SMN loss dysregulates microtubule-associated proteins in spinal muscular atrophy model.

Spinal muscular atrophy (SMA) is a rare neurodegenerative disease caused by the absence of survival motor neuron (SMN) protein. SMN loss results in impairments of the cytoskeleton, including microtubules and regulatory proteins. However, the contribution of microtubule-associated proteins (MAPs) to microtubule dysregulations in SMA is not fully understood. In this study, we investigated neuronal MAPs responsible for the microtubule stability and growth, including MAP1A, MAP2, MAP6, MAP7, EB1, and EB3 using an in vitro model of SMA. Decreased MAP2 and EB3 levels were found in SMN-deficient motor neuron-like cells, and EB3 protein level was also relevant to MAP1B. SMN loss leads to an increase in EB3 comet numbers at proximal neurites, indicating increased microtubule growth. Our findings suggest that SMN deficiency simultaneously causes dysregulations of several MAPs, contributing to the perturbations of microtubule dynamics in SMA.

Oligomerization and cell surface expression of recombinant GABAA receptors tagged in the δ subunit.

The γ-Aminobutyric acid type A receptors (GABAARs) are heteropentameric chloride channels responsible for primary inhibition in the mammalian brain. Studies have shown the expression of recombinant GABAAR subunits tagged with the green fluorescent protein (GFP), a 26.9 kDa protein that exhibits bright green fluorescence when exposed to light in the blue to ultraviolet range. This allows the formation of recombinant proteins essential for the development of relevant in-vitro and in-vivo methodologies. Among the GABAAR subunits, the δ subunit was never tagged in its cytoplasmic domain, an evolutionary conserved domain found in between the third and the fourth transmembrane domains. In this study, first, we have cloned the mouse cDNAs encoding for the δ, α1, ß2 subunits of GABAARs, and then developed two fusion proteins of δ subunit each tagged with the GFP variant, EGFP (enhanced GFP) at unique sites in the cytoplasmic domain. The recombinant proteins were expressed alone or in combination with α1 and/or ß2 subunits in neuroblastoma 2a cells. Live cell confocal microscopy indicated that the cytoplasmically tagged δ subunits were targeted to the cell membrane when expressed in the presence of α1 and ß2 subunits in neuroblastoma 2a cells. However, this was not observed when they were expressed alone or only with α1 or ß2 subunits in the same cell line. These results confirm the general oligomerization and targeting pattern of GABAAR subtypes described in the other in-vitro studies in the literature. Thus, our results suggest that the EGFP tagging in the ctoplasmic domain did not interfere with the oligomerization and cell surface expression of recombinant δ subunits. To our knowledge, this is the first study showing the generation, expression and preliminary analysis of the δ-GABAARs tagged in the cytoplasmic domain of the δ subunit which can be further elaborated to probe intracellular protein interactions of GABAARs via the δ subunit.

Fast Screening of Protein-Protein Interactions Using Förster Resonance Energy Transfer (FRET-) Based Fluorescence Plate Reader Assay in Live Cells.

Protein-protein interactions (PPIs) have great importance for intracellular signal transduction and sustaining the homeostasis of an organism. Thus, the identification of PPIs is necessary to better understand the downstream signaling functions of the proteins in healthy and pathological conditions. Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) is a powerful tool for detecting PPIs in living cells. In literature, FRET analysis methods such as donor photobleaching (FLIM), acceptor photobleaching, spectral imaging, and the three-filter cube method (sensitized emission) are abundantly applied to investigate PPIs; however, they require various expensive instrumentations, and their calculation methods are very time consuming. Since confocal microscopy applications and live cell-based techniques of FRET are very costly, scientists sometimes prefer plate readers for FRET experiments. However, plate reader applications also have many disadvantages and considerations compared to confocal fluorescence microscopy, and complex calculation procedures should be performed. To overcome these problems, we propose a FRET-based high-throughput assay method with a standard monochromator-based microplate reader, which is generally available in most biochemistry laboratories, and an alternative calculation procedure. This rapid, low cost, and effective analysis method enables the scientists to prescreen PPIs in living cells as a preliminary study and quick glance at the experiment before preparing the whole experimental setup with the expensive instrumentations. Additionally, the alternative calculation procedure provides the FRET area comparison without complex bleed-through calculations in a non-conventional manner by shortening the analysis processes with this quick and uncomplicated spectral representation.

Somatic copy number variant load in neurons of healthy controls and Alzheimer's disease patients.

The possible role of somatic copy number variations (CNVs) in Alzheimer's disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using low-coverage scWGS on pyramidal neurons dissected via both laser capture microdissection (LCM) and fluorescence activated cell sorting (FACS) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA1, hippocampal CA3, and the cerebellum. Among reliably detected somatic CNVs identified in 1301 cells obtained from the brains of 13 AD patients and 7 healthy controls, deletions were more frequent compared to duplications. Interestingly, we observed slightly higher frequencies of CNV events in cells from AD compared to similar numbers of cells from controls (4.1% vs. 1.4%, or 0.9% vs. 0.7%, using different filtering approaches), although the differences were not statistically significant. On the technical aspects, we observed that LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with FACS. To reduce within-cell read depth variation, we proposed a principal component analysis-based denoising approach that significantly improves signal-to-noise ratios. Lastly, we showed that LCM-isolated neurons in AD harbour slightly more read depth variability than neurons of controls, which might be related to the reported hyperploid profiles of some AD-affected neurons.

