Systems biology of fungal infection

Sodium lignosulfonate causes cell membrane perturbation in the human fungal pathogen Candida albicans

Underestimating fungal infections led to a gap in the development of antifungal medication. However, rising rates of morbidity and mortality with fungal infection have revealed an alarming rise in antifungal resistance also. Due to the eukaryotic properties of fungi and the close evolutionary similarity between fungal cells and human hosts, therapeutic targeting of Candida infections is troublesome, along with the development of resistance. The discovery of new antifungals is so far behind schedule that the antifungal pipeline is nearly empty. Previously, we have reported the activity and susceptibility of Sodium lignosulfonate (LIG) against C. albicans. In this work, we have established the mechanistic actions of LIG's activity. We performed flow cytometric analysis for membrane integrity, ergosterol binding assay, crystal violet assay, and membrane leakage assay to analyze quantitatively that the C. albicans membrane is being disrupted in response to LIG. Electron microscopic analysis with SEM and TEM confirmed changes in Candida cellular morphology and membrane perturbation respectively. These findings indicated that LIG causes cell membrane damage in C. albicans. This knowledge about LIG's mechanism of action against C. albicans could be used to explore it further as a lead antifungal molecule to develop it as a potent candidate for antifungal therapeutics in the future.

Synthesis, characterization, molecular dynamic simulation, and biological assessment of cinnamates linked to imidazole/benzimidazole as a CYP51 inhibitor

A class of 2-(1H-imidazol-1-yl)-1-phenylethyl cinnamates 6a-6j and 2-(1H-benzo[d]imidazol-1-yl)-1-phenylethyl cinnamates 7a-7j were synthesized, and their synthesis was validated using various spectroscopic techniques like IR, NMR, and Mass spectrometry. In addition, the compounds were assessed for in-vitro antibacterial against gram-positive and gram-negative strains and in-vitro antifungal against six different fungal strains. Compounds 6 g, 7 b, 7f, and 7 g exhibited significant activity against all bacterial strains ranging from MIC = 12.5-50 µg/mL, and compounds 6 g, 7 b, and 7 g exhibited considerable activity against all fungal strains ranging from MFC = 125-200 µg/mL. A molecular docking study indicated that compounds 6 g, 7 b, 7 g, and 7j could be lodged in the active pocket and inhibit C. albicans Sterol 14α-demethylase (CYP51) protein via various interactions, and these studies validate the antifungal results. Different parameters from the 100 ns MD simulation study are investigated to evaluate the dynamic stability of protein-ligand complexes. According to the MD simulation study, the proposed compounds effectively kept their molecular interaction and structural integrity within the C. albicans Sterol 14-demethylase. Compounds 6 g, 7 b, and 7 g are promising lead compounds in searching for novel antifungal drug-like molecules. Furthermore, in silico ADME indicates that these compounds possess drug-like physicochemical properties to be orally bioavailable.Communicated by Ramaswamy H. Sarma.

Computational systems biology in disease modeling and control, review and perspectives

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.

Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses

Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.

A data library of Candida albicans functional genomic screens

Functional genomic screening of genetic mutant libraries enables the characterization of gene function in diverse organisms. For the fungal pathogen Candida albicans, several genetic mutant libraries have been generated and screened for diverse phenotypes, including tolerance to environmental stressors and antifungal drugs, and pathogenic traits such as cellular morphogenesis, biofilm formation and host-pathogen interactions. Here, we compile and organize C. albicans functional genomic screening data from ∼400 screens, to generate a data library of genetic mutant strains analyzed under diverse conditions. For quantitative screening data, we normalized these results to enable quantitative and comparative analysis of different genes across different phenotypes. Together, this provides a unique C. albicans genetic database, summarizing abundant phenotypic data from functional genomic screens in this critical fungal pathogen.

Uncovering the Role of Metabolism in Oomycete-Host Interactions Using Genome-Scale Metabolic Models

Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the "mass-flow" through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host-pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.

The impact of the Fungus-Host-Microbiota interplay upon Candida albicans infections: current knowledge and new perspectives

Candida albicans is a major fungal pathogen of humans. It exists as a commensal in the oral cavity, gut or genital tract of most individuals, constrained by the local microbiota, epithelial barriers and immune defences. Their perturbation can lead to fungal outgrowth and the development of mucosal infections such as oropharyngeal or vulvovaginal candidiasis, and patients with compromised immunity are susceptible to life-threatening systemic infections. The importance of the interplay between fungus, host and microbiota in driving the transition from C. albicans commensalism to pathogenicity is widely appreciated. However, the complexity of these interactions, and the significant impact of fungal, host and microbiota variability upon disease severity and outcome, are less well understood. Therefore, we summarise the features of the fungus that promote infection, and how genetic variation between clinical isolates influences pathogenicity. We discuss antifungal immunity, how this differs between mucosae, and how individual variation influences a person's susceptibility to infection. Also, we describe factors that influence the composition of gut, oral and vaginal microbiotas, and how these affect fungal colonisation and antifungal immunity. We argue that a detailed understanding of these variables, which underlie fungal-host-microbiota interactions, will present opportunities for directed antifungal therapies that benefit vulnerable patients.

