Abstract (Expand)

The PspC and Hic proteins of Streptococcus pneumoniae are some of the most variable microbial immune evasion proteins identified to date. Due to structural similarities and conserved binding profiles, it was assumed for a long time that these pneumococcal surface proteins represent a protein family comprised of eleven subgroups. Recently, however, the evaluation of more proteins revealed a greater diversity of individual proteins. In contrast to previous assumptions a pattern evaluation of six PspC and five Hic variants, each representing one of the previously defined subgroups, revealed distinct structural and likely functionally regions of the proteins, and identified nine new domains and new domain alternates. Several domains are unique to PspC and Hic variants, while other domains are also present in other virulence factors encoded by pneumococci and other bacterial pathogens. This knowledge improved pattern evaluation at the level of full-length proteins, allowed a sequence comparison at the domain level and identified domains with a modular composition. This novel strategy increased understanding of individual proteins variability and modular domain composition, enabled a structural and functional characterization at the domain level and furthermore revealed substantial structural differences between PspC and Hic proteins. Given the exceptional genomic diversity of the multifunctional PspC and Hic proteins a detailed structural and functional evaluation need to be performed at the strain level. Such knowledge will also be useful for molecular strain typing and characterizing PspC and Hic proteins from new clinical S. pneumoniae strains.

Authors: S. Du, C. Vilhena, S. King, A. Sahagun-Ruiz, S. Hammerschmidt, Christine Skerka, Peter Zipfel

Date Published: 18th Jan 2021

Journal: Sci Rep

Abstract (Expand)

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

Authors: P. Patumcharoenpol, M. Nakphaichit, Gianni Panagiotou, A. Senavonge, N. Suratannon, W. Vongsangnak

Date Published: 6th Jan 2021

Journal: PLoS Comput Biol

Abstract (Expand)

Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal-bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.

Authors: Mohammad Mirhakkak, Sascha Schäuble, Tilman Klassert, S. Brunke, Philipp Brandt, D. Loos, R. V. Uribe, F. Senne de Oliveira Lino, Y. Ni, Slavena Vylkova, Hortense Slevogt, Bernhard Hube, Esther Weiß, M. O. A. Sommer, Gianni Panagiotou

Date Published: 15th Dec 2020

Journal: ISME J

Abstract (Expand)

Most unicellular organisms live in communities and express different phenotypes. Many efforts have been made to study the population dynamics of such complex communities of cells, coexisting as well-coordinated units. Minimal models based on ordinary differential equations are powerful tools that can help us understand complex phenomena. They represent an appropriate compromise between complexity and tractability; they allow a profound and comprehensive analysis, which is still easy to understand. Evolutionary game theory is another powerful tool that can help us understand the costs and benefits of the decision a particular cell of a unicellular social organism takes when faced with the challenges of the biotic and abiotic environment. This work is a binocular view at the population dynamics of such a community through the objectives of minimal modelling and evolutionary game theory. We test the behaviour of the community of a unicellular social organism at three levels of antibiotic stress. Even in the absence of the antibiotic, spikes in the fraction of resistant cells can be observed indicating the importance of bet hedging. At moderate level of antibiotic stress, we witness cyclic dynamics reminiscent of the renowned rock-paper-scissors game. At a very high level, the resistant type of strategy is the most favourable.

Authors: R. Garde, Jan Ewald, A. T. Kovacs, Stefan Schuster

Date Published: 3rd Nov 2020

Journal: Open Biol

Abstract (Expand)

The problem with cancer tissue is that its intratumoral heterogeneity and its complexity is extremely high as cells possess, depending on their location and function, different mutations, different mRNA expression and the highest intricacy in the protein pattern. Prior to genomic and proteomic analyses, it is therefore indispensable to identify the exact part of the tissue or even the exact cell. Laser-based microdissection is a tried and tested technique able to produce pure and well-defined cell material for further analysis with proteomic and genomic techniques. It sheds light on the heterogeneity of cancer or other complex diseases and enables the identification of biomarkers. This review aims to raise awareness for the reconsideration of laser-based microdissection and seeks to present current state-of-the-art combinations with omic techniques.

