1. Home
  2. Projects Index
  3. FungiNet B - Bioinformatics projects

FungiNet B - Bioinformatics projects

Overview
No description specified

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

SEEK ID: https://funginet.hki-jena.de/projects/16

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

Organisms: Aspergillus fumigatus, Candida albicans

INF PALs: No PALs for this Project

Project created: 1st Oct 2012

Related items

Johannes Balkenhol

Projects: B2, FungiNet B - Bioinformatics projects, FungiNet total, B1

Institutions: Julius Maximilians University Würzburg, Biocenter, Department of Bioinformatics

Axel Brakhage project_administratorprogramme_administrator

Projects: FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, FungiNet total, A1, Z1

Institutions: Friedrich Schiller University Jena, Institute of Microbiology, Dept. of Microbiology and Molecular Biology, Friedrich Schiller University Jena, Jena School for Microbial Communication, Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

Andreas Caput

Projects: B1, FungiNet B - Bioinformatics projects, FungiNet total

Institutions: Julius Maximilians University Würzburg, Biocenter, Department of Bioinformatics

Thomas Dandekar project_administrator

Projects: FungiNet B - Bioinformatics projects, FungiNet total, B1, B2

Institutions: Julius Maximilians University Würzburg, Biocenter, Department of Bioinformatics

Stefanie Dietrich : inactive since 2020-06-09

Projects: B4, FungiNet total, FungiNet B - Bioinformatics projects

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

2 hidden items
7 hidden items
9 hidden items
Ortholog clusters
FungiNet B - Bioinformatics projects, B1

This is the result of OrthoMCL analysis.

Creator: Shishir K Gupta

Contributor: Shishir K Gupta

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
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
3 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
8 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
1 hidden item
Kruppel-like Factor 4 modulates interleukin-6 release in human dendritic cells after in vitro stimulation with Aspergillus fumigatus and Candida albicans
FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF

Abstract (Expand)

Invasive fungal infections are associated with high mortality rates and are mostly caused by the opportunistic fungi Aspergillus fumigatus and Candida albicans. Immune responses against these fungi are … still not fully understood. Dendritic cells (DCs) are crucial players in initiating innate and adaptive immune responses against fungal infections. The immunomodulatory effects of fungi were compared to the bacterial stimulus LPS to determine key players in the immune response to fungal infections. A genome wide study of the gene regulation of human monocyte-derived dendritic cells (DCs) confronted with A. fumigatus, C. albicans or LPS was performed and Kruppel-like factor 4 (KLF4) was identified as the only transcription factor that was down-regulated in DCs by both fungi but induced by stimulation with LPS. Downstream analysis demonstrated the influence of KLF4 on the interleukine-6 expression in human DCs. Furthermore, KLF4 regulation was shown to be dependent on pattern recognition receptor ligation. Therefore KLF4 was identified as a controlling element in the IL-6 immune response with a unique expression pattern comparing fungal and LPS stimulation.

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

Date Published: 28th Jun 2016

Journal: Sci Rep

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

36 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

14 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