The impact of episporic modification of Lichtheimia corymbifera on virulence and interaction with phagocytes

Fungal infections caused by the ancient lineage Mucorales are emerging and increasingly reported in humans. Comprehensive surveys on promising attributes from a multitude of possible virulence factors are limited and so far, focused on Mucor and Rhizopus. This study addresses a systematic approach to monitor phagocytosis after physical and enzymatic modification of the outer spore wall of Lichtheimia corymbifera, one of the major causative agents of mucormycosis. Episporic modifications were performed and their consequences on phagocytosis, intracellular survival and virulence by murine alveolar macrophages and in an invertebrate infection model were elucidated. While depletion of lipids did not affect the phagocytosis of both strains, delipidation led to attenuation of LCA strain but appears to be dispensable for infection with LCV strain in the settings used in this study. Combined glucano-proteolytic treatment was necessary to achieve a significant decrease of virulence of the LCV strain in Galleria mellonella during maintenance of the full potential for spore germination as shown by a novel automated germination assay. Proteolytic and glucanolytic treatments largely increased phagocytosis compared to alive resting and swollen spores. Whilst resting spores barely (1–2%) fuse to lysosomes after invagination in to phagosomes, spore trypsinization led to a 10-fold increase of phagolysosomal fusion as measured by intracellular acidification. This is the first report of a polyphasic measurement of the consequences of episporic modification of a mucormycotic pathogen in spore germination, spore surface ultrastructure, phagocytosis, stimulation of Toll-like receptors (TLRs), phagolysosomal fusion and intracellular acidification, apoptosis, generation of reactive oxygen species (ROS) and virulence.

Microglial Response to Aspergillus flavus and Candida albicans: Implications in Endophthalmitis

Aspergillus flavus is the most common etiology of fungal endophthalmitis in India, while Candida albicans is the causative agent in the West. In this study, we determined the role of microglial cells in evoking an inflammatory response following an infection with A. flavus and C. albicans strains isolated from patients with endophthalmitis. Microglia (CHME-3) cells were infected with A. flavus and C. albicans and the expression of Toll-Like Receptors (TLRs), cytokines and Matrix metalloproteinases (MMPs) were assessed at various time intervals. A. flavus infected cells induced higher expressions of TLR-1, -2, -5, -6, -7 and -9 and cytokines such as IL-1α, IL-6, IL-8, IL-10 and IL-17. In contrast, C. albicans infected microglia induced only TLR-2 along with the downregulation of IL-10 and IL-17. The expression of MMP-9 (Matrix metalloproteinase-9) was however upregulated in both A. flavus and C. albicans infected microglia. These results indicate that microglial cells have the ability to incite an innate response towards endophthalmitis causing fungal pathogens via TLRs and inflammatory mediators. Moreover, our study highlights the differential responses of microglia towards yeast vs. filamentous fungi.

Hybrid Agent-Based Modeling of Aspergillus fumigatus Infection to Quantitatively Investigate the Role of Pores of Kohn in Human Alveoli

The healthy state of an organism is constantly threatened by external cues. Due to the daily inhalation of hundreds of particles and pathogens, the immune system needs to constantly accomplish the task of pathogen clearance in order to maintain this healthy state. However, infection dynamics are highly influenced by the peculiar anatomy of the human lung. Lung alveoli that are packed in alveolar sacs are interconnected by so called Pores of Kohn. Mainly due to the lack of in vivo methods, the role of Pores of Kohn in the mammalian lung is still under debate and partly contradicting hypotheses remain to be investigated. Although it was shown by electron microscopy that Pores of Kohn may serve as passageways for immune cells, their impact on the infection dynamics in the lung is still unknown under in vivo conditions. In the present study, we apply a hybrid agent-based infection model to quantitatively compare three different scenarios and discuss the importance of Pores of Kohn during infections of Aspergillus fumigatus. A. fumigatus is an airborne opportunistic fungus with rising incidences causing severe infections in immunocompromised patients that are associated with high mortality rates. Our hybrid agent-based model incorporates immune cell dynamics of alveolar macrophages – the resident phagocytes in the lung – as well as molecular dynamics of diffusing chemokines that attract alveolar macrophages to the site of infection. Consequently, this model allows a quantitative comparison of three different scenarios and to study the importance of Pores of Kohn. This enables us to demonstrate how passaging of alveolar macrophages and chemokine diffusion affect A. fumigatus infection dynamics. We show that Pores of Kohn alter important infection clearance mechanisms, such as the spatial distribution of macrophages and the effect of chemokine signaling. However, despite these differences, a lack of passageways for alveolar macrophages does impede infection clearance only to a minor extend. Furthermore, we quantify the importance of recruited macrophages in comparison to resident macrophages.

Modeling Early Stages of Bone and Joint Infections Dynamics in Humans: A Multi-Agent, Multi-System Based Model

