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Survival Strategies of Pathogenic Candida Species in Human Blood Show Independent and Specific Adaptations.
INF, B3 (E), C1, C3

Abstract (Expand)

Only four species, Candida albicans, C. glabrata, C. parapsilosis, and C. tropicalis, together account for about 90% of all Candida bloodstream infections and are among the most common causes of invasive … fungal infections of humans. However, virulence potential varies among these species, and the phylogenetic tree reveals that their pathogenicity may have emerged several times independently during evolution. We therefore tested these four species in a human whole-blood infection model to determine, via comprehensive dual-species RNA-sequencing analyses, which fungal infection strategies are conserved and which are recent evolutionary developments. The ex vivo infection progressed from initial immune cell interactions to nearly complete killing of all fungal cells. During the course of infection, we characterized important parameters of pathogen-host interactions, such as fungal survival, types of interacting immune cells, and cytokine release. On the transcriptional level, we obtained a predominantly uniform and species-independent human response governed by a strong upregulation of proinflammatory processes, which was downregulated at later time points after most of the fungal cells were killed. In stark contrast, we observed that the different fungal species pursued predominantly individual strategies and showed significantly different global transcriptome patterns. Among other findings, our functional analyses revealed that the fungal species relied on different metabolic pathways and virulence factors to survive the host-imposed stress. These data show that adaptation of Candida species as a response to the host is not a phylogenetic trait, but rather has likely evolved independently as a prerequisite to cause human infections.IMPORTANCE To ensure their survival, pathogens have to adapt immediately to new environments in their hosts, for example, during the transition from the gut to the bloodstream. Here, we investigated the basis of this adaptation in a group of fungal species which are among the most common causes of hospital-acquired infections, the Candida species. On the basis of a human whole-blood infection model, we studied which genes and processes are active over the course of an infection in both the host and four different Candida pathogens. Remarkably, we found that, while the human host response during the early phase of infection is predominantly uniform, the pathogens pursue largely individual strategies and each one regulates genes involved in largely disparate processes in the blood. Our results reveal that C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis all have developed individual strategies for survival in the host. This indicates that their pathogenicity in humans has evolved several times independently and that genes which are central for survival in the host for one species may be irrelevant in another.

Authors: P. Kammer, S. McNamara, Thomas Wolf, Theresia Conrad, Stefanie Allert, F. Gerwien, Kerstin Hünniger, Oliver Kurzai, Reinhard Guthke, Bernhard Hube, Jörg Linde, S. Brunke

Date Published: 6th Oct 2020

Journal: mBio

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin.
INF, A1, C5

Abstract (Expand)

BACKGROUND: Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still … remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration. RESULTS: Here, we made use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall which leads to a compensatory stress response. We analyzed the omics data using two different approaches: First, we applied a simple, classical approach by comparing lists of differentially expressed genes (DEGs), differentially synthesized proteins (DSyPs) and differentially secreted proteins (DSePs); second, we used a recently published module-detecting approach, ModuleDiscoverer, to identify regulatory modules from PPINs in conjunction with the experimental data. Our results demonstrate that regulatory modules show a notably higher overlap between the different molecular levels and time points than the classical approach. The additional structural information provided by regulatory modules allows for topological analyses. As a result, we detected a significant association of omics data with distinct biological processes such as regulation of kinase activity, transport mechanisms or amino acid metabolism. We also found a previously unreported increased production of the secondary metabolite fumagillin by A. fumigatus upon exposure to caspofungin. Furthermore, a topology-based analysis of potential key factors contributing to drug-caused side effects identified the highly conserved protein polyubiquitin as a central regulator. Interestingly, polyubiquitin UbiD neither belonged to the groups of DEGs, DSyPs nor DSePs but most likely strongly influenced their levels. CONCLUSION: Module-detecting approaches support the effective integration of multilevel omics data and provide a deep insight into complex biological relationships connecting these levels. They facilitate the identification of potential key players in the organism's stress response which cannot be detected by commonly used approaches comparing lists of differentially abundant molecules.

