WT1 is essential for normal kidney development, and genetic alterations are associated with Wilms’ tumor, Denys Drash (DDS), and Frasier syndromes. Although generally considered a transcription factor this study has revealed that WT1 interacts with an essential splicing factor, U2AF65, and associates with the splicing machinery. WT1 is alternatively spliced and isoforms that include three amino acids, KTS, show stronger interaction with U2AF65 in vitro and better colocalization with splicing factors in vivo. Interestingly a mutation associated with DDS enhanced both −KTS WT1 binding to U2AF65 and splicing-factor colocalization. These data illustrate the functional importance of WT1 isoforms and suggest that WT1 plays a role in pre-mRNA splicing.

The number and disposition of flagella harbored by eubacteria are regulated by a specific trait successfully maintained over generations. The genes governing the number of flagella inBacillus subtilishave never been identified, although theifmlocus has long been recognized to influence the motility phenotype of this microorganism. The characterization of a spontaneousifmmutant ofB. subtilis, displaying diverse degrees of cell flagellation in both liquid and solid media, raised the question of how theifmlocus governs the number and assembly of functional flagella. The major finding of this investigation is the characterization of a newly identified dicistronic operon, namedswrA, that controls both swimming motility and swarming differentiation inB. subtilis. Functional analysis of theswrAoperon allowedswrAA(previously namedswrA[D. B. Kearns, F. Chu, R. Rudner, and R. Losick, Mol. Microbiol.52:357-369, 2004]) to be the first gene identified inB. subtilisthat controls the number of flagella in liquid environments and the assembly of flagella in response to cell contact with solid surfaces. Evidence is given that the second gene of the operon,swrAB, is essential for enabling the surface-adhering cells to undergo swarming differentiation. Preliminary data point to a molecular interaction between the two gene products.

In HeLa cells metabolically labeled in vivo with [32P] orthophosphate in the presence of okadaic acid the concentration of phosphorylated A1 protein was increased significantly as compared to controls. Purified recombinant hnRNP protein A1 served as an excellent substrate in vitro for the catalytic subunit of cAMP-dependent protein kinase (PKA) and for casein kinase II (CKII). Thin layer electrophoresis of A1 acid hydrolysates showed the protein to be phosphorylated exclusively on serine residue by both kinases. V8 phosphopeptide maps revealed that the target site(s) of in vitro phosphorylation are located in the C-terminal region of A1. Phosphoamino acid sequence analysis and site directed mutagenesis identified Ser 199 as the sole phosphoamino acid in the protein phosphorylated by PKA. Phosphorylation introduced by PKA resulted in the suppression of the ability of protein A1 to promote strand annealing in vitro, without any detectable effect on its nucleic acid binding capacity. This finding indicates that phosphorylation of a single serine residue in the C-terminal domain may significantly alter the properties of protein A1.

Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design. Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain. This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.