Authors: Ferdinand Von Eggeling, Franziska Hoffmann

Date Published: 25th Jun 2020

Journal: Proteomics

Abstract (Expand)

BACKGROUND: Viruses are important components of microbial communities modulating community structure and function; however, only a couple of tools are currently available for phage identification and analysis from metagenomic sequencing data. Here we employed the random forest algorithm to develop VirMiner, a web-based phage contig prediction tool especially sensitive for high-abundances phage contigs, trained and validated by paired metagenomic and phagenomic sequencing data from the human gut flora. RESULTS: VirMiner achieved 41.06% +/- 17.51% sensitivity and 81.91% +/- 4.04% specificity in the prediction of phage contigs. In particular, for the high-abundance phage contigs, VirMiner outperformed other tools (VirFinder and VirSorter) with much higher sensitivity (65.23% +/- 16.94%) than VirFinder (34.63% +/- 17.96%) and VirSorter (18.75% +/- 15.23%) at almost the same specificity. Moreover, VirMiner provides the most comprehensive phage analysis pipeline which is comprised of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition) and supports two-group comparison when the input (metagenomic sequence data) includes different conditions (e.g., case and control). Application of VirMiner to an independent cohort of human gut metagenomes obtained from individuals treated with antibiotics revealed that 122 KEGG orthology and 118 Pfam groups had significantly differential abundance in the pre-treatment samples compared to samples at the end of antibiotic administration, including clustered regularly interspaced short palindromic repeats (CRISPR), multidrug resistance, and protein transport. The VirMiner webserver is available at . CONCLUSIONS: We developed a comprehensive tool for phage prediction and analysis for metagenomic samples. Compared to VirSorter and VirFinder-the most widely used tools-VirMiner is able to capture more high-abundance phage contigs which could play key roles in infecting bacteria and modulating microbial community dynamics. TRIAL REGISTRATION: The European Union Clinical Trials Register, EudraCT Number: 2013-003378-28 . Registered on 9 April 2014.

Authors: T. Zheng, J. Li, Y. Ni, K. Kang, M. A. Misiakou, L. Imamovic, B. K. C. Chow, A. A. Rode, P. Bytzer, M. Sommer, Gianni Panagiotou

Date Published: 19th Mar 2019

Journal: Microbiome

Abstract (Expand)

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans. We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement-associated autoimmune diseases.

Authors: S. N. Lang, S. Germerodt, C. Glock, Christine Skerka, Peter Zipfel, Stefan Schuster

Date Published: 20th Feb 2019

Journal: PLoS One

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

Abstract (Expand)

Rationale: The liver is a central organ not only for metabolism but also immune function. Life-threatening infections of both bacterial and fungal origin can affect liver function but it is yet unknown whether molecular changes differ depending on the pathogen. We aimed to determine whether the hepatic host response to bacterial and fungal infections differs in terms of hepatic metabolism and liver function. Methods: We compared murine models of infection, including bacterial peritoneal contamination and infection (PCI), intraperitoneal and systemic C. albicans infection, at 6 and 24 h post-infection, to sham controls. The molecular hepatic host response was investigated by the detection of regulatory modules based on large-scale protein-protein interaction networks and expression data. Topological analysis of these regulatory modules was used to reveal infection-specific biological processes and molecular mechanisms. Intravital microscopy and immunofluorescence microscopy were used to further analyze specific aspects of pathophysiology such as cholestasis. Results: Down-regulation of lipid catabolism and bile acid synthesis was observed after 6 h in all infection groups. Alterations in lipid catabolism were characterized by accumulation of long chain acylcarnitines and defective beta-oxidation, which affected metabolism by 6 h. While PCI led to an accumulation of unconjugated bile acids (BA), C. albicans infection caused accumulation of conjugated BA independent of the route of infection. Hepatic dye clearance and transporter expression revealed reduced hepatic uptake in fungal infections vs. defects in secretion following polybacterial infection. Conclusion: Molecular phenotypes of lipid accumulation and cholestasis allow differentiation between pathogens as well as routes of infection at early stages in mice. Targeted metabolomics could be a useful tool for the profiling of infected/septic patients and the type of pathogen, with subsequent customization and targeting of therapy.

Authors: Barbara Schaarschmidt, S. Vlaic, A. Medyukhina, S. Neugebauer, S. Nietzsche, F. A. Gonnert, J. Rodel, M. Singer, M. Kiehntopf, Marc Thilo Figge, Ilse Jacobsen, Michael Bauer, A. T. Press

Date Published: 8th Aug 2018

Journal: Theranostics

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 (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

Abstract (Expand)

The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage-Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host-pathogen interaction of macrophages and C. albicans For this, we study the population dynamics of the macrophage-C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells.

