Activation of regulatory T cells triggers specific changes in glycosylation associated with Siglec-1-dependent inflammatory responses [version 1; peer review: 2 approved]

Background: Siglec-1 is a macrophage lectin-like receptor that mediates sialic acid-dependent cellular interactions. Its upregulation on macrophages in autoimmune disease was shown previously to promote inflammation through suppressing the expansion of regulatory T cells (Tregs). Here we investigate the molecular basis for Siglec-1 binding to Tregs using in vitro-induced cells as a model system. Methods: Glycosylation changes that affect Siglec‑1 binding were studied by comparing activated and resting Tregs using RNA-Seq, glycomics, proteomics and binding of selected antibodies and lectins. A proximity labelling and proteomics strategy was used to identify Siglec-1 counter-receptors expressed on activated Tregs. Results: Siglec-1 binding was strongly upregulated on activated Tregs, but lost under resting conditions. Glycomics revealed changes in N-glycans and glycolipids following Treg activation and we observed changes in expression of multiple 'glycogenes' that could lead to the observed increase in Siglec-1 binding. Proximity labelling of intact, living cells identified 49 glycoproteins expressed by activated Tregs that may function as Siglec-1 counter-receptors. These represent ~5% of the total membrane protein pool and were mainly related to T cell activation and proliferation. We demonstrate that several of these counter-receptors were upregulated following activation of Tregs and provide initial evidence that their altered glycosylation may also be important for Siglec-1 binding. Conclusions: We provide the first comprehensive analysis of glycan changes that occur in activated Tregs, leading to recognition by the macrophage lectin, Siglec-1 and suppression of Treg expansion. We furthermore provide insights into glycoprotein counter-receptors for Siglec-1 expressed by activated Tregs that are likely to be important for suppressing Treg expansion

Exosomes of adult human fibroblasts cultured on 3D silk fibroin nonwovens intensely stimulate neoangiogenesis

Bombyx mori silk fibroin (SF) is a biomacromolecule allowing to assemble scaffolds 7 for tissue engineering/regeneration purposes because of its cellular adhesiveness, high 8 biocompatibility, and low immunogenicity. Earlier work showed that two types of 3D-SF-based 9 nonwovens (3D-SFnws) implanted into mouse subcutaneous tissue were promptly vascularised via plates in exosome-depleted medium. DNA amounts and D-glucose consumption revealed HDFs 17 undefined molecular mechanisms. This study used nontumorigenic adult human dermal fibroblasts (HDFs) adhering to a third type of 3D-SFnws to test whether HDFs release exosomes whose contents promote neoangiogenesis

Pressure-dependent EPANET extension

In water distribution systems (WDSs), the available flow at a demand node is dependent on the pressure at that node. When a network is lacking in pressure, not all consumer demands will be met in full. In this context, the assumption that all demands are fully satisfied regardless of the pressure in the system becomes unreasonable and represents the main limitation of the conventional demand driven analysis (DDA) approach to WDS modelling. A realistic depiction of the network performance can only be attained by considering demands to be pressure dependent. This paper presents an extension of the renowned DDA based hydraulic simulator EPANET 2 to incorporate pressure-dependent demands. This extension is termed “EPANET-PDX” (pressure-dependent extension) herein. The utilization of a continuous nodal pressure-flow function coupled with a line search and backtracking procedure greatly enhance the algorithm’s convergence rate and robustness. Simulations of real life networks consisting of multiple sources, pipes, valves and pumps were successfully executed and results are presented herein. Excellent modelling performance was achieved for analysing both normal and pressure deficient conditions of the WDSs. Detailed computational efficiency results of EPANET-PDX with reference to EPANET 2 are included as well

Studying the Salt Dependence of the Binding of σ70 and σ32 to Core RNA Polymerase Using Luminescence Resonance Energy Transfer