Diagnosis and management of bone and joint infections (BJI) is a challenging task. The high intra and inter patient's variability in terms of clinical presentation makes it impossible to rely on a systematic description or classical statistical analysis for its diagnosis. Advances can be achieved through a better understanding of the system behavior that results from the interactions between the components at a micro-scale level, which is difficult to mastered using traditional methods. Multiple studies from the literature report factors and interactions that affect the dynamics of the BJI system. The objectives of this study were (i) to perform a systematic review to identify relevant interactions between agents (cells, pathogens) and parameters values that characterize agents and interactions, and (ii) to develop a two dimensional computational model of the BJI system based on the results of the systematic review. The model would simulate the behavior resulting from the interactions on the cellular and molecular levels to explore the BJI dynamics, using an agent-based modeling approach. The BJI system's response to different microbial inoculum levels was simulated. The model succeeded in mimicking the dynamics of bacteria, the innate immune cells, and the bone mass during the first stage of infection and for different inoculum levels in a consistent manner. The simulation displayed the destruction in bone tissue as a result of the alteration in bone remodeling process during the infection. The model was used to generate different patterns of system behaviors that could be analyzed in further steps. Simulations results suggested evidence for the existence of latent infections. Finally, we presented a way to analyze and synthesize massive simulated data in a concise and comprehensive manner based on the semi-supervised identification of ordinary differential equations (ODE) systems. It allows to use the known framework for temporal and structural ODE analyses and therefore summarize the whole simulated system dynamical behavior. This first model is intended to be validated by in vivo or in vitro data and expected to generate hypotheses to be challenged by real data. Step by step, it can be modified and complexified based on the test/validation iteration cycles.

Visualizing fungicide action: an in vivo tool for rapid validation of fungicides with target location HOG pathway

BACKGROUND

The mitogen-activated protein kinase MoHog1p was fused with a green fluorescent protein (GFP) in the filamentous fungus Magnaporthe oryzae. The MoHOG1::GFP mutant was found to be an excellent tool visualizing in vivo fungicide-dependent translocation of MoHog1p into the nucleus. Validation of pathway specificity was achieved by generating fluorescence-labelled MoHog1p in the ΔMohik1 'loss of function' mutant strain.

RESULTS

GFP-labelled MoHog1p expressed in the wildtype and in ΔMohik1 demonstrates that fludioxonil is acting on the HOG pathway and even more precisely that fungicide action is dependent on the group III histidine kinase MoHik1p. GFP-tagged MoHog1p translocated into the nucleus upon fungicide treatment in the MoHOG1::GFP mutant within seconds, but did not do so in the ΔMohik1/HOG1::GFP mutant.

CONCLUSION

Here, we developed a rapid in vivo tool for fluorescent-based validation of fungicides targeting the HOG-signaling pathway. Furthermore, using the fluorescent mutants generated in this study, we are able to visualize that fungicide action is dependent on the histidine kinase MoHik1p but operates in a different mechanism of pathway activation compared to osmotic stress. © 2018 Society of Chemical Industry.

Comparative Assessment of Aspergillosis by Virtual Infection Modeling in Murine and Human Lung

Aspergillus fumigatus is a ubiquitous opportunistic fungal pathogen that can cause severe infections in immunocompromised patients. Conidia that reach the lower respiratory tract are confronted with alveolar macrophages, which are the resident phagocytic cells, constituting the first line of defense. If not efficiently removed in time, A. fumigatus conidia can germinate causing severe infections associated with high mortality rates. Mice are the most extensively used model organism in research on A. fumigatus infections. However, in addition to structural differences in the lung physiology of mice and the human host, applied infection doses in animal experiments are typically orders of magnitude larger compared to the daily inhalation doses of humans. The influence of these factors, which must be taken into account in a quantitative comparison and knowledge transfer from mice to humans, is difficult to measure since in vivo live cell imaging of the infection dynamics under physiological conditions is currently not possible. In the present study, we compare A. fumigatus infection in mice and humans by virtual infection modeling using a hybrid agent-based model that accounts for the respective lung physiology and the impact of a wide range of infection doses on the spatial infection dynamics. Our computer simulations enable comparative quantification of A. fumigatus infection clearance in the two hosts to elucidate (i) the complex interplay between alveolar morphometry and the fungal burden and (ii) the dynamics of infection clearance, which for realistic fungal burdens is found to be more efficiently realized in mice compared to humans.

Methodologies for Assessing Disease Tolerance in Pigs

Features of intensive farming can seriously threaten pig homeostasis, well-being and productivity. Disease tolerance of an organism is the adaptive ability in preserving homeostasis and at the same time limiting the detrimental impact that infection can inflict on its health and performance without affecting pathogen burden per se. While disease resistance (DRs) can be assessed measuring appropriately the pathogen burden within the host, the tolerance cannot be quantified easily. Indeed, it requires the assessment of the changes in performance as well as the changes in pathogen burden. In this paper, special attention is given to criteria required to standardize methodologies for assessing disease tolerance (DT) in respect of infectious diseases in pigs. The concept is applied to different areas of expertise and specific examples are given. The basic physiological mechanisms of DT are reviewed. Disease tolerance pathways, genetics of the tolerance-related traits, stress and disease tolerance, and role of metabolic stress in DT are described. In addition, methodologies based on monitoring of growth and reproductive performance, welfare, emotional affective states, sickness behavior for assessment of disease tolerance, and methodologies based on the relationship between environmental challenges and disease tolerance are considered. Automated Precision Livestock Farming technologies available for monitoring performance, health and welfare-related measures in pig farms, and their limitations regarding DT in pigs are also presented. Since defining standardized methodologies for assessing DT is a serious challenge for biologists, animal scientists and veterinarians, this work should contribute to improvement of health, welfare and production in pigs.