Authors: Theresia Conrad, Olaf Kniemeyer, S. G. Henkel, T. Kruger, D. J. Mattern, V. Valiante, Reinhard Guthke, Ilse Jacobsen, Axel Brakhage, S. Vlaic, Jörg Linde

Date Published: 20th Oct 2018

Journal: BMC Syst Biol

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.
INF, B1

Abstract (Expand)

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. … However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Authors: S. Vlaic, Theresia Conrad, C. Tokarski-Schnelle, M. Gustafsson, U. Dahmen, Reinhard Guthke, Stefan Schuster

Date Published: 11th Jan 2018

Journal: Sci Rep

Specific and Novel microRNAs Are Regulated as Response to Fungal Infection in Human Dendritic Cells.
INF, A2, B3 (E), C3

Abstract (Expand)

Within the last two decades, the incidence of invasive fungal infections has been significantly increased. They are characterized by high mortality rates and are often caused by Candida albicans and … Aspergillus fumigatus. The increasing number of infections underlines the necessity for additional anti-fungal therapies, which require extended knowledge of gene regulations during fungal infection. MicroRNAs are regulators of important cellular processes, including the immune response. By analyzing their regulation and impact on target genes, novel therapeutic and diagnostic approaches may be developed. Here, we examine the role of microRNAs in human dendritic cells during fungal infection. Dendritic cells represent the bridge between the innate and the adaptive immune systems. Therefore, analysis of gene regulation of dendritic cells is of particular significance. By applying next-generation sequencing of small RNAs, we quantify microRNA expression in monocyte-derived dendritic cells after 6 and 12 h of infection with C. albicans and A. fumigatus as well as treatment with lipopolysaccharides (LPS). We identified 26 microRNAs that are differentially regulated after infection by the fungi or LPS. Three and five of them are specific for fungal infections after 6 and 12 h, respectively. We further validated interactions of miR-132-5p and miR-212-5p with immunological relevant target genes, such as FKBP1B, KLF4, and SPN, on both RNA and protein level. Our results indicate that these microRNAs fine-tune the expression of immune-related target genes during fungal infection. Beyond that, we identified previously undiscovered microRNAs. We validated three novel microRNAs via qRT-PCR. A comparison with known microRNAs revealed possible relations with the miR-378 family and miR-1260a/b for two of them, while the third one features a unique sequence with no resemblance to known microRNAs. In summary, this study analyzes the effect of known microRNAs in dendritic cells during fungal infections and proposes novel microRNAs that could be experimentally verified.

Authors: Andreas Dix, K. Czakai, I. Leonhardt, K. Schaferhoff, M. Bonin, Reinhard Guthke, Hermann Einsele, Oliver Kurzai, Jürgen Löffler, Jörg Linde

Date Published: 11th Mar 2017

Journal: Front Microbiol

Use of systems biology to decipher host microbial interactions and predict pathological consequences
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

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; (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilising high-throughput data (e.g. SNP detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as 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, also the prediction of large-scale networks will be feasible that can used for multi-dimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalised medicine with high-throughput data and generation of multi-scale host-pathogen interaction models.

Authors: Andreas Dix, S. Vlaic, Reinhard Guthke, Jörg Linde

Date Published: 27th Apr 2016

Journal: Clin Microbiol Infect

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

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.

Authors: Reinhard Guthke, S. Gerber, Theresia Conrad, S. Vlaic, S. Durmus, T. Cakir, F. E. Sevilgen, Ekaterina Shelest, Jörg Linde

Date Published: 22nd Apr 2016

Journal: Front Microbiol

How to Predict Molecular Interactions between Species
FungiNet B - Bioinformatics projects, INF

Abstract (Expand)

Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, … with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.

Authors: Sylvie Schulze, J. Schleicher, Reinhard Guthke, Jörg Linde

Date Published: 31st Mar 2016

Journal: Front Microbiol

Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk … for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy toward this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of hematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time reverse transcription polymerase chain reaction (RT-PCR) assay and we also found a down-regulation of S100B in A. fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.