Allergen fragrance molecules: a potential relief for COVID-19.

BACKGROUND: The latest coronavirus SARS-CoV-2, discovered in China and rapidly spread Worldwide. COVID-19 affected millions of people and killed hundreds of thousands worldwide. There are many ongoing studies investigating drug(s) suitable for preventing and/or treating this pandemic; however, there are no specific drugs or vaccines available to treat or prevent SARS-CoV-2 as of today. METHODS: Fifty-eight fragrance materials, which are classified as allergen fragrance molecules, were selected and used in this study. Docking simulations were carried out using four functional proteins; the Covid19 Main Protase (MPro), Receptor binding domain (RBD) of spike protein, Nucleocapsid, and host Bromodomain protein (BRD2), as target macromolecules. Three different software, AutoDock, AutoDock Vina (Vina), and Molegro Virtual Docker (MVD), running a total of four different docking protocol with optimized energy functions were used. Results were compared with the five molecules reported in the literature as potential drugs against COVID-19. Virtual screening was carried out using Vina, molecules satisfying our cut-off (- 6.5 kcal/mol) binding affinity was confirmed by MVD. Selected molecules were analyzed using the flexible docking protocol of Vina and AutoDock default settings. RESULTS: Ten out of 58 allergen fragrance molecules were selected for further docking studies. MPro and BRD2 are potential targets for the tested allergen fragrance molecules, while RBD and Nucleocapsid showed weak binding energies. According to AutoDock results, three molecules, Benzyl Cinnamate, Dihydroambrettolide, and Galaxolide, had good binding affinities to BRD2. While Dihydroambrettolide and Galaxolide showed the potential to bind to MPro, Sclareol and Vertofix had the best calculated binding affinities to this target. When the flexible docking results analyzed, all the molecules tested had better calculated binding affinities as expected. Benzyl Benzoate and Benzyl Salicylate showed good binding affinities to BRD2. In the case of MPro, Sclareol had the lowest binding affinity among all the tested allergen fragrance molecules. CONCLUSION: Allergen fragrance molecules are readily available, cost-efficient, and shown to be safe for human use. Results showed that several of these molecules had comparable binding affinities as the potential drug molecules reported in the literature to target proteins. Thus, these allergen molecules at correct doses could have significant health benefits.

Three dimensional structure prediction of panomycocin, a novel Exo-ß-1,3-glucanase isolated from Wickerhamomyces anomalus NCYC 434 and the computational site-directed mutagenesis studies to enhance its thermal stability for therapeutic applications.

Panomycocin is a naturally produced potent antimycotic/antifungal protein secreted by the yeast Wickerhamomyces anomalus NCYC 434 with an exo-ß-1,3-glucanase activity. In this study the three dimensional structure of panomycocin was predicted and the computational site-directed mutagenesis was performed to enhance its thermal stability in liquid formulations over the body temperature for topical therapeutic applications. Homology modeling was performed with MODELLER and I-TASSER. Among the generated models, the model with the lowest energy and DOPE score was selected for further loop modeling. The loop model was optimized and the reliability of the model was confirmed with ERRAT, Verify 3D and Ramachandran plot values. Enhancement of the thermal stability of the model was done using contemporary servers and programs such as SPDBViewer, CNA, I-Mutant2.0, Eris, AUTO-MUTE and MUpro. In the region outside the binding site of the model Leu52 Arg, Phe223Arg and Gly254Arg were found to be the best thermostabilizing mutations with 6.26 K, 6.26 K and 8.27 K increases, respectively. In the binding site Glu186Arg was found to be the best thermostabilizer mutation with a 9.58 K temperature increase. The results obtained in this study led us to design a mutant panomycocin that can be used as a novel antimycotic/antifungal drug in a liquid formulation for topical applications over the normal body temperature.

GPCRsort-responding to the next generation sequencing data challenge: prediction of G protein-coupled receptor classes using only structural region lengths.

Next generation sequencing (NGS) and the attendant data deluge are increasingly impacting molecular life sciences research. Chief among the challenges and opportunities is to enhance our ability to classify molecular target data into meaningful and cohesive systematic nomenclature. In this vein, the G protein-coupled receptors (GPCRs) are the largest and most divergent receptor family that plays a crucial role in a host of pathophysiological pathways. For the pharmaceutical industry, GPCRs are a major drug target and it is estimated that 60%-70% of all medicines in development today target GPCRs. Hence, they require an efficient and rapid classification to group the members according to their functions. In addition to NGS and the Big Data challenge we currently face, an emerging number of orphan GPCRs further demand for novel, rapid, and accurate classification of the receptors since the current classification tools are inadequate and slow. This study presents the development of a new classification tool for GPCRs using the structural features derived from their primary sequences: GPCRsort. Comparison experiments with the current known GPCR classification techniques showed that GPCRsort is able to rapidly (in the order of minutes) classify uncharacterized GPCRs with 97.3% accuracy, whereas the best available technique's accuracy is 90.7%. GPCRsort is available in the public domain for postgenomics life scientists engaged in GPCR research with NGS: http://bioserver.ceng.metu.edu.tr/GPCRSort .