The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools

Cannabis cultivation in order to produce drugs is forbidden in Switzerland. Thus, law enforcement authorities regularly ask forensic laboratories to determinate cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. As required by the EU official analysis protocol the THC rate of cannabis is measured from the flowers at maturity. When laboratories are confronted to seedlings, they have to lead the plant to maturity, meaning a time consuming and costly procedure. This study investigated the discrimination of fibre type from drug type Cannabis seedlings by analysing the compounds found in their leaves and using chemometrics tools. 11 legal varieties allowed by the Swiss Federal Office for Agriculture and 13 illegal ones were greenhouse grown and analysed using a gas chromatograph interfaced with a mass spectrometer. Compounds that show high discrimination capabilities in the seedlings have been identified and a support vector machines (SVMs) analysis was used to classify the cannabis samples. The overall set of samples shows a classification rate above 99% with false positive rates less than 2%. This model allows then discrimination between fibre and drug type Cannabis at an early stage of growth. Therefore it is not necessary to wait plants' maturity to quantify their amount of THC in order to determine their chemotype. This procedure could be used for the control of legal (fibre type) and illegal (drug type) Cannabis production.

Microparticles in stored red blood cells: an approach using flow cytometry and proteomic tools.

BACKGROUND AND OBJECTIVES: Microparticles (MPs) are small phospholipid vesicles of less than 1 microm, shed in blood flow by various cell types. These MPs are involved in several biological processes and diseases. MPs have also been detected in blood products; however, their role in transfused patients is unknown. The purpose of this study was to characterize those MPs in blood bank conditions. MATERIALS AND METHODS: Qualitative and quantitative experiments using flow cytometry or proteomic techniques were performed on MPs derived from erythrocytes concentrates. In order to count MPs, they were either isolated by various centrifugation procedures or counted directly in erythrocyte concentrates. RESULTS: A 20-fold increase after 50 days of storage at 4 degrees C was observed (from 3370 +/- 1180 MPs/microl at day 5 to 64 850 +/- 37 800 MPs/microl at day 50). Proteomic analysis revealed changes of protein expression comparing MPs to erythrocyte membranes. Finally, the expression of Rh blood group antigens was shown on MPs generated during erythrocyte storage. CONCLUSIONS: Our work provides evidence that storage of red blood cell is associated with the generation of MPs characterized by particular proteomic profiles. These results contribute to fundamental knowledge of transfused blood products.

Expanding molecular modeling and design tools to non-natural sidechains.

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.