bluuebunny/crossref_metadata_2025 huggingface dataset:
Sympathetic neurons synthesize and secrete pro‐nerve growth factor protein
Authors: Hasan Wohaib , Pedchenko Tetyana , Krizsan‐Agbas Dora , Baum Laura , Smith Peter G.
Published: September 2003

Postmitotic sympathetic neuronal survival is dependent upon nerve growth factor (NGF) provided by peripheral targets, and this dependency serves as a central tenet of the neurotrophic hypothesis. In some other systems, NGF has been shown to play an autocrine role, although the pervasiveness and significance of this phenomenon within the nervous system remain unclear. We show here that rat sympathetic neurons synthesize and secrete NGF. NGF mRNA is expressed in nearly half of superior cervical ganglion sympathetic neurons at embryonic day 17, rising to over 90% in the early postnatal period, and declining in the adult. Neuronal immunoreactivity is reduced when retrograde transport is interrupted by axotomy, but persists in a subpopulation of neurons despite diminished mRNA expression, suggesting that intrinsic protein synthesis occurs. Cultured neonatal neurons express NGF mRNA, which is maintained even when they are undergoing apoptosis. To determine which NGF isoforms are secreted, we performed metabolic labeling and immunoprecipitation of NGF‐immunoreactive proteins synthesized by cultured NGF‐dependent and ‐independent neurons. Conditioned medium contained high molecular weight NGF precursor proteins, which varied depending upon the state of NGF dependence. Mature NGF was undetectable by these methods. High molecular weight NGF isoforms were also detected in ganglion homogenates, and persisted at diminished levels following axotomy. We conclude that sympathetic neurons express NGF mRNA, and synthesize and secrete pro‐NGF protein. These findings suggest that a potential NGF‐sympathetic neuron autocrine loop may exist in this prototypic target‐dependent system, but that the secreted forms of this neurotrophin apparently do not support neuronal survival. © 2003 Wiley Periodicals, Inc. J Neurobiol
38–53, 2003
20‐hydroxyecdysone inhibits the mitotic activity of neuronal precursors in the developing mushroom bodies of the honeybee, Apis mellifera
Authors: Malun Dagmar , Moseleit Ariane D. , Grünewald Bernd
Published: September 2003

The mushroom bodies (MBs) within the brain of the honeybee, Apis mellifera, are prominent paired neuropil structures consisting of a lateral and a median subunit. The intrinsic MB neurons (Kenyon cells) of each of these subunits are generated in four distinct proliferation centers, each associated with a calyx. Previous BrdU studies revealed that neurogenesis of Kenyon cells starts at the first larval stage (L1) by symmetrical cell division of Kenyon precursor cells, and ceases abruptly at a midpupal stage (P5). In the present work, we confirmed these results using the antiphospho histone H3 mitosis marker to label mitotically active cells in a cell culture system, in histological sections, and in whole‐mount brain preparations. To elucidate whether the steroid hormone ecdysone plays a role in the termination of Kenyon cell neurogenesis, we manipulated the hormone titer by injecting 20‐hydroxyecdysone (20E) into animals of those pupal stages (P0/1, P3, P4) in which neurogenesis of Kenyon cells was still extensive. The effects of 20E were evaluated by determining the number of mitotically active cells in confocal microscopic images of squash preparations of the MB proliferation centers. In all pupal stages studied, 20E caused a reduction of mitotic activity, indicating its involvement in the cessation of Kenyon cell neurogenesis. © 2003 Wiley Periodicals, Inc. J Neurobiol 57: 1‐14, 2003
Aberrant neuronal connectivity in CHL1‐deficient mice is associated with altered information processing‐related immediate early gene expression
Authors: Montag‐Sallaz Monique , Baarke Andrea , Montag Dirk
Published: September 2003