Antifungal and Cytotoxic Activities of Sixty Commercially-Available Essential Oils

There is an urgent and unmet need for new antifungal therapies. Global fungal infection rates continue to rise and fungal infections pose increasing burdens on global healthcare systems. Exacerbating the situation, the available antifungal therapeutic arsenal is limited and development of new antifungals has been slow. Current antifungals are known for unwanted side effects including nephrotoxicity and hepatotoxicity. Thus, the need for new antifungals and new antifungal targets is urgent and growing. A collection of 60 commercially-available essential oils has been screened for antifungal activity against Aspergillus niger, Candida albicans, and Cryptococcus neoformans, as well as for cytotoxic activity against MCF-7 and MDA-MB-231 human breast tumor cell lines; the chemical compositions of the essential oils have been determined by gas chromatography-mass spectrometry (GC-MS). Ten essential oils showed remarkable antifungal and cytotoxic activities: Indian, Australian, and Hawaiian sandalwoods; melissa; lemongrass; cilantro; cassia; cinnamon; patchouli; and vetiver.

Quantitative Simulations Predict Treatment Strategies Against Fungal Infections in Virtual Neutropenic Patients

The condition of neutropenia, i.e., a reduced absolute neutrophil count in blood, constitutes a major risk factor for severe infections in the affected patients. Candida albicans and Candida glabrata are opportunistic pathogens and the most prevalent fungal species in the human microbiota. In immunocompromised patients, they can become pathogenic and cause infections with high mortality rates. In this study, we use a previously established approach that combines experiments and computational models to investigate the innate immune response during blood stream infections with the two fungal pathogens C. albicans and C. glabrata. First, we determine immune-reaction rates and migration parameters under healthy conditions. Based on these findings, we simulate virtual patients and investigate the impact of neutropenic conditions on the infection outcome with the respective pathogen. Furthermore, we perform in silico treatments of these virtual patients by simulating a medical treatment that enhances neutrophil activity in terms of phagocytosis and migration. We quantify the infection outcome by comparing the response to the two fungal pathogens relative to non-neutropenic individuals. The analysis reveals that these fungal infections in neutropenic patients can be successfully cleared by cytokine treatment of the remaining neutrophils; and that this treatment is more effective for C. glabrata than for C. albicans.

Dimensionality of Motion and Binding Valency Govern Receptor-Ligand Kinetics As Revealed by Agent-Based Modeling

Mathematical modeling and computer simulations have become an integral part of modern biological research. The strength of theoretical approaches is in the simplification of complex biological systems. We here consider the general problem of receptor–ligand binding in the context of antibody–antigen binding. On the one hand, we establish a quantitative mapping between macroscopic binding rates of a deterministic differential equation model and their microscopic equivalents as obtained from simulating the spatiotemporal binding kinetics by stochastic agent-based models. On the other hand, we investigate the impact of various properties of B cell-derived receptors—such as their dimensionality of motion, morphology, and binding valency—on the receptor–ligand binding kinetics. To this end, we implemented an algorithm that simulates antigen binding by B cell-derived receptors with a Y-shaped morphology that can move in different dimensionalities, i.e., either as membrane-anchored receptors or as soluble receptors. The mapping of the macroscopic and microscopic binding rates allowed us to quantitatively compare different agent-based model variants for the different types of B cell-derived receptors. Our results indicate that the dimensionality of motion governs the binding kinetics and that this predominant impact is quantitatively compensated by the bivalency of these receptors.

SCF Ubiquitin Ligase F-box Protein Fbx15 Controls Nuclear Co-repressor Localization, Stress Response and Virulence of the Human Pathogen Aspergillus fumigatus

F-box proteins share the F-box domain to connect substrates of E3 SCF ubiquitin RING ligases through the adaptor Skp1/A to Cul1/A scaffolds. F-box protein Fbx15 is part of the general stress response of the human pathogenic mold Aspergillus fumigatus. Oxidative stress induces a transient peak of fbx15 expression, resulting in 3x elevated Fbx15 protein levels. During non-stress conditions Fbx15 is phosphorylated and F-box mediated interaction with SkpA preferentially happens in smaller subpopulations in the cytoplasm. The F-box of Fbx15 is required for an appropriate oxidative stress response, which results in rapid dephosphorylation of Fbx15 and a shift of the cellular interaction with SkpA to the nucleus. Fbx15 binds SsnF/Ssn6 as part of the RcoA/Tup1-SsnF/Ssn6 co-repressor and is required for its correct nuclear localization. Dephosphorylated Fbx15 prevents SsnF/Ssn6 nuclear localization and results in the derepression of gliotoxin gene expression. fbx15 deletion mutants are unable to infect immunocompromised mice in a model for invasive aspergillosis. Fbx15 has a novel dual molecular function by controlling transcriptional repression and being part of SCF E3 ubiquitin ligases, which is essential for stress response, gliotoxin production and virulence in the opportunistic human pathogen A. fumigatus.

The opportunistic human fungal pathogen Aspergillus fumigatus is the most prevalent cause for severe fungal infections in immunocompromised hosts. A major virulence factor of A. fumigatus is its ability to rapidly adapt to host conditions during infection. The rapid response to environmental changes underlies a well-balanced system of production and degradation of proteins. The degradation of specific target proteins is mediated by ubiquitin-protein ligases (E3), which mark their target proteins with ubiquitin for proteasomal degradation. Multisubunit SCF Cullin1 Ring ligases (CRL) are E3 ligases where the F-box subunit functions as a substrate-specificity determining adaptor. A comprehensive control of protein production includes global co-repressors as the conserved Ssn6(SsnF)-Tup1(RcoA) complex, which reduces transcription on multiple levels. We have identified a novel connection between protein degradation and synthesis through an F-box protein. Fbx15 can be incorporated into SCF E3 ubiquitin ligases and controls upon stress the nuclear localization of the SsnF. Fbx15 plays a critical role for A. fumigatus adaptation and is essential for virulence in a murine infection model. Fbx15 is a fungal-specific protein and therefore a potential target for future drug development.

