Structure of the PilZ-FimXEAL-c-di-GMP complex responsible for the regulation of bacterial type IV pilus biogenesis

Signal transduction pathways mediated by cyclic-bis(3'→5')-dimeric GMP (c-di-GMP) control many important and complex behaviors in bacteria. C-di-GMP is synthesized through the action of GGDEF domains that possess diguanylate cyclase activity and is degraded by EAL or HD-GYP domains with phosphodiesterase activity. There is mounting evidence that some important c-di-GMP-mediated pathways require protein-protein interactions between members of the GGDEF, EAL, HD-GYP and PilZ protein domain families. For example, interactions have been observed between PilZ and the EAL domain from FimX of Xanthomonas citri (Xac). FimX and PilZ are involved in the regulation of type IV pilus biogenesis via interactions of the latter with the hexameric PilB ATPase associated with the bacterial inner membrane. Here, we present the crystal structure of the ternary complex made up of PilZ, the FimX EAL domain (FimXEAL) and c-di-GMP. PilZ interacts principally with the lobe region and the N-terminal linker helix of the FimXEAL. These interactions involve a hydrophobic surface made up of amino acids conserved in a non-canonical family of PilZ domains that lack intrinsic c-di-GMP binding ability and strand complementation that joins β-sheets from both proteins. Interestingly, the c-di-GMP binds to isolated FimXEAL and to the PilZ-FimXEAL complex in a novel conformation encountered in c-di-GMP-protein complexes in which one of the two glycosidic bonds is in a rare syn conformation while the other adopts the more common anti conformation. The structure points to a means by which c-di-GMP and PilZ binding could be coupled to FimX and PilB conformational states

Stenodactilina e lanceolina: due nuove potenti “ribosome-inactivating proteins” bicatenarie. Purificazione, caratterizzazione chimico-fisica e studio dei meccanismi citotossici

Two lectins, called lanceolin and stenodactylin, were purified by affinity chromatography on CL Sepharose 6B from the caudices of the Passifloraceae Adenia lanceolata and Adenia stenodactyla, respectively. They are glycoproteins with Mw of 61,243 (lanceolin) and 63,131 daltons (stenodactylin), consisting of an enzymatic A chain linked to a larger B chain with lectin properties, with N-terminal amino acid sequences similar to that of volkensin, the toxic lectin from Adenia volkensii. These two lectins agglutinate red blood cells, inhibit protein synthesis in a cell-free system as well as in whole cells, and depurinate ribosomes and DNA, but not tRNA or poly(A). They are highly toxic to cells, in which they induce apoptosis and strongly inhibit protein synthesis, and to mice, with LD50s 8.16 mg/kg (lanceolin) and 2.76 mg/kg (stenodactylin) at 48 hours after administration. Thus, lanceolin and stenodactylin have all the properties of the toxic type 2 ribosomeinactivating proteins (RIPs). Further experiments were conducted in order to clarify the effects of these RIPs in cells. We investigated the cronological relationship between cytotoxic activity, indirectly evaluated as inhibition of protein synthesis, and loss of cell viability in NB100 cell line. The induction of apoptosis was assessed by determining caspases 3 and 7 levels, which increase 8-16 hours earlier than the beginning of protein synthesis inhibition. This suggest that the arrest of protein synthesis is not a central event in the pathway of cell poisoning by RIPs. The high toxicity and the induction of cell death only by apoptosis and not by necrosis in two muscular cell lines (TE671 and RD/18) suggest that lanceolin and stenodactylin may be potential candidates for experimental chemoablation in strabism and blepharospasm. These results show that lanceolin and stenodactylin are amongst the most potent toxins of plant origin.

HIF1α regulates Mitochondrial biogenesis and Cellular senescence induced by Gamma Radiation