In humans, loss or alteration of the CHL1/CALL gene may contribute to mental impairment associated with the 3p‐syndrome, caused by distal deletions of the short (p) arm of chromosome 3, and schizophrenia. Mice deficient for the Close Homologue of L1 (CHL1) show aberrant connectivity of hippocampal mossy fibers and olfactory sensory axons, suggesting participation of CHL1 in the establishment of neuronal networks. Furthermore, behavioral studies showed that CHL1‐deficient mice react differently towards novel experimental environments. These data raise the hypothesis that processing of information, possibly novel versus familiar, may be altered in the absence of CHL1. To test this hypothesis, brain activities were investigated after presentation of a novel, familiar, or neutral gustatory stimulus using metabolic mapping with (14C)‐2‐deoxyglucose (2‐DG) and analysis of mRNA expression of the immediate early genes (IEGs) c‐fos and arg 3.1/arc by in situ hybridization. 2‐DG labeling revealed only small differences between CHL1‐deficient and wild‐type littermate mice. In contrast, while the specific novelty‐induced increase in c‐fos expression was maintained in most of the brain areas analyzed, c‐fos mRNA expression was similar after the novel and familiar taste in several brain areas of the CHL1‐deficient mice. Furthermore, in these mutants, arg 3.1/arc expression was slightly reduced after the novel taste and increased after the familiar taste, leading to a similar arg 3.1/arc mRNA expression after both stimuli. Our results indicate that, in contrast to controls, CHL1‐deficient mice might process novel and familiar information similarly and suggest that the altered neuronal connectivity in these mutants disturbs information processing at the molecular level. © 2003 Wiley Periodicals, Inc. J Neurobiol 57: 67–80, 2003
S100 immunoreactive glial cells in the forebrain and midbrain of the lizard Gallotia galloti during ontogeny
Authors: del Mar Romero‐Alemán María , Monzón‐Mayor Maximina , Yanes Carmen , Arbelo‐Galván Juan Francisco , Lang Dirk , Renau‐Piqueras Jaime , Negrín‐Martínez Ciro
Published: September 2003

We identified S100 immunoreactive cells in the brain of the lizard Gallotia galloti during ontogeny using immunohistochemical techniques for light and electron microscopy. In double labeling experiments with antibodies specific for S100A1 and S100B (anti‐S100) and proliferative cell nuclear antigen (anti‐PCNA), myelin basic protein (anti‐MBP), phosphorylated neurofilaments (SMI‐31), glial fibrillary acidic protein (anti‐GFAP), or glutamine synthetase (anti‐GS), we detected S100‐like immunoreactivity in glial cells but never in neurons. Restricted areas of the ventricular zone were stained in the hypothalamus from E32 to postnatal stages, and in the telencephalon at E35, E36, and in adults. S100 immunoreactivity was observed predominantly in scattered PCNA‐negative cells that increased in number from E35 to the adult stage in the myelinated tracts of the brain and had the appearance of oligodendrocytes. Quantitative analysis revealed that all of the S100‐positive glial cells were GFAP‐negative, whereas most of the S100‐positive glial cells were GS‐positive. Ultrastructurally, most of these S100‐positive/GS‐positive glial cells resembled oligodendrocytes of light and medium electron density. In adult lizards, a small subpopulation of astrocyte‐like cells was also stained in the pretectum. We conclude that in the lizard S100 can be considered a marker of a subpopulation of oligodendrocytes rather than of astrocytes, as is the case in mammals. The S100‐positive subpopulation of oligodendrocytes in the lizard could represent cells actively involved in the process of myelination during development and in the maintenance of myelin sheaths in the adult. © 2003 Wiley Periodicals, Inc. J Neurobiol 57: 54–66, 2003
Different regulation of Purkinje cell dendritic development in cerebellar slice cultures by protein kinase Cα and ‐β
Authors: Gundlfinger Anja , Kapfhammer Josef P. , Kruse Friederike , Leitges Michael , Metzger Friedrich
Published: September 2003

Activity of protein kinase C (PKC), and in particular the PKCγ‐isoform, has been shown to strongly affect and regulate Purkinje cell dendritic development, suggesting an important role for PKC in activity‐dependent Purkinje cell maturation. In this study we have analyzed the role of two additional Ca2+‐dependent PKC isoforms, PKCα and ‐β, in Purkinje cell survival and dendritic morphology in slice cultures using mice deficient in the respective enzymes. Pharmacological PKC activation strongly reduced basal Purkinje cell dendritic growth in wild‐type mice whereas PKC inhibition promoted branching. Purkinje cells from mice deficient in PKCβ, which is expressed in two splice forms by granule but not Purkinje cells, did not yield measurable morphological differences compared to respective wild‐type cells under either experimental condition. In contrast, Purkinje cell dendrites in cultures from PKCα‐deficient mice were clearly protected from the negative effects on dendritic growth of pharmacological PKC activation and showed an increased branching response to PKC inhibition as compared to wild‐type cells. Together with our previous work on the role of PKCγ, these data support a model predicting that normal Purkinje cell dendritic growth is mainly regulated by the PKCγ‐isoform, which is highly activated by developmental processes. The PKCα isoform in this model forms a reserve pool, which only becomes activated upon strong stimulation and then contributes to the limitation of dendritic growth. The PKCβ isoform appears to not be involved in the signaling cascades regulating Purkinje cell dendritic maturation in cerebellar slice cultures. © 2003 Wiley Periodicals, Inc. J Neurobiol 57: 95–109, 2003
Protein profiles of medulloblastoma cell lines DAOY and D283: Identification of tumor‐related proteins and principles
Authors: Peyrl Andreas , Krapfenbauer Kurt , Slavc Irene , Yang Jae‐Won , Strobel Thomas , Lubec Gert
Published: September 2003

