Nuclear pore complexes (NPCs) form gateways that control molecular exchange between the nucleus and the cytoplasm. They impose a diffusion barrier to macromolecules and enable the selective transport of nuclear transport receptors with bound cargo. The underlying mechanisms that establish these permeability properties remain to be fully elucidated but require unstructured nuclear pore proteins rich in Phe-Gly (FG)-repeat domains of different types, such as FxFG and GLFG. While physical modeling and in vitro approaches have provided a framework for explaining how the FG network contributes to the barrier and transport properties of the NPC, it remains unknown whether the number and/or the spatial positioning of different FG-domains along a cylindrical, ∼40 nm diameter transport channel contributes to their collective properties and function. To begin to answer these questions, we have used DNA origami to build a cylinder that mimics the dimensions of the central transport channel and can house a specified number of FG-domains at specific positions with easily tunable design parameters, such as grafting density and topology. We find the overall morphology of the FG-domain assemblies to be dependent on their chemical composition, determined by the type and density of FG-repeat, and on their architectural confinement provided by the DNA cylinder, largely consistent with here presented molecular dynamics simulations based on a coarse-grained polymer model. In addition, high-speed atomic force microscopy reveals local and reversible FG-domain condensation that transiently occludes the lumen of the DNA central channel mimics, suggestive of how the NPC might establish its permeability properties.
There has been a significant drive to deliver nanotechnological solutions to biosensing, yet there remains an unmet need in the development of biosensors that are affordable, integrated, fast, capable of multiplexed detection, and offer high selectivity for trace analyte detection in biological fluids. Herein, some of these challenges are addressed by designing a new class of nanoscale sensors dubbed nanopore extended field-effect transistor (nexFET) that combine the advantages of nanopore single-molecule sensing, field-effect transistors, and recognition chemistry. We report on a polypyrrole functionalized nexFET, with controllable gate voltage that can be used to switch on/off, and slow down single-molecule DNA transport through a nanopore. This strategy enables higher molecular throughput, enhanced signal-to-noise, and even heightened selectivity via functionalization with an embedded receptor. This is shown for selective sensing of an anti-insulin antibody in the presence of its IgG isotype.
Over the past decades, atomic force microscopy (AFM) has emerged as an increasingly powerful tool to study the dynamics of biomolecules at nanometer length scales. However, the more stochastic the nature of such biomolecular dynamics, the harder it becomes to distinguish them from AFM measurement noise. Rapid, stochastic dynamics are inherent to biological systems comprising intrinsically disordered proteins. One role of such proteins is in the formation of the transport barrier of the nuclear pore complex (NPC): the selective gateway for macromolecular traffic entering or exiting the nucleus. Here, we use AFM to observe the dynamics of intrinsically disordered proteins from two systems: the transport barrier of native NPCs and the transport barrier of a mimetic NPC made using a DNA origami scaffold. Analyzing data recorded with 50–200 ms temporal resolution, we highlight the importance of drift correction and appropriate baseline measurements in such experiments. In addition, we describe an autocorrelation analysis to quantify time scales of observed dynamics and to assess their veracity—an analysis protocol that lends itself to the quantification of stochastic fluctuations in other biomolecular systems. The results reveal the surprisingly slow rate of stochastic, collective transitions inside mimetic NPCs, highlighting the importance of FG-nup cohesive interactions.
Co-block polymer surfaces provide a platform on which to visualize DNA–protein interactions by atomic force microscopy at nanometre resolution.
Over the past decades, atomic force microscopy (AFM) has emerged as an increasingly powerful tool to study the dynamics of biomolecules at nanometre length scales. However, the more stochastic the nature of such biomolecular dynamics, the harder it becomes to distinguish them from AFM measurement noise. Rapid, stochastic dynamics are inherent to biological systems comprising intrinsically disordered proteins. One role of such proteins is in the formation of the transport barrier of the nuclear pore complex (NPC): the selective gateway for macromolecular traffic entering or exiting the nucleus. Here, we use AFM to observe the dynamics of intrinsically disordered proteins from two systems: the transport barrier of native NPCs, and the transport barrier of a mimetic NPC made using a DNA origami scaffold. Analysing data recorded with 50-200 ms temporal resolution, we highlight the importance of drift correction and appropriate baseline measurements in such experiments. In addition, we describe an auto-correlation analysis to quantify time scales of observed dynamics and to assess their veracityan analysis protocol that lends itself to the quantification of stochastic fluctuations in other biomolecular systems. The results reveal the surprisingly slow rate of stochastic, collective transitions inside mimetic NPCs, highlighting the importance of FG-nup cohesive interactions.
DNA-protein interactions are vital to cellular function, with key roles in the regulation of gene expression and genome maintenance. Atomic force microscopy (AFM) offers the ability to visualize DNA-protein interactions at nanometre resolution in nearphysiological buffers, but it requires that the DNA be adhered to the surface of a solid substrate. This presents a problem when working at biologically relevant protein concentrations, where protein may be present at large excess in solution; much of the biophysically relevant information can therefore be occluded by non-specific protein binding to the underlying substrate. Here we explore the use of PLL x -b-PEG y block copolymers to achieve selective adsorption of DNA on a mica surface.Through varying both the number of lysine and ethylene glycol residues in the block copolymers, we show selective adsorption of DNA on mica that is functionalized with a PLL 10 -b-PEG 113 / PLL 1000-2000 mixture as viewed by AFM imaging in a solution containing high concentrations of streptavidin. We show that this selective adsorption extends to DNA-protein complexes, through the use of biotinylated DNA and streptavidin, and demonstrate that DNA-bound streptavidin can be unambiguously distinguished by in-liquid AFM in spite of an excess of unbound streptavidin in solution.
The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). Articles published 09/2018-09/2020 were searched for using MEDLINE, EMBASE, and the Cochrane Register. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics -the target condition). Risk of bias and applicability was assessed with QADAS 2 methodology. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. Fifteen studies were included with 1038 patients in training sets and 233 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% (10/15) of studies. External hold-out test sets were used in 27% (4/15) to give ranges of diagnostic accuracy measures: recall = 0.70-1.00; specificity = 0.67-0.90; precision = 0.78-0.88; F1 score = 0.74-0.94; balanced accuracy = 0.74-0.83; AUC = 0.80-0.85. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.
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