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

A computational model of invasive aspergillosis in the lung and the role of iron

Background

Invasive aspergillosis is a severe infection of immunocompromised hosts, caused by the inhalation of the spores of the ubiquitous environmental molds of the Aspergillus genus. The innate immune response in this infection entails a series of complex and inter-related interactions between multiple recruited and resident cell populations with each other and with the fungal cell; in particular, iron is critical for fungal growth.

Results

A computational model of invasive aspergillosis is presented here; the model can be used as a rational hypothesis-generating tool to investigate host responses to this infection. Using a combination of laboratory data and published literature, an in silico model of a section of lung tissue was generated that includes an alveolar duct, adjacent capillaries, and surrounding lung parenchyma. The three-dimensional agent-based model integrates temporal events in fungal cells, epithelial cells, monocytes, and neutrophils after inhalation of spores with cellular dynamics at the tissue level, comprising part of the innate immune response. Iron levels in the blood and tissue play a key role in the fungus’ ability to grow, and the model includes iron recruitment and consumption by the different types of cells included. Parameter sensitivity analysis suggests the model is robust with respect to unvalidated parameters, and thus is a viable tool for an in silico investigation of invasive aspergillosis.

Conclusions

Using laboratory data from a mouse model of invasive aspergillosis in the context of transient neutropenia as validation, the model predicted qualitatively similar time course changes in fungal burden, monocyte and neutrophil populations, and tissue iron levels. This model lays the groundwork for a multi-scale dynamic mathematical model of the immune response to Aspergillus species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0275-2) contains supplementary material, which is available to authorized users.

Systems Biology Approaches for Host-Fungal Interactions: An Expanding Multi-Omics Frontier

Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic "two systems" biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host-fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale.

An Omics Perspective on Candida Infections: Toward Next-Generation Diagnosis and Therapy

Candida species can cause severe infections associated with high morbidity and mortality. Therefore, it is essential to gain more insight into the anti-fungal host defense response. The advent of omics technology and development of advanced systems biology tools has permitted to approach this in an unbiased and quantitative manner. This review summarizes the insights gained on anti-Candida immunity from genetic-, transcriptome-, proteome-, metabolome-, microbiome-, mycobiome-, and computational systems biology studies and discusses practical aspects and future perspectives.

Identification and characterization of an anti-oxidative stress-associated mutant of Aspergillus fumigatus transformed by Agrobacterium tumefaciens

Aspergillus fumigatus is one of the most common opportunistic pathogenic fungi, surviving in various environmental conditions. Maintenance of the redox homeostasis of the fungus relies upon the well‑organized regulation between reactive oxygen species generated by immune cells or its own organelles, and the activated anti‑oxidative stress mechanism. To investigate such a mechanism, the present study obtained a number of randomly‑inserted mutants of A. fumigatus, mediated by Agrobacterium tumefaciens. In addition, a high throughput hydrogen peroxide screening system was established to examine ~1,000 mutants. A total of 100 mutants exhibited changes in hydrogen peroxide sensitivity, among which a significant increase in sensitivity was observed in the AFM2658 mutant. Further investigations of the mutant were also performed, in which the sequence of this mutant was characterized using thermal asymmetric interlaced‑polymerase chain reaction. This revealed that the insertion site was located on chromosome 2 afu1_92, and the 96 bp sequence was knocked out, which partially comprised a sequence localized between the integral membrane protein coding region and the helix‑loop‑helix transcription factor coding region. A decrease in the levels of anti‑oxidative stress‑associated mRNAs were observed, and an increase in reactive oxygen species were detected using fluorescence. The results of the present study demonstrated that this sequence may have a protective role in A. fumigatus in the presence of oxidative stress.

Functional analysis of Paracoccidioides brasiliensis 14-3-3 adhesin expressed in Saccharomyces cerevisiae

BACKGROUND

14-3-3 proteins comprise a family of eukaryotic multifunctional proteins involved in several cellular processes. The Pb14-3-3 of Paracoccidioides brasiliensis seems to play an important role in the Paracoccidioides-host interaction. Paracoccidioides brasiliensis is an etiological agent of paracoccidioidomycosis, which is a systemic mycosis that is endemic in Latin America. In the initial steps of the infection, Paracoccidioides spp. synthetizes adhesins that allow it to adhere and invade host cells. Therefore, the aim of this work was to perform a functional analysis of Pb14-3-3 using Saccharomyces cerevisiae as a model.

RESULTS

The functional analysis of Pb14-3-3 was performed in S. cerevisiae, and it was found that Pb14-3-3 partially complemented S. cerevisiae proteins Bmh1p and Bmh2p, which are recognized as two yeast 14-3-3 homologues. When we evaluated the adhesion profile of S. cerevisiae transformants, Pb14-3-3 acted as an adhesin in S. cerevisiae; however, Bmh1p did not show this function. The influence of Pb14-3-3 in S. cerevisiae ergosterol pathway was also evaluated and our results showed that Pb14-3-3 up-regulates genes involved in ergosterol biosynthesis.

