Gibbsbyers8666

Using spectroscopic rulers to track multiple conformations of single biomolecules and their dynamics have revolutionized the understanding of structural dynamics and its contributions to biology. While the FRET-based ruler reports on inter-dye distances in the 3-10 nm range, other spectroscopic techniques, such as protein-induced fluorescence enhancement (PIFE), report on the proximity between a dye and a protein surface in the shorter 0-3 nm range. Regardless of the method of choice, its use in measuring freely-diffusing biomolecules one at a time retrieves histograms of the experimental parameter yielding separate centrally-distributed sub-populations of biomolecules, where each sub-population represents either a single conformation that stayed unchanged within milliseconds, or multiple conformations that interconvert much faster than milliseconds, and hence an averaged-out sub-population. In single-molecule FRET, where the reported parameter in histograms is the inter-dye FRET efficiency, an intrinsically disordered protein, such as the α-Synuclein monomer in buffer, was previously reported as exhibiting a single averaged-out sub-population of multiple conformations interconverting rapidly. While these past findings depend on the 3-10 nm range of the FRET-based ruler, we sought to put this protein to the test using single-molecule PIFE, where we track the fluorescence lifetime of site-specific sCy3-labeled α-Synuclein proteins one at a time. Interestingly, using this shorter range spectroscopic proximity sensor, sCy3-labeled α-Synuclein exhibits several lifetime sub-populations with distinctly different mean lifetimes that interconvert in 10-100 ms. These results show that while α-Synuclein might be disordered globally, it nonetheless attains stable local structures. In summary, in this work we highlight the advantage of using different spectroscopic proximity sensors that track local or global structural changes one biomolecule at a time.Adipose tissue provides a rich and accessible source of multipotent stem cells, which are able to self-renew. These adipose-derived stem cells (ADSCs) provide a consistent ex vivo cellular system that are functionally like that of in vivo adipocytes. Use of ADSCs in biomedical research allows for cellular investigation of adipose tissue metabolic regulation and function. ADSC differentiation is necessary for adequate adipocyte expansion, and suboptimal differentiation is a major mechanism of adipose dysfunction. Understanding changes in ADSC differentiation is crucial to understanding the development of metabolic dysfunction and disease. The protocols described in this manuscript, when followed, will yield mature adipocytes that can be used for several in vitro functional tests to assess ADSC metabolic function, including but not limited to assays measuring glucose uptake, lipolysis, lipogenesis, and secretion. Rhesus macaques (Macaca mulatta) are physiologically, anatomically, and evolutionarily similar to humans and as such, their tissues and cells have been used extensively in biomedical research and for development of treatments. Here, we describe ADSC isolation using fresh subcutaneous and omental adipose tissue obtained from 4-9-year old rhesus macaques. Adipose tissue samples are enzymatically digested in collagenase followed by filtration and centrifugation to isolate ADSCs from the stromal vascular fraction. Isolated ADSCs are proliferated in stromal media followed by approximately 14-21 days of differentiation using a cocktail of 0.5 μg/mL dexamethasone, 0.5 mM isobutyl methylxanthine, and 50 μM indomethacin in stromal media. Mature adipocytes are observed at approximately 14 days of differentiation. ONO-7300243 In this manuscript, we describe protocols for ADSC isolation, proliferation, and differentiation in vitro. Although, we have focused on ADSCs from rhesus macaque adipose tissue, these protocols can be utilized for adipose tissue obtained from other animals with minimal adjustments.In fragment-based drug discovery, hundreds or often thousands of compounds smaller than ~300 Da are tested against the protein of interest to identify chemical entities that can be developed into potent drug candidates. Since the compounds are small, interactions are weak, and the screening method must therefore be highly sensitive; moreover, structural information tends to be crucial for elaborating these hits into lead-like compounds. Therefore, protein crystallography has always been a gold-standard technique, yet historically too challenging to find widespread use as a primary screen. Initial XChem experiments were demonstrated in 2014 and then trialed with academic and industrial collaborators to validate the process. Since then, a large research effort and significant beamtime have streamlined sample preparation, developed a fragment library with rapid follow-up possibilities, automated and improved the capability of I04-1 beamline for unattended data collection, and implemented new tools for data manag-week turn-around for the main protease.A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.