1. Home
  2. People Index
  3. Andreas Dix

Andreas Dix

About Andreas Dix:
No description specified

SEEK ID: https://funginet.hki-jena.de/people/45

Location:  Germany

Expertise: Not specified

Tools: Not specified

ORCID: Not specified

help Andreas's tags

This person has not yet tagged anything.

Related items

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

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

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

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

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

4 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