Abstract
Candida albicans causes two types of major infections in humans:
superficial infections, such as skin and mucosal infection, and
life-threatening systemic infections, like airway and catheter-related
blood stream infections. It is a polymorphic fungus with two distinct
forms (yeast and hyphal) and the morphological plasticity is strongly
associated with many disease causing proteins. In this study, 137
hyphae associated proteins from Candida albicans (C. albicans) were
collected from different sources to create a Protein-Protein
Interaction (PPI) network. Out of these, we identified 18 hub proteins
(Hog1, Hsp90, Cyr1, Cdc28, Pkc1, Cla4, Cdc42, Tpk1, Act1, Pbs2, Bem1,
Tpk2, Ras1, Cdc24, Rim101, Cdc11, Cdc10 and Cln3) that were the most
important ones in hyphae development. Ontology and functional
enrichment analysis of these proteins could categorize these hyphae
associated proteins into groups like signal transduction, kinase
activity, biofilm formation, filamentous growth, MAPK signaling etc.
Functional annotation analysis of these proteins showed that the
protein kinase activity to be essential for hyphae formation in
Candida. Additionally, most of the proteins from the network were
predicted to be localized on cell surface or periphery, suggesting them
as the main protagonists in inducing infections within the host. The
complex hyphae formation phenomenon of C. albicans is an attractive
target for exploitation to develop new antifungals and anti-virulence
strategies to combat C. albicans infections. We further tried to
characterize few of the most crucial proteins, especially the kinases
by their sequence and structural prospects. Therefore, through this
article an attempt to understand the hyphae forming protein network
analysis has been made to unravel and elucidate the complex
pathogenesis processes with the principal aim of systems biological
research involving novel Bioinformatics strategies to combat fungal
infections.
Keywords: Bioinformatics, Microbiology
1. Introduction
Candida albicans is a pathogenic fungus belonging to the family
Saccharomycetaceae which causes life-threatening infections in humans
with mortality rate of 40–60% [[29]1, [30]2, [31]3]. It is an
opportunistic pathogen causing circumscribed infections of the skin,
nails, and mucocutaneous membranes in healthy people, whereas, becomes
aggressive in immune deficient patients due to malignancy, inherited
disease, concurrent infection, or medical intervention [[32]4, [33]5,
[34]6, [35]7, [36]8]. Among Candida spp., C. albicans, the main
pathogen in this genus, is responsible for the majority of all forms of
candidiasis [37][9]. In nosocomial urinary tract infections,
approximately 80% is caused by C. albicans [[38]10, [39]11]. Indeed, in
the United States, the fourth most common hospital borne systemic
infections are caused by Candida sp. with crude mortality rates of up
to 50% [[40]12, [41]13]. Approximately 75% of women are prone to get
infected from vulvovaginal candidiasis (VVC) at least once in their
lifespan with 40–50% chance of additional episode [[42]14, [43]15].
Furthermore, 5–8% amongst them suffer from at least four recurrent VVC
in a year [44][16].
C. albicansis a polymorphic fungus that can grow either as ovoid-shaped
budding yeast (blastopore), as elongated ellipsoid cells with
compressions at the septa (pseudohyphae) or as parallel walled true
hyphae [45][17]. The yeast form is believed to be primarily involve in
dissemination, whereas, hyphal form shows more invasiveness [[46]18,
[47]19]. Candida species infect the host by these significant virulent
morphological structures-pseudohyphae, (e.g., Candida tropicalis,
Candida parapsilosis, Candida guilliermondii, and Candida lusitaniae)
[[48]20, [49]21] and hyphae (C. albicans, C. dubliniensis, and
C. tropicalis) [50][17]. The genome sequences of different Candida
species have indicated that many of the genes involved in yeast to
filamentous transition are evolutionarily conserved [51][22]. It is
significantly noted that the approximately 85% of identified
filamentous genes of C. albicans are homolog to other Candida species
[52][23]. C. albicans shows greater expansion in the number of genes
relative to most of the other Candida species belonging to the same
family. Consistent with this thought, less pathogenic Candida species
have reduced the ability to produce virulent factors that are required
for adhesion and invasion in the host cell in comparison to pathogenic
hyphae forming C. albicans [53][24]. The hyphae formation is an
important part in the infection process of C. albicans, as it helps by
promoting tissue penetration in host epithelial and endothelial cells
and also avoids host immune system [[54]25, [55]26]. C. albicans shows
morphological plasticity and the transition from yeast to filamentous
form in the host is a critical virulence determinant of infections
[56][27]. The hyphal morphogenesis has always been associated with
virulent proteins that govern simultaneously in a co-regulated fashion
both virulence and hyphal growth [[57]28, [58]29, [59]30]. Therefore,
it is necessary to understand the mechanisms of hyphae formation in
C. albicans and the role of virulent proteins to elucidate the complex
pathogenesis processes.
