Current Opinion in Microbiology 2012, 15:440–446
This review comes from a themed issue on Host–microbe interactions: Fungi
Edited by Mihai G Netea and Gordon D Brown
For a complete overview see the
Issue
and the
Editorial
Available online 19th June 2012
1369-5274/$ – see front matter, © 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.mib.2012.05.001
Introduction
In ecology and immunology, tolerance usually refers to host mitigation of the fitness
costs of an infection [1]. This is distinct from resistance, whereby the host reduces
the microorganism burden. These costs may tip the balance of an immune response towards
tolerance of environmental microorganisms, including fungi. Modern pressures on the
immune system and the natural composition human microbiome have partially resulted
from the expansion of fungi in fermented foods, including opportunistic pathogens
colonizing humans. This is particularly important for intestinal tissues, where mucosal
immunity faces life-long challenges by beneficial and detrimental microbes [2,3].
These microbes, including pathogenic fungi, possess a molecular arsenal to escape
diverse defense mechanisms of immunocompetent hosts. It is thought that the co-evolution
of opportunistic pathogens with their healthy host may aid in their ability to exploit
host defenses and remain tolerated [4].
The history of host and fungal interactions will strongly influence resistance against
and tolerance to microorganisms. Cross-talk mechanisms during host–pathogen interactions
will impact the outcome of infections and further influence subsequent pathogen exposure.
As a result, genome-wide studies have gained in popularity to investigate global response
patterns to infections from both the host and pathogen side. However, biological interpretations
of genome-wide studies are limited to only a fraction of the theoretically possible
interactions between genes, environmental conditions, and life cycles taking part
in a host–pathogen setting (Figure 1). The enormous complexity underlying the host–pathogen
interplay when considering the theoretically possible genetic interactions of even
a few genes, necessitates the simplification of systems to cellular or pathway levels.
A systems biology approach at different levels — genomic, proteomic, and metabolomic — is
an emerging strategy to better understand the pathophysiology of infectious processes
and their underlying mechanisms during host–pathogen interactions [5,6].
Systems biology is a rapidly evolving integrative approach that connects many disciplines
and aims to create a quantitative and predictive understanding of biological processes.
Systems biology has evolved by two parallel approaches: ‘top-down’ network inference,
reconstruction, and modeling based on functional genomics data, and the ‘bottom-up’
approach of modeling well-defined circuits based on their functional conservation
with other systems. Systems biology approaches follow iterative cycles of modeling
and data generation, based on a given biological and testable hypothesis [7]. Recent
seminal reviews highlight the power of these different approaches in the dissection
of mammalian innate immunity [8••,9–12], the reconstruction of immune signaling, transcriptional
networks [13•
], and host–pathogen interactions [14,5,6].
This review will address recent work aimed at investigating the transition of opportunistic
fungi, with a focus on Candida spp., from the commensal to the pathogenic state, emphasizing
fungal mechanisms to escape host immune surveillance. We will discuss new approaches
in functional genomics that facilitate modeling, and those which are aimed at understanding
the fungal response in the host environment. Furthermore, we discuss the advantages
of combining different approaches to gain a better understanding of how the cross-talk
between fungal pathogens and their hosts shapes the progress and outcome of invasive
infections.
Host perspectives
Innate and adaptive immune responses are responsible for recognizing, responding,
and adapting to opportunistic microbial pathogens, including fungi [15•,16]. These
responses determine whether microbes require the activation of pathogen-specific defense
or attack mechanisms [17,18]. Recognition of fungal pathogens by innate immune cells
elicits immune responses by engaging multiple cell-bound, soluble, or intracellular
receptors, in a stage-specific and cell-specific manner [19••
]. To date, hundreds of proteins and genes have been implicated in the innate immune
response [20]. The transcriptional response to a microbial stimulus is further tailored
to both the stimulus and the responsive immune cell [21]. The analysis of the transcriptome
of human dendritic cells (DCs) to Aspergillus fumigatus, C. albicans, and S. cerevisiae
showed how the expression of immune-relevant genes increases depending on the morphology,
life-stage, and incubation period with the fungus [22•,23,24]. Models of downstream
signal transduction networks using gene expression data have been generated based
on similarities in expression profiles in related species, the prediction of shared
regulatory motifs, and their integration at the pathway level [25]. Recently, a combination
of a forward-genomics and reverse-genomics approach enabled the reconstruction of
transcriptional and regulatory networks driving the immune response in DCs to a viral
infection [26••
]. The resultant network model investigated how pathogen-sensing pathways achieve
specificity and the influence of a single regulator on mediating inflammatory genes
and viral responses depending on the timing of the regulator activation. A regulatory
network of potential interactions between microRNAs and mRNAs is an additional level
of complexity of how pathogens could manipulate host cell responses [27].
The extent to which early transcriptional regulatory events determine the decision-making
process in immune cells responding to different pathogenic fungi is still an open
question. However, an increasing number of databases are collecting and annotating
functional information. For example, the InnateDB, curates the innate immunity interactome
[28], and ImmGen collects immunological microarray data (www.immgen.org). The further
development of cell-specific bioinformatic tools to analyze the response in macrophages
[29] or DCs [30•
] will allow for the classification of stimuli by their species-specific transcriptional
programs governing fungal recognition (Rizzetto et al., unpublished observation).