CONCLUSIONS

Our data showed that Pb14-3-3 was able to partially complement Bmh1p and Bmh2p proteins in S. cerevisiae; however, we suggest that Pb14-3-3 has a differential role as an adhesin. In addition, Pb-14-3-3 may be involved in Paracoccidioides spp. ergosterol biosynthesis which makes it an interest as a therapeutic target.

Integrated inference and evaluation of host-fungi interaction networks

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data.

Adhesion-dependent rupturing of Saccharomyces cerevisiae on biological antimicrobial nanostructured surfaces

Recent studies have shown that some nanostructured surfaces (NSS), many of which are derived from surfaces found on insect cuticles, rupture and kill adhered prokaryotic microbes. Most important, the nanoscale topography is directly responsible for this effect. Although parameters such as cell adhesion and cell wall rigidity have been suggested to play significant roles in this process, there is little experimental evidence regarding the underlying mechanisms involving NSS-induced microbial rupture. In this work, we report the NSS-induced rupturing of a eukaryotic microorganism, Saccharomyces cerevisiae. We show that the amount of NSS-induced rupture of S. cerevisiae is dependent on both the adhesive qualities of the yeast cell and the nanostructure geometry of the NSS. Thus, we are providing the first empirical evidence that these parameters play a direct role in the rupturing of microbes on NSS. Our observations of this phenomenon with S. cerevisiae, particularly the morphological changes, are strikingly similar to that reported for bacteria despite the differences in the yeast cell wall structure. Consequently, NSS provide a novel approach for the control of microbial growth and development of broad-spectrum microbicidal surfaces.

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions

Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely stratification of sepsis patients by distinguishing hyper-inflammatory from paralytic phases in immune dysregulation.

Deciphering chemokine properties by a hybrid agent-based model of Aspergillus fumigatus infection in human alveoli

The ubiquitous airborne fungal pathogen Aspergillus fumigatus is inhaled by humans every day. In the lung, it is able to quickly adapt to the humid environment and, if not removed within a time frame of 4–8 h, the pathogen may cause damage by germination and invasive growth. Applying a to-scale agent-based model of human alveoli to simulate early A. fumigatus infection under physiological conditions, we recently demonstrated that alveolar macrophages require chemotactic cues to accomplish the task of pathogen detection within the aforementioned time frame. The objective of this study is to specify our general prediction on the as yet unidentified chemokine by a quantitative analysis of its expected properties, such as the diffusion coefficient and the rates of secretion and degradation. To this end, the rule-based implementation of chemokine diffusion in the initial agent-based model is revised by numerically solving the spatio-temporal reaction-diffusion equation in the complex structure of the alveolus. In this hybrid agent-based model, alveolar macrophages are represented as migrating agents that are coupled to the interactive layer of diffusing molecule concentrations by the kinetics of chemokine receptor binding, internalization and re-expression. Performing simulations for more than a million virtual infection scenarios, we find that the ratio of secretion rate to the diffusion coefficient is the main indicator for the success of pathogen detection. Moreover, a subdivision of the parameter space into regimes of successful and unsuccessful parameter combination by this ratio is specific for values of the migration speed and the directional persistence time of alveolar macrophages, but depends only weakly on chemokine degradation rates.

The Aspergillus fumigatus cell wall integrity signaling pathway: drug target, compensatory pathways, and virulence

Aspergillus fumigatus is the most important airborne fungal pathogen, causing severe infections with invasive growth in immunocompromised patients. The fungal cell wall (CW) prevents the cell from lysing and protects the fungus against environmental stress conditions. Because it is absent in humans and because of its essentiality, the fungal CW is a promising target for antifungal drugs. Nowadays, compounds acting on the CW, i.e., echinocandin derivatives, are used to treat A. fumigatus infections. However, studies demonstrating the clinical effectiveness of echinocandins in comparison with antifungals currently recommended for first-line treatment of invasive aspergillosis are still lacking. Therefore, it is important to elucidate CW biosynthesis pathways and their signal transduction cascades, which potentially compensate the inhibition caused by CW- perturbing compounds. Like in other fungi, the central core of the cell wall integrity (CWI) signaling pathway in A. fumigatus is composed of three mitogen activated protein kinases. Deletion of these genes resulted in severely enhanced sensitivity of the mutants against CW-disturbing compounds and in drastic alterations of the fungal morphology. Additionally, several cross-talk interactions between the CWI pathways and other signaling pathways are emerging, raising the question about their role in the CW compensatory mechanisms. In this review we focused on recent advances in understanding the CWI signaling pathway in A. fumigatus and its role during drug stress response and virulence.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Image-based systems biology of infection

The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image-based systems biology approach. This review demonstrates how image-based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach--imaging, quantitative characterization, and modeling--and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image-derived data to simulate host-pathogen interactions in silico.

Defining the transcriptomic landscape of Candida glabrata by RNA-Seq

Candida glabrata is the second most common pathogenic Candida species and has emerged as a leading cause of nosocomial fungal infections. Its reduced susceptibility to antifungal drugs and its close relationship to Saccharomyces cerevisiae make it an interesting research focus. Although its genome sequence was published in 2004, little is known about its transcriptional dynamics. Here, we provide a detailed RNA-Seq-based analysis of the transcriptomic landscape of C. glabrata in nutrient-rich media, as well as under nitrosative stress and during pH shift. Using RNA-Seq data together with state-of-the-art gene prediction tools, we refined the annotation of the C. glabrata genome and predicted 49 novel protein-coding genes. Of these novel genes, 14 have homologs in S. cerevisiae and six are shared with other Candida species. We experimentally validated four novel protein-coding genes of which two are differentially regulated during pH shift and interaction with human neutrophils, indicating a potential role in host–pathogen interaction. Furthermore, we identified 58 novel non-protein-coding genes, 38 new introns and condition-specific alternative splicing. Finally, our data suggest different patterns of adaptation to pH shift and nitrosative stress in C. glabrata, Candida albicans and S. cerevisiae and thus further underline a distinct evolution of virulence in yeast.