In this work, we have implemented several bioinformatics and systems
biology approach combining text mining methods to analyze and interpret
the Protein-Protein Interaction (PPI) network of proteins that are
involved in C. albicans phenotypic plasticity by conversion from yeast
to hyphae formation and development. We used Saccharomyces cerevisiae,
a fungal model organism as a control for the validation of interactions
and different aspects related to Candida hyphae forming proteins. With
the limitation of antifungal drug availability and enhancing
populations of susceptible patients, it is essential to understand the
mechanisms of hyphae formation in order to develop new strategies for
treating candidiasis. We have tried to infer the essential proteins and
their regulation through the network analysis, which will be highly
beneficial to understand the resistance mechanisms as well as for
further development of anti-fungal therapy.
2. Materials and methods
2.1. Dataset preparations and validations
Proteins that are known to participate in hyphae formations in
C. albicans were collected from literature and Candida Genome Database
(CGD). The CGD is a well-organized repository containing various kinds
of genomic, proteomic, morphology and annotation related information
from four Candida species including C. albicans [60][31]. The proteomic
data of C. albicans revealed that more than 70% of enlisted proteins
are uncharacterized. Orthology analyses of these proteins were carried
out by using NCBI BLAST [61][32] with other Candida species and
Saccharomyces cerevisiae. We used Saccharomyces cerevisiae, a fungal
model organism, as control for the validation and analyses of different
aspects of protein-protein interactions (PPI) amongst the hyphae
forming Candida proteins. All the PPI data were retrieved from several
online resources (eg. STRINGS, BIOGRID, CGD & UNIPROT) and literatures
[[62]33, [63]34, [64]35]. The STRINGS and BIOGRID are known for storing
information about both physically and functionally interacting
proteins, whereas the CGD & UNIPROT contain information of interactions
validated by experimental findings. Following the aforementioned
databases, we built a full interaction dataset containing the details
of the proteins that have well-defined roles in hyphae developments
using an in-house pipeline written in Perl. The final datasets
consisted of a total of 137 proteins and 714 unique interactions. On
the basis of their roles, the proteins were classified as either
promoting hyphae formation or suppressing the process. The interactions
were also marked as either physical or functional.
2.2. Building and visualizing the network
The total PPI datasets were arranged in a network form by Cytoscape
3.6.1 [65][36]. The Cytoscape defines PPI networks as graphs in terms
of nodes and edges, which represent the proteins and their associated
interactions, respectively. All the edges are considered as
‘unidirectional’ in the network, and duplicate edges including
‘self-loops’ were removed. The same Cytoscape was further used for
visualization and analyzing the network using its diverse plugins
integrated for multiple functions. Independent colour codes were used
for distinguishing the nodes for their functions and topological
attributes.
2.3. Network topology analysis
Topological analyses were performed by employing “Network Analyzer”
plugin of Cytoscape [66][37]. The quality of network architecture can
be validated by different topological attributes such as degree k,
clustering coefficient C(n), Betweenness centrality BC(n) and Closeness
centrality CC(n). Degree k defines the number of directly connected
neighbors of a node. The clustering coefficient C(n) is a measure of
the degree of a node that has a greater probability to cluster together
in the network. Similarly, Betweenness centrality BC(n)is the relative
frequency of all paired shortest paths of a particular node in a
network gives the information about the extent of interactions that a
node mediates in a network. Closeness centrality CC(n) shows the
spreading of information of a node and is defined as the reciprocal of
the average shortest path length in a network [[67]38, [68]39, [69]40,
[70]41, [71]42, [72]43]. CC(n) would represent the strength of the
interactions. A shorter path length would mean a stronger interaction.
Another important attribute is the node degree distribution P(k), which
aids to declare whether a network is random or scale-free, and is
calculated by fitting the power law using equation
[MATH: y=axb :MATH]
; where ‘
[MATH: a :MATH]
’ is a constant and ‘
[MATH: b :MATH]
’ is denoted as an exponent. In this study, power law of P(k) has been
used to evaluate the robustness of the network [73][38].