While the analysis of gene expression is commonly used to study the activation of
immune cells, proteomics constitute a complementary approach providing a direct view
on protein levels as well as their activities. Proteomics however poses additional
challenges, including cost and the technical limitations to make the process quantitative
[31•
]. Moreover, mRNA expression levels are not necessarily correlated with protein production,
hampering the comparative analysis of these data sets. A recent study combined a comprehensive
quantitative proteome and transcriptome analysis on immature and cytokine-matured
human DCs [32]. Although the overall correlation between differential mRNA and protein
expression was low, the correlation between components of DC relevant pathways was
significantly higher, underscoring that the integration of related data sets at the
pathway level can significantly increase the predictive power of multiple -omics analyses.
Recently, a global investigation of the macrophage phosphoproteome and its dynamic
changes upon TLR activation has been identified [33]. Functional bioinformatic analyses
confirmed already known players of the TLR-mediated signaling and identified new transcriptional
regulators previously not implicated in TLR-induced gene expression.
Pathogen perspectives
Fungal adaptation to host immune surveillance
Fungal pathogens have developed sophisticated means to evade or persist in the host,
despite normal immune surveillance [34]. The use of genome-wide technologies to study
global transcriptional changes has revealed the complexity of fungal adaptation to
various host niches. Recent studies provide insights into the mechanisms of adaptation
during infection, which include: the expression of anti-phagocytic functions and specific
nutrient acquisition systems, the remodeling of central carbon metabolism, and the
hypoxia response [35,36]. Virulence factor expression is, to a large extent, embedded
in the regulation of functions needed for growth in the mammalian hosts. Pioneering
early work on the differential gene expression of fungi phagocytosed by immune cells
including macrophages, neutrophils, and granulocytes, revealed, among others, a dynamic
response to nutrient starvation, oxidative stress, and iron limitation. Attempts by
fungi and especially Candida spp. to adapt to the damaging effects of the environment
via the activation of genes encoding antioxidant and detoxifying enzymes, and iron
uptake proteins were shown [37]. A physiological role for cell surface superoxide
dismutases in detoxifying reactive oxygen species (ROS) in innate immune cells and
facilitating immune evasion was found [38]. In addition, autophagy and pexophagy mechanisms
are important virulence traits of fungi to enable persistence and survival [39,40].
Notably, a global model of iron homeostasis in A. fumigatus has integrated data from
Northern blot analysis, microarray expression, transcription factor knock-out mutants,
and the occurrence of transcription factor binding motifs in regulatory regions of
the genes to predict new transcription factor to target interactions [41].
Fungi may also evade the immune system by changing virulence gene expression at different
infection stages upon encountering host-conditions. For example, a novel flow cytometry-based
technique showed how changes in fungal gene expression profiles occurring over time
influenced patient outcomes with clinical strains of Cryptococcus neoformans [42,43].
Using an in vitro oral candidiasis model, C. albicans mutants defecting in regulators
of hyphal formation were attenuated in their ability to invade and damage epithelial
cells [44]. The further use of microarray and RNA-seq technology in conjunction with
in vitro infection models could be used to further investigate the role of stage-specific
virulence gene expression.
Genome dynamics of fungal pathogens
Many fungal clinical isolates display a large degree of genetic and genomic heterogeneity.
Segmental or whole-chromosome aneuploidy can be a source of selectable phenotypic
variation in fungal species [45], conferring a selective advantage in a host setting
[46]. For example, exposure to specific antifungal drugs increases the frequency of
adaptive events, promoting drug resistance in independent lineages of C. albicans
cells [47]. Additionally, loss of heterozygosity events is elevated in C. albicans
in response to oxidative, heat, and antifungal drug stress in vitro [48•
]. Although rare, even S. cerevisiae may become an opportunistic pathogen under very
specific conditions or genetic alterations [49]. Hence, cell population dynamics and
evolutionary forces imposed by host stress and other factors may represent the driving
force of genomic plasticity in fungal pathogens that enable colonization of various
host niches. Strain variability and surface alterations could also explain differences
in the host immune response [50], providing new opportunities to model host immune
system interactions. Pathogenicity itself could reflect adaptive advantages conferred
by the acquisition of virulence traits in different strains, thereby increasing pathogen
fitness.
Contrary to S. cerevisiae, C. albicans lacks a complete sexual cycle, impeding efficient
genetic analyses and limiting systems biology approaches with this obligatory diploid
fungus. Under certain environmental conditions, C. albicans can switch from to a mating-competent
state [51]. This transition modulates metabolic preferences, antifungal drug resistance,
niche distribution, and host immune cell-specific interactions among many others,
and is therefore an important consideration in the investigation of fungal fitness
within host niches. Comparative genomics studies have the potential to identify new
virulence-associated gene networks [5,52]. The number of sequenced fungal genomes
publically available has significantly expanded in recent years [53]. In addition,
the Candida Genome Database (CGD) and the Aspergillus database, among others now offer
multiple species, facilitating these comparisons. The availability of genomic datasets
studying specifically host–fungi interactions have also expanded (Table 1), along
with the number of software platforms available for the analysis and integration of
genome-wide data sets [54]. Exploring commonalities and differences among fungi could
be used to further understand the genetic basis for pathogenic phenotypes.
