The T cell response to major grass allergens is regulated and includes IL-10 production in atopic but not in non-atopic subjects.

BACKGROUND: The incidence of allergic diseases is increasing in industrialized countries and the immunological mechanisms leading to tolerance or allergy are poorly understood. Cytokines with suppressive abilities and CD4(+)CD25(+) regulatory T cells have been suggested to play a central role in allergen-specific responses. The aim was to determine whether major grass allergens induce production of suppressive cytokines in allergic and healthy subjects and to examine the inhibitory effect of these cytokines on allergic responses. METHODS: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy and grass-allergic donors and stimulated with the major grass allergens Phl p 1 or Phl p 5. The effects of endogenous IL-10 and/or TGF-beta on proliferation and cytokine production were determined by use of blocking antibodies. In addition, the number of CD4(+)CD25(+) T cells and their expression of chemokine receptors were investigated by flow cytometry. RESULTS: Phl p 1 and Phl p 5 induced IL-10 production, which down-regulated proliferation and cytokine production, in PBMC cultures from atopic but not from non-atopic donors. Comparable frequencies of CD4(+)CD25(+) T cells were present in PBMCs in the two groups, but fewer cells from atopic donors were CD4(+)CD25(+)CCR4(+) and more cells were CD4(+)CD25(+)CLA(+) compared to healthy donors. CONCLUSION: Allergen-specific responses of grass allergic patients but not in non-atopic subjects are influenced by regulatory cytokines produced in response to the important allergens. Differences in CD4(+)CD25(+) T cell expression of chemokine receptors in allergic compared to non-atopic donors could suggest that the homing of CD4(+)CD25(+) T cells is important for the regulation of allergen-specific responses.

Exploring abiotic stress on asynchronous protein metabolism in single kernels of wheat studied by NMR spectroscopy and chemometrics.

Extreme climate events are being recognized as important factors in the effects on crop growth and yield. Increased climatic variability leads to more frequent extreme conditions which may result in crops being exposed to more than one extreme event within a growing season. The aim of this study was to examine the implications of different drought treatments on the protein fractions in grains of winter wheat using (1)H nuclear magnetic resonance spectroscopy followed by chemometric analysis. Triticum aestivum L. cv. Vinjett was studied in a semi-field experiment and subjected to drought episodes either at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the (1)H NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development during grain-filling. The results from both the (1)H NMR spectra of methanol extracts and the (1)H HR-MAS NMR of single kernels showed that a single drought event during the generative stage had as strong an influence on protein metabolism as two consecutive events of drought. By contrast, a drought event at the vegetative growth stage had little effect on the parameters investigated. For the first time, (1)H HR-MAS NMR spectra of grains taken during grain-filling were analysed by an advanced multiway model. In addition to the results from the chemical protein analysis and the (1)H HR-MAS NMR spectra of single kernels indicating that protein metabolism is influenced by multiple drought events, the (1)H NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits.

Non-haem iron availability from pork meat: Impact of heat treatments and meat protein dose.

Pork meat heated at 60, 80, 100, and 120°C (1h), raw pork meat, BSA, casein and haemoglobin were examined for their effects on in vitro iron availability measured as Fe(2+)-dialysability, and on iron-reducing capacity following in vitro protein digestion (IVPD-dialysis). The pepsin-digested samples of meat heated at 80, 100, and 120°C resulted in increased in vitro iron availability. Generally, the capacity to reduce Fe(3+) to Fe(2+) was higher in the pepsin digests, whereas Fe(2+) decreased significantly after pepsin+pancreatin digestion and only part of the Fe(2+) was dialysable. Regardless of protein concentration, casein had no effect on in vitro iron availability, while pork meat protein treated at 120°C showed dose dependency reaching a plateau at 50mg protein/ml. In conclusion, the major effects on iron availability in vitro was shown in pepsin digests under conditions mimicking those in the duodenal lumen and heat-treatment of meat at 120°C showed the most pronounced effects.

Induction of systemic CTL responses in melanoma patients by dendritic cell vaccination: cessation of CTL responses is associated with disease progression.

Two HLA-A2-positive patients with advanced stage IV melanoma were treated with monocyte-derived dendritic cells (DC) pulsed with either tumor peptide antigens from gp100, MART-1 and MAGE-3 alone or in combination with autologous oncolysates. Clinically, the rapid progression of disease was substantially stalled and both patients were alive for more than 15 months after initiation of therapy. Specific CTL reactivity against several tumor antigens was detectable in peripheral blood, which declined just before reactivation of disease progression. Furthermore, CD3 zeta-chain expression detected by Western lotting was decreased in PBL at this time. In summary, our data confirm that DC-based vaccinations induce peptide-specific T cells in the peripheral blood of advanced-stage melanoma patients. Although successful induction of systemic tumor antigen-specific CTL may not lead to objective clinical tumor regression, their presence are indicative of a prolonged survival.

