Metabolite profiling of antidepressant drug action reveals novel drug targets beyond monoamine elevation

Currently used antidepressants elevate monoamine levels in the synaptic cleft. There is good reason to assume that this is not the only source for antidepressant therapeutic activities and that secondary downstream effects may be relevant for alleviating symptoms of depression. We attempted to elucidate affected biochemical pathways downstream of monoamine reuptake inhibition by interrogating metabolomic profiles in DBA/2Ola mice after chronic paroxetine treatment. Metabolomic changes were investigated using gas chromatography-mass spectrometry profiling and group differences were analyzed by univariate and multivariate statistics. Pathways affected by antidepressant treatment were related to energy metabolism, amino acid metabolism and hormone signaling. The identified pathways reveal further antidepressant therapeutic action and represent targets for drug development efforts. A comparison of the central nervous system with blood plasma metabolite alterations identified GABA, galactose-6-phosphate and leucine as biomarker candidates for assessment of antidepressant treatment effects in the periphery

Azidobupramine, an Antidepressant-Derived Bifunctional Neurotransmitter Transporter Ligand Allowing Covalent Labeling and Attachment of Fluorophores

The aim of this study was to design, synthesize and validate a multifunctional antidepressant probe that is modified at two distinct positions. The purpose of these modifications was to allow covalent linkage of the probe to interaction partners, and decoration of probe-target complexes with fluorescent reporter molecules. The strategy for the design of such a probe (i.e., azidobupramine) was guided by the need for the introduction of additional functional groups, conveying the required properties while keeping the additional moieties as small as possible. This should minimize the risk of changing antidepressant-like properties of the new probe azidobupramine. To control for this, we evaluated the binding parameters of azidobupramine to known target sites such as the transporters for serotonin (SERT), norepinephrine (NET), and dopamine (DAT). The binding affinities of azidobupramine to SERT, NET, and DAT were in the range of structurally related and clinically active antidepressants. Furthermore, we successfully visualized azidobupramine-SERT complexes not only in SERT-enriched protein material but also in living cells stably overexpressing SERT. To our knowledge, azidobupramine is the first structural analogue of a tricyclic antidepressant that can be covalently linked to target structures and further attached to reporter molecules while preserving antidepressant-like properties and avoiding radioactive isotopes

Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php