Infection modeling and microbial arcades
Spatio-temporal modeling of infection dynamics is an emerging field to incorporate
the dynamics of pathogenesis [55,6]. One approach is evolutionary game theory, an
application of game theory mathematics based on the relationship between the behavior
of an organism and its evolution, or co-evolution, with other species. These studies
formulate a simplified infection in silico and predict pathogen fitness by identifying
game rules, often from genome-wide expression data. Most recently, it has been used
to describe infections including: mixed viral infections of Arabidopsis thaliana [56],
persistent bacterial infections [57], a simulated multi-species biofilm [58], and
the mechanisms enabling survival of C. albicans inside macrophages [59••
]. For C. albicans, the outcome was analyzed based on the mean evolutionary cost of
a cell population to obtain a positive fitness and the infection strategy employed
by C. albicans to enable proliferation in the host was hypothesized be responsive
to this cost. These studies emphasize the importance of analyzing microbes as adaptive
social components of biological systems, because of their ability to sense and respond
to the requirements of their own population, and that of their environment [60••
].
Computational modeling has been used to reconstruct the complex network between the
immune cells and the bacterial pathogen Mycobacterium tubercolosis. On the basis of
known interactions of the bacteria during infection, they estimated the influence
of specific factors, such as an increase in specific cytokines or vaccination, on
bacterial clearance and thereby identified the overall propensity for the bacteria
to persist in the host under a wide range of conditions [61].
Most modeling approaches use genome-wide microarray expression data or RNA-seq. RNA-seq
provides the advantage of simultaneous expression profiling of genes of the pathogens
and their hosts, reducing concerns about platform-dependent effects. In addition,
RNA-seq can potentially be used to investigate allelic variants of a transcript, and
the evolution of microorganisms within its host. Small-scale network inference from
the simultaneous analysis of C. albicans and DCs from M. musculus has predicted novel
host–pathogen genetic interactions [62••
]. Furthermore, a genome-wide inference network using C. albicans has identified a
number of candidate antifungal target genes [63]. These studies emphasize the advantages
of simplifying genome-wide expression data using modeling and inference techniques
to identify novel interactions and strategies utilized by the host and pathogen during
infection.
Significant hurdles remain in order to use infection modeling on a large scale. One
major limitation is that the experimental data is generated at different time scales.
The transcriptional response of fungi takes place after minutes, proteomics from minutes
to hours, and the subsequent immune response to the fungus from hours to days or even
weeks. Choosing a mathematical approach to relate these time scales is not trivial.
Moreover, the use of different units, strains, and animal models between laboratories
can limit the ability to compare data sets. There been a push to standardize genome-wide
data sets, including the Minimum Information for Biological and Biomedical Investigations
(MIBBI, http://mibbi.sourceforge.net/), which will significantly aid in dataset comparison
between laboratories. Relatedly, the maintenance and integration of new and existing
fungal databases is needed to make the available information accessible and decrease
the bottleneck for data analysis. Curation based on data models that incorporate pathway
information [64] will make it easier to integrate new types of data sets, such as
metabolomics, proteomics or host–pathogen data sets, as they become available.
Conclusions and outlook
A frequent critique of systems biology is that the massive influx of data has led
to a fundamental loss of perspective because data generation has outpaced our capacity
and ability to analyze them. It is therefore easy to loose the scale in which -omics
data is biologically meaningful. Taking a lesson from Schrödinger's philosophy, the
understanding of inner workings of the eye does not bring one closer to the perception
of color: the additional information is irrelevant to the question. In other words,
the biological context and proper parameter estimation of biological data sets is
the key to generate models of predictive power. An initial definition of the system
and its potential impact on the interacting species it contains is therefore required
for analysis, including responses that determine pathogen clearance or host killing.
Understanding the evolution of fungal strategies to survive and infect the host requires
simultaneous investigation of microorganism–host interactions in both pathogenic and
commensal species. Lessons learned from modeling the cell cycle show the importance
of obtaining time course information either at the whole genome, or at the single
molecule level, including the identification of biologically meaningful parameters,
to obtain identifiable models. Developing strategies for the integration of multiple
and complementary — quantitative -omics data sets, in a dynamic manner, will also
be essential to further our understanding of microbial infections by reducing available
data sets into testable models.
The host immune response is a complex entity and its behavior cannot be investigated
in isolation from the environment that is driving adaptive changes, such as host immune
defense. Systems biology holds the promise of helping us to obtain holistic views
on the extent of this environment, and to generate predictions of host–microbe behavior
and disease outcome. Combining the major schools of thought of mathematical modeling
and functional genomics is a promising to solution to reach the goals of deciphering
infectious processes and eventually improving therapeutic approaches to fungal infections.
Conflict of interest
The authors have declared that no conflicts of interest.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted
as:
• of special interest
•• of outstanding interest