Biomedical subjects

Susanne Jacobsen

Publications and source records attributed to Susanne Jacobsen.

10 recordsLinked to original sources

PubMed2007

Multivariate data analysis of proteome data.

We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image processing software, transfer of data, multivariate data analysis, interpretation of the results, and finally we return to biology.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2006

Application of near-infrared and Fourier transform infrared spectroscopy in the characterization of ligand-induced conformation changes in folate binding protein purified from bovine milk: influence of buffer type and pH.

Fourier transform infrared (FT-IR) and near-infrared (NIR) spectroscopy have been applied to detect structural alterations in folate binding protein (FBP) induced by ligation in different buffer types. The amide I region pointed to a beta-sheet to alpha-helix transition upon ligation in acetate and phosphate buffers, and the formation of intermolecular beta-sheet was indicated at pH 5.0, in agreement with a dimerization of FBP taking place at this pH. The ligand-induced changes in the 2100-2300 nm NIR region were significant for FBP in acetate and phosphate buffers of pH 5.0, and the variations were interpreted as secondary structure changes, based on previous assignments of secondary structures to the combination bands in the NIR region. In the case of acetate buffer, variations in the amide combination bands agreed with the amide I analysis, but for the other buffer types some discrepancies were found and explained by side-chain contributions to the NIR, which could reflect the tertiary and quaternary structure differences. NIR spectra of FBP at pH 7.4 and 5.0 revealed contradictory effects on the side chains, reflecting different polymerization events at the two pH values, whereas the amide I region indicated similar changes at the two pH values. Therefore, we suggest that FT-IR and NIR spectroscopy may complement each other, such that the two techniques in combination may give information on all three types of protein conformational changes. While the secondary structure changes are revealed by FT-IR, the tertiary and quaternary structure changes are reflected in the NIR spectra, although the general influence of the latter changes on the NIR spectra remains to be confirmed.

Animals↗

PubMed2005

Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable for bread-making purposes) were investigated. The samples were collected from grains from 15 until 45 days post-anthesis (dpa). Gluten proteins from wheat grains were extracted and subsequently analysed by mass spectrometry. Discrimination partial least-squares regression and soft independent modelling of class analogy were used to determine the quality of new and unknown wheat samples. With these methods, we were able to predict correctly the end-use qualities at every stage investigated. This new fast technique, based on the rapidity of mass spectrometry combined with the objectivity of multivariate data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later.

Edible Grain↗

PubMed2004

Explorative data analysis of two-dimensional electrophoresis gels.

Methods for classification of two-dimensional (2-DE) electrophoresis gels based on multivariate data analysis are demonstrated. Two-dimensional gels of ten wheat varieties are analyzed and it is demonstrated how to classify the wheat varieties in two qualities and a method for initial screening of gels is presented. First, an approach is demonstrated in which no prior knowledge of the separated proteins is used. Alignment of the gels followed by a simple transformation of data makes it possible to analyze the gels in an automated explorative manner by principal component analysis, to determine if the gels should be further analyzed. A more detailed approach is done by analyzing spot volume lists by principal components analysis and partial least square regression. The use of spot volume data offers a mean to investigate the spot pattern and link the classified protein patterns to distinct spots on the gels for further investigation. The explorative approach in analysis of 2-D gels makes it possible, in a fast and convenient way, to screen many gels in order to determine the protein patterns that form clusters and could be selected for further examination.

Diagnostic Imaging↗

PubMed2004

Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality.

Rapid methods for the identification of wheat varieties and their end-use quality have been developed. The methods combine the analysis of wheat protein extracts by mass spectrometry with partial least-squares regression in order to predict the variety or end-use quality of unknown wheat samples. The whole process takes approximately 30 min. Extracts of alcohol-soluble storage proteins (gliadins) from wheat were analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Partial least-squares regression was subsequently applied using these mass spectra for making models that could predict the wheat variety or end-use quality. Previously, an artificial neural network was used to identify wheat varieties based on their protein mass spectra profiles. The present study showed that partial least-squares regression is at least as useful as neural networks for this identification. Furthermore, it was demonstrated that partial least-squares regression could be used to predict wheat end-use quality, which has not been possible using neural networks.

Denmark↗

PubMed2004

Multivariate approaches in plant science.

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

Algorithms↗

PubMed2002

Comparison of ethanol-soluble proteins from different rye (Secale cereale) varieties by two-dimensional electrophoresis.

The major storage proteins from six rye varieties, grown under the same conditions in 1997 and 1998 in Rønhave, Denmark, were analyzed by two-dimensional (2-D) polyacrylamide gel electrophoresis. The proteins were extracted from ground rye kernels with 70% ethanol and separated by 2-D electrophoresis. The gels were scanned, compared using ImageMaster software and the data sets were analyzed by principal component analysis (PCA) using THE UNSCRAMBLER software. Afterwards MATLAB was used to make a cluster analysis of the varieties based on PCA. The analysis of the gels showed, that the protein patterns (number of different proteins and their isoelectric points and molecular weights) from the six rye varieties were different. Based on the presence of unique cultivar-specific spots it was possible to differentiate between all six varieties if the two harvest years were investigated separately. When the results were combined from the two years five varieties could be differentiated. The results from the PCA confirmed the finding of the unique spots and cluster analysis was made in order to illustrate the results. The combination of the results from 2-D electrophoresis and other grain characteristics showed that one protein spot was located close to the parameters bread volume and bread height.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2002

Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis.

The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.

Journal Article↗

PubMed2002

Determination of wheat quality by mass spectrometry and multivariate data analysis.

Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis. Proteome analysis of storage proteins from the wheat gluten complex based on two-dimensional electrophoresis and analysis of the N-terminal sequence has revealed a protein homologous to gamma-gliadins, tentatively associated with quality and within the molecular weight range 27-35 kDa. Further examinations of gliadin data based on mass spectrometry revealed that quality among wheat varieties could be determined by means of principal component analysis. Further examinations by interval partial least squares made it possible to encircle an overall optimal molecular weight interval from 31.5 to 33.7 kDa. The use of multivariate analysis on data from mass spectrometry has thus shown to be a promising technique to minimize the number of two-dimensional gels within the field of proteome analysis.

Gliadin↗

PubMed1997

A phosphate-starvation-inducible outer-membrane protein of Pseudomonas fluorescens Ag1 as an immunological phosphate-starvation marker.

A phosphate-starvation-inducible outer-membrane protein of Pseudomonas fluorescens Ag1, expressed at phosphate concentrations below 0.08-0.13 mM, was purified and characterized. The purification method involved separation of outer-membrane proteins by SDS-PAGE and extraction of the protein from nitrocellulose or PVDF membranes after electrotransfer of proteins to the membranes. The N-terminal amino acid sequence of the purified protein, called Psi1, did not show homology to any known proteins, and in contrast to the phosphate-specific porin OprP of P. aeruginosa its mobility in SDS-PAGE was not affected by solubilization temperature. An antiserum against Psi1 recognized a protein of M, 55,000 in four other P. fluorescens strains among 24 tested strains representing Pseudomonas rRNA homology group I, showing antigenic heterogeneity within this group. A method for immunofluorescence microscopy involving cell permeabilization was adapted to visualize cell-specific expression of Psi1 in P. fluorescens exposed to limiting amounts of phosphate. This approach should be useful for further exploration of Psi1 as a marker to study the availability of phosphate to P. fluorescens in natural environments.

Amino Acid Sequence↗