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Reinhard Guthke

About Reinhard Guthke:

Bioprocess data analysis (data mining)
Transcriptomics and proteomics data analysis and modelling
Gene Regulatory Network inference (Reverse engineering)
Decision making support systems in diagnostics, therapy and biotechnology

SEEK ID: https://funginet.hki-jena.de/people/2

Location:  Germany

Expertise: Mathematical Modelling, Gene Regulatory Network Inference, Integrative Data Analysis

Tools: Matlab, R, ODE

ORCID: Not specified

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B3 (E): inactive since 2018-06-07

Predictive modelling of host-fungal pathogen interactions by reconstruction of gene regulatory networks

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

Public web page: http://www.funginet.de/project-b3.html

FungiNet total: inactive since 2018-06-04
No description specified

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

Public web page: http://www.funginet.de/home.html

FungiNet C - Candida projects: inactive since 2018-06-12
No description specified

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

Public web page: http://www.funginet.de/project-area-c.html

FungiNet B - Bioinformatics projects: inactive since 2018-06-04
No description specified

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

Public web page: http://www.funginet.de/project-area-b.html

FungiNet A - Aspergillus projects: inactive since 2018-06-04
No description specified

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

Public web page: http://www.funginet.de/project-area-a.html

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DMykG 2015 Programmheft
FungiNet total, FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF, Z2
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Creator: Reinhard Guthke

Contributor: Reinhard Guthke

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DMykG_2015 Programmheft
FungiNet total, FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF, Z2
No description specified

Creator: Reinhard Guthke

Contributor: Reinhard Guthke

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Annual Meeting CRC/TR 124 FungiNet - 2014 - Program
FungiNet total
No description specified

Creator: Reinhard Guthke

Contributor: Reinhard Guthke

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[DEMO] Response of hepatocytes to rapid change in the medium composition (gene regulatory network): Model 1100 "freshMedia"
FungiNet total, FungiNet B - Bioinformatics projects

Primary hepatocytes were exposed to a stimulus by exchanging culture medium, thereby simulating changes in the blood composition. The expression of genes at different time points was recorded. Differentially expressed genes were clustered using fuzzy c-means algorithm into five groups. The arcs of the possible network were identified using the NetGenerator algorithm under the restriction of biological knowledge. The analysis was restricted to the main metabolic pathways of hepatocytes. The reverse
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Creators: Thomas Wolf, Wolfgang Schmidt-Heck, Reinhard Guthke

Contributor: Thomas Wolf

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SOP Processing of RNA-Seq Data
FungiNet B - Bioinformatics projects

RNA-Seq is a method to measure the transcript/gene expression by adaption of High-throughput sequencing technologies. In contrast to microarrays where the measurement is based on a hybridization process, RNA-Seq sequences cDNA in order to get information about a sample's RNA content.

Creators: Reinhard Guthke, Jörg Linde, Steffen Priebe

Contributor: Reinhard Guthke

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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

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SFB FungiNet: Interns Seminar in Jena A5 & B1 - 9.9.2014

9. September 2014

A5 und B1

Slevogt / Schuster

Start Date: 9th Sep 2014

End Date: Not specified

Event Website: Not specified

Country:  Germany

City: Jena

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