Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

<p>Abstract</p> <p>Background</p> <p>In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant <it>Aspergillus nidulans </it>(mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.</p> <p>Results</p> <p>More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different <it>A. nidulans </it>strains.</p> <p>Conclusion</p> <p>Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.</p

MarVis: a tool for clustering and visualization of metabolic biomarkers

<p>Abstract</p> <p>Background</p> <p>A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.</p> <p>Results</p> <p>We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.</p> <p>Conclusion</p> <p>MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.</p

Aspergillus Bibliography 2008

This bibliography attempts to cover genetical and biochemical publications on Aspergillus nidulans and also includes selected references to related species and topics. Entries have been checked as far as possible, but please tell me of any errors and omissions. Authors are kindly requested to send a copy of each article to the FGSC for its reprint collection

A functional genomics study of extracellular protease production by Aspergillus niger

The objective of the project described in this thesis was to study the complex induction of extracellular proteases in the filamentous fungus Aspergillus niger using information gathered with functional genomics technologies. A special emphasis is given to the requirements for performing a successful systems biology study and addressing the challenges met in analyzing the large, information-rich data sets generated with functional genomics technologies. The role that protease activity plays in strain and process development of A. niger and other aspergilli is reviewed. The influence of several environmental factors on the production of extracellular proteases of A. niger in controlled batch cultivations was studied. Samples generated in this study were used for analysis with different functional genomics technologies. With a shotgun proteomics approach the A. niger secretome under different experimental conditions was determined. Furthermore, the effect of different quantitative phenotypes related to protease or glucoamylase activity on the information content of a metabolomics data set was investigated. Finally, the clustering of co-expressed genes is described. First, a set of conserved genes was used to construct gene co-expression networks. Subsequently, all protein-coding A. niger genes, including hypothetical and poorly conserved genes, were integrated into the co-expression analysis.UBL - phd migration 201

Comparative genomics and transcriptomics elucidate virulence mechanisms and host responses in infectious diseases

The main thematic area of the present thesis is the development and application of bioinformatics pipelines, namely whole-genome sequence (WGS) analysis and transcriptome profile analysis. These pipelines were applied to study the fungal pathogen Aspergillus fumigatus (Manuscripts I, III, and IV) and the early human immune mechanisms activated in response to different types of pathogens (bacteria, fungi, and co-infections) in sepsis patients (Manuscript II). The comparative genomic and transcriptomic analyses applied in my thesis have significantly improved our understanding of fungal pathogenicity as well as the pathogen-specific immune response mechanisms of the human host. Next to a number of novel insights, my work included in this thesis has generated a large number of new hypotheses based on big-data analysis, offering the scientific community the possibility to design exciting new research to confirm them in future experimental studies and bring us closer to actual precision medicine for infectious diseases