Reciprocal secretion of proteins by the bacterial type III machines of plant and animal pathogens suggests universal recognition of mRNA targeting signals

Bacterial pathogens of both animals and plants use type III secretion machines to inject virulence proteins into host cells. Although many components of the secretion machinery are conserved among different bacterial species, the substrates for their type III pathways are not. The Yersinia type III machinery recognizes some secretion substrates via a signal that is encoded within the first 15 codons of yop mRNA. These signals can be altered by frameshift mutations without affecting secretion of the encoded polypeptides, suggesting a mechanism whereby translation of yop mRNA is coupled to the translocation of newly synthesized polypeptide. We report that the type III machinery of Erwinia chrysanthemi cloned in Escherichia coli recognizes the secretion signals of yopE and yopQ. Pseudomonas syringae AvrB and AvrPto, two proteins exported by the recombinant Erwinia machine, can also be secreted by the Yersinia type III pathway. Mapping AvrPto sequences sufficient for the secretion of reporter fusions in Yersinia revealed the presence of an mRNA secretion signal. We propose that 11 conserved components of type III secretion machines may recognize signals that couple mRNA translation to polypeptide secretion.

Isolation and characterization of neutral-lipid-containing organelles and globuli-filled plastids from Brassica napus tapetum

The monolayer tapetum cells of the maturing flowers of Brassica napus contain abundant subcellular globuli-filled plastids and special lipid particles, both enriched with lipids that are supposed to be discharged and deposited onto the surface of adjacent maturing pollen. We separated the two organelles by flotation density gradient centrifugation and identified them by electron microscopy. The globuli-filled plastids had a morphology similar to those described in other plant species and tissues. They had an equilibrium density of 1.02 g/cm3 and contained neutral esters and unique polypeptides. The lipid particles contained patches of osmiophilic materials situated among densely packed vesicles and did not have an enclosing membrane. They exhibited osmotic properties, presumably exerted by the individual vesicles. They had an equilibrium density of 1.05 g/cm3 and possessed triacylglycerols and unique polypeptides. Several of these polypeptides were identified, by their N-terminal sequences or antibody cross-reactivity, as oleosins, proteins known to be associated with seed storage oil bodies. The morphological and biochemical characteristics of the lipid particles indicate that they are novel organelles in eukaryotes that have not been previously isolated and studied. After lysis of the tapetum cells at a late stage of floral development, only the major plastid neutral ester was recovered, whereas the other abundant lipids and proteins of the two tapetum organelles were present in fragmented forms or absent on the pollen surface.

The elasticity and failure of fluid-filled cellular solids: Theory and experiment

We extend and apply theories of filled foam elasticity and failure to recently available data on foods. The predictions of elastic modulus and failure mode dependence on internal pressure and on wall integrity are borne out by photographic evidence of distortion and failure under compressive loading and under the localized stress applied by a knife blade, and by mechanical data on vegetables differing only in their turgor pressure. We calculate the dry modulus of plate-like cellular solids and the cross over between dry-like and fully fluid-filled elastic response. The bulk elastic properties of limp and aging cellular solids are calculated for model systems and compared with our mechanical data, which also show two regimes of response. The mechanics of an aged, limp beam is calculated, thus offering a practical procedure for comparing experiment and theory. This investigation also thereby offers explanations of the connection between turgor pressure and crispness and limpness of cellular materials.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.