Medulloblastoma is the most frequent malignant brain tumor in children and is considered to be of neuroectodermal origin. Two main representative cell lines, DAOY and D283, are widely used in studies of medulloblastoma. The former shows expression of neuronal and glial elements whereas the latter is assigned to neuronal lineages. We decided to systematically study the proteome of these cell lines in order to find novel and known proteins that could serve as candidate markers or could be of interest as specific antigens for future vaccines. We studied DAOY and D283 by two‐dimensional gel electrophoresis with subsequent matrix‐assisted laser desorption/ionization identification. A series of identified medulloblastoma proteins were already described in many other malignancies of different origin. An antiapoptotic principle, Ded protein, was observed in both cell lines. Several hypothetical proteins, that were never described at the protein level but only predicted from nucleic acid sequences, could be identified. We conclude that medulloblastoma proteins SYT interacting protein, similar to glucose related protein 58 kDa, hypothetical 37.5 kDa protein, serologically defined colon cancer antigen 10, hepatocellular carcinoma‐associated antigen 59, X‐ray repair complementing defective repair in CHO 5, hypothetical protein Q96ir7, nit protein 2 and hypothetical protein Q96e67, have been described in a series of other malignancies possibly indicating a role for those in tumor biology and pathomechanisms. The antiapoptotic principle, Ded protein, found in both cell lineages may stand for immortalization but could also determine malignancy per se in medulloblastoma.
Mitochondrial proteome: Altered cytochrome c oxidase subunit levels in prostate cancer
Authors: Herrmann Paul C. , Gillespie John W. , Charboneau Lu , Bichsel Verena E. , Paweletz Cloud P. , Calvert Valerie S. , Kohn Elise C. , Emmert‐Buck Michael R. , Liotta Lance A. , Petricoin Emanuel F.
Published: September 2003

Laser capture microdissection was combined with reverse phase protein lysate arrays to quantitatively analyze the ratios of mitochondrial encoded cytochrome c oxidase subunits to nuclear encoded cytochrome c oxidase subunits, and to correlate the ratios with malignant progression in human prostate tissue specimens. Cytochrome c oxidase subunits I‐III comprise the catalytic core of the enzyme and are all synthesized from mitochondrial DNA. The remaining subunits (IV–VIII) are synthesized from cellular nuclear DNA. A significant (P < 0.001, 30/30 prostate cases) shift in the relative concentrations of nuclear encoded cytochrome c oxidase subunits IV, Vb, and VIc compared to mitochondrial encoded cytochrome c oxidase subunits I and II was noted during the progression of prostate cancer from normal epithelium through premalignant lesions to invasive carcinoma. Significantly, this shift was discovered to begin even in the premalignant stage. Reverse phase protein lysate array‐based observations were corroborated with immunohistochemistry, and extended to a few human carcinomas in addition to prostate. This analysis points to a role for nuclear DNA encoded mitochondrial proteins in carcinogenesis; underscoring their potential as targets for therapy while highlighting the need for full characterization of the mitochondrial proteome.
Developmentally induced changes of the proteome in the protozoan parasite Leishmania donovani
Authors: Bente Meike , Harder Simone , Wiesgigl Martina , Heukeshoven Jochen , Gelhaus Christoph , Krause Eberhard , Clos Joachim , Bruchhaus Iris
Published: September 2003

In order to proceed through their life cycle, protozoan parasites of the genus Leishmania cycle between sandflies and mammals. This change of environment correlates with the differentiation from the promastigote stage (insect form) to the amastigote stage (intracellular mammalian form). The molecular basis underlying this major transformation is poorly understood so far; however, heat shock protein 90 (HSP90) appears to play a pivotal role. To further elucidate this process we identified proteins expressed preferentially in either of the two life cycle stages. By using two‐dimensional (2‐D) gel electrophoresis we observed defined changes in the protein pattern. A total of approximately 2000 protein spots were visualized. Of these, 31 proteins were present only in promastigotes. The abundance of 65 proteins increased during heat‐induced in vitro amastigote differentiation, while a decreased abundance is observed for four proteins late in amastigote differentiation. Further analyses using matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry and peptide mass fingerprinting 67 protein spots were identified representing 41 different proteins known from databases and eight hypothetical proteins. Further studies showed that most of the stage‐specific proteins fall into five groups of functionally related proteins. These functional categories are: (i) stress response (e.g. heat, oxidative stress); (ii) cytoskeleton and cell membrane; (iii) energy metabolism and phosphorylation; (iv) cell cycle and proliferation; and (v) amino acid metabolism. Very similar changes in the 2‐D protein pattern were obtained when in vitro amastigote differentiation was induced either by pharmacological inhibition of HSP90 or by a combination of heat stress and acidic pH supporting the critical role for HSP90 in life cycle control.
Multiple approaches to data‐mining of proteomic data based on statistical and pattern classification methods
Authors: Tatay Jacob W. , Feng Xin , Sobczak Nancy , Jiang Hao , Chen Chin‐Fu , Kirova Roumyana , Struble Craig , Wang Nan Jiang , Tonellato Peter J.
Published: September 2003

