Design and Characterization of a Multistage Peptide-Based Vaccine Platform to Target Mycobacterium tuberculosis Infection.

The complex immunopathology ofMycobacterium tuberculosis(Mtb) is one of the main challenges in developing a novel vaccine against this pathogen, particularly regarding eliciting protection against both active and latent stages. Multistage vaccines, which contain antigens expressed in both phases, represent a promising strategy for addressing this issue, as testified by the tuberculosis vaccine clinical pipeline. Given this approach, we designed and characterized a multistage peptide-based vaccine platform containing CD4+ and CD8+ T cell epitopes previously validated for inducing a relevant T cell response against Mtb. After preliminary screening, CFP10 (32-39), GlfT2 (4-12), HBHA (185-194), and PPE15 (1-15) were selected as promising candidates, and we proved that the PM1 pool of these peptides triggered a T cell response in Mtb-sensitized human peripheral blood mononuclear cells (PBMCs). Taking advantage of the use of thiol-maleimide chemoselective ligation, we synthesized a multiepitope conjugate (Ac-CGHP). Our results showed a structure-activity relationship between the conjugation and a higher tendency to fold and assume an ordered secondary structure. Moreover, the palmitoylated conjugate (Pal-CGHP) comprising the same peptide antigens was associated with an enhanced cellular uptake in human and murine antigen-presenting cells and a better immunogenicity profile. Immunization study, conducted in BALB/c mice, showed that Pal-CGHP induced a significantly higher T cell proliferation and production of IFNγ and TNFα over PM1 formulated in the Sigma Adjuvant System

A Chemocentric Approach to the Identification of Cancer Targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided

Prenylation Inhibition-Induced Cell Death in Melanoma: Reduced Sensitivity in BRAF Mutant/PTEN Wild-Type Melanoma Cells.

While targeted therapy brought a new era in the treatment of BRAF mutant melanoma, therapeutic options for non-BRAF mutant cases are still limited. In order to explore the antitumor activity of prenylation inhibition we investigated the response to zoledronic acid treatment in thirteen human melanoma cell lines with known BRAF, NRAS and PTEN mutational status. Effect of zoledronic acid on proliferation, clonogenic potential, apoptosis and migration of melanoma cells as well as the activation of downstream elements of the RAS/RAF pathway were investigated in vitro with SRB, TUNEL and PARP cleavage assays and videomicroscopy and immunoblot measurements, respectively. Subcutaneous and spleen-to-liver colonization xenograft mouse models were used to evaluate the influence of zoledronic acid treatment on primary and disseminated tumor growth of melanoma cells in vivo. Zoledronic acid more efficiently decreased short-term in vitro viability in NRAS mutant cells when compared to BRAF mutant and BRAF/NRAS wild-type cells. In line with this finding, following treatment decreased activation of ribosomal protein S6 was found in NRAS mutant cells. Zoledronic acid demonstrated no significant synergism in cell viability inhibition or apoptosis induction with cisplatin or DTIC treatment in vitro. Importantly, zoledronic acid could inhibit clonogenic growth in the majority of melanoma cell lines except in the three BRAF mutant but PTEN wild-type melanoma lines. A similar pattern was observed in apoptosis induction experiments. In vivo zoledronic acid did not inhibit the subcutaneous growth or spleen-to-liver colonization of melanoma cells. Altogether our data demonstrates that prenylation inhibition may be a novel therapeutic approach in NRAS mutant melanoma. Nevertheless, we also demonstrated that therapeutic sensitivity might be influenced by the PTEN status of BRAF mutant melanoma cells. However, further investigations are needed to identify drugs that have appropriate pharmacological properties to efficiently target prenylation in melanoma cells

Classification methods for 3D objects in laserscanning data

The object classification can play an important role in a lot of applications of airborne laserscanning data. The filtering process and the subsequent DTM generation using airborne laserscanning data can be significantly improved by classification of non-terrain objects (e.g. vegetation, buildings etc.). On the other hand classification can be also the first step of object-specific modelling, like vegetation or building reconstruction for 3D city models, design of telecommunication networks, urban planning or disaster management. A pixel-wise classification – especially when using laserscannning data- is limited in terms of reliability of its results. Therefore, the first step of this approach will be a segmentation of 3D objects. For each segment object-specific features (e.g. height texture, shape etc.) are extracted and used for subsequent classification process. In this phase the method is based on raster data. For segmentation a normalised DSM (nDSM) is generated by subtracting the original laser data (DSM) from a rough DTM (created by a strong filtering of the DSM). Now 3D objects can be segmented by means of specific a region growing algorithm on this nDSM. Different kind of object-oriented features are calculated for each segment, like height texture, border gradients, first/last pulse height differences, shape parameters or laser intensities. For classification two methods have been applied, on one hand a fuzzy logic classification, on the other hand a statistical method (maximum likelihood). The fuzzy logic approach resulted in an overall classification rate of about 95 % for test site ‘Salem ’ (hilly terrain) and about 90 % for test site ‘Karlsruhe ’ (flat terrain). The confusion matrix for ‘Salem ’ show that buildings were erroneous classified as trees (5%) resp. trees as buildings (4%). The mos