The study of protein-protein interactions is becoming increasingly important for understanding the regulation of many cellular processes. The ability to quantify the strength with which two binding partners interact is desirable but the accurate determination of equilibrium binding constants is a difficult process. The use of Luminescence Resonance Energy Transfer (LRET) provides a homogeneous binding assay that can be used for the detection of protein-protein interactions. Previously, we developed an LRET assay to screen for small molecule inhibitors of the interaction of σ70 with theβ' coiled-coil fragment (amino acids 100–309). Here we describe an LRET binding assay used to monitor the interaction of E. coli σ70 and σ32 with core RNA polymerase along with the controls to verify the system. This approach generates fluorescently labeled proteins through the random labeling of lysine residues which enables the use of the LRET assay for proteins for which the creation of single cysteine mutants is not feasible. With the LRET binding assay, we are able to show that the interaction of σ70 with core RNAP is much more sensitive to NaCl than to potassium glutamate (KGlu), whereas the σ32 interaction with core RNAP is insensitive to both salts even at concentrations >500 mM. We also find that the interaction of σ32 with core RNAP is stronger than σ70 with core RNAP, under all conditions tested. This work establishes a consistent set of conditions for the comparison of the binding affinities of the E.coli sigma factors with core RNA polymerase. The examination of the importance of salt conditions in the binding of these proteins could have implications in both in vitro assay conditions and in vivo function

Multi-length scale characterization of compression on metal foam flow-field based fuel cells using X-ray computed tomography and neutron radiography

The mechanical compression of metal foam flow-field based polymer electrolyte fuel cells (PEFCs) is critical in determining the interfacial contact resistance with gas diffusion layers (GDLs), reactant flow and water management. The distinct scale between the pore structure of metal foams and the entire flow-field warrant a multi-length scale characterization that combines ex-situ tests of compressed metal foam samples and in-operando analysis of operating PEFCs using X-ray computed tomography (CT) and neutron radiography. An optimal ‘medium’ compression was found to deliver a peak power density of 853 mW cm−2. The X-ray CT data indicates that the compression process significantly decreases the mean pore size and narrows the pore size distribution of metal foams. Simulation results suggest compressing metal foam increases the pressure drop and gas velocity, improving the convective liquid water removal. This is in agreement with the neutron imaging results that demonstrates an increase in the mass of accumulated liquid water with minimum compression compared to the medium and maximum compression cases. The results show that a balance between Ohmic resistance, water removal capacity and parasitic power is imperative for the optimal performance of metal foam based PEFCs

Sodium Superionic Conductors (NASICONs) as Cathode Materials for Sodium-Ion Batteries

Sodium-ion batteries (SIBs) have developed rapidly owing to the high natural abundance, wide distribution, and low cost of sodium. Among the various materials used in SIBs, sodium superion conductor (NASICON)-based electrode materials with remarkable structural stability and high ionic conductivity are one of the most promising candidates for sodium storage electrodes. Nevertheless, the relatively low electronic conductivity of these materials makes them display poor electrochemical performance, significantly limiting their practical application. In recent years, the strategies of enhancing the inherent conductivity of NASICON-based cathode materials have been extensively studied through coating the active material with a conductive carbon layer, reducing the size of the cathode material, combining the cathode material with various carbon materials, and doping elements in the bulk phase. In this paper, we review the recent progress in the development of NASICON-based cathode materials for SIBs in terms of their synthesis, characterization, functional mechanisms, and performance validation/optimization. The advantages and disadvantages of such SIB cathode materials are analyzed, and the relationship between electrode structures and electrochemical performance as well as the strategies for enhancing their electrical conductivity and structural stability is highlighted. Some technical challenges of NASICON-based cathode materials with respect to SIB performance are analyzed, and several future research directions are also proposed for overcoming the challenges toward practical applications

Propensity score analysis in the Genetic Analysis Workshop 17 simulated data set on independent individuals

Genetic Analysis Workshop 17 provided simulated phenotypes and exome sequence data for 697 independent individuals (209 case subjects and 488 control subjects). The disease liability in these data was influenced by multiple quantitative traits. We addressed the lack of statistical power in this small data set by limiting the genomic variants included in the study to those with potential disease-causing effect, thereby reducing the problem of multiple testing. After this adjustment, we could readily detect two common variants that were strongly associated with the quantitative trait Q1 (C13S523 and C13S522). However, we found no significant associations with the affected status or with any of the other quantitative traits, and the relationship between disease status and genomic variants remained obscure. To address the challenge of the multivariate phenotype, we used propensity scores to combine covariates with genetic risk factors into a single risk factor and created a new phenotype variable, the probability of being affected given the covariates. Using the propensity score as a quantitative trait in the case-control analysis, we again could identify the two common single-nucleotide polymorphisms (C13S523 and C13S522). In addition, this analysis captured the correlation between Q1 and the affected status and reduced the problem of multiple testing. Although the propensity score was useful for capturing and clarifying the genetic contributions of common variants to the disease phenotype and the mediating role of the quantitative trait Q1, the analysis did not increase power to detect rare variants