A second stimulus required for enhanced antifungal activity of human neutrophils in blood is provided by anaphylatoxin C5a

Polymorphonuclear neutrophilic granulocytes (PMN) as cellular components of innate immunity play a crucial role in the defense against systemic Candida albicans infection. To analyze stimuli that are required for PMN activity during C. albicans infection in a situation similar to in vivo, we used a human whole-blood infection model. In this model, PMN activation 10 min after C. albicans infection was largely dependent on the anaphylatoxin C5a. Most importantly, C5a enabled blood PMN to overcome filament-restricted recognition of C. albicans and allowed efficient elimination of nonfilamentous C. albicans cph1Δ/efg1Δ from blood. Major PMN effector mechanisms, including oxidative burst, release of secondary granule contents and initial fungal phagocytosis could be prevented by blocking C5a receptor signaling. Identical effects were achieved using a humanized Ab specifically targeting human C5a. Phagocytosis of C. albicans 10 min postinfection was mediated by C5a-dependent enhancement of CD11b surface expression on PMN, thus establishing the C5a-C5aR-CD11b axis as a major modulator of early anti-Candida immune responses in human blood. In contrast, phagocytosis of C. albicans by PMN 60 min postinfection occurred almost independently of C5a and mainly contributed to activation of phagocytically active PMN at later time points. Our results show that C5a is a critical mediator in human blood during C. albicans infection.

Interference of Aspergillus fumigatus with the immune response

Aspergillus fumigatus is a saprotrophic filamentous fungus and also the most prevalent airborne fungal pathogen of humans. Depending on the host’s immune status, the variety of diseases caused by A. fumigatus ranges from allergies in immunocompetent hosts to life-threatening invasive infections in patients with impaired immunity. In contrast to the majority of other Aspergillus species, which are in most cases nonpathogenic, A. fumigatus features an armory of virulence determinants to establish an infection. For example, A. fumigatus is able to evade the human complement system by binding or degrading complement regulators. Furthermore, the fungus interferes with lung epithelial cells, alveolar macrophages, and neutrophil granulocytes to prevent killing by these immune cells. This chapter summarizes the different strategies of A. fumigatus to manipulate the immune response. We also discuss the potential impact of recent advances in immunoproteomics to improve diagnosis and therapy of an A. fumigatus infection.

Agent-based model of human alveoli predicts chemotactic signaling by epithelial cells during early Aspergillus fumigatus infection

Aspergillus fumigatus is one of the most important human fungal pathogens, causing life-threatening diseases. Since humans inhale hundreds to thousands of fungal conidia every day, the lower respiratory tract is the primary site of infection. Current interaction networks of the innate immune response attribute fungal recognition and detection to alveolar macrophages, which are thought to be the first cells to get in contact with the fungus. At present, these networks are derived from in vitro or in situ assays, as the peculiar physiology of the human lung makes in vivo experiments, including imaging on the cell-level, hard to realize. We implemented a spatio-temporal agent-based model of a human alveolus in order to perform in silico experiments of a virtual infection scenario, for an alveolus infected with A. fumigatus under physiological conditions. The virtual analog captures the three-dimensional alveolar morphology consisting of the two major alveolar epithelial cell types and the pores of Kohn as well as the dynamic process of respiration. To the best of our knowledge this is the first agent-based model of a dynamic human alveolus in the presence of respiration. A key readout of our simulations is the first-passage-time of alveolar macrophages, which is the period of time that elapses until the first physical macrophage-conidium contact is established. We tested for random and chemotactic migration modes of alveolar macrophages and varied their corresponding parameter sets. The resulting first-passage-time distributions imply that randomly migrating macrophages fail to find the conidium before the start of germination, whereas guidance by chemotactic signals derived from the alveolar epithelial cell associated with the fungus enables a secure and successful discovery of the pathogen in time.

Fungal model systems and the elucidation of pathogenicity determinants

Fungi have the capacity to cause devastating diseases of both plants and animals, causing significant harvest losses that threaten food security and human mycoses with high mortality rates. As a consequence, there is a critical need to promote development of new antifungal drugs, which requires a comprehensive molecular knowledge of fungal pathogenesis. In this review, we critically evaluate current knowledge of seven fungal organisms used as major research models for fungal pathogenesis. These include pathogens of both animals and plants; Ashbya gossypii, Aspergillus fumigatus, Candida albicans, Fusarium oxysporum, Magnaporthe oryzae, Ustilago maydis and Zymoseptoria tritici. We present key insights into the virulence mechanisms deployed by each species and a comparative overview of key insights obtained from genomic analysis. We then consider current trends and future challenges associated with the study of fungal pathogenicity.

