title: Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification.
abstract: BACKGROUND
  In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.
  
  
  RESULTS
  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 A. nidulans strains.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs Lyngby, Denmark. irene@cbs.dtu.dk
authors.forename: Irene
authors.initials: I
authors.lastname: Kouskoumvekaki
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Yang
authors.forename: Svava O
authors.initials: SO
authors.lastname: Jónsdóttir
authors.forename: Lisbeth
authors.initials: L
authors.lastname: Olsson
authors.forename: Gianni
authors.initials: G
authors.lastname: Panagiotou
mesh_terms: D001185:Artificial Intelligence; D001233:Aspergillus nidulans / Q000201:enzymology / Q000235:genetics / Q000378:metabolism*; D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism*; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods; D024861:Genetic Enhancement; D005838:Genotype; D008660:Metabolism / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015202:Protein Engineering; D040901:Proteomics; D012459:Salicylates / Q000378:metabolism
chemical_list: D015415:Biomarkers; D012459:Salicylates; C005236:6-methylsalicylic acid