RNA-aptamers-in-droplets (RAPID) high-throughput screening for secretory phenotypes.

Synthetic biology and metabolic engineering seek to re-engineer microbes into living foundries for the production of high value chemicals. Through a design-build-test cycle paradigm, massive libraries of genetically engineered microbes can be constructed and tested for metabolite overproduction and secretion. However, library generation capacity outpaces the rate of high-throughput testing and screening. Well plate assays are flexible but with limited throughput, whereas droplet microfluidic techniques are ultrahigh-throughput but require a custom assay for each target. Here we present RNA-aptamers-in-droplets (RAPID), a method that greatly expands the generality of ultrahigh-throughput microfluidic screening. Using aptamers, we transduce extracellular product titer into fluorescence, allowing ultrahigh-throughput screening of millions of variants. We demonstrate the RAPID approach by enhancing production of tyrosine and secretion of a recombinant protein in Saccharomyces cerevisiae by up to 28- and 3-fold, respectively. Aptamers-in-droplets affords a general approach for evolving microbes to synthesize and secrete value-added chemicals.Screening libraries of genetically engineered microbes for secreted products is limited by the available assay throughput. Here the authors combine aptamer-based fluorescent detection with droplet microfluidics to achieve high throughput screening of yeast strains engineered for enhanced tyrosine or streptavidin production

Integrated Screening for Arginase Inhibitors

Arginase is an enzyme that catalyzes the formation of L-ornithine and urea from L-arginine. L-arginine is also a substrate for nitric oxide synthase (NOS), resulting in the formation of nitric oxide (NO) which is a key vasodilator. Not surprisingly, arginase inhibitors are being studied to treat various diseases, including hypertension, erectile dysfunction, atherosclerosis, wound healing and myocardial reperfusion injury. Recently, the use of virtual screening and docking to identify and characterize novel arginase inhibitors as potential therapeutics to treat leshmania infections has been reported in the literature. Hence, there is interest in the development of new and improved arginase inhibitors. Here, we describe the use of an iterative in silico and in vitro work-flow for identifying novel arginase inhibitors. The first in silico arm of the work-flow involves the use of library design, virtual screening, docking, and consensus scoring to identify predicted hit compounds. The in vitro arm involves rapid assaying of predicted hits in an optimized arginase assay. Confirmed hits are passed into the second in silico arm which involves ligand-based screening, docking, and consensus scoring. The crank is turned on the in silico – in vitro – in silico cycle until a promising candidate for hit-to-lead optimization has been identified. Preliminary results appear encouraging, providing hope that a novel arginase drug candidate will be identified and that our computational work-flow will prove useful on other targets

Computational Design of Novel Insulin Degrading Enzyme Inhibitors

Human insulin degrading enzyme (IDE) plays a role in the proteolytic cleavage of insulin, glucagon, and other short, hydrophobic peptides with roles in glucose and cellular metabolism. Because of IDE’s role in insulin clearance, IDE inhibitors may hold promise as therapies for potentiating insulin signaling in patients suffering from type 2 diabetes mellitus. IDE is a large (~100 kDa) chambered protease of the conserved M16A subfamily of zinc metalloproteases. The enzyme adopts a structure that is analogous to a clamshell formed by the joining of the N terminal and C terminal domains. The characteristic zinc binding and catalytic motif (HXXEH) is positioned within the enzyme’s N terminus, while C terminal residues also play important roles in substrate binding and catalysis. Here, we describe the use of a computational work-flow for identifying novel IDE inhibitors. The work flow integrates mutation-based active site structural analysis, virtual screening, docking and fragment-based design. Initial computational results appear promising and should lead to assay testing in the near future

Multifunctional PA6 composites using waste glass fiber and green metal organic framework/graphene hybrids