Cellular response to γ-rays is mediated by ATM-p53 axis. When p53 is phosphorylated, it can transactivate several genes to induce permanent cell cycle arrest (senescence) or apoptosis. Epithelial and mesenchymal cells are more resistant to radiation-induced apoptosis and respond mainly by activating senescence. Hence, tumor cells in a senescent state might remain as “dormant” malignant in fact through disruption of p53 function, cells may overcome growth arrest. Oncocytic features were acquired in the recurring neoplasia after radiation therapy in patient with colonrectal cancer. Oncocytic tumors are characterized by aberrant biogenesis and are mainly non-aggressive neoplasms. Their low proliferation degree can be explained by chronic destabilization of HIF1α, which presides to adaptation to hypoxia and also plays a pivotal role in hypoxia-related radio-resistance. The aim of the present thesis was to verify whether mitochondrial biogenesis can be induced following radiation treatment, in relation of HIF1α status and whether is predictive of a senescence response. In this study was demonstrate that mitochondrial biogenesis parameters like mitochondrial DNA copy number could be used for the prediction of hypoxic status of tissue after radiation treatment. γ-rays induce an increase of mitochondrial mass and function, in response to a genotoxic stress that pushes cells into senescence. Mitochondrial biogenesis is only indirectly regulated by p53, whose activation triggers a MDM2-mediated HIF1α degradation, leading to the release of PGC-1β inhibition by HIF1α. On the other hand, this protein blunts the mitochondrial response to γ-rays as well as the induction of p21-mediated cell senescence, indicating prevalence of the hypoxic over the genotoxic response. Finally in vivo, post-radiotherapy mtDNA copy number increase well correlates with lack of HIF1α increase in the tissue, concluding this may be a useful molecular tool to infer the trigger of a hypoxic response during radiotherapy, which may lead to failure of activation of senescence.

Ribosome-inactivating proteins and their immunotoxins for cancer therapy: insights into the mechanism of cell death

Ribosome-inactivating proteins (RIPs) are a family of plant toxic enzymes that permanently damage ribosomes and possibly other cellular substrates, thus causing cell death involving different and still not completely understood pathways. The high cytotoxic activity showed by many RIPs makes them ideal candidates for the production of immunotoxins (ITs), chimeric proteins designed for the selective elimination of unwanted or malignant cells. Saporin-S6, a type 1 RIP extracted from Saponaria officinalis L. seeds, has been extensively employed to construct anticancer conjugates because of its high enzymatic activity, stability and resistance to conjugation procedures, resulting in the efficient killing of target cells. Here we investigated the anticancer properties of two saporin-based ITs, anti-CD20 RTX/S6 and anti-CD22 OM124/S6, designed for the experimental treatment of B-cell NHLs. Both ITs showed high cytotoxicity towards CD20-positive B-cells, and their antitumor efficacy was enhanced synergistically by a combined treatment with proteasome inhibitors or fludarabine. Furthermore, the two ITs showed differencies in potency and ability to activate effector caspases, and a different behavior in the presence of the ROS scavenger catalase. Taken together, these results suggest that the different carriers employed to target saporin might influence saporin intracellular routing and saporin-induced cell death mechanisms. We also investigated the early cellular response to stenodactylin, a recently discovered highly toxic type 2 RIP representing an interesting candidate for the design and production of a new IT for the experimental treatment of cancer.

DARPins recognizing the tumor-associated antigen EpCAM selected by phage and ribosome display and engineered for multivalency

Designed Ankyrin Repeat Proteins (DARPins) represent a novel class of binding molecules. Their favorable biophysical properties such as high affinity, stability and expression yields make them ideal candidates for tumor targeting. Here, we describe the selection of DARPins specific for the tumor-associated antigen epithelial cell adhesion molecule (EpCAM), an approved therapeutic target on solid tumors. We selected DARPins from combinatorial libraries by both phage display and ribosome display and compared their binding on tumor cells. By further rounds of random mutagenesis and ribosome display selection, binders with picomolar affinity were obtained that were entirely monomeric and could be expressed at high yields in the cytoplasm of Escherichia coli. One of the binders, denoted Ec1, bound to EpCAM with picomolar affinity (K(d)=68 pM), and another selected DARPin (Ac2) recognized a different epitope on EpCAM. Through the use of a variety of bivalent and tetravalent arrangements with these DARPins, the off-rate on cells was further improved by up to 47-fold. All EpCAM-specific DARPins were efficiently internalized by receptor-mediated endocytosis, which is essential for intracellular delivery of anticancer agents to tumor cells. Thus, using EpCAM as a target, we provide evidence that DARPins can be conveniently selected and rationally engineered to high-affinity binders of various formats for tumor targeting.

On Ribosome Load, Codon Bias and Protein Abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.

On Ribosome Load, Codon Bias and Protein Abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.