Carob germ proteins have been shown to have functional properties similar to wheat gluten enabling formulation and production of yeast leavened gluten-free baked goods from a true dough rather than a stiff batter. The purpose of this research was to optimize the production of wheat-free bread containing carob germ flour, corn starch, NaCl, sucrose, hydroxypropyl methylcellulose (HPMC), and H₂O. A key criterion was to formulate viscoelastic dough similar to wheat dough. To that end, response surface methodology (RSM) was used to determine optimal levels of carob germ flour, H₂O, and HPMC. Components varied as follows: 4.94%-15.05% for carob germ flour, 0.05%-3.75% HPMC, and 65.25%-83.75% H₂O (percents are on a flour basis, where carob germ flour in combination with maize starch equals 100%). Sucrose, NaCl, and yeast were held constant at 2%. Bread parameters evaluated were specific volume and crumb hardness, where the largest specific volume and the lowest value for crumb hardness were considered most desirable. The optimum formula as determined by RSM consisted of 7% carob germ flour, 93% maize starch, 2% HPMC, and 80% H₂O with predicted crumb hardness of ~200 g of force and a specific volume of ~3.5 cm³/g. When proof time was optimized, a specific volume of ~5.6 ml/g and crumb hardness value of ~156 g of force was observed. Carob germ flour may be used as an alternative to wheat flour in formulating viscoelastic dough and high quality gluten-free bread. PRACTICAL APPLICATION: Celiac disease affects approximately 1% of the world's population. Sufferers of the disease must consume a gluten-free diet. Currently, gluten-free baked products are made from batters and lack the ability to be made from dough based systems which limits the overall processability and product variety. This research is aimed at the utilization of carob germ protein and its ability to form dough to produce an optimal gluten-free bread formulation. This will help to alleviate problems in processability and product variety associated with gluten-free baked goods.

Intact yeast cells treated with alkali cations took up plasmid DNA. Li+, Cs+, Rb+, K+, and Na+ were effective in inducing competence. Conditions for the transformation of Saccharomyces cerevisiae D13-1A with plasmid YRp7 were studied in detail with CsCl. The optimum incubation time was 1 h, and the optimum cell concentration was 5 x 10(7) cells per ml. The optimum concentration of Cs+ was 1.0 M. Transformation efficiency increased with increasing concentrations of plasmid DNA. Polyethylene glycol was absolutely required. Heat pulse and various polyamines or basic proteins stimulated the uptake of plasmid DNA. Besides circular DNA, linear plasmid DNA was also taken up by Cs+-treated yeast cells, although the uptake efficiency was considerably reduced. The transformation efficiency with Cs+ or Li+ was comparable with that of conventional protoplast methods for a plasmid containing ars1, although not for plasmids containing a 2 microns origin replication.

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

The proto-oncogene bcl-2 inhibits apoptotic and necrotic neural cell death. Expression of Bcl-2 in the GT1-7 neural cell line prevented death as a result of glutathione depletion. Intracellular reactive oxygen species and lipid peroxides rose rapidly in control cells depleted of glutathione, whereas cells expressing Bcl-2 displayed a blunted increase and complete survival. Modulation of the increase in reactive oxygen species influenced the degree of cell death. Yeast mutants null for superoxide dismutase were partially rescued by expression of Bcl-2. Thus, Bcl-2 prevents cell death by decreasing the net cellular generation of reactive oxygen species.

Mitogen-activated protein (MAP) kinase cascades are activated in response to various extracellular stimuli, including growth factors and environmental stresses. A MAP kinase kinase kinase (MAPKKK), termed ASK1, was identified that activated two different subgroups of MAP kinase kinases (MAPKK), SEK1 (or MKK4) and MKK3/MAPKK6 (or MKK6), which in turn activated stress-activated protein kinase (SAPK, also known as JNK; c-Jun amino-terminal kinase) and p38 subgroups of MAP kinases, respectively. Overexpression of ASK1 induced apoptotic cell death, and ASK1 was activated in cells treated with tumor necrosis factor-alpha (TNF-alpha). Moreover, TNF-alpha-induced apoptosis was inhibited by a catalytically inactive form of ASK1. ASK1 may be a key element in the mechanism of stress- and cytokine-induced apoptosis.