Glass fiber-polyamide 6 (PA6) composites are widely used for various automotive applications, yet the ability to exhibit multifunctional properties and the cost of it remains challenging. Herein this work introduces a cost-effective approach for utilization of waste glass fiber (GF), green aluminium metal organic framework (Al-MOF), and industry-grade graphene nanoplatelets (GNPs) for the fabrication of multifunctional PA6 thermoplastic composites with enhanced mechanical performance and fire retardancy. The results demonstrate that hybrid filler of Al-MOF and GNPs have a synergistic effect in improving the mechanical properties and fire retardancy of GF reinforced PA6 composites. Compared to the neat PA6, the PA6 composite containing 20 wt% GFs, 5 wt% GNPs, and 5 wt% Al-MOF exhibited ~97% and ~93% improvements in tensile and flexural strength, respectively. Also, compared to the neat PA6, 27 and 55°C increases were observed in glass transition temperature (Tg) and heat deflection temperature, respectively. Thermal stability and fire retardancy of the GFs/PA6 composites were significantly improved when hybridized with GNPs and Al-MOF

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent

Status of Muon Collider Research and Development and Future Plans

The status of the research on muon colliders is discussed and plans are outlined for future theoretical and experimental studies. Besides continued work on the parameters of a 3-4 and 0.5 TeV center-of-mass (CoM) energy collider, many studies are now concentrating on a machine near 0.1 TeV (CoM) that could be a factory for the s-channel production of Higgs particles. We discuss the research on the various components in such muon colliders, starting from the proton accelerator needed to generate pions from a heavy-Z target and proceeding through the phase rotation and decay ($\pi \to \mu \nu_{\mu}$) channel, muon cooling, acceleration, storage in a collider ring and the collider detector. We also present theoretical and experimental R & D plans for the next several years that should lead to a better understanding of the design and feasibility issues for all of the components. This report is an update of the progress on the R & D since the Feasibility Study of Muon Colliders presented at the Snowmass'96 Workshop [R. B. Palmer, A. Sessler and A. Tollestrup, Proceedings of the 1996 DPF/DPB Summer Study on High-Energy Physics (Stanford Linear Accelerator Center, Menlo Park, CA, 1997)].Comment: 95 pages, 75 figures. Submitted to Physical Review Special Topics, Accelerators and Beam

THE ROLE OF INTERDEPENDENCE IN THE MICRO-FOUNDATIONS OF ORGANIZATION DESIGN: TASK, GOAL, AND KNOWLEDGE INTERDEPENDENCE

Interdependence is a core concept in organization design, yet one that has remained consistently understudied. Current notions of interdependence remain rooted in seminal works, produced at a time when managers’ near-perfect understanding of the task at hand drove the organization design process. In this context, task interdependence was rightly assumed to be exogenously determined by characteristics of the work and the technology. We no longer live in that world, yet our view of interdependence has remained exceedingly task-centric and our treatment of interdependence overly deterministic. As organizations face increasingly unpredictable workstreams and workers co-design the organization alongside managers, our field requires a more comprehensive toolbox that incorporates aspects of agent-based interdependence. In this paper, we synthesize research in organization design, organizational behavior, and other related literatures to examine three types of interdependence that characterize organizations’ workflows: task, goal, and knowledge interdependence. We offer clear definitions for each construct, analyze how each arises endogenously in the design process, explore their interrelations, and pose questions to guide future research

The bacterial stressosome:a modular system that has been adapted to control secondary messenger signaling

SummaryThe stressosome complex regulates downstream effectors in response to environmental signals. In Bacillus subtilis, it activates the alternative sigma factor σB, leading to the upregulation of the general stress regulon. Herein, we characterize a stressosome-regulated biochemical pathway in Moorella thermoacetica. We show that the presumed sensor, MtR, and the scaffold, MtS, form a pseudo-icosahedral structure like that observed in B. subtilis. The N-terminal domain of MtR is structurally homologous to B. subtilis RsbR, despite low sequence identity. The affinity of the switch kinase, MtT, for MtS decreases following MtS phosphorylation and not because of structural reorganization. Dephosphorylation of MtS by the PP2C type phosphatase MtX permits the switch kinase to rebind the stressosome to reset the response. We also show that MtT regulates cyclic di-GMP biosynthesis through inhibition of a GG(D/E)EF-type diguanylate cyclase, demonstrating that secondary messenger levels are regulated by the stressosome