Authors: Andreas Dix, K. Czakai, J. Springer, M. Fliesser, M. Bonin, Reinhard Guthke, A. L. Schmitt, Hermann Einsele, Jörg Linde, Jürgen Löffler

Date Published: 21st Mar 2016

Journal: Front Microbiol

Proteomic Profiling of Serological Responses to Aspergillus fumigatus Antigens in Patients with Invasive Aspergillosis
Z2

Abstract (Expand)

Aspergillus fumigatus is the species that most commonly causes the opportunistic infection invasive aspergillosis (IA) in patients being treated for hematological malignancies. Little is known about … the A. fumigatus proteins that trigger the production of Aspergillus-specific IgG antibodies during the course of IA. To characterize the serological response to A. fumigatus protein antigens, mycelial proteins were separated by 2-D gel electrophoresis. The gels were immunoblotted with sera from patients with probable and proven IA and control patients without IA. We identified 49 different fungal proteins, which gave a positive IgG antibody signal. Most of these antigens play a role in primary metabolism and stress responses. Overall, our analysis identified 18 novel protein antigens from A. fumigatus. To determine whether these antigens can be used as diagnostic or prognostic markers or exhibit a protective activity, we employed supervised machine learning with decision trees. We identified two candidates for further analysis, the protein antigens CpcB and Shm2. Heterologously produced Shm2 induced a strongly proinflammatory response in human peripheral blood mononuclear cells after in vitro stimulation. In contrast, CpcB did not activate the immune response of PBMCs. These findings could serve as the basis for the development of an immunotherapy of IA.

Authors: J. Teutschbein, S. Simon, J. Lother, J. Springer, P. Hortschansky, C. O. Morton, Jürgen Löffler, Hermann Einsele, E. Conneally, T. R. Rogers, Reinhard Guthke, Axel Brakhage, Olaf Kniemeyer

Date Published: 15th Mar 2016

Journal: J Proteome Res

Draft Genome Sequences of Fungus Aspergillus calidoustus
INF

Abstract (Expand)

Here, we report the draft genome sequence of Aspergillus calidoustus (strain SF006504). The functional annotation of A. calidoustus predicts a relatively large number of secondary metabolite gene … clusters. The presented genome sequence builds the basis for further genome mining.

Authors: F. Horn, Jörg Linde, D. J. Mattern, G. Walther, Reinhard Guthke, K. Scherlach, K. Martin, Axel Brakhage, L. Petzke, Vito Valiante

Date Published: 12th Mar 2016

Journal: Genome Announc

Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational … systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

Authors: J. Schleicher, Theresia Conrad, M. Gustafsson, G. Cedersund, Reinhard Guthke, Jörg Linde

Date Published: 10th Feb 2016

Journal: Brief Funct Genomics

Editorial: Computational Systems Biology of Pathogen-Host Interactions
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract

Not specified

Authors: S. Durmus, T. Cakir, Reinhard Guthke

Date Published: 4th Feb 2016

Journal: Front Microbiol

Candida albicans infection leads to barrier breakdown and a MAPK/NF-kappaB mediated stress response in the intestinal epithelial cell line C2BBe1
FungiNet B - Bioinformatics projects, INF

Abstract (Expand)

Intestinal epithelial cells (IEC) form a tight barrier to the gut lumen. Paracellular permeability of the intestinal barrier is regulated by tight junction proteins and can be modulated by microorganisms … and other stimuli. The polymorphic fungus Candida albicans, a frequent commensal of the human mucosa has the capacity of traversing this barrier and establishing systemic disease within the host. Infection of polarized C2BBe1 IEC with wild-type C. albicans led to a transient increase of transepithelial electric resistance (TEER) before subsequent barrier disruption, accompanied by a strong decline of junctional protein levels and substantial, but considerably delayed cytotoxicity. Time-resolved microarray-based transcriptome analysis of C. albicans challenged IEC revealed a prominent role of NF-kappaB and MAPK signaling pathways in the response to infection. Hence, we inferred a gene regulatory network based on differentially expressed NF-kappaB and MAPK pathway components and their predicted transcriptional targets. The network model predicted activation of GDF15 by NF-kappaB was experimentally validated. Furthermore, inhibition of NF-kappaB activation in C. albicans infected C2BBe1 cells led to enhanced cytotoxicity in the epithelial cells. Taken together our study identifies NF-kappaB activation as an important protective signaling pathway in the response of epithelial cells to C. albicans. This article is protected by copyright. All rights reserved.