2.4. Identification of hub proteins
Hub proteins are the ones which have the maximum number of interacting
partners in a network [74][43]. Here, the proteins containing more than
20 interactions were considered as Hub proteins, estimated from
topological parameter degree k. Additionally, the Cytohubba plugin of
Cytoscape was used to identify the hub proteins/nodes from the network
[75][44]. There are a total of 11 different methods implemented in
Cytohubba to analyze the network feature to rank the nodes accordingly.
2.5. Modular analysis and sub-network generation
Molecular complex detection (MCODE) plugin of Cytoscape was used to
identify highly connected local sub-networks from the total PPI network
[76][45]. Module identification is based on the principle of two
interacting proteins having high probabilities of interactions with
each other. The MCODE algorithm generates the modular clusters from PPI
network through vertex weighting using local neighborhood density and
outward traversal from dense protein node to discover dense regions.
The parameters set for modular analysis were of degree cutoff = 2,
haircut = true, node score cutoff = 0.2, k-score = 2, and maximum depth
= 100. Top five clusters were considered further on the basis of MCODE
score ≥4.
2.6. Functional enrichment and ontology analysis
Functional annotations of top scoring clusters were performed online at
DAVID Bioinformatics Resources server [77][46]. Overall annotation
analyses of the whole PPI network were executed using ClueGO plugin of
Cytoscape [78][47]. ClueGO is known for integrating Gene Ontology (GO)
terms as well as KEGG/BioCarta pathway terms and generates functionally
organized networks on the basis of their annotations. Several gene
ontology (GO) terms such as biological processes, molecular function
and cellular components for C. albicans were retrieved and subjected to
ClueGO analyses. Finally, two-sided hypergeometric test
(enrichment/depletion), with Bonferroni steps down for pV correction at
0.05 significance level (p-value) and kappa score of 0.4 were set as
threshold to analyze the network.
2.7. Structure modeling of candida kinase domain
Amino acid sequence of Candida Chk1 kinase domain (AA, 358–637) was
downloaded from Uniprot (ID: [79]Q5AHA0), and subjected to the
template-based threading and modeling server I-TASSER [80][48]. The
best modeled structure generated by I-TASSER was further refined by
Smart Minimizer of Discovery Studio (DS) 2.5 with RMS gradient of 0.1,
consequently, its stereochemistry was checked through SAVES server
[81]http://services.mbi.ucla.edu/SAVES.
3. Results
3.1. Construction of network
PPI network analysis is a crucial approach towards the understanding of
the mechanisms of complex biological reactions and their possible
outcomes. In the present work, we focused on building comprehensive
network of proteins to analyze their modes of interactions leading to
hyphae formation in C. albicans. For this, we collected the information
of a total of 137 potent proteins having association with hyphae
formation and regulation from published literatures [[82]49, [83]50,
[84]51, [85]52, [86]53] and other online resources [Candida Genome
Database; Saccharomyces Genome Database; STRING; BioGRID; UniProt;
PubMed; DAVID]. We used the aforementioned collected information to
analyze the modes of interactions (i.e. physical or genetic) and
integrated them within the network through Cytoscape. Finally, the PPI
network was constructed with experimentally validated interactions
consisting of 137 nodes and 714 edges. Among the 137 proteins, 101
proteins were identified as promoting hyphal growth, whereas 36 were
supposed to be suppressing the hyphae formations. General features of
the network were presented in [87]Fig. 1.
Fig. 1.
[88]Fig. 1
[89]Open in a new tab
General representation of the network; (a) Node sizes were set on their
degrees. The nodes were colored by their betweenness centrality values
(red to grey). (b) Node sizes were set on their degrees. The nodes were
colored by their roles in hyphae formation and development
(red-promoting; green-suppressing). (c) Interactions among the hyphae
promoting nodes. (d) Interactions among the hyphae suppressing nodes.
3.2. Topology analysis
PPI networks or biological networks show distinctive topological
characteristics, which make them different from other random networks
([90]Fig. 2). The most important feature is the power law of node
degree distribution which gives information about the robustness of the
network [91][43]. It has been stated that the exponent form of the
power law in any scale free biological network should be less than 2
[[92]38,[93]43]. In our case, the exponent ‘b’ was found to be -0.833,
which signifies its reliability and the importance of hubs in the
network ([94]Fig. 2a).
Fig. 2.
[95]Fig. 2
[96]Open in a new tab
Topological attributes of the network; (a) Node degree distribution of
the network with power fitted. (b) Distribution of topological
coefficients. (c) Betweenness centrality. (d) Closeness centrality.
