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Michael Edberg Hansen

Publications and source records attributed to Michael Edberg Hansen.

5 recordsLinked to original sources

A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants.

Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.

Fermentation↗

X-Hitting: an algorithm for novelty detection and dereplication by UV spectra of complex mixtures of natural products.

A major challenge in lead discovery is to detect well-known and trivial compounds rapidly, a process known as dereplication, so that isolation, structure elucidation, and pharmacological investigations can be focused on novel compounds. In this paper, we present a new algorithm, X-hitting, based on cross sample comparison of full UV spectra from HPLC analysis of highly complex natural product extracts/samples. X-Hitting allows automatic identification of known compounds but more important also allows finding of potentially new or similar compounds. We demonstrate this new algorithm by automatic identification of known structures, a task we call cross-hitting, and tentative identification of potentially new bioactive compounds, a task we call new-hitting, in HPLC data from analysis of fungal extracts. Both tasks are illustrated using 18 important reference compounds and complex fungal extracts obtained from isolates in the IBT Culture Collection held at BioCentrum-DTU, Technical University of Denmark. The receiver operating characteristics statistic is used to evaluate the performance of the compound predictor, and it was found that compounds could be identified with high confidence (AUC approximately 0.98). Based on high confidence in retrieving identical spectra, the method is extended to include similar but still different spectra.

Algorithms↗

Automated and unbiased classification of chemical profiles from fungi using high performance liquid chromatography.

In this paper we present a method for unbiased/unsupervised classification and identification of closely related fungi, using chemical analysis of secondary metabolite profiles created by HPLC with UV diode array detection. For two chromatographic data matrices a vector of locally aligned full spectral similarities is calculated along the retention time axis. The vector depicts the evaluating of the alikeness between two fungal extracts based upon eluted compounds and corresponding UV-absorbance spectra. For assessment of the chemotaxonomic grouping the vector is condensed to one similarity describing the overall degree of similarity between the profiles. Two sets of data were used in this study: One set was used in the method development and a second dataset used for method validation. First we developed a method for evaluating the secondary metabolite production from closely related Penicillium species. Then the algorithm was validated on fungal isolates belonging to the genus Alternaria. The results showed that the species may be segregated into taxa in full accordance with published taxonomy.

Algorithms↗

A new matching algorithm for high resolution mass spectra.

We present a new matching algorithm designed to compare high-resolution spectra. Whereas existing methods are bound to compare fixed intervals of ion masses, the accurate mass spectrum (AMS) distance method presented here is independent of any alignment. Based on the Jeffreys-Matusitas (JM) distance, a difference between observed peaks across pairs of spectra can be calculated, and used to find a unique correspondence between the peaks. The method takes into account that there may be differences in resolution of the spectra. The algorithm is used for indexing in a database containing 80 accurate mass spectra from an analysis of extracts of 80 isolates representing the nine closely related species in the Penicillium series Viridicata. Using this algorithm we can obtain a retrieval performance of approximately 97-98% that is comparable with the best of the existing methods (e.g., the dot-product distance). Furthermore, the presented method is independent of any variable alignment procedures or binning.

Algorithms↗

Visual clone identification of Penicillium commune isolates.

A method for visual clone identification of Penicillium commune isolates was developed. The method is based on images of fungal colonies acquired after growth on a standard medium and involves a high degree of objectivity, which in future studies will make it possible for non-experts to perform a qualified identification of different species as well as clones within a species. A total of 77 P. commune isolates from a cheese dairy were 3-point inoculated on Yeast Extract Sucrose (YES) agar and incubated for 7 days at 25 degrees C. After incubation, the isolates were classified into groups containing the same genotype determined by DNA fingerprinting (AFLP). Each genotype also has a specific phenotype such as different colony colours. By careful image acquisition, colours were measured in a reproducible way. Prior to image analysis, each image was corrected with respect to colour, geometry and self-illumination, thereby gaining a set of directly comparable images. A method for automatic extraction of a given number of concentric regions was used. Using the positions of the regions, a number of relevant features--capturing colour and colour-texture from the surface of the fungal colonies--was extracted for further analysis. We introduced the Jeffreys-Matusitas (JM) distance between the feature distributions to express the similarity between regions in two colonies, and to evaluate the overall (weighted) similarity. The nearest neighbour (NN) classification rule was used. On a dataset from 137 isolates, we obtained a "leave-one-out" cross-validation identification rate of approximately 93-98% compared with the result of DNA fingerprinting.

Clone Cells↗