Authors: M. Bohringer, S. Pohlers, Sylvie Schulze, Daniela Albrecht-Eckardt, J. Piegsa, M. Weber, R. Martin, Kerstin Hünniger, Jörg Linde, Reinhard Guthke, Oliver Kurzai

Date Published: 12th Jan 2016

Journal: Cell Microbiol

Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, INF

Abstract (Expand)

Mitogen activated protein kinases (MAPKs) are highly conserved in eukaryotic organisms. In pathogenic fungi, their activities were assigned to different physiological functions including drug adaptation … and resistance. Aspergillus fumigatus is a human pathogenic fungus, which causes life-threatening invasive infections. Therapeutic options against invasive mycoses are still limited. One of the clinically used drugs is caspofungin, which specifically targets the fungal cell wall biosynthesis. A systems biology approach, based on comprehensive transcriptome data sets and mathematical modeling, was employed to infer a regulatory network and identify key interactions during adaptation to caspofungin stress in A. fumigatus. Mathematical modeling and experimental validations confirmed an intimate cross talk occurring between the cell wall-integrity and the high osmolarity-glycerol signaling pathways. Specifically, increased concentrations of caspofungin promoted activation of these signalings. Moreover, caspofungin affected the intracellular transport, which caused an additional osmotic stress that is independent of glucan inhibition. High concentrations of caspofungin reduced this osmotic stress, and thus decreased its toxic activity. Our results demonstrated that MAPK signaling pathways play a key role during caspofungin adaptation and are contributing to the paradoxical effect exerted by this drug.

Authors: R. Altwasser, C. Baldin, J. Weber, Reinhard Guthke, O. Kniemeyer, Axel Brakhage, Jörg Linde, V. Valiante

Date Published: 10th Sep 2015

Journal: PLoS One

Draft Genome Sequence of the Fungus Penicillium brasilianum MG11
FungiNet A - Aspergillus projects

Abstract (Expand)

The genus Penicillium belongs to the phylum Ascomycota and includes a variety of fungal species important for food and drug production. We report the draft genome sequence of Penicillium brasilianum … MG11. This strain was isolated from soil, and it was reported to produce different secondary metabolites.

Authors: F. Horn, Jörg Linde, D. J. Mattern, G. Walther, Reinhard Guthke, Axel Brakhage, V. Valiante

Date Published: 5th Sep 2015

Journal: Genome Announc

Computing the various pathways of penicillin synthesis and their molar yields
FungiNet B - Bioinformatics projects

Abstract (Expand)

More than 80 years after its discovery, penicillin is still a widely used and commercially highly important antibiotic. Here, we analyse the metabolic network of penicillin synthesis in Penicillium … chrysogenum based on the concept of elementary flux modes. In particular, we consider the synthesis of the invariant molecular core of the various subtypes of penicillin and the two major ways of incorporating sulfur: transsulfuration and direct sulfhydrylation. 66 elementary modes producing this invariant core are obtained. These show four different yields with respect to glucose, notably (1/2), 2/5, 1/3, and 2/7, with the highest yield of (1/2) occurring only when direct sulfhydrylation is used and alpha-aminoadipate is completely recycled. In the case of no recycling of this intermediate, we find the maximum yield to be 2/7. We compare these values with earlier literature values. Our analysis provides a systematic overview of the redundancy in penicillin synthesis and a detailed insight into the corresponding routes. Moreover, we derive suggestions for potential knockouts that could increase the average yield.