The data‐mining challenge presented is composed of two fundamental problems. Problem one is the separation of forty‐one subjects into two classifications based on the data produced by the mass spectrometry of protein samples from each subject. Problem two is to find the specific differences between protein expression data of two sets of subjects. In each problem, one group of subjects has a disease, while the other group is nondiseased. Each problem was approached with the intent to introduce a new and potentially useful tool to analyze protein expression from mass spectrometry data. A variety of methodologies, both conventional and nonconventional were used in the analysis of these problems. The results presented show both overlap and discrepancies. What is important is the breadth of the techniques and the future direction this analysis will create.
Analysis of human serum proteins by liquid phase isoelectric focusing and matrix‐assisted laser desorption/ionization‐mass spectrometry
Authors: Wang Michael Z. , Howard Brandon , Campa Michael J. , Patz Edward F. , Fitzgerald Michael C.
Published: September 2003

Direct matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry (MALDI‐TOF MS) analysis of human serum yielded ion signals from only a fraction of the total number of peptides and proteins expected to be in the sample. We increased the number of peptide and protein ion signals observed in the MALDI‐TOF mass spectra analysis of human serum by using a prefractionation protocol based on liquid phase isoelectric focusing electrophoresis. This pre‐fractionation technique facilitated the MALDI‐TOF MS detection of as many as 262 different peptide and protein ion signals from human serum. The results obtained from three replicate fractionation experiments on the same serum sample indicated that 148 different peptide and protein ion signals were reproducibly detected using our isoelectric focusing and MALDI‐TOF MS protocol.
Identification and validation of a potential lung cancer serum biomarker detected by matrix‐assisted laser desorption/ionization‐time of flight spectra analysis
Authors: Howard Brandon A. , Wang Michael Z. , Campa Michael J. , Corro Christina , Fitzgerald Michael C. , Patz Edward F.
Published: September 2003

Many abnormalities detected in the thorax by routine conventional imaging studies are benign, yet all require further evaluation because of the concern for cancer. To address this deficiency and develop a serum biomarker for lung cancer, we designed a matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry (MALDI‐TOF MS) based platform to display the proteins present in the serum of patients with or without lung cancer, and then challenged the scientific community to analyze these data with the aim of determining specific ion signal differences among the resulting spectra. The most statistically significant ion peak identified by the various analysis algorithms that differentiated the serum of patients with lung cancer from the serum of individuals without lung cancer was found at m/z 11 702. We identified the protein responsible for this ion peak as serum amyloid A (SAA; Mr = 11 682.7) by partial purification followed by in‐gel digestion and peptide mapping. By enzyme‐linked immunosorbent assay, we showed SAA to be present at 286 ng/mL in the serum of cancer patients vs. 34.1 ng/mL in the serum of individuals without cancer. These data suggest that the combination of MALDI‐TOF MS and computer analysis can be a powerful tool in the search for serum biomarkers of lung cancer and other diseases.
Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method
Authors: Lee Kwan R. , Lin Xiwu , Park Daniel C. , Eslava Sergio
Published: September 2003

There are many data mining techniques for processing and general learning of multivariate data. However, we believe the wavelet transformation and latent variable projection method are particularly useful for spectroscopic and chromatographic data. Projection based methods are designed to handle hugely multivariate nature of such data effectively. For the actual analysis of the data we have used latent variable projection methods such as principal component analysis (PCA) and partial least squares projection to latent structures based discriminant analysis (PLS‐DA) to analyze the raw data presented to the participants of the First Duke Proteomics Data Mining Conference. PCA was used to solve problem #1 (clustering problem) and the PLS‐DA was used to solve problem #2 (classification problem). The idea of internal and external cross‐validation was used to validate the model obtained from the classification analysis. The simple two‐component PLS‐DA model obtained from the analysis performed well. The model has completely separated the two groups from all the data. The same model applied on two‐thirds of the data showed good performance by external validation with independent test set of remaining 13 specimens obtained by setting aside the spectra of every third specimen (accuracy of 85%).
Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum
Authors: Neville Padraic , Tan Pei‐Yi , Mann Geoffrey , Wolfinger Russ
Published: September 2003