Classification of wheat varieties: use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorpiton/ionization-time of flight-mass spectrometry and an artificial neural network.

Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat varieties. Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) was applied to three pairs of wheat varieties, which can not be classified correctly by ANN. By 2-D PAGE the varieties in the three pairs can be discriminated and these six wheat varieties can be separated from each other, which could not be separated by MALDI-TOF-MS and NN.

Identification of barley and rye varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks.

Cereal varieties are normally identified using time-consuming methods such as visual examination of either the intact grain or one-dimensional electrophoretic patterns of the grain storage proteins. A fast method for identification of wheat (Triticum aestivum L.) varieties has previously been developed, which combines analysis of alcohol-soluble wheat proteins (gliadins) using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks. Here we have applied the same method for the identification of both barley (Hordeum vulgare L.) and rye (Secale cereale L.) varieties. For barley, 95% of the mass spectra were correctly classified. This is an encouraging result, since in earlier experiments only a grouping into subsets of varieties was possible. However, the method was not useful in the classification of rye, due to the strong similarity between mass spectra of different varieties.

Poor correspondence between predicted and experimental binding of peptides to class I MHC molecules.

Naturally processed peptides presented by class I major histocompatibility complex (MHC) molecules display a characteristic allele specific motif of two or more essential amino acid side chains, the so-called peptide anchor residues, in the context of an 8-10 amino acid long peptide. Knowledge of the peptide binding motif of individual class I MHC molecules permits the selection of potential peptide antigens from proteins of infectious organisms that could induce protective T-cell-mediated immunity. Several methods have been developed for the prediction of potential class I MHC binding peptides. One is based on a simple scanning for the presence of primary peptide anchor residues in the sequence of interest. A more sophisticated technology is the utilization of predictive computer algorithms. Here, we have analyzed the experimental binding of 84 peptides selected on the basis of the presence of peptide binding motifs for individual class I MHC molecules. The actual binding was compared with the results obtained when analyzing the same peptides by two well-known, publicly available computer algorithms. We conclude that there is no strong correlation between actual and predicted binding when using predictive computer algorithms. Furthermore, we found a high number of false-negatives when using a predictive algorithm compared to simple scanning for the presence of primary anchor residues. We conclude that the peptide binding assay remains an important step in the identification of cytotoxic T lymphocyte (CTL) epitopes which can not be substituted by predictive algorithms.

Phosphorylated peptides can be transported by TAP molecules, presented by class I MHC molecules, and recognized by phosphopeptide-specific CTL.

CTL recognize short peptide fragments presented by class I MHC molecules. In this study, we examined the effect of phosphorylation on TAP transport, binding to class I MHC molecules, and recognition by CTL of peptide fragments from known phosphorylated oncogene proteins or virus phosphoproteins. We show that phosphopeptides can be efficiently transported from the cytosol to the endoplasmic reticulum by the TAP. Furthermore, we show that phosphorylation can have a neutral, negative, or even a positive effect on peptide binding to class I MHC. Finally, we have generated phosphopeptide-specific CTL that discriminate between the phosphorylated and the nonphosphorylated versions of the peptide. We conclude that phosphopeptide-specific CTL responses are likely to constitute a subset of the class I MHC-restricted CTL repertoire in vivo.

An assay for peptide binding to HLA-Cw*0102.

The assembly assay for peptide binding to class I major histocompatibility complex (MHC) molecules is based on the ability of peptides to stabilize MHC class I molecules synthesized by transporter associated with antigen processing (TAP)-deficient cell. The TAP-deficient cell line T2 has previously been used in the assembly assay to analyze peptide binding to HLA-A*0201 and -B*5101. In this study, we have extended this technique to assay for peptides binding to endogenous HLA-Cw*0102 molecules. We have analyzed the peptide binding of 20 peptides with primary anchor motifs for HLA-Cw*0102. One-third of the peptides analyzed bound with high affinity, half of the peptides examined did not bind, whereas the remaining peptides displayed intermediate binding activity. Interest in HLA-C molecules has increased significantly in recent years, since it has been shown that HLA-C molecules both can present peptides to cytotoxic T lymphocytes (CTL) and in addition are able to inhibit natural killer (NK)-mediated lysis.

Identification of wheat varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and an artificial neural network

A novel tool for variety identification of wheat (Triticum aestivum L.) has been developed: an artificial neural network (ANN) is used to classify the gliadin fraction analysed by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS). The robustness of this novel method with respect to various experimental parameters has been tested. The results can be summarised: (i) With this approach 97% of the wheat varieties can be classified correctly with a corresponding correlation coefficient of 1.0, (ii) The method is fast since the time of extracting gliadins from flour can be reduced to 20 min without significant decrease in overall performance, (iii) The storage of flour or extracts under standard conditions does not influence the classification ability (i. e. the generalisation ability) of the method, and (iv) The classification obtained is not influenced by the identity of the operator making the analysis. This study demonstrates that a combination of an ANN and MALDI-TOFMS analysis of the gliadin fraction provides a fast and reliable tool for the variety identification of wheat. Copyright 1999 John Wiley & Sons, Ltd.