Authors: M. T. Prausse, S. Schauble, Reinhard Guthke, Stefan Schuster

Date Published: 19th Aug 2015

Journal: Biotechnol Bioeng

Hitting the caspofungin salvage pathway of human-pathogenic fungi with the novel lasso peptide humidimycin (MDN-0010)
FungiNet A - Aspergillus projects, B3 (E)

Abstract (Expand)

Fungal infections have increased dramatically in the last 2 decades, and fighting infectious diseases requires innovative approaches such as the combination of two drugs acting on different targets or … even targeting a salvage pathway of one of the drugs. The fungal cell wall biosynthesis is inhibited by the clinically used antifungal drug caspofungin. This antifungal activity has been found to be potentiated by humidimycin, a new natural product identified from the screening of a collection of 20,000 microbial extracts, which has no major effect when used alone. An analysis of transcriptomes and selected Aspergillus fumigatus mutants indicated that humidimycin affects the high osmolarity glycerol response pathway. By combining humidimycin and caspofungin, a strong increase in caspofungin efficacy was achieved, demonstrating that targeting different signaling pathways provides an excellent basis to develop novel anti-infective strategies.

Authors: Vito Valiante, M. C. Monteiro, J. Martin, R. Altwasser, N. El Aouad, I. Gonzalez, Olaf Kniemeyer, E. Mellado, S. Palomo, N. de Pedro, I. Perez-Victoria, J. R. Tormo, F. Vicente, F. Reyes, O. Genilloud, Axel Brakhage

Date Published: 8th Jun 2015

Journal: Antimicrob Agents Chemother

A review on computational systems biology of pathogen-host interactions
FungiNet B - Bioinformatics projects

Abstract (Expand)

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.

Authors: S. Durmus, T. Cakir, A. Ozgur, Reinhard Guthke

Date Published: 9th Apr 2015

Journal: Front Microbiol

Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF

Abstract (Expand)

Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used … detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to classify if a sample contains a bacterial, fungal, or mock-infection. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional test dataset comprising Cryptococcus neoformans infections. Furthermore, the classifier is robust to Gaussian noise, indicating correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.

Authors: Andreas Dix, Kerstin Hünniger, M. Weber, Reinhard Guthke, Oliver Kurzai, Jörg Linde

Date Published: 11th Mar 2015

Journal: Front Microbiol

Data- and knowledge-based modeling of gene regulatory networks: an update
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network … inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.

Authors: Jörg Linde, Sylvie Schulze, S. G. Henkel, Reinhard Guthke

Date Published: 2nd Mar 2015

Journal: EXCLI J

Draft Genome Sequence and Gene Annotation of the Entomopathogenic Fungus Verticillium hemipterigenum
FungiNet A - Aspergillus projects, INF

Abstract (Expand)

Verticillium hemipterigenum (anamorph Torrubiella hemipterigena) is an entomopathogenic fungus and produces a broad range of secondary metabolites. Here, we present the draft genome sequence of the … fungus, including gene structure and functional annotation. Genes were predicted incorporating RNA-Seq data and functionally annotated to provide the basis for further genome studies.

Authors: F. Horn, A. Habel, D. H. Scharf, J. Dworschak, Axel Brakhage, Reinhard Guthke, C. Hertweck, Jörg Linde

Date Published: 24th Jan 2015

Journal: Genome Announc

Defining the transcriptomic landscape of Candida glabrata by RNA-Seq
FungiNet C - Candida projects, INF

Abstract (Expand)

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.

Authors: Jörg Linde, S. Duggan, M. Weber, F. Horn, Patricia Sieber, D. Hellwig, Konstantin Riege, Manja Marz, R. Martin, Reinhard Guthke, Oliver Kurzai

Date Published: 13th Jan 2015

Journal: Nucleic Acids Res

Microevolution of Candida albicans in macrophages restores filamentation in a nonfilamentous mutant
FungiNet C - Candida projects, INF

Abstract (Expand)