We bring a “spectrum” of classical data mining and statistical analysis methods to bear on discrimination of two groups of spectra from 24 diseased and 17 normal patients. Our primary goal is to accurately estimate the generalizability of this small dataset. After an aggressive preprocessing step that reduces consideration to only 55 peaks, we conduct over 35 out‐of‐sample cross‐validation simulations of logistic regression, binary decision trees, and linear discriminant analysis. Misclassification rates grow worse as the size of the holdout sample increases, with many exceeding 30 percent. The ability to generalize is clearly tempered by the statistical, instrumentation, and biophysical characteristics of the study.
Clustering mass spectrometry data using order statistics
Authors: Slotta Douglas J. , Heath Lenwood S. , Ramakrishnan Naren , Helm Rich , Potts Malcolm
Published: September 2003

Mass spectrometry data is inherently uncertain. Rather than compare peak heights across samples, a comparison can be made of the relative ordering of the peak height across samples. Order statistics are used to provide a distance metric between each ordered list of peak heights from the samples. A principal component analysis is performed on the set of distance vectors to highlight to important components.
Discriminant models for high‐throughput proteomics mass spectrometer data
Authors: Purohit Parul V. , Rocke David M.
Published: September 2003

We use several different multivariate analysis methods to discriminate between diseased and healthy patients using protein mass spectrometer data provided by Duke University. Two problems were presented by the university; one in which the responses (diseased or healthy) of the patients were not known and second, when the responses were known. In the latter case, the data can be used as a ‘training’ set. We attempted both problems. In particular, we use principle component analysis along with clustering methods to discriminate for the first problem set and partial least squares coupled with logistic and discriminant methods when the responses were known. In addition, we were able to detect regions of interest in the spectrum where there were differences in the protein patterns between healthy and diseased patients. There was considerable effort involved in the preprocessing of the data. We used a binning approach to reduce the number of variables rather than peak heights or peak areas. We performed a square root transformation on the data to help stabilize the variance; this in turn made a significant improvement in clustering results.
Protocols for disease classification from mass spectrometry data
Authors: Wagner Michael , Naik Dayanand , Pothen Alex
Published: September 2003

We report our results in classifying protein matrix‐assisted laser desorption/ionization‐time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross‐validation studies with four selected proteins yielded misclassification rates in the 10–15% range for all the classification methods. Three of these proteins or protein fragments are down‐regulated and one up‐regulated in lung cancer, the disease under consideration in this data set. When cross‐validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross‐validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.
Tree‐based disease classification using protein data
Authors: Zhu Hongtu , Yu Chang‐Yung , Zhang Heping
Published: September 2003

A reliable and precise classification of diseases is essential for successful diagnosis and treatment. Using mass spectrometry from clinical specimens, scientists may find the protein variations among disease and use this information to improve diagnosis. In this paper, we propose a novel procedure to classify disease status based on the protein data from mass spectrometry. Our new tree‐based algorithm consists of three steps: projection, selection and classification tree. The projection step aims to project all observations from specimens into the same bases so that the projected data have fixed coordinates. Thus, for each specimen, we obtain a large vector of ‘coefficients’ on the same basis. The purpose of the selection step is data reduction by condensing the large vector from the projection step into a much lower order of informative vector. Finally, using these reduced vectors, we apply recursive partitioning to construct an informative classification tree. This method has been successfully applied to protein data, provided by the Department of Radiology and Chemistry at Duke University.
Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer
Authors: Markey Mia K. , Tourassi Georgia D. , Floyd Carey E.
Published: September 2003

A classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass‐to‐charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross‐validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000–10 000, 20 000–30 000, 45 000–60 000, and >125 000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 ± 0.07 for leave‐one‐out cross‐validation.
A comprehensive approach to the analysis of matrix‐assisted laser desorption/ionization‐time of flight proteomics spectra from serum samples
Authors: Baggerly Keith A. , Morris Jeffrey S. , Wang Jing , Gold David , Xiao Lian‐Chun , Coombes Kevin R.
Published: September 2003

For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable‐width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1–B8). After data processing, we found the intensities at the following mass to charge (m/z) values to be useful discriminators: 3077, 12 886 and 74 263. Using these values, we were able to achieve an overall classification accuracy of 38/41 (92.6%). Perfect classification could be achieved by adding two additional peaks, at 2476 and 6955. We identified these values by applying a genetic algorithm to a filtered list of m/z values using Mahalanobis distance between the group means as a fitness function.
Machine learning approaches to lung cancer prediction from mass spectra
Authors: Hilario Melanie , Kalousis Alexandros , Müller Markus , Pellegrini Christian
Published: September 2003

We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten‐fold cross‐validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676‐mass data set and on a prescreened 124‐mass data set. Highest predictive accuracies (1–2 off‐sample misclassifications) were achieved by a multilayer perceptron and Naïve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.
A prototype methodology combining surface‐enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions
Authors: Mian Shahid , Ball Graham , Hornbuckle Jo , Holding Finn , Carmichael James , Ellis Ian , Ali Selman , Li Geng , McArdle Stephanie , Creaser Colin , Rees Robert
Published: September 2003