Other various parameters of the PPI networks, such as clustering
coefficient C(n), network centralization, and network density were
found to be 0.325, 0.280, and 0.077, respectively. The maximum value of
average clustering coefficient (0.325) was observed to justify the
network with impressive measurement of nodes to be clustered together.
Similarly, the network centralization score signified the importance of
each node with good resemblance to the ratio of actual connections to
the total possible connections within a network (density). The number
of shortest paths was 18632 in the Candida hyphae PPI network, which
would indicate that the nature of connectivity of the proteins was
relatively high. The result also reveal that the transmission of
biological information in the network was achieved through only a few
steps as these proteins were involved in hyphae formation in the
species by responding to various physiological and environmental clues.
Similarly, the value of the degree centrality could identify the
important nodes in the network on the basis of number of interactions,
which were distinguished in terms of shared pathways or biological
processes. The distribution of closeness centrality CC(n), and
Betweenness centrality BC(n) were presented in [97]Fig.2c and 2d. CC(n)
of a node in a network gives the idea about information that is passed
from one node to another by measuring the number of shortest paths
passing through the nodes from a PPI network [98][38]. Here Hog1 had
the highest value of 0.57142857. Likewise, the BC(n) analysis of nodes
revealed the proteins that could act as bridges or connect distant
proteins together in the network.
3.3. Hub protein analysis
Both the network centrality as well as CytoHubba plugin was used to
identify the hub proteins throughout the network. In this network, the
Hsp90 and Hog1 proteins exhibited the highest BC(n) presuming these two
to act as bridges, or bottlenecks, and were necessarily responsible for
keeping the other nodes of the network intact ([99]Fig. 3a). Top ten
hub proteins identified by Bottleneck, MCC and Edge betweenness
algorithms of CytoHubba were presented in [100]Fig. 3b. As observed
from other PPI networks, a node degree of less than 20 maybe considered
to have not so important roles in the said biological process (i.e. not
solely performing) [101][54]. Those were non-seed proteins and were not
considered as hubs. In our study the top five hub proteins each from
promoting and suppressing groups were presented in [102]Fig.3c and d.
From the above analysis, it could possibly be stated that the following
18 proteins, viz., Hog1, Hsp90, Cyr1, Cdc28, Pkc1, Cla4, Cdc42, Tpk1,
Act1, Pbs2, Bem1, Tpk2, Ras1, Cdc24, Rim101, Cdc11, Cdc10 & Cln3 were
the proteins that might have the highest degrees, betweenness and
closeness centrality values and could act as hubs or bottlenecks in the
PPI network, among which 10 were suggested to promote and 8 to suppress
the hyphae formation ([103]Table 1). In a cellular system, it has been
proposed that most interacting networks follow the overall broad-scale
topology, where less number of proteins is regarded as hubs and most
proteins interact with fewer partners [104][55]. The current network
would represent only the interconnection among the proteins that are
involved in hyphae formation, which is just a part of whole
interactomes. However, such investigations might pave the first step
towards the understanding of hyphae forming mechanisms in C. albicans
from a systems biology point of view.
Fig. 3.
[105]Fig. 3
[106]Open in a new tab
Representation of hub proteins. (a) Circle view of the whole network
identifying the nodes with more than 20°. (b) Hub proteins identified
by CytoHubba using different algorithms. (c) Individual interactions of
top five hyphae promoting proteins. (d) Individual interactions of top
five hyphae suppressing proteins.
Table 1.
Topological attributes for hub proteins.
Name Degree Betweenness Centrality BC(n) Closeness Centrality CC(n)
Bottleneck MCC Edge Betweenness
Hog1 48 0.12396158 0.57142857 14 26625 2275.935
Hsp90 42 0.16951442 0.56903766 14 12597 3112.285
Cyr1 38 0.0654404 0.54183267 10 31090 1201.486
Cdc28 35 0.0660763 0.51515152 7 6064 1213.161
Pkc1 34 0.07775958 0.5210728 6 2221 1427.666
Cla4 34 0.05254553 0.51711027 13 16936 964.736
Cdc42 34 0.03810069 0.50746269 14 18462 699.5287
Tpk1 31 0.04288084 0.5210728 5 27301 787.2923
Act1 31 0.04014922 0.5112782 19 4035 737.1397
Pbs2 30 0.03608228 0.50184502 4 4674 662.4706
Bem1 28 0.01798415 0.4981685 3 16672 330.189
Tpk2 26 0.02883792 0.51711027 3 29211 529.4643
Ras1 24 0.03621418 0.50746269 2 29045 664.8924
Cdc24 24 0.01369953 0.48398577 3 15754 251.5233
Rim101 22 0.03041436 0.49275362 4 813 558.4076
Cdc11 21 0.0380035 0.48398577 2 372 697.7443
Cdc10 21 0.02138152 0.46896552 2 302 392.5646
Cln3 20 0.03490596 0.48571429 3 159 640.8734
[107]Open in a new tab
Proteins that participates in suppressing hyphae were mentioned in
italics.