Following antifungal treatment, Candida albicans, and other human pathogenic fungi can undergo microevolution, which leads to the emergence of drug resistance. However, the capacity for microevolutionary … adaptation of fungi goes beyond the development of resistance against antifungals. Here we used an experimental microevolution approach to show that one of the central pathogenicity mechanisms of C. albicans, the yeast-to-hyphae transition, can be subject to experimental evolution. The C. albicans cph1Delta/efg1Delta mutant is nonfilamentous, as central signaling pathways linking environmental cues to hyphal formation are disrupted. We subjected this mutant to constant selection pressure in the hostile environment of the macrophage phagosome. In a comparatively short time-frame, the mutant evolved the ability to escape macrophages by filamentation. In addition, the evolved mutant exhibited hyper-virulence in a murine infection model and an altered cell wall composition compared to the cph1Delta/efg1Delta strain. Moreover, the transcriptional regulation of hyphae-associated, and other pathogenicity-related genes became re-responsive to environmental cues in the evolved strain. We went on to identify the causative missense mutation via whole genome- and transcriptome-sequencing: a single nucleotide exchange took place within SSN3 that encodes a component of the Cdk8 module of the Mediator complex, which links transcription factors with the general transcription machinery. This mutation was responsible for the reconnection of the hyphal growth program with environmental signals in the evolved strain and was sufficient to bypass Efg1/Cph1-dependent filamentation. These data demonstrate that even central transcriptional networks can be remodeled very quickly under appropriate selection pressure.

Authors: A. Wartenberg, Jörg Linde, R. Martin, M. Schreiner, F. Horn, Ilse Jacobsen, S. Jenull, Thomas Wolf, K. Kuchler, Reinhard Guthke, Oliver Kurzai, A. Forche, C. d'Enfert, S. Brunke, Bernhard Hube

Date Published: 4th Dec 2014

Journal: PLoS Genet

Draft Genome Sequence of Streptomyces iranensis
FungiNet A - Aspergillus projects, INF

Abstract (Expand)

Streptomyces iranensis HM 35 has been shown to exhibit 72.7% DNA-DNA similarity to the important drug rapamycin (sirolimus)-producing Streptomyces rapamycinicus NRRL5491. Here, we report the genome … sequence of HM 35, which represents a partially overlapping repertoire of secondary metabolite gene clusters with S. rapamycinicus, including the gene cluster for rapamycin biosynthesis.

Authors: F. Horn, V. Schroeckh, T. Netzker, Reinhard Guthke, Axel Brakhage, Jörg Linde

Date Published: 19th Jul 2014

Journal: Genome Announc

The novel globin protein fungoglobin is involved in low oxygen adaptation of Aspergillus fumigatus
FungiNet A - Aspergillus projects, INF, Z2

Abstract (Expand)

The human pathogenic fungus Aspergillus fumigatus normally lives as a soil saprophyte. Its environment includes poorly oxygenated substrates that also occur during tissue invasive growth of the fungus … in the human host. Up to now, few cellular factors have been identified that allow the fungus to efficiently adapt its energy metabolism to hypoxia. Here, we cultivated A. fumigatus in an O2 -controlled fermenter and analysed its responses to O2 limitation on a minute timescale. Transcriptome sequencing revealed several genes displaying a rapid and highly dynamic regulation. One of these genes was analysed in detail and found to encode fungoglobin, a previously uncharacterized member of the sensor globin protein family widely conserved in filamentous fungi. Besides low O2 , iron limitation also induced transcription, but regulation was not entirely dependent on the two major transcription factors involved in adaptation to iron starvation and hypoxia, HapX and SrbA respectively. The protein was identified as a functional haemoglobin, as binding of this cofactor was detected for the recombinant protein. Gene deletion in A. fumigatus confirmed that haem-binding fungoglobins are important for growth in microaerobic environments with O2 levels far lower than in hypoxic human tissue.

Authors: F. Hillmann, Jörg Linde, N. Beckmann, M. Cyrulies, M. Strassburger, T. Heinekamp, H. Haas, Reinhard Guthke, Olaf Kniemeyer, Axel Brakhage

Date Published: 7th Jul 2014

Journal: Mol Microbiol

Regulatory networks controlling nitrogen sensing and uptake in Candida albicans
FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF

Abstract (Expand)

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.

