Assembly and adhesive properties of curli : A stationary phase-specific surface organelle in gram-negative enteric bacteria

Assembly and adhesive properties of curli: A stationaryphase-specific surface organelle in gram-negative enteric bacteria. Mårten Hammar The natural environments of bacteria are most often characterizedby suboptimal conditions for growth, e. g., limited supply ofnutrients. Consequently, periods of dormancy or negligible growth arethe rule rather than the exception. Starvation leads to profounddevelopmental changes. The most dramatic response to starvation isthe generation of dormant spores by many species of gram-positivebacteria and the generation of multicellular fruiting bodies bymyxobacteria. In contrast, many gram negative bacteria, e. g.,enteric bacteria like Escherichia coli and Salmonellaenterica, do not enter a dormant state but maintain a low levelof metabolism combined with an increased resistance to environmentalstresses. Although not considered a typical starvation-inducedresponse, bacteria express a variety of surface structures inresponse to diverse environmental conditions. Often, these structuresare proteinaceous filaments extending out from the bacterial cellsurface. These structures serve the purpose of mediating contactbetween the bacterium and a eukaryotic cell surface, a tissue matrixor serum protein, or to other bacteria, conspecific or of otherspecies. These interactions are often the commited steps leading tosubsequent colonization of an epithelial surface, entry into a hostcell, exchange of DNA between bacteria, or development of a bacterialcommunity organized as biofilms, colonies or multicellular fruitingbodies. In Escherichia coli, a novel type of fimbriae-likestructure denoted curli was recently discovered. This surfaceorganelle is characterized by a distinct morphology, a high bindingaffinity for many eukaryotic proteins, and a unique pattern ofstationary phase-dependent expression. To further understand the mechanisms of curli biogenesis and theregulation of curli expression, a genetic analysis was performed toidentify the genetic determinants involved in these processes. Thisthesis presents the identification of six genes, csgDEFG and csgBA,the products of which are specifically involved in curli production. CsgA is the major subunit protein of the curli filament. CsgB is asurface-exposed protein required for polymerization of CsgA subunitsinto curli filaments and for anchoring of these to the cell surface.CsgG is an outer membrane-anchored periplasmic lipoprotein whichstabilizes the CsgB and CsgA proteins. CsgF, together with CsgB,participates in nucleation of CsgA subunits. A cell deficient innucleation function secretes soluble CsgA monomers or oligomers tothe growth medium. These CsgA subunits are polymerization-competent,i. e., they spontaneously polymerize to curli fibers on the surfaceof any cell presenting a functional nucleator (CsgB). This process ofextracellular formation of curli indicates a novel pathway offimbriae biogenesis, the extracellular nucleation-precipitationpathway. The addition of subunits to the growing filament seems to bedriven by mass action and guided only by the diffusion gradientbetween the source of secreted subunits and the growing curli tip. The adhesive properties of curli are dependent on CsgA subunitspolymerized in the presence of CsgE. Thus, inactivation of the csgEgene results in curli deficient in binding. A similar type ofadhesion-deficient curli is produced by cells devoid of Nacetylglucosamine-6-phosphate deacetylase. Several regulatory proteins controlling the expression of curliwere identified. CsgD is required for transcription of the csgBAoperon. OmpR, a transcriptional activator responding to osmolarity,and d, a stationary phase-specific sigma factor, activatestranscription of the csgDEFG operon. Mutations in several other lociaffect transcription of the csg genes, indicating a complexregulation of curli expression where different signals are mtegrated. Key words: Escherichia colil/curli/ fimbriae/ csg/starvation ISBN 91-628-2605-

Combining evidence of preferential gene-tissue relationships from multiple sources

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity

Combining evidence of preferential gene-tissue relationships from multiple sources

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity

Example GRHPR of how the total score is calculated (see main text).

<p>Example GRHPR of how the total score is calculated (see main text).</p

The agreement of the 191 specific genes with strong support and highest coverage, based on our predictions, to TiGER, PaGenBase (TiSGeD) and HPA.

<p>No info means missing data.</p

Training/Testing.

<p>The percentage shows the agreement of the detection and HuGEindex prediction. Specific and ubiquitously expressed genes are all considered as positive if agrees with the prediction. The rows show the dataset used for training, i.e. to estimate the parameters. The columns show the testing on the other datasets.</p

Flow diagram illustrating the procedure for predicting preferentially expressed genes from multiple datasets.

<p>The following steps are taken: 1) The methods (Bayes Factor, ROKU-SPM and Decision F = Decision function) are applied to each dataset separately, 2) The consensus vote combines the output from the methods, 3) The inner-score combines the output from several probe sets and 4) The total score integrates the results from all datasets into a common result.</p