The Saccharomyces cerevisiae SEC14 Gene Encodes a Cytosolic Factor That Is Required for Transport of Secretory Proteins from the Yeast Golgi Complex

We have obtained and characterized a genomic clone of SEC14, a Saccharomyces cerevisiae gene whose product is required for export of yeast secretory proteins from the Golgi complex. Gene disruption experiments indicated that SEC14 is an essential gene for yeast vegetative growth. Nucleotide sequence analysis revealed the presence of an intron within the SEC14 structural gene, and predicted the synthesis of a hydrophilic polypeptide of 35 kD in molecular mass. In confirmation, immunoprecipitation experiments demonstrated SEC14p to be an unglycosylated polypeptide, with an apparent molecular mass of some 37 kD, that behaved predominantly as a cytosolic protein in subcellular fractionation experiments. These data were consistent with the notion that SEC14p is a cytosolic factor that promotes protein export from yeast Golgi. Additional radiolabeling experiments also revealed the presence of SEC14p-related polypeptides in extracts prepared from the yeasts Kluyveromyces lactis and Schizosaccharomyces pombe. Furthermore, the K. lactis SEC14p was able to functionally complement S. cerevisiae sec14ts defects. These data suggested a degree of conservation of SEC14p structure and function in these yeasts species

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera from each patient were analyzed before and after treatment, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite the small number of patients, highly significant and consistent differences were observed before and after response to therapy in all patients. Of 282 spectral peaks identified, 23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences were observed regardless of whether patients responded to conventional therapy or to MRA. These peaks represent potential biomarkers of active disease. One such peak was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles from serum samples obtained at the time of active disease were compared between the two patient groups. Nine peaks had mean signal intensities significantly different (P < 0.001) between active disease in patients who responded to conventional therapy and in patients who failed to respond, suggesting a possible profile predictive of response. Collectively, these data demonstrate the presence of serum proteomic profiles in SJIA that are reflective of active disease and suggest the feasibility of using the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of novel biomarkers in autoimmune diseases

Assessing the Statistical Significance of the Achieved Classification Error of Classifiers Constructed using Serum Peptide Profiles, and a Prescription for Random Sampling Repeated Studies for Massive High-Throughput Genomic and Proteomic Studies

source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error.We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE

Detection and assignment of TP53 mutations in tumor DNA using peptide mass signature genotyping.

This report describes the application of a new approach to tumor genotyping called peptide mass signature genotyping (PMSG) that is particularly suited to detecting minority sequences in a DNA sample. Detecting minority sequences is essential for accurate tumor genotyping because tumor resections are generally a mixture of malignant and non-malignant cells, with the mutations of interest often outnumbered by the corresponding wild-type alleles. To explore the suitability of PMSG for tumor genotyping, 25 human squamous cell carcinomas of the head and neck, as well as a set of cell lines derived from those tumors, were analyzed for mutations in exons 5 to 8 of the TP53 gene, the exons that encode the DNA-binding domains of the p53 protein. PMSG identified mutations in 11 tumor DNA samples, whereas dideoxy sequencing of the same samples detected mutations in only four. Currently, PMSG can be used to detect mutations that are present in only 20% of the sample DNA, and we expect that this threshold will be lowered significantly as the PMSG process is improved. Hum Mutat 22:158-165, 2003.</p

Detection of cystic fibrosis mutations by peptide mass signature genotyping.

BACKGROUND: The diversity of genetic mutations and polymorphisms calls for the development of practical detection methods capable of assessing more than one patient/one nucleotide position per analysis. METHODS: We developed a new method, based on peptide mass signature genotyping (PMSG), for the detection of DNA mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Exons of the gene were amplified, cloned, and expressed in Escherichia coli as peptide fusions, in natural as well as unnatural reading frames. Peptide analytes were purified by immobilized metal affinity chromatography and analyzed by matrix-assisted, laser desorption/ionization time-of-flight mass spectrometry. Synthetic and natural DNA samples with the 25 mutations recommended for CFTR carrier screening (Grody et al. Genet Med 2001;3:149-54) were assessed using the PMSG test for the CFTR gene. RESULTS: Peptide analytes ranged from 6278 to 17 454 Da and varied 30-fold in expression; highly expressing peptides were observed by electron microscopy to accumulate as inclusion bodies. Peptides were reliably recovered from whole-cell lysates by a simple purification method. CFTR mutations caused detectable changes in resulting mass spectrometric profiles, which were >95% reliably detected in blinded testing of replicate synthetic heterozygous DNA samples. Mutation detection was possible with both sample pooling and multiplexing. The PMSG CFTR test was used to determine compound heterozygous mutations in DNA samples from cystic fibrosis patients, which were confirmed by direct DNA sequencing. CONCLUSIONS: The PMSG test of the CFTR gene demonstrates unique capabilities for determining the sequence status of a DNA target by sensitively monitoring the mass of peptides, natural or unnatural, generated from that target.</p