Authors: Sybille Dühring, Jan Ewald, S. Germerodt, C. Kaleta, Thomas Dandekar, Stefan Schuster

Date Published: 14th Jul 2017

Journal: J R Soc Interface

Abstract (Expand)

The human restricted pathogen Moraxella catarrhalis is an important causal agent for exacerbations in chronic obstructive lung disease (COPD) in adults. In such patients, increased numbers of granulocytes are present in the airways, which correlate with bacteria-induced exacerbations and severity of the disease. Our study investigated whether the interaction of M. catarrhalis with the human granulocyte-specific carcinoembryonic antigen-related cell adhesion molecule (CEACAM)-3 is linked to NF-kappaB activation, resulting in chemokine production. Granulocytes from healthy donors and NB4 cells were infected with M. catarrhalis in the presence of different inhibitors, blocking antibodies and siRNA. The supernatants were analysed by ELISA for chemokines. NF-kappaB activation was determined using a luciferase reporter gene assay and chromatin-immunoprecipitation. We found evidence that the specific engagement of CEACAM3 by Moraxella catarrhalis ubiquitous surface protein A1 (UspA1) results in the activation of pro-inflammatory events, such as degranulation of neutrophils, ROS production and chemokine secretion. The interaction of UspA1 with CEACAM3 induced the activation of the NF-kappaB pathway via Syk and the Card9 pathway and was dependent on the phosphorylation of the CEACAM3 ITAM -like motif. These findings suggest that the CEACAM3 signalling in neutrophils is able to specifically modulate airway inflammation caused by infection with M. catarrhalis.

Authors: A. Heinrich, K. A. Heyl, E. Klaile, Tobias Müller, Tilman Klassert, A. Wiessner, K. Fischer, R. R. Schumann, U. Seifert, K. Riesbeck, A. Moter, B. B. Singer, S. Bachmann, Hortense Slevogt

Date Published: 3rd Apr 2016

Journal: Cell Microbiol

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

Abstract (Expand)

Fungal pathogens must assimilate local nutrients to establish an infection in their mammalian host. We focus on carbon, nitrogen, and micronutrient assimilation mechanisms, discussing how these influence host-fungus interactions during infection. We highlight several emerging trends based on the available data. First, the perturbation of carbon, nitrogen, or micronutrient assimilation attenuates fungal pathogenicity. Second, the contrasting evolutionary pressures exerted on facultative versus obligatory pathogens have led to contemporary pathogenic fungal species that display differing degrees of metabolic flexibility. The evolutionarily ancient metabolic pathways are conserved in most fungal pathogen, but interesting gaps exist in some species (e.g., Candida glabrata). Third, metabolic flexibility is generally essential for fungal pathogenicity, and in particular, for the adaptation to contrasting host microenvironments such as the gastrointestinal tract, mucosal surfaces, bloodstream, and internal organs. Fourth, this metabolic flexibility relies on complex regulatory networks, some of which are conserved across lineages, whereas others have undergone significant evolutionary rewiring. Fifth, metabolic adaptation affects fungal susceptibility to antifungal drugs and also presents exciting opportunities for the development of novel therapies.

Authors: I. V. Ene, S. Brunke, A. J. Brown, Bernhard Hube

Date Published: 4th Sep 2014

Journal: Cold Spring Harb Perspect Med

Abstract (Expand)

Beyond its well-documented role in reproduction, embryogenesis and maintenance of body tissues, vitamin A has attracted considerable attention due to its immunomodulatory effects on both the innate and the adaptive immune responses. In infectious diseases, vitamin A has been shown to have a host-protective effect in infections of bacterial, viral or protozoan origin. Nevertheless, its impact in fungal infections remains unknown. Meanwhile, the frequency of invasive mycoses keeps on growing, with Candida albicans being the major opportunistic fungal pathogen and associated with high mortality. In the present work, we explored the impact of all-trans retinoic acid (atRA), the most active metabolite of vitamin A, on the innate immune response against C. albicans in human monocytes. Our results show a strong immunomodulatory role for atRA, leading to a significant down-regulation of the fungi-induced expression and secretion of the pro-inflammatory cytokines TNFalpha, IL6 and IL12. Moreover, atRA significantly suppressed the expression of Dectin-1, a major fungal pattern recognition receptor, as well as the Dectin-1-dependent cytokine production. Both RAR-dependent and RAR-independent mechanisms seem to play a role in the atRA-mediated immunomodulation. Our findings open a new direction to elucidate the role of vitamin A on the immune function during fungal infections.

Authors: Tilman Klassert, A. Hanisch, J. Brauer, E. Klaile, K. A. Heyl, M. K. Mansour, J. M. Tam, J. M. Vyas, Hortense Slevogt

Date Published: 17th Aug 2014

Journal: Med Microbiol Immunol

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