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI‐TOF matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic ‘fingerprints’ indicative of treatment regimen for chemosensitive (MCF‐7, T47D) and chemoresistant (MCF‐7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign ‘class’ for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot‐strap statistical validation techniques and that biomarker ion patterns indicative of response/non‐response phenotypes are associated with MCF‐7 and MCF‐7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF‐7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF‐7/ADR were compared against those derived from MCF‐7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.
Proteomic analysis of acrylamide gel separated proteins immobilized on polyvinylidene difluoride membranes following proteolytic digestion in the presence of 80% acetonitrile
Authors: Bunai Keigo , Nozaki Manabu , Hamano Miyuki , Ogane Shinya , Inoue Taro , Nemoto Tadashi , Nakanishi Hiroshi , Yamane Kunio
Published: September 2003

Two‐dimensional polyacrylamide gel electrophoresis (2‐D PAGE) combined with mass spectrometry (MS) is a highly accurate and sensitive means of identifying proteins. We have developed a novel method for digesting proteins on polyvinylidene difluoride (PVDF) membranes for subsequent matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) MS analysis. After Tricine sodium dodecyl sulfate (SDS)‐PAGE, separated proteins were electroblotted onto PVDF membranes in a semidry discontinuous buffer system, visualized by staining with Coomassie Blue, excised, digested with trypsin or lysC in 80% acetonitrile, and then analyzed by MALDI‐TOF MS. This method has several advantages over in‐gel digestion in terms of sample handling, sensitivity, and time. We identified 105 fmol of Bacillus subtilis SecA and 100∼500 fmol of standard proteins. We also analyzed the submembrane protein fraction solubilized by 1% n‐dodecyl‐β‐D‐maltoside from B. subtilis membranes after separation by 2‐D PAGE, and identified 116 protein spots. This method can detect proteins at the 10∼50 fmol level by pooling more than ten identical electroblotted protein spots.
Profiling of cytokine expression by biotin‐labeled‐based protein arrays
Authors: Lin Ying , Huang Ruochun , Chen Li‐Pai , Lisoukov Henry , Lu Zhen‐Hai , Li Shiyong , Wang Cheng C. , Huang Ruo‐Pan
Published: September 2003

Global analysis of protein expression holds great promise in basic research and patient care. Previously we demonstrated that multiple cytokines could be detected simultaneously using an enzyme‐linked immunosorbent assay protein array system with high sensitivity and specificity. In this paper, we described a biotin‐labeled‐based protein array system to detect multiple cytokines simultaneously from biological samples. In this new approach, proteins from a variety of biological sources are labeled with biotin. The biotin‐labeled proteins are then incubated with antibody chips. Targeted proteins are captured by the array antibodies spotted on the antibody chips. The presence of targeted proteins is detected using Cy3‐ or Cy5‐conjugated streptavidin and signals are imaged by laser scanner. The system also can be easily adapted to a two‐color binding assay, allowing measurement of the levels of proteins in a test sample with respect to a reference sample at the same chip. To demonstrate its potential applications, we applied this technology to profile human cytokines, chemokines, growth factors, angiogenic factors and proteases in estrogen receptor (ER)+ and ER− cells. These results suggest that biotin‐labeled‐based antibody chip technology can provide a practical and powerful means of profiling hundreds or thousands of proteins for research and clinical purposes.
Application of sensitive fluorescent dyes in linkage of laser microdissection and two‐dimensional gel electrophoresis as a cancer proteomic study tool
Authors: Kondo Tadashi , Seike Masahiro , Mori Yasuharu , Fujii Kazuyasu , Yamada Tesshi , Hirohashi Setsuo
Published: September 2003

The combination of laser microdissection and two‐dimensional gel electrophoresis (2‐D PAGE) has been developed to perform proteomic analysis on specific populations of cells in cancer tissues. However, as conventional low sensitivity silver staining was used for spot detection, the microdissection required to obtain an adequate amount of protein for 2‐D PAGE is laborious and only a restricted number of protein spots could be visualized. As a consequence, this technology was impractical for direct clinical applications and had a limited impact on cancer studies. To solve these problems, we developed an application in which fluorescent dyes label the proteins extracted from microdissected tissues prior to 2‐D PAGE separation. In this application, a small amount of protein, less than 6.6 μg, was enough to generate a 2‐D profile with approximately 1500 protein spots. This technique was applied to compare the proteome of normal intestinal epithelium with that of adenoma in Min mice. Thirty‐seven protein spots reproducibly showed significant differences in intensities. Mass spectrometric analysis and Western blotting identified eight of them, including prohibitin, 14‐3‐3zeta, tropomyosin 3 and Hsp84. These results indicate that fluorescence labeling of proteins from microdissected tissues prior to 2‐D PAGE is a powerful cancer proteomic study tool.
Proteome of monocytes primed with lipopolysaccharide: Analysis of the abundant proteins
Authors: Gadgil Himanshu S. , Pabst Karen M. , Giorgianni Francesco , Umstot Edward S. , Desiderio Dominic M. , Beranova‐Giorgianni Sarka , Gerling Ivan C. , Pabst Michael J.
Published: September 2003