Determinants of decision-making in the screening procedure of a community psychiatric service.

This study is a predictor analysis of the screening procedure followed by a psychiatric service for a period of 1 year preceding and a period of 1 year following the introduction of community psychiatry. Throughout this period, the psychiatric service consisted of a local service within the catchment area and a central service at a psychiatric hospital outside the area. At the time of the reorganization, the responsibility for the psychiatric service was transferred from the public health authorities to the social services. Before the reorganization, screenings were conducted on the basis of referral papers or simply as a result of telephone communication. After the reorganization, the screening procedure was intensified by means of a pre-examination. One aim of the reorganization was to ensure that the severely mentally ill take priority over patients characterized predominantly by social strain. Patients with manic-depressive psychosis and other psychoses showed a significantly increased probability of being accepted for treatment, whereas those with schizophrenia showed no significant increase, irrespective of the service reorganization. Similarly, manic-depressive psychosis and other psychoses (not schizophrenia) were significant predictors of hospitalization at the mental hospital outside the catchment area as well as hospitalization in the local facilities, irrespective of the service reorganization. Indicators of social strain were not given higher priority following the service reorganization.

From image processing to classification: IV. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for breadmaking.

The end-use quality of products made from doughs consisting of wheat flour and water is often dependent upon the storage (gluten) proteins of the grain endosperm. Today the electrophoretic patterns of the high molecular weight (HMW) glutenin subunits are used for quality selections in wheat breeding programs in several countries. In this study, we used two multivariate techniques to classify digitized patterns from isoelectric focusing of gliadins and glutenins: a two-layered neural network architecture consisting of a self-organizing feature map and a feed-forward classifier [1], and discriminant analysis [2,3]. Three groups of seven wheat varieties (Triticum aestivum L.), associated with poor, medium or good properties in relation to bread-making quality, were used. The best classification results were obtained by the neural network model, based on data from the gliadin fraction: it was possible to classify varieties associated with poor or good quality, with recognition rates of 70 and 69%, respectively. The statistical method was better suited to solve the classification problem when the data was based on the glutenin fraction: if a specific variety was already known to be non-poor, this method enabled us to classify the medium- and good-quality classes with recognition rates of 90 and 88%, respectively. The results obtained were confirmed by correlation coefficients.

From image processing to classification: III. Matching patterns by shifting and stretching.

A method for the classification of electrophoretic patterns is described and tested on a data set representing ten wheat varieties. The method attempts to match each electropherogram to each variety by a transformation involving displacement and stretching along the x-axis. This is done essentially by the method of least squares, which uses only the information contained in the electropherogram itself to adjust it to the variety in question. The method is completely automatic and works extremely well by classifying 98% of the spectra correctly, judged by cross validation.

From image processing to classification: I. Modeling disturbances of isoelectric focusing patterns.

In order to optimize the conditions for evaluation of isoelectric focusing (IEF) patterns by digital image processing, the sources of error in determination of the pI values were analyzed together with the influence of a varying background. The effects of band distortions, in the spectra of the individual lanes, were examined. In order to minimize the effect of these distortions, optimal conditions for handling IEF patterns by digital image processing were elucidated. The systematic part of the global deformation on the gels was investigated and an algorithm was developed by which it was possible to correct for a part of the individual distortions. The effects of various corrections for lane distortions were illustrated by classification, using different types of discriminant analysis. Finally the background disturbances were examined, and described by a mathematical model.

From image processing to classification: II. Classification of electrophoretic patterns using self-organizing feature maps and feed-forward neural networks.

In a recent study, isoelectric focusing patterns were classified with a neural network using the back-propagation algorithm [1]. In order to further study the classification process and to generalize the presentation of electrophoretic patterns, Kohonen's self-organizing feature maps [2] were applied in this study. Although these feature maps are very efficient in many pattern recognition tasks, our data proved to be too complex for classification with an unsupervised system. Therefore, a second supervised network on top of the feature map was necessary. As in [3], a feed-forward network trained by the back-propagation algorithm was used. The final system allows us to correctly classify 90% of all wheat varieties. Moreover, the system proved to be reliable, reasonable in training time and shows the same accuracy in different experimental setups.

Spectroscopic investigations on the highly purified lactoperoxidase Fe(III)-heme catalytic site.

