohyphal morphogenesis.

The morphology transformation of C. albicans has

been proven to be important in the pathogenesis of

C. albicans. In the end, the hydrolase, CAF16, exerts

SSD1

CAL0003938

CAL0001390

KAR2

CAL0004978

CAL0004601

NPL3

NAM7

CCR4

CAL0005908

CAL0004040

GDH2

CAL0002811

BUD14

SMT3

MHP1

ARP9

CAL0004008

CAL0000592

CLC1

HAT2

SDS24

ERG24

BNI1

ERG1

CAL0004558

CAL0004693

CDC7

ACT1

CAL0003932

SEC2

PGA4

GNP1

PHR2

CAL0000788

FUM11

GCV3

SCL1

ACH1

CAL0003509

GAP2

CAF0007087

ARG3

CAL0005816

RPN1

UAP1

RPN10

KSP1

CAL0005225

ZDS1

CDC28

CAF16

CLA4

MKC1

AAT21

ISW2

CAL0005376

CAL0004824

FGR32

PEX13

MFG1

RBK1

CYP5

CAL0002898

MET10

CAL0000831

CAL0003798

AKL1

OFD1

HAT1

MET2

PHO85

Redox status

Budding, ﬁlament growth, and cell cycle

Figure 4: The ten proteins highest in relevance scores in

the pathogen-pathogen region and their ﬁrst neighbors (The

meaning of node color, edge color, and line style are the

same as Figure 2.).

its inﬂuence on the RNA polymerase II although the

speciﬁc affected genes are still unknown.

3.2.3 The Host-Pathogen Region

In the host-pathogen region, we also selected 10 pro-

teins from the host and the pathogen, respectively.

Those interspecies proteins form more complicated

interaction networks in the host-pathogenregion (Fig-

ure 5). Meanwhile, the functional structure is slightly

different comparing to the host-host and pathogen-

pathogen regions. A possible mechanism how redox

status in the host and pathogen correlates is shown

in the extracted IDN, i.e., the interaction between

thioredoxin (txn) and RiboNucleotide Reductase 1

(RNR1). In addition to the redox role of RNR1,

RNR1 also has impact on the iron utility, ﬁlament

growth, and cell cycle. This implicates the effect of

redox status on the pathogen is multifaceted. One

of the interactions of RNR1, the link between RNR1

and CAL0003932, is attenuated in the adaptive im-

mune response. This implies the ability of adap-

tive immunity to attenuate the deubiquitination and

degradation of proteasome in the pathogen. How-

ever, CAL0003932 has not been well-characterized.

A group of chemokine-related host proteins consti-

tutes another similar function in the host-pathogen re-

gion. Comparing to the chemokine-related function

in the host-host region, its roles in the HPIs are more

interesting. CAG1, the entry how chemokine-related

functions affect the pathogen, is related to the hyphal

growth, mating, and bioﬁlm formation of pathogen,

which are all important in the pathogenesis. Besides

the same functions (redox status and chemokines) in

the pathogen-pathogen and host-host regions, there

are several functions which can only be seen in the

host-pathogen region, that is drug responses, fatty

acid, glycine metabolism, and circadian. The inter-

actions among TAF60, gtf2a2, polr2e emerge in the

adaptive immunity. TAF60, a transcription factor, is

EssentialProteinsandFunctionalModulesintheHost-PathogenInteractionsfromInnatetoAdaptiveImmunity-C.