We performed a proteomic analysis of monocytes primed by lipopolysaccharide (LPS) in vitro, using two‐dimensional gels stained with Coomassie blue. We found 16 proteins of ∼500 detected that either increased or decreased in abundance as a result of priming by LPS (14 with P ≤ 0.05). The proteins were identified by comparing the masses of their tryptic peptides with those of all known proteins, using matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry and the SWISS‐PROT database. Identities were confirmed by matching the sequence of several tryptic peptides, using liquid chromatography electrospray‐ionization quadrupole ion trap mass spectrometry. There were increases in the protective enzymes superoxide dismutase and catalase, in four calgranulins, in the cytokine pre‐B cell enhancing factor, and in annexin 2, macrophage capping protein, transketolase, pyruvate kinase, and serine/threonine protein kinase 10. Proteins that decreased in abundance were integrin alpha‐IIB, protein disulfide isomerase, and platelet‐activating factor acetylhydrolase. Many of these altered proteins have interesting functions in inflammation.
Editorial: Proteomics 9/2003
Authors: Campa Michael J. , Fitzgerald Michael J. , Patz Edward F.
Published: September 2003

No abstracts.
Treatment of mice with a herbal preparation (Mentat) protects against radiation‐induced mortality
Authors: Jagetia Ganesh Chandra , Shrinath Baliga Manjeshwar
Published: September 2003

The effect of various doses (0, 5, 10, 20, 40, 80, 100, 120 and 160 mg/kg b. wt.) of 50% ethanolic extract of mentat (a herbal preparation) was studied on the survival of mice exposed to 10 Gy of γ‐radiation. Treatment of mice with different doses of mentat consecutively for ﬁve days before irradiation delayed the onset of mortality and reduced the symptoms of radiation sickness when compared with the non‐drug treated irradiated controls. Most of the doses of mentat provided protection against the gastrointestinal (GI) death, however, the highest protection against GI death was observed for 80 mg/kg mentat. This was also true for bone marrow deaths, where the highest number of survivors were observed at 30 days post‐irradiation in this group (i.e. 80 mg/kg) when compared with the other doses of mentat. The evaluation of acute toxicity showed that mentat was non‐toxic up to a dose of 1.5 g/kg b. wt., where no drug‐induced mortality was observed. The LD50 dose of mentat was found to be 1.75 g/kg b. wt. Our study demonstrates that mentat can provide good radioprotection at a dose of 80 mg/kg, which is far below its toxic dose. Copyright © 2003 John Wiley & Sons, Ltd.
Inhibitory phenolic amides on lipopolysaccharide‐induced nitric oxide production in RAW 264.7 cells from Beta vulgaris var. cicla seeds
Authors: Kim Youngleem , Han Mi Seon , Lee Ji Suk , Kim Jinwoong , Kim Young Choong
Published: September 2003

Four phenolic compounds were isolated from Beta vulgaris L. var. cicla L. (Chenopodiaceae). These isolated compounds were identiﬁed as N‐cis‐feruloyl 3‐O‐methyldopamine (1), N‐cis‐feruloyl tyramine (2), N‐trans‐feruloyl 3‐O‐methyldopamine (3), N‐trans‐feruloyl tyramine (4), respectively, by spectroscopic analysis. The phenolic amides 1–4 exhibited modest inhibitory activity on LPS‐activated nitric oxide production dose‐dependently in RAW 264.7 cells. Copyright © 2003 John Wiley & Sons, Ltd.
The in vitro effect of aqueous extract of Nigella sativa seeds on nitric oxide production
Authors: Mahmood M. Saeed , Gilani A. H. , Khwaja Afsheen , Rashid Ayesha , Ashfaq M. K.
Published: September 2003

