Microevolution of Candida albicans in macrophages restores filamentation in a nonfilamentous mutant
FungiNet C - Candida projects, INF

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

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

Data- and knowledge-based modeling of gene regulatory networks: an update
FungiNet B - Bioinformatics projects, INF, B3 (E)

Abstract (Expand)

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network … inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.

Authors: Jörg Linde, Sylvie Schulze, S. G. Henkel, Reinhard Guthke

Date Published: 2nd Mar 2015

Journal: EXCLI J

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