The role of Mss11 in Candida albicans biofilm formation

Candida albicans is an opportunistic human pathogen that can form a biofilm on biotic or inert surfaces such as epithelia and clinical devices. In this study, we examine the formation of C. albicans biofilm by establishing a key gene-centered network based on protein-protein interaction (PPI) and gene expression datasets. Starting from C. albicans Cph1 and Efg1, transcription factors associated with morphogenesis of biofilm formation, a network elucidates the complex cellular process and predicts potential unknown components related to biofilm formation. Subsequently, we analyzed the functions of Mss11 among these identified proteins to test the efficiency of the proposed computational approach. MSS11-deleted mutants were compared with a wild-type strain, indicating that the mutant is defective in forming a mature biofilm and partially attenuates the virulence of C. albicans in an infected mouse model. Finally, a DNA microarray analysis was conducted to identify the potential target genes of C. albicans Mss11. The findings of this study clarify complex gene or protein interaction during the biofilm formation process of C. albicans, supporting the application of a systems biology approach to study fungal pathogenesis.

Regulatory networks controlling nitrogen sensing and uptake in Candida albicans

Nitrogen is one of the key nutrients for microbial growth. During infection, pathogenic fungi like C. albicans need to acquire nitrogen from a broad range of different and changing sources inside the host. Detecting the available nitrogen sources and adjusting the expression of genes for their uptake and degradation is therefore crucial for survival and growth as well as for establishing an infection. Here, we analyzed the transcriptional response of C. albicans to nitrogen starvation and feeding with the infection-relevant nitrogen sources arginine and bovine serum albumin (BSA), representing amino acids and proteins, respectively. The response to nitrogen starvation was marked by an immediate repression of protein synthesis and an up-regulation of general amino acid permeases, as well as an up-regulation of autophagal processes in its later stages. Feeding with arginine led to a fast reduction in expression of general permeases for amino acids and to resumption of protein synthesis. The response to BSA feeding was generally slower, and was additionally characterized by an up-regulation of oligopeptide transporter genes. From time-series data, we inferred network interaction models for genes relevant in nitrogen detection and uptake. Each individual network was found to be largely specific for the experimental condition (starvation or feeding with arginine or BSA). In addition, we detected several novel connections between regulator and effector genes, with putative roles in nitrogen uptake. We conclude that C. albicans adopts a particular nitrogen response network, defined by sets of specific gene-gene connections for each environmental condition. All together, they form a grid of possible gene regulatory networks, increasing the transcriptional flexibility of C. albicans.

Interactive exploration of integrated biological datasets using context-sensitive workflows

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.

Deciphering metabolic traits of the fungal pathogen Aspergillus fumigatus: redundancy vs. essentiality

Incidence rates of infections caused by environmental opportunistic fungi have risen over recent decades. Aspergillus species have emerged as serious threat for the immunecompromised, and detailed knowledge about virulence-determining traits is crucial for drug target identification. As a prime saprobe, A. fumigatus has evolved to efficiently adapt to various stresses and to sustain nutritional supply by osmotrophy, which is characterized by extracellular substrate digestion followed by efficient uptake of breakdown products that are then fed into the fungal primary metabolism. These intrinsic metabolic features are believed to be related with its virulence ability. The plethora of genes that encode underlying effectors has hampered their in-depth analysis with respect to pathogenesis. Recent developments in Aspergillus molecular biology allow conditional gene expression or comprehensive targeting of gene families to cope with redundancy. Furthermore, identification of essential genes that are intrinsically connected to virulence opens accurate perspectives for novel targets in antifungal therapy.

Comparison of transcriptome technologies in the pathogenic fungus Aspergillus fumigatus reveals novel insights into the genome and MpkA dependent gene expression

BACKGROUND

The filamentous fungus Aspergillus fumigatus has become the most important airborne fungal pathogen causing life-threatening infections in immuno-compromised patients. Recently developed high-throughput transcriptome and proteome technologies, such as microarrays, RNA deep-sequencing, and LC-MS/MS of peptide mixtures, are of enormous value for systematically investigating pathogenic organisms. In the field of infection biology, one of the priorities is to collect and standardise data, in order to generate datasets that can be used to investigate and compare pathways and gene responses involved in pathogenicity. The "omics" era provides a multitude of inputs that need to be integrated and assessed. We therefore evaluated the potential of paired-end mRNA-Seq for investigating the regulatory role of the central mitogen activated protein kinase (MpkA). This kinase is involved in the cell wall integrity signalling pathway of A. fumigatus and essential for maintaining an intact cell wall in response to stress.

RESULTS

The comparison of the transcriptome and proteome of an A. fumigatus wild-type strain with an mpkA null mutant strain revealed that 70.4% of the genome was found to be expressed and that MpkA plays a significant role in the regulation of many genes involved in cell wall remodelling, oxidative stress and iron starvation response, and secondary metabolite biosynthesis. Moreover, absence of the mpkA gene also strongly affects the expression of genes involved in primary metabolism. The data were further processed to evaluate the potential of the mRNA-Seq technique. We comprehensively matched up our data to published transcriptome studies and were able to show an improved data comparability of mRNA-Seq experiments independently of the technique used. Analysis of transcriptome and proteome data revealed only a weak correlation between mRNA and protein abundance.

CONCLUSIONS

High-throughput analysis of MpkA-dependent gene expression confirmed many previous findings that this kinase is important for regulating many genes involved in metabolic pathways. Our analysis showed more than 2000 differentially regulated genes. RNA deep-sequencing is less error-prone than established microarray-based technologies. It also provides additional information in A. fumigatus studies and as a result is more suitable for the creation of extensive datasets.