The in vitro effect of aqueous extract of Nigella sativa seeds on nitric oxide (NO) production by murine macrophages was studied. Murine peritoneal macrophages were pre‐incubated with the extract and then activated with Escherichia coli lipopolysaccharride. NO production was measured after 24 hours by spectrophotometry. The plant extract caused a dose‐dependent decrease in NO production. Dialyzed preparation of the extract did not affect NO production. However, the boiled fraction of the extract resulted in a dose‐dependent inhibition of NO apparently comparable to that of the whole extract. These results indicate that the aqueous extract of N. sativa seeds exhibits an inhibitory effect on nitric oxide production by murine macrophages and the active component(s) is/are non‐protein in nature. In view of the fact that nitric oxide is a pro‐inﬂammatory mediator, this study validates the traditional use of the Nigella sativa seeds for the treatment of rheumatism. Copyright © 2003 John Wiley & Sons, Ltd.
Antitussive activity of Abies webbiana Lindl. leaf extract against sulphur dioxide‐induced cough reﬂex in mice
Authors: Nayak S. S. , Ghosh A. K. , Srikanth K. , Debnath B. , Jha T.
Published: September 2003

The methanol extract of A. webbiana Lindl was evaluated for its effect on a cough model induced by sulphur dioxide gas in mice. When administered orally it exhibited signiﬁcant antitussive activity compared with the control in a dose dependent manner. The antitussive activity of the extract was compared with that of codeine phosphate, a prototype antitussive agent. The A. webbiana leaf extract (400 and 600 mg/kg) showed maximum inhibition of cough frequency by 71.69% and 78.67%, respectively, when compared with the control group and was comparable in effect to codeine phosphate. Copyright © 2003 John Wiley & Sons, Ltd.
Bioassay‐guided isolation and identiﬁcation of antifungal compounds from ginger
Authors: Ficker C. , Smith M. L. , Akpagana K. , Gbeassor M. , Zhang J. , Durst T. , Assabgui R. , Arnason J. T.
Published: September 2003

A bioassay‐guided isolation of antifungal compounds from an African land race of ginger, Zingiber ofﬁcinale Roscoe, led to the identiﬁcation of [6], [8] and [10]‐gingerols and [6]‐gingerdiol as the main antifungal principles. The compounds were active against 13 human pathogens at concentrations of <1 mg/mL. The gingerol content of the African land race was at least 3 × higher than that of typical commercial cultivars of ginger. Therefore, ginger extracts standardized on the basis of the identiﬁed compounds, could be considered as antifungal agents for practical therapy. Copyright © 2003 John Wiley & Sons, Ltd.
Tourism dynamics: Cyprus' hotel value chain and proﬁtability
Authors: Georgantzas Nicholas C.
Published: September 2003

In response to the Cyprus Hotel Association's aim to test how Cyprus' year 2010 ofﬁcial strategy may affect tourist arrivals, hotel bed capacity and proﬁtability, this study looks at Cyprus' hotel value chain within the island's tourism customer–supplier value chain. A system dynamics simulation model helps compute multiple strategic scenarios of what might happen to Cyprus' tourism over the next 40 years. The faithful reproduction of historical tourism and bed capacity data through a 40‐year time horizon shows the model's usefulness. The computed scenarios assess Cyprus' hotel value‐chain sensitivity to policy parameters, tourism growth and tourism seasonality, along with their potential effects on the hotel value chain and proﬁtability. Copyright © 2003 John Wiley & Sons, Ltd.
XPS inelastic background optimization: algorithm and results
Authors: Jo Masatoshi
Published: September 2003

The x‐ray photoelectron spectrum of a solid is a superposition of the peaks and the inelastic background. Using the background optimization technique it is possible to make a precise separation of them. The separated peaks and background have shown very physically consistent results in relation to the peak intensity, peak shape, loss function, etc. The most remarkable point of the present formalism is that very little sample‐specific knowledge is needed for the analysis. In this paper, details of the method and the results obtained both previously and recently on the metals Cu, Al and Ag will be reviewed. Copyright © 2003 John Wiley & Sons, Ltd.
Estimating the size of an illicit‐drug‐using population
Authors: Simeone Ronald , Holland Lynn , Viveros‐Aguilero Roman
Published: September 2003

This paper describes a new method for estimating the size of an illicit‐drug‐using population. It is designed to overcome certain limitations of registry‐based techniques that require both comprehensive site coverage and unique case identifiers, and which do not typically provide estimates of the number of drug users who are currently active. The approach involves collecting retrospective self‐report data on the careers of individuals who appear at drug treatment programmes. A model is developed that corrects for the selection bias introduced by the sampling plan, and which allows us to estimate the rate at which drug users generate treatment admission events during spells of use. The size of the drug‐using population is estimated by dividing the estimated total number of treatment admissions that are generated during some fixed interval of time by the estimated rate at which individuals generate such events. The technique is tested in a series of simulation studies which demonstrate that accurate estimates of the size of the drug using population can be obtained in this manner. Analytical expressions for confidence intervals about the population estimates are derived as part of the exercise. Limitations of the approach and other potential applications are discussed. Copyright © 2003 John Wiley & Sons, Ltd.