BC(n) value indicates the extent of interactions that a node mediates
in a network.
CC(n) represents the degree of a node that has a greater probability to
cluster together.
Bottleneck, MCC, Edge Betweenness scores are the output of three
algorithms used by CytoHubba plugins to generate the hub proteins.
3.4. Sub-network and enrichment analysis
The sub-networks generated by MCODE plugin were ranked on the basis of
their confidence score, which is an indicator of their likeliness to
form real protein complexes [108][45]. Out of five clusters detected by
MCODE, three clusters (MCODE score: 6.833, 4.333 & 4) were selected for
enrichment analysis. The first two modules contained 13 nodes of each
and edges of 41 and 26, respectively; whereas the third one had 16
nodes and 30 edges ([109]Fig. 4). All the three modules were found to
be associated with many statistically significant GO terms. The
proteins present in cluster 1 were found to belong to the following
classes: Nucleotide-binding (P value: 3.1E-10),
Serine/threonine-protein kinase (P value: 3.2E-7), Cellular response to
starvation (P value: 3.4E-7), Filamentous growth (P value: 1.3E-6) and
cAMP-mediated signaling (P value: 3.8E-6).
Fig. 4.
[110]Fig. 4
[111]Open in a new tab
Sub-networks generated by MCODE.
On the other hand, the proteins in cluster 2 were annotated with
versatile functions such as MAPK signaling pathway - yeast (P value:
1.3E-16), small GTPase mediated signal transduction (P value: 4.7E-11),
filamentous growth (P value: 2.1E-9), small GTPase superfamily (P
value: 1.4E-8), nucleotide-binding (P value: 4.2E-8) and fungal-type
cell wall organization (P value: 8.1E-8). Similarly, the cluster 3 was
detected as the largest one and associated with MAPK signaling pathway
(P value: 6.7E-15), two-component regulatory system (P value: 2.3E-11),
kinase (P value: 6.4E-8), signal transduction histidine kinase (P
value: 7.6E-7), phosphoprotein (P value: 1.4E-6), and cellular response
to farnesol (P value: 2.0E-6). Total 12 previously identified hub
proteins were rediscovered in these clusters, where Cyr1, Ras1, Tpk2,
Tpk1, Cdc28, Hsp90 were detected in cluster 1, Hog1 & Cdc42 in cluster
2, and Pbs2, Cdc24, Rim101 & Bem1 in cluster 3.
3.5. Classification of interactions on the basis of enrichment analysis
The ClueGO plugin of Cytoscape was used to create the network of
over-represented nodes based on predefined kappa score level. It
generates a dynamical network structure from a gene list of interest
and projects functionally grouped terms by means of kappa statistics to
link the attributes in the network [112][56]. The ontology and pathway
enrichment analysis of the whole set of proteins produced three
different functional characterization terms such as biological process,
molecular function and cellular components.
Annotations of the proteins against 6971 reference gene sets were
functionally grouped in important biological process such as
intracellular signal transduction (GO:0035556), regulation of
filamentous growth of a population of unicellular organisms
(GO:1900428), cellular response to oxygen-containing compound
(GO:1901701), single-species biofilm formation (GO:0044010),
interaction with host (GO:0051701), signal transduction by protein
phosphorylation (GO:0023014), negative regulation of filamentous growth
of a population of unicellular organisms (GO:1900429), cellular
response to abiotic stimulus (GO:0071214), filamentous growth of a
population of unicellular organisms in response to chemical stimulus
(GO:0036171), positive regulation of response to external stimulus
(GO:0032103) ([113]Fig. 5 and supplementary table Table S1). In total,
130 proteins were clustered in any category of biological process. Out
of 137, seven proteins like Tsp1, Ydr174, Yel1, Yer67, Yer73, Ylr63 and
Ymr90 could not be grouped and remained un-annotated.
Fig. 5.