Purification of the lactoperoxidase (LPO) major cationic isoenzyme was significantly improved by the use of preparative chromatographic and electrophoretic methods combined with analytical electrophoretic techniques and image processing. A detailed report is given of the experimental procedure. Furthermore, electron paramagnetic resonance has played a fundamental role in evaluating the enzyme purity against lactoferrin and minor LPO isoenzyme components in setting the final steps of the purification. With the aim to completely clarify the Fe(III)-heme high-spin nature of the native LPO, two samples of lactoperoxidase, LPO1 and LPO2 (RZ = 0.95) from farm and commercial milk, respectively, were purified and characterized in particular by electron paramagnetic resonance (EPR) spectroscopy, in comparison with a commercial preparation (LPOs). The LPO1 EPR spectrum, at physiological pH, is clearly indictive of the presence of an iron(III)-heme high-spin catalytic site in the native enzyme. On the contrary, in the LPO2 spectrum a thermal equilibrium between high- and low-spin iron(III)-heme species is present. The low-spin component of the spectrum has been assigned to an LPO-NO2- adduct due to the presence of some nitrite impurities originating either from commercial unpasteurized milk or from external sources. The LPOs EPR spectrum shwos the presence of some spurious lines in the g approximately equal to 6 and 4 regions due to the minor LPO isoenzyme components and to lactoferrin, respectively. The LPO EPR spectra previously reported in the literature contain a variable number of spurious lines in the g approximately equal to 4 and 2 regions as a consequence of lactoferrin impurity and LPO low-spin adducts with endogenous or exogenous anions. Furthermore, the interaction of LPO with its native substrate (the thiocyanate anion), which previously was shown by NMR and EPR (at high substrate concentration) spectroscopies, has been confirmed by EPR at low temperature and low substrate concentration and by optical spectroscopy at room temperature and high substrate concentration as a function of pH. The LPO activity at optimum pH (approximately equal to 4-5) has been measured in phosphate and acetate buffer using as an oxidizable substrate the system dimethylamino benzoic acid 3-methyl-2-benzothiazolinone hydrazone hydrochloride monohydrate (DMAB-MBTH), which was considered a good chromogen for other peroxidases such as HRP and zucchini peroxidases. The LPO vs SCN- activity at optimum pH (approximately equal to 5.5) has been measured in phosphate and acetate buffer.(ABSTRACT TRUNCATED AT 400 WORDS)

Classification of wheat varieties by isoelectric focusing patterns of gliadins and neural network.

Classification of wheat varieties, using isoelectric focusing patterns of the gliadins, image processing and neural networks, is described. The method was compared to a statistical classification method, discriminant analysis. The isoelectric point and the area of each band were calculated by image processing. Different methods of presenting the electrophoretic patterns to the neural network were studied. The most effective method was transformation of the electrophoretic pattern to a small (11 x 47 pixels) representation of the original digitized image, which was presented to the neural network as a vector. The neural network was trained with a number of patterns and tested with new patterns from different electrophoretic runs of the same wheat varieties. In this study we used ten different wheat varieties and the neural network was able to classify 95.5% of the patterns correctly. The statistical classification method classified the same data set 91.8% correctly. We conclude that both the neural network and discriminant analysis were able to classify the patterns correctly with a high degree of certainty. The patterns that were misclassified were indistinguishable by visual inspection.

Evidence of a common regulation of IgE and IgG-subclass antibodies in humans during immunotherapy.

Based on a 3-year prospective study of 20 pollen-allergic patients, where a detailed analysis of the IgE, IgG1 and IgG4 immune response was performed, we propose that a common regulatory mechanism exists between the IgE and IgG1 synthesis and between IgE and IgG4 synthesis during immunotherapy. It was found that the IgE immune response to a number of antigens was quantitatively diminished during the period of immunotherapy when IgG1 was present early (week 12), and for other antigens there was a rise in IgE without an early IgG1 antibody response. Additionally, it was found that for some antigens a rise in IgE antibodies was contrasted by a fall in the IgG4 antibody response and for other antigens the opposite was true, indicating a regulatory mechanism between the IgE and the IgG4 synthesis. A statistical analysis showed that these findings were statistically significant at the 0.01% level for the IgE/IgG1 relationship and at the 0.05% level for the IgE/IgG4 relationship. These findings could have implications for future immunotherapy regimens.

Classification of crossed immunoelectrophoretic patterns using digital image processing and artificial neural networks.

A method is presented which makes it possible to present crossed immunoelectrophoretic patterns to an artificial neural network. The electrophoretic patterns are presented for the artificial neural network as three-dimensional vectors and it is shown that it is possible with this representation to train the network to learn the patterns and classify them. It was found that the ability to generalize was substantially increased by the addition of noise to the input patterns during training. Furthermore, the addition of noise decreased the number of presentations needed to reach the predetermined error level. The trained neural network was able to classify all distorted patterns correctly within an error range of 1%.