Authors: S. Ramachandra, Jörg Linde, Matthias Brock, Reinhard Guthke, Bernhard Hube, S. Brunke

Date Published: 20th Mar 2014

Journal: PLoS One

Regulation of fungal secondary metabolism
FungiNet total, FungiNet A - Aspergillus projects

Abstract (Expand)

Fungi produce a multitude of low-molecular-mass compounds known as secondary metabolites, which have roles in a range of cellular processes such as transcription, development and intercellular … communication. In addition, many of these compounds now have important applications, for instance, as antibiotics or immunosuppressants. Genome mining efforts indicate that the capability of fungi to produce secondary metabolites has been substantially underestimated because many of the fungal secondary metabolite biosynthesis gene clusters are silent under standard cultivation conditions. In this Review, I describe our current understanding of the regulatory elements that modulate the transcription of genes involved in secondary metabolism. I also discuss how an improved knowledge of these regulatory elements will ultimately lead to a better understanding of the physiological and ecological functions of these important compounds and will pave the way for a novel avenue to drug discovery through targeted activation of silent gene clusters.

Author: Axel A Brakhage

Date Published: 26th Nov 2012

Journal: Nat. Rev. Microbiol.

Comparison of transcriptome technologies in the pathogenic fungus Aspergillus fumigatus reveals novel insights into the genome and MpkA dependent gene expression
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects

Abstract

ABSTRACT:

Authors: Sebastian Müller, Clara Baldin, Marco Groth, Reinhard Guthke, Olaf Kniemeyer, Axel A Brakhage, Vito Valiante

Date Published: 2nd Oct 2012

Journal: BMC Genomics

An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells
FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF

Abstract (Expand)

The ability to adapt to diverse micro-environmental challenges encountered within a host is of pivotal importance to the opportunistic fungal pathogen Candida albicans. We have quantified C. albicans … and M. musculus gene expression dynamics during phagocytosis by dendritic cells in a genome-wide, time-resolved analysis using simultaneous RNA-seq. A robust network inference map was generated from this dataset using NetGenerator, predicting novel interactions between the host and the pathogen. We experimentally verified predicted interdependent sub-networks comprising Hap3 in C. albicans, and Ptx3 and Mta2 in M. musculus. Remarkably, binding of recombinant Ptx3 to the C. albicans cell wall was found to regulate the expression of fungal Hap3 target genes as predicted by the network inference model. Pre-incubation of C. albicans with recombinant Ptx3 significantly altered the expression of Mta2 target cytokines such as IL-2 and IL-4 in a Hap3-dependent manner, further suggesting a role for Mta2 in host-pathogen interplay as predicted in the network inference model. We propose an integrated model for the functionality of these sub-networks during fungal invasion of immune cells, according to which binding of Ptx3 to the C. albicans cell wall induces remodeling via fungal Hap3 target genes, thereby altering the immune response to the pathogen. We show the applicability of network inference to predict interactions between host-pathogen pairs, demonstrating the usefulness of this systems biology approach to decipher mechanisms of microbial pathogenesis.

Authors: L. Tierney, Jörg Linde, S. Muller, S. Brunke, J. C. Molina, Bernhard Hube, U. Schock, Reinhard Guthke, K. Kuchler

Date Published: 12th Mar 2012

Journal: Front Microbiol

Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects

Abstract (Expand)

