1. Home
  2. People Index
  3. Reinhard Guthke

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

help Reinhard's tags

Related items

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 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

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

C5: inactive since 2018-06-12

Gastrointestinal colonisation and mechanisms of translocation of Candida albicans through gut mucosa

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

Public web page: http://www.funginet.de/project-c5.html

4 hidden items
4 hidden items
5 hidden items
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

Download
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

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

Creator: Reinhard Guthke

Contributor: Reinhard Guthke

Download
2 hidden items
[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
...

Creators: Thomas Wolf, Wolfgang Schmidt-Heck, Reinhard Guthke

Contributor: Thomas Wolf

Download
6 hidden items
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

Contributors: Reinhard Guthke, Sascha Schäuble

Download
3 hidden items
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

27 hidden items
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

11 hidden items
Powered by
 (v.1.9.1)
About INF | FungiNet | Funding and Programmes | Credits | Imprint
Copyright © 2008 - 2019 The University of Manchester and HITS gGmbH