[114]Fig. 5
[115]Open in a new tab
ClueGO analysis of top scoring clusters from biological process.
Based on molecular function ontology, annotations of these proteins
were classified into major molecular functions such as protein kinase
activity (GO:0004672), purine nucleoside binding (GO:0001883), protein
serine/threonine/tyrosine kinase activity (GO:0004712), protein kinase
regulator activity (GO:0019887), calcium ion transmembrane transporter
activity (GO:0015085), MAP kinase activity (GO:0004707),
dolichyl-phosphate-mannose-protein mannosyltransferase activity
(GO:0004169) and actin binding (GO:0003779) ([116]Fig. 6 and Table S2).
Fig. 6.
[117]Fig. 6
[118]Open in a new tab
ClueGO analysis of top scoring clusters from molecular function point.
Similarly, the major cellular components were functionally categorized
into cell cortex (GO:0005938), hyphal tip (GO:0001411), fungal-type
cell wall (GO:0009277) ([119]Fig. 7 and Table S3).
Fig. 7.
[120]Fig. 7
[121]Open in a new tab
ClueGO analysis of top scoring clusters of cellular components.
3.6. Enrichment of proteins solely present in C. albicans and morphology
analysis
We prepared a list of 17 proteins that were unique in C. albicans and
were not present in any yeast family. Functional annotations of their
biological processes suggested that they can be grouped in only five
categories such as MAPK cascade (GO:0000165), adhesion involved in
single-species biofilm formation (GO:0043709) cell adhesion involved in
single-species biofilm formation (GO:0043709), negative regulation of
response to stimulus (GO:0048585), and regulation of filamentous growth
of a population of unicellular organisms in response to pH (GO:1900741)
([122]Fig. 8). From our dataset, 95 proteins of C. albicans have the
potentiality to contribute towards virulence, 19 belong to cell
adhesion group, 17 showed resistances to drugs/chemicals and 10 were
found to be involved in host cell induction. By a comparative study, we
observed that Bcr1 was a unique virulence protein in C. albicans which
involved in symbiotic interaction and has roles in biofilm formation.
Similarly, the Mkc1 was also a unique virulence protein which has shown
resistance and participates in induction of host cell. The details were
presented in [123]Fig. 9 and Table S4.
Fig. 8.
[124]Fig. 8
[125]Open in a new tab
ClueGO analysis of unique genes from C. albicans in different
biological process.
Fig. 9.
[126]Fig. 9
[127]Open in a new tab
Venn diagram of proteins involved in different morphological features;
(a) Categorization of proteins into virulence, cell adhesion, host
resistant protein, induction to host, and proteins that are unique in
C. albicans. (b) Small categorization of virulence, showed resistances
and that were unique in C. albicans.
3.7. Characterization of crucial proteins
The top 20% of nodes having higher degrees, functional enrichment
analysis and multiple morphological features reveals that the proteins
with kinase activity were predominant. Among them Hog1, Ssk2, Pbs2,
Chk1, Cdc28, Tpk2, Pkc1& Cla4 were the leading kinases showing variable
roles in candida hyphae formation. Sequence alignment of this large
family of kinase proteins showed <30% of sequence similarity among
themselves ([128]Fig. 10a). Additionally many of them are already
declared as drug target due to their virulence property in candidiasis
[[129]57, [130]58, [131]59, [132]60, [133]61, [134]62]. Among them, a
histidine kinase protein Chk1 that promotes hyphae formation, has been
least studied. It is a large multifunctional protein of 2,471 amino
acid lengths, an essential virulent protein in Candida, and a non-human
homolog. structure of Candida Chk1 kinase domain is shown in
[135]Fig. 10b.
Fig. 10.
[136]Fig. 10
[137]Open in a new tab
Characterization of kinase proteins in hyphae formation; (a) Sequence
alignment of kinase domains, (b) Structural representation of modeled
Chk1 kinase domain.
4. Discussion
Recent studies have revealed the pathological importance of C. albicans
through its hyphae formation. The proteins responsible for hyphae
formation are considered as the integral components for the major
virulence strategy of C. albicans. Expressions and the interactions of
these proteins are believed to exert various cellular functions,
adaptation to adverse conditions, and inducing pathogenesis. Network
analysis in the article is an informative tool to direct novel
experimentation to provide further insight into the mechanism of
pathogenesis and virulence of C. albicans. Hence, the understanding of
these PPIs is essential to study the pathogenic mechanisms in
C. albicans and also for developing new therapeutic strategies. In this
work, we constructed a network of 137 proteins that have role in hyphae
development and studied their functions by network topology, hub,
clustering, and functional enrichment analysis.
