On the way toward systems biology of Aspergillus fumigatus infection

In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

BACKGROUND

Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis.

MATERIALS & METHOD

In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.

CONCLUSION

Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.

How to invade a susceptible host: cellular aspects of aspergillosis

Diseases caused by Aspergillus spp. and in particular A. fumigatus are manifold and affect individuals suffering from immune dysfunctions, among them immunocompromised ones. The determinants of whether the encounter of a susceptible host with infectious propagules of this filamentous saprobe results in infection have been characterized to a limited extent. Several cellular characteristics of A. fumigatus that have evolved in its natural environment contribute to its virulence, among them general traits as well as particular ones that affect interaction with the mammalian host. Among the latter, conidial constituents, cell wall components, secreted proteins as well as extrolites shape the tight interaction of A. fumigatus with the host milieu and also contribute to evasion from immune surveillance.

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.

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.

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.

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.

Aerially transmitted human fungal pathogens: what can we learn from metagenomics and comparative genomics?

In the last few decades, aerially transmitted human fungal pathogens have been increasingly recognized to impact the clinical course of chronic pulmonary diseases, such as asthma, cystic fibrosis or chronic obstructive pulmonary disease. Thanks to recent development of culture-free high-throughput sequencing methods, the metagenomic approaches are now appropriate to detect, identify and even quantify prokaryotic or eukaryotic microorganism communities inhabiting human respiratory tract and to access the complexity of even low-burden microbe communities that are likely to play a role in chronic pulmonary diseases. In this review, we explore how metagenomics and comparative genomics studies can alleviate fungal culture bottlenecks, improve our knowledge about fungal biology, lift the veil on cross-talks between host lung and fungal microbiota, and gain insights into the pathogenic impact of these aerially transmitted fungi that affect human beings. We reviewed metagenomic studies and comparative genomic analyses of carefully chosen microorganisms, and confirmed the usefulness of such approaches to better delineate biology and pathogenesis of aerially transmitted human fungal pathogens. Efforts to generate and efficiently analyze the enormous amount of data produced by such novel approaches have to be pursued, and will potentially provide the patients suffering from chronic pulmonary diseases with a better management. This manuscript is part of the series of works presented at the "V International Workshop: Molecular genetic approaches to the study of human pathogenic fungi" (Oaxaca, Mexico, 2012).

Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0

BACKGROUND

Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time.

RESULTS

The NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed.

CONCLUSIONS

NetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit.

Systems biology of fungal infection

Elucidation of pathogenicity mechanisms of the most important human-pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections. A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviors in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions. We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modeling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.