Biomedical subjects

Ib Søndergaard

Publications and source records attributed to Ib Søndergaard.

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↗

PubMed2005

Monitoring lipase-catalyzed interesterification for bulky fat modification with FT-IR/NIR spectroscopy.

This work demonstrates the application of FT-IR and FT-NIR spectroscopy to monitor the enzymatic interesterification process for bulky fat modification. The reaction was conducted between palm stearin and coconut oil (70:30, w/w) with the catalysis of Lipozyme TL IM at 70 degrees C in a batch reactor. The blends and interesterified fat samples in liquid form were measured by attenuated total reflectance based FT-IR (spectra region, 1516-781 cm(-1)) and transmission mode based FT-NIR (spectra region, 5369-4752 cm(-1)) with the temperature of both controlled at 70 degrees C. The samples in solid form were also measured by reflectance-based FT-NIR (spectra regions, 7037-6039 and 5995-5612 cm(-1)) at room temperature. Calibrations of FT-IR and FT-NIR for conversion degrees (evaluated by triglyceride profile), solid fat contents (SFC), and dropping points of interesterified products were carried out by using partial least-squares regression. High correlations (r > 0.96) were obtained from cross validations of the data estimated by FT-IR, FT-NIR, and the above-mentioned conventional analytical methods, except for correlations (r = 0.90-0.95) between FT-IR and SFC profiles. Overall, FT-NIR spectroscopy coupled with transmission mode measured at 70 degrees C had the highest correlations, which also had the closest conditions to the sampled products in the process, indicating a great potential for implementation as an on-line control for monitoring the enzymatic interesterification process.

Calibration↗

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

Spontaneous T-cell responses against peptides derived from the Taxol resistance-associated gene-3 (TRAG-3) protein in cancer patients.

Expression of the cancer-testis antigen Taxol resistance-associated gene-3 (TRAG-3) protein is associated with acquired paclitaxel (Taxol) resistance, and is expressed in various cancer types; e.g., breast cancer, leukemia, and melanoma. Thus, TRAG-3 represents an attractive target for immunotherapy of cancer. To identify HLA-A*02.01-restricted epitopes from TRAG-3, we screened cancer patients for spontaneous cytotoxic T-cell responses against TRAG-3-derived peptides. The TRAG-3 protein sequence was screened for 9mer and 10mer peptides possessing HLA-A*02.01-binding motifs. Of 12 potential binders, 9 peptides were indeed capable of binding to the HLA-A*02.01 molecule, with binding affinities ranging from strong to weak binders. Subsequently, lymphocytes from cancer patients (9 breast cancer patients, 12 melanoma patients, and 13 patients with hematopoietic malignancies) were analyzed for spontaneous reactivity against the panel of peptides by ELISpot assay. Spontaneous immune responses were detected against 8 epitope candidates in 7 of 9 breast cancer patients, 7 of 12 melanoma patients, and 5 of 13 patients with hematopoietic malignancies. In several cases, TRAG-3-specific CTL responses were scattered over several epitopes. Hence, no immunodominance of any single peptide was observed. Furthermore, single-peptide responses were detected in 2 of 12 healthy HLA-A2(+) donors, but no responses were detectable in 9 HLA-A2(-) healthy donors or 4 HLA-A2(-) melanoma patients. The identified HLA-A*02.01-restricted TRAG-3-derived epitopes are targets for spontaneous immune responses in breast cancer, hematopoietic cancer, and melanoma patients. Hence, these epitopes represent potential target structures for future therapeutic vaccinations against cancer, possibly appropriate for strategies that combine vaccination and chemotherapy; i.e., paclitaxel treatment.

Antigens, Neoplasm↗

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↗