Topological analysis confirmed our network as biologically scale-free
and robust. From the average clustering coefficient and number of
shortest path values, it was ascertained that the connectivity among
proteins were very high. Centrality analysis of our predicted hyphae
network yielded information regarding hubs which further helped to
identify the hub proteins using CytoHubba. Out of the total 137
proteins, 18 possesses more than 20° among which Act1, Cdc28, Cdc42,
Cla4 & Cyr1 were identified as interactome with the largest connections
and were also involved in the promotion of hyphal growth, whereas Hog1,
Hsp90, Pbs2, Pkc1 &Tpk1 were recognized as major suppressor of hyphal
growth with the largest number of connections.
Modular analysis by MCODE produce three large clusters of proteins that
were highly connected in the network based on their functional
properties. Ontology and functional enrichment analyses of these
clusters revealed that the proteins in these clusters were represented
in groups such as nucleotide-binding, kinase activity, GTPase activity,
filamentous growth, MAPK signaling, and other signaling pathways.
Twelve out of the 18 identified hub proteins were reestablished within
these clusters suggesting that these proteins were the key players in
hyphae development in C. albicans.
Functional annotation analyzed by ClueGO provides a broad
classification of these proteins and their involvement in various
biological activities in addition to hyphal growth. Similarly, the
ClueGO predicted three large clusters of proteins categorized on the
basis of molecular function, biological process and cellular
components. Within the molecular function, protein kinase activity is
the largest one having 21 numbers of nodes and also kinase regulatory
activity showed six nodes. Hence, the pathways related to kinase
activity can be considered as one of the most important paths in
Candida hyphae formation. Additionally, most of the proteins from the
network were predicted to be localized on cell cortex or periphery of
the fungi, which means they would act as the main protagonist in
inducing infections within the host. Regulation of filamentous growth
is the biological process that covered maximum number of protein nodes.
Cyr1 or Cdc35 is an essential enzyme of C. albicans that is associated
in integrating the environmental signals from a range of sources
responsible for hyphae formation. Induction of hyphae is further
transmitted through interaction of Cyr1 with Ras1 and Cap1 [138][63].
This study postulated Cyr1 as one of the hub proteins having 38
connections. Cyr1 and its interacting partners were predicted to be
involved in biological processes such as interaction with host,
cellular response to oxygen containing compound and regulation of
response to stress. Additionally, Cyr1 was found to bear the highest
numbers of biological activities from the list of proteins considered.
Act1 is a hyphal tip associated protein and is required for hyphae
elongation through hyphal tip polarization [139][64]. It also helps in
localizing the Cdc42 during the hyphal development. Cdc42 plays the
role of master regulator of polarity control and is known to interact
with many PAK family kinases during the filament growth [140][65]. It
is also proved that the Cdc28 controls the activities of Cdc42 and
other hyphae associated proteins. Hence, the repression or inhibition
of Cdc28 can disrupt the hyphal formation in Candida. The interactomes
of these above proteins were predicted to be involved in biological
processes such as mitotic cell cycle process, cell morphogenesis,
regulation of filamentous growth, intracellular signal transduction,
and regulation of cellular component organization. Act1 was found to
interact with many hyphae regulating proteins.
In our network, Hog1 and Hsp90 possessed the maximum number of
connections and were believed to be hyphae suppressing proteins in
C. albicans. Lowering the Hog1 basal activity can promote Brg1
expression for hyphal elongation [141][66]. Similarly, Hsp90 regulates
hyphal development by regulating Cyr1 and repressing Ras1-PKA signaling
[142][67]. Hog1 was predicted with 11 biological processes and is
involved in many regulatory pathways including regulation of
filamentous growth and regulation of response to extracellular
response. Both Hog1 and Hsp90 were found to form many genetic and
physical interactions with the nodes that are involved in either hyphae
development or suppression. Among the other hub proteins from hyphae
suppressing group Pbs2, Pkc1, Tpk2 and Cdc24 were mainly associated
with the others for their activation or inactivation purposes. The
interacting partners of these proteins were predicted to be involved in
processes like signal transduction and regulation of response to
stimulus.