albicans-zebrafishInfectionModel

RPM2

per1b

cry2b

AMS1

DOG1

per2

arntl1b

cry2a

pygb

gys2

azi1

CAR2

HRR25

DCP2

SES1

NIP1

CAL0001603

ERG20

IPL1

EMP24

CAL0003394

HEM15

FAS2

CAL0001016

CAF0006974

CAL0004978

mhc1uba

PDX3

LAT1

ORC1

SHM1

VMA4

OLE1

actr3b

CAL0006149

CAL0005659

FUM11

SEC24

NSP1

UAP1

CAL0002752

CAL0000821

DPS1-1

CDC60

FAA4

GCV1

CDC28

CAL0002889

shmt1

RPN10

SYS1

RIC1

RPN1

VPH1

CAL0002020

GLN3

CAL0003403

YME1

PHR2

CAL0001938

TRK1

CAL0002617

CAL0000581

RHO3

SNZ1

ARF2

CAL0003207

CAL0000524

MES1

ERG26

UBP13

CAL0004665

CAL0006275

VAC8

CAL0000165

polr2eb

GSC1

gtf2a2

ADE2

FUN31

CAL0001155

PAP1

CAG1

gnai1

gnat2

STE18

ENSDARP00000111107

ENSDARP00000105159

ccl-c5a

si:dkey-269d20.3

CAL0000531

CHO1

CAL0003208

CAL0003987

CAL0004670

CAL0002919

ADE13

CAL0000243

GNP1

ERG1

CAL0002715

HEM13

CAL0003071

TPK2

CAL0006120

BRE1

BCY1

ppp1r1b

fabp1b.1

CMP1

GCV2

ABP1

CMD1

CUE5

CAL0001326

TAF60

SCS7

TAF19

CAL0003530

JAB1

si:ch211-204c21.1

GLO3

CAF0007003

HIS1

GRE3

MET15

GLN4

PGK1

CAL0006081

RPT6

GPA2

ARO8

CAL0004190

CAL0000101

GDE1

SIN3

FBA1

CAL0004479

MNT1

TKL1

CAL0003932

ARG1

ARO3

CTA3

MNT2

CAL0003043

SEC18

CAL0001235

CAL0002259

RPN2

CAL0000804

CAL0005635

ARG5,6

HSP70

ILV5

CAL0003768

SPT5

CAL0004266

SSD1

GVP36

CAL0001316

CAL0003837

CAL0006116

GDI1

CAL0005180

LEU1

CAL0004574

CAL0003861

CAL0001798

eif5b

NAM7

ISN1

NPL3

SSU81

CAL0001588

CAL0005131

CAL0000927

MCM1

CAL0000380

CCT3

CAL0000827

dusp4

HEM14

CAF0007058

NTH1

CEK1

CAL0004008

txn

PKC1

CAL0000331

smurf1

CAL0004305

URA7

THR1

CRM1

LEU4

AKL1

ESA1

POL3

ENSDARP00000076901

RNR1

RAD52

SHM2

HSM3

CAL0004051

CAL0006158

CAF0007423

GLK1

ILV2

CEF3

MSH6

MET13

ARG3

SMT3

pold1

POL2

RAD32

RSP5

PMS1

Chemokines

Circadian

Figure 5: The ten host and pathogen proteins highest in rele-

vance scores in the pathogen-pathogen region and their ﬁrst

neighbors (The meaning of node color, edge color, and line

style are the same as Figure 2.).

responsible for the drug responses in the pathogen and

gtf2a2 and polr2e are related to the gene transcription

in the host. Their interactions stand for a possible

mechanism how the effects of HPIs are exerted into

the gene level. Other proteins for drug response are

SEC24 and LEU4, which are not well-characterized.

The fatty acid binding protein (fabp1b.1) of the host

shows the involvement of fatty acid in the innate and

adaptive immune responses. This is another proof for

the hypothesis of a local and systemic crosstalk be-

tween adipocytes and monocytes mediated by fatty

acids (Kopp et al., 2009). This fatty acid binding

protein links to the protein of the pathogen, SIN3,

related to the ﬁlament growth. The interaction be-

tween fatty acid binding protein and SIN3 becomes

more negative in the adaptive immunity. The third

function, glycine metabolism, has been implicated the

contribution to the infectious capacity of the pathogen

(Flynn et al., 2010). The ﬁnal and interesting function

is the circadian rhythm in the host and pathogen. The

circadian rhythm-related proteins of the host (cry2a,

cry2b, and per2) and the pathogen (HRR25) form a

sub-network in the host-pathogen region. The circa-

dian rhythms in the host and pathogen are correlated

and there are plenty of the pathogens functions (yeast-

hyphal switch, gene transcription, pathogenesis, etc.)

are affected through HRR25.

4 CONCLUSION

In this study, the dynamic network modeling is used

to identify the complex and dynamic HPIs during two

different types of immune system. Based on the high-

throughput expression level measurements, two HP-

PPINs are constructed and then compared with each

other. We found that the pathogen may change the in-

teractions between proteins rather than recruit a whole

new set of proteins to react with the host. Hence,

we proposed the relevance score to quantify the dif-

ference of the regulation of a protein between the

innate and adaptive HP-PPIN. The relationships be-

tween the HPIs and several proteins of higher rele-

vance scores are veriﬁed by literatures. Also, sev-

eral not well-known proteins but with higher rele-

vance scores are suggested their roles in the HPIs.

Moreover, the circadian-, redox status-, angiogenesis-

, and coagulation-related functions are correlated in

the HPIs. The interplays among the four functional

modules cause the changes in the mechanisms of the

pathogenesis, responses to stresses, and cell cycle in

the C. albicans. In the end, these proteins of higher

relevance scores and the four functional modules are

regarded as the essential components in the HPIs as

well as the potential targets for future drug develop-

ments.

ACKNOWLEDGEMENTS

Ministry of Science and Technology, R.O.C. (Taiwan)

[MOST 103-2745-E-007-001-ASP].

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EssentialProteinsandFunctionalModulesintheHost-PathogenInteractionsfromInnatetoAdaptiveImmunity-C.

albicans-zebrafishInfectionModel