BACKGROUND: In System Biology, iterations of wet-lab experiments followed by modelling approaches and model-inspired experiments describe a cyclic workflow. This approach is especially useful for the … inference of gene regulatory networks based on high-throughput gene expression data. Experiments can verify or falsify the predicted interactions allowing further refinement of the network model. Aspergillus fumigatus is a major human fungal pathogen. One important virulence trait is its ability to gain sufficient amounts of iron during infection process. Even though some regulatory interactions are known, we are still far from a complete understanding of the way iron homeostasis is regulated. RESULTS: In this study, we make use of a reverse engineering strategy to infer a regulatory network controlling iron homeostasis in A. fumigatus. The inference approach utilizes the temporal change in expression data after a change from iron depleted to iron replete conditions. The modelling strategy is based on a set of linear differential equations and offers the possibility to integrate known regulatory interactions as prior knowledge. Moreover, it makes use of important selection criteria, such as sparseness and robustness. By compiling a list of known regulatory interactions for iron homeostasis in A. fumigatus and softly integrating them during network inference, we are able to predict new interactions between transcription factors and target genes. The proposed activation of the gene expression of hapX by the transcriptional regulator SrbA constitutes a so far unknown way of regulating iron homeostasis based on the amount of metabolically available iron. This interaction has been verified by Northern blots in a recent experimental study. In order to improve the reliability of the predicted network, the results of this experimental study have been added to the set of prior knowledge. The final network includes three SrbA target genes. Based on motif searching within the regulatory regions of these genes, we identify potential DNA-binding sites for SrbA. Our wet-lab experiments demonstrate high-affinity binding capacity of SrbA to the promoters of hapX, hemA and srbA. CONCLUSIONS: This study presents an application of the typical Systems Biology circle and is based on cooperation between wet-lab experimentalists and in silico modellers. The results underline that using prior knowledge during network inference helps to predict biologically important interactions. Together with the experimental results, we indicate a novel iron homeostasis regulating system sensing the amount of metabolically available iron and identify the binding site of iron-related SrbA target genes. It will be of high interest to study whether these regulatory interactions are also important for close relatives of A. fumigatus and other pathogenic fungi, such as Candida albicans.

Authors: Jörg Linde, P. Hortschansky, E. Fazius, Axel Brakhage, Reinhard Guthke, H. Haas

Date Published: 19th Jan 2012

Journal: BMC Syst Biol

FungiDB: an integrated functional genomics database for fungi
FungiNet B - Bioinformatics projects

Abstract (Expand)

FungiDB (http://FungiDB.org) is a functional genomic resource for pan-fungal genomes that was developed in partnership with the Eukaryotic Pathogen Bioinformatic resource center (http://EuPathDB.org). … FungiDB uses the same infrastructure and user interface as EuPathDB, which allows for sophisticated and integrated searches to be performed using an intuitive graphical system. The current release of FungiDB contains genome sequence and annotation from 18 species spanning several fungal classes, including the Ascomycota classes, Eurotiomycetes, Sordariomycetes, Saccharomycetes and the Basidiomycota orders, Pucciniomycetes and Tremellomycetes, and the basal 'Zygomycete' lineage Mucormycotina. Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yeast two hybrid interaction data. The underlying genomic sequence and annotation combined with functional data, additional data from the FungiDB standard analysis pipeline and the ability to leverage orthology provides a powerful resource for in silico experimentation.

Authors: Jason E Stajich, Todd Harris, Brian P Brunk, John Brestelli, Steve Fischer, Omar S Harb, Jessica C Kissinger, Wei Li, Vishal Nayak, Deborah F Pinney, Chris J Stoeckert, David S Roos

Date Published: 7th Nov 2011

Journal: Nucleic Acids Res.

FungiFun: a web-based application for functional categorization of fungal genes and proteins
INF

Abstract (Expand)

FungiFun assigns functional annotations to fungal genes or proteins and performs gene set enrichment analysis. Based on three different classification methods (FunCat, GO and KEGG), FungiFun categorizes … genes and proteins for several fungal species on different levels of annotation detail. It is web-based and accessible to users without any programming skills. FungiFun is the first tool offering gene set enrichment analysis including the FunCat categorization. Two biological datasets for Aspergillus fumigatus and Candida albicans were analyzed using FungiFun, providing an overview of the usage and functions of the tool. FungiFun is freely accessible at https://www.omnifung.hki-jena.de/FungiFun/.

Authors: S. Priebe, Jörg Linde, D. Albrecht, Reinhard Guthke, A. A. Brakhage

Date Published: 10th Nov 2010

Journal: Fungal Genet Biol

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