Among the hyphae forming proteins showing kinase activity, Hog1, Cek1 &
Mkc1 proteins are the most essential kinases. In favorable condition
hyphae formation takes place through Cek1 pathway. The activated Cek1
participates in the morphological transition through Cph1 hyphae
specific transcription factor. The Cek1 protein is dephosphorylated by
the phosphatase protein Cpp1. Cpp1 in turn is activated by Hog1
[143][68]. Thus Hog1 pathway suppresses the Cek1 activity and restrains
the hyphae formation. The activated Hog1 protein also phosphorylate
Mkc1 pathway which in turn promote cell wall integrity. Mkc1 is also
stimulated by upstream protein Pkc1 [144][69]. Through this study Hog1
stress adaptation kinase pathway core components Ssk2, Pbs2 and Hog1
were found to be the most important hub hyphae proteins which are
deduced in this study. Hog1 belongs to the MAP kinase family protein
known for suppressing hyphae formation. It is a virulent and essential
protein in Candida. Though its human homolog is present, it is
considered as a drug target [145][57]. Pbs2 belongs to the MAP kinase
family protein suppressing hyphae formation. It is a nonessential
protein having human homolog [146][70]. Ssk2 and Pkc1 are also MAP
kinase family proteins which suppress hyphae formation, and are already
reported as drug target [[147]57, [148]61]. Tpk2 belongs to the
cAMP-PKA kinase family protein that is nonessential and are known to
suppress the hyphae formation, but reported as drug target [149][71].
Cdc28 is an essential cyclin dependent kinase family protein in Candida
that promotes the hyphae formation [150][72]. Chk1 is a histidine
kinase protein that plays a crucial role in the yeast to hyphae
transformation, biofilm formation, virulence, quorum sensing, peroxide
adaptation, cell wall composition and triazole resistance [[151]73,
[152]74, [153]75, [154]76, [155]77, [156]78, [157]79, [158]80]. During
C. albicans infection Chk1 is needed for the survival in neutrophils
and adherence to esophageal cells in human [159][81]. From the above
stated functions of the hub kinase protein Chk1, it is found to be an
essential protein, different from its human homolog and we propose to
consider it as a therapeutic or drug target for candidiasis involving
hyphae formation. The findings on kinase pathways and the presence of
predominant kinase hub proteins involved in hyphae formation make them
suitable candidates which can be considered as potential targets for
prevention of hyphae formation as well as for the development of new
antifungal strategies.
The proteins that showed multiple morphological features were Bcr1,
Mkc1, Hwp1, Als1, Pmr1, Sod5, Cek1 and Pmt2. Most of them had direct
interactions with the hyphae suppressing proteins such as Hog1, Hsp90
and Nrg1. Among these, Pmr1 is the most explored one, which shares 13
connections in the network, having positive role in hyphae formation
and development. The other proteins require further attention to
understand their role in hyphae formation in C. albicans. It is also to
be noted that the hyphae formation is induced under different cellular
conditions. However, in this case we considered only those data which
were verified by wet-lab experiments. The data used in our study were
generated considering the cellular conditions. Since the proposed model
in this work was based on the experimentally verified data, the model
took into account the different cellular conditions inherently.
Overall, this study emphasizes on the involvement of major hyphae
forming proteins in different cellular physiology of C. albicans, and
their respective interactions necessary for the pathophysiology. From
the study, it was concluded that the metabolic processes such as
cAMP-mediated signaling, MAPK pathway and protein kinase pathway are
significant for the morphogenesis and pathological activity of
C. albicans. Proteins that are involved in both positive and negative
regulation of hyphae formation are explored through network analyses
and have also been described for their potential therapeutic targets.
Further studies are in progress to elucidate the mechanisms for
regulation of hyphae formation in C. albicans.
Declarations
Author contribution statement
Sanjib Das, Rajabrata Bhuyan, Angshuman Bagchi, Tanima Saha: Conceived
and designed the experiments; Performed the experiments; Analyzed and
interpreted the data; Contributed reagents, materials, analysis tools
or data; Wrote the paper.
Funding statement
This work was supported by grants from BTIS net programme of DBT,
Ministry of Science and Technology, Government of India, New Delhi.
Angshuman Bagchi and Tanima Saha also received financial assistance
from Personal Research Grant 2018–2019 provided by University of
Kalyani.
Competing interest statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
Contributor Information
Angshuman Bagchi, Email: angshumanb@gmail.com.
Tanima Saha, Email: sahatanima@klyuniv.ac.in, sahatanima@yahoo.co.in.
Appendix A. Supplementary data
The following is the supplementary data related to this article:
Revised_Supplementary_Material
[160